Guerino Casassanta (Minas Gerais, 1894-1962) foi professor em Belo Horizonte, exerceu o cargo de Inspetor de Instrução, sendo professor de Psicologia no Instituto Estadual de Educação, e um autor de livros principalmente sobre pedagogia, sendo a sua obra mais citada o livro Jornais Escolares (1939).

Livros
Amaral (2013, p.125 e 126) cita no artigo Os jornais estudantis Ecos Gonzagueanos e Estudante: apontamentos sobre o ensino secundário católico e laico (Pelotas/RS, 1930 a 1960) as reflexões de Casasanta: No que  tange  à  análise  de  jornais  estudantis  ou, como  eram  especificados  pela bibliografia da época, jornais escolares, no período pós 1930, é fundamental a leitura de Guerino  Casasanta  que  publicou, em  1939,o  livro Jornais escolares, resultado de  um inquérito  sobre  jornais  escolares,  realizado  em  Minas  Gerais  no  ano  de  1933,  quando ocupava o cargo de inspetor de ensino do Estado.

Nessa obra, o autor fundamenta a importância pedagógica do jornal escolar para os alunos e para a escola, aponta objetivamente as possibilidades de sua inserção junto ao currículo escolar e apresenta em detalhes as etapas de sua elaboração e organização, ou seja, dá as dicas de como fazer um tipo ideal de jornal escolar.

Nesse trabalho, o aluno, sob  a  supervisão  de  seus  professores,  exerceria  uma  atividade  que  o  prepararia  para  o futuro,  vivenciando  situações que  levassem  a:  "preparar  o  indivíduo  a  viver  numa democracia;  tornar  o  indivíduo  guia  de  si  mesmo;  ensinar  o  valor  da  cooperação; despertar o interesse do educando pela escola; despertar no educando os sentimentos de ordem e de legalidade, etc." (Casasanta, 1939, p. 39-40).

Casasanta (1939) reitera a importância do jornal escolar e afirma que:

A  escola  deve  ser  uma  família,  devendo  predominar  aí  o  espírito coletivo.  O  jornal  alimenta  esse  espírito,  promove  a  cooperação, estimula as  iniciativas.  É  o  traço  de  união  entre  seus  membros.As  atividades escolares,  como,  aliás,  todas  as  atividades  sociais,  requerem  estímulo  e incitamento. A publicidade é um meio de êxito e sucesso. O jornal escolar pode  manter  vivas  as atividades,  incentivando  o  entusiasmo  entre  os alunos,  levando-os  a  empregar  nelas  todo  o  esforço  e  toda  a  atenção.O jornal  une  a  escola  à  sociedade,  pondo-a  constantemente  a  par  de  sua vida e de suas realizações. Estabelece, assim, um entendimento recíproco, interessando o povo na obra escolar.O jornal leva aos pais, aos ex-alunos e  a  todos  as  notícias  da  escola.  Matem-se,  dessa  forma,  sempre  vivo  o interesse  daqueles  que  viveram  na  escola  e  cujas  notícias  lhes  são particularmente  gratas.As  notícias  davida  escolar,  de  suas  iniciativas  e atividades,  suscitarão  iguais  procedimentos  a  outros  estabelecimentos.Não se poderá dizer que o treino do aluno no jornal escolar seja indício de que,  mais  tarde,  se  trone  um  jornalista  [...]  Entretanto  o  exercício  que o jornal  escolar facilita,  poderá  ser  o  ponto  de  partida  da  revelação  de  uma tendência. (p. 40 e 41)

Além dos pressupostos citados, o autor assinala que o jornal escolar repercutiria no desenvolvimento  de  qualidades  que  as  matérias  de  ensino  não  incentivavam,  senão  em pequena  escala,  tais  como  a  iniciativa,  a  liderança  e  as  características  pessoais  dos alunos. O jornal seria uma forma de estabelecer intercâmbio entre as escolas, de registrara história da mesma, de propagar a escola, de atrair para ela o interesse da população: "o que  devemos  esperar  do  jornal  é  que  seja  educativo.É  meio  ótimo  para  que  a personalidade da criança se patenteie, indicando os seus anseios, as suas tendências, as suas aptidões" (Ibid.,p. 41).

O trabalho de Casasanta também é mencionado por Pinheiro (2004). A autora  menciona que Casasanta traça prescrições detalhadas para o desenvolvimento satisfatório do jornal escolar sistematizadas a partir da experiência.

Segundo o autor, o texto deveria ser de cunho nitidamente infantil, mas dirigido pelo professor. Esta orientação é um exemplo de como deveria ser a linguagem do jornal escolar ideal, que despertasse o interesse e a curiosidade infantis. O jornal escolar, segundo Casasanta é caracterizado como um jogo, uma brincadeira que prepara para o futuro. O autor afirmava que, se o jornal não tiver a participação ativa do aluno, será uma atividade inteiramente perdida e defende essa participação desde o processo de criação do periódico como condição para que se torne um eficiente instrumento de progresso e crescimento. O autor confirma que o jornal escolar está incluído no rol das atividades extra-classe e destaca os valores genéricos dessas atividades, tomando o cuidado, porém, de atribuí-los especificamente aos jornais escolares. São eles: Preparar o indivíduo a viver numa democracia; tornar o indivíduo guia de si mesmo; ensinar o valor da cooperação; despertar o interesse do educando pela escola; despertar no educando os sentimentos de ordem e de legalidade, etc.

Em 1932, um artigo assinado por Guerino Casassanta (Santos, 2019, p.76-77) manifestava a preocupação com os prejuízos de se confundir retardados com os anormais. E faz uma defesa da homogeneização das classes, de modo que os retardados pudessem ter o destino certo, com educação que lhes era devida e necessária:

O trabalho de homogeneização das classes, que se tem processado em nossos grupos escolares, motivou o estudo de um problema sério e relevante: o destino dos retardados. Sabe-se que essas crianças apresentam um crescimento mental deficiente, muito abaixo da média normal e, em consequência, com pequenas probabilidades de vencerem e progredirem. Ao contrário do que se possa supor, essas crianças requerem um cuidado todo especial pelas dificuldades de adaptação ao meio social e, por isso, destinadas, em regra, a aumentar o número dos inúteis, dos viciosos e dos pervertidos. A escola tem o dever de examinar cuidadosamente a situação dessas crianças e procurar solucioná-la de maneira que, mais dia, menos dia, não se vejam elas na impossibilidade de viver honestamente. (REVISTA DO ENSINO, 1932, p. 38).

Escolas
Em Minas Gerais, existem escolas com o nome do Prof. Guerino Casassanta.

São elas:

 Escola Estadual Professor Guerino Casassanta na Rua Treze de Maio, 2009. Bela Vista. Ouro Fino-MG (Link)
 Escola Estadual Professor Guerino Casassanta na Rua São Miguel, 269. São Miguel Arcanjo. Ribeirão das Neves - MG (Link)

Guerino Casasanta


السد هي إحدى القرى اللبنانية من قرى قضاء المتن في محافظة جبل لبنان.

المراجع

أماكن مأهولة في قضاء المتن
مدن لبنان

السد (المتن)


Livredning er en dansk undervisningsfilm fra 1948 instrueret af Carl Otto Petersen og Poul Schüssler og efter manuskript af Carl Otto Petersen.

Handling
Instruktionsfilm for svømmere, der må forudsættes at have en smule kendskab til livredning. Beregnet til anvendelse i skoler og foreninger.

Referencer

Eksterne henvisninger

Dokumentarfilm fra Danmark
Undervisningsfilm fra Danmark
Danske film fra 1948

Livredning (dokumentarfilm fra 1948)


Corendon Turizm Grubu bünyesinde kurulan Corendon Airlines, Türk tescilli bir hava yolu operatörüdür. Corendon Turizm Grubu çatısı altında ayrıca Hollanda ve Belçika merkezli tur operatörleri; Avrupa, Türkiye ve Karayipler'de otelleri bulunan bir otel zinciri ile incoming şirketi yer almaktadır.

Tarihçe

Corendon Airlines, 2004 yılında kurulmuş ve ilk ticari uçuşunu 12 Nisan 2005 tarihinde Sabiha Gökçen'den Amsterdam'a gerçekleştirmiştir. 2011 yılında Hollanda tescilli Corendon Dutch Airlines, 26 Mayıs 2017'de Corendon Airlines Europe olmak üzere iki kardeş şirket kurmuştur.

IATA üyesi Corendon Airlines; ayrıca AOC, IOSA, JAR-OPS 1, ISO 9001, ISO 10002, ISO 14001, ISO 27001, JAR 145 ve Engelsiz Havayolu belgelerine sahiptir. 11 Nisan 2014'te Almanya'dan Antalya'ya uçuşlar düzenlemeye başlayan Corendon Airlines, pazarın en büyük tur operatörleri ile yaptığı iş birliği sonucu her sene sunduğu koltuk sayısını artırarak yükselen bir başarı grafiği sergilemiştir.

Corendon Airlines, biri Türkiye, 2'si Avrupa bayraklı olmak üzere 3 hava yolu şirketiyle yılda yaklaşık 6 milyon turist taşımaktadır. Türkiye'ye en fazla turist getiren havayollarından biri konumunda olan havayolu, operasyon ağına ve tanıtım faaliyetlerine yaptığı yatırımlarla Hollanda, Belçika, İskandinav ülkeleri, Almanya gibi destinasyonlarda ismini Türkiye ile özdeşleştirmiştir.

Uçuş noktaları
Full charter, split charter, sub charter ve wet lease operasyonları yöneten havayolu; Hollanda, Belçika, Almanya, İsviçre, Avusturya, İspanya, İskandinav ülkeleri, Finlandiya, İtalya, Fransa, Çekya, Romanya, Polonya, İsrail ve Mısır başta olmak üzere 40'tan fazla ülkede 145 havalimanından uçuşlar düzenlemektedir.

Türkiye'deki operasyon merkezleri olan Antalya, İzmir, Trabzon, Kayseri'nin yanı sıra; Almanya'nın Hannover, Köln, Münster ve Nürnberg havalimanlarında uçaklarını base eden havayolu, 2021 senesinde Düsseldorf ve İsviçre'nin Basel meydanlarını da operasyon merkezleri arasına ekleyecek.

Avrupa'dan Türkiye, İspanya, İtalya Yunanistan, Mısır gibi popüler tatil destinasyonlarına uçuşlar düzenleyen Corendon Airlines, bunların yanı sıra Türkiye'nin farklı şehirlerinden Avrupa'ya direkt uçuşlar düzenlemektedir.

Filo

Yönetim Kurulu Başkanlığı'nı Yıldıray Karaer'in yürüttüğü Corendon Airlines'ın filosunda 14'ü Türkiye; 3'ü Hollanda, 6'sı Malta tescilli olmak üzere 23 adet Boeing 737-800 bulunmaktadır.

Olay ve Kırım Raporları

 Corendon Havayolları, dünyadaki tek emniyet ve ürün derecelendirmesi yapan havacılık sitesi AirlineRatings.com tarafından 2016 yılında yapılan değerlendirmede, tam puan anlamına gelen 7 yıldız alarak dünyadaki 448 hava yolundan sadece 137 tanesinin seçilebildiği emniyetli havayolları arasına girdi.

Dış bağlantılar
 Corendon Hava Yolları
 Corendon Hotels&Resorts

Kaynakça

Türkiye merkezli havayolu şirketleri
2004'te kurulan şirketler
2004'te Türkiye'de kurulan oluşumlar

Corendon Airlines


إبينا  هي مدينة يابانية تقع في محافظة كاناغاوا.
تم تأسيس المدينة في 1 نوفمبر، 1971.

جغرافيا
تقع مدينة إبينا تقريبا في منتصف محافظة كاناغاوا على الضفة اليسرى لنهر ساغامي.

مواصلات

قطارات
 خط ساغامي
 خط أوداكيو
 خط ساغامي الرئيسي

توأمة
لإبينا (كاناغاوا) اتفاقيات توأمة مع:
 نوبوريبيتسو (18 مايو 2015 - )

أعلام

 جون ياماموتو
 أكيكو كيجيموتا
 ناوكو كيجيموتا

مراجع

وصلات خارجية
 الموقع الرسمي باللغة الإنكليزية
 الموقع الرسمي باللغة اليابانية

مدن محافظة كاناغاوا

إبينا (كاناغاوا)


Celallı, Samsun ilinin Kavak ilçesine bağlı bir mahalledir.

Tarihçe
mahallenin tarihi hakkında söylenen rivayet şu sekildedir:1747 yılında köye ilk Celal adında bir kişinin geldiği yerlestiği ve buraya onun adının verildiği iddia edilmektedir.

Coğrafya
Samsun il merkezine 66 km, Kavak ilçesine ise 14 km uzaklıktadır.

Nüfus

Kavak'ın mahalleleri

Celallı, Kavak


A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single type of input, like music, or multiple inputs within and across platforms like news, books and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.

Overview
Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also known as the personality-based approach), as well as other systems such as knowledge-based systems. Collaborative filtering approaches build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties.

We can demonstrate the differences between collaborative and content-based filtering by comparing two early music recommender systems – Last.fm and Pandora Radio.
 Last.fm creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique.
 Pandora uses the properties of a song or artist (a subset of the 400 attributes provided by the Music Genome Project) to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach.

Each type of system has its strengths and weaknesses. In the above example, Last.fm requires a large amount of information about a user to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems. Whereas Pandora needs very little information to start, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed).

Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data.

Recommender systems have been the focus of several granted patents.

History

Elaine Rich created the first recommender system in 1979, called Grundy. She looked for a way to recommend a user a book she might like. Her idea was to create a system that asks the user specific questions and assigns her stereotypes depending on her answers. Depending on a users stereotype, she would then get a recommendation for a book she might like.

Another early recommender system, called a "digital bookshelf", was described in a 1990 technical report by Jussi Karlgren at Columbia University, and implemented at scale and worked through in technical reports and publications from 1994 onwards by Jussi Karlgren, then at SICS,

and research groups led by Pattie Maes at MIT, Will Hill at Bellcore, and Paul Resnick, also at MIT

whose work with GroupLens was awarded the 2010 ACM Software Systems Award.

Montaner provided the first overview of recommender systems from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems.  Herlocker provides an additional overview of evaluation techniques for recommender systems, and Beel et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges.

Approaches

Collaborative filtering

One approach to the design of recommender systems that has wide use is collaborative filtering. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. The system generates recommendations using only information about rating profiles for different users or items. By locating peer users/items with a rating history similar to the current user or item, they generate recommendations using this neighborhood. Collaborative filtering methods are classified as memory-based and model-based. A well-known example of memory-based approaches is the user-based algorithm, while that of model-based approaches is Matrix factorization (recommender systems).

A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation as first implemented by Allen.

When building a model from a user's behavior, a distinction is often made between explicit and implicit forms of data collection.

Examples of explicit data collection include the following:
 Asking a user to rate an item on a sliding scale.
 Asking a user to search.
 Asking a user to rank a collection of items from favorite to least favorite.
 Presenting two items to a user and asking him/her to choose the better one of them.
 Asking a user to create a list of items that he/she likes (see Rocchio classification or other similar techniques).

Examples of implicit data collection include the following:
 Observing the items that a user views in an online store.
 Analyzing item/user viewing times.
 Keeping a record of the items that a user purchases online.
 Obtaining a list of items that a user has listened to or watched on his/her computer.
 Analyzing the user's social network and discovering similar likes and dislikes.

Collaborative filtering approaches often suffer from three problems: cold start, scalability, and sparsity.
 Cold start: For a new user or item, there is not enough data to make accurate recommendations. Note: one commonly implemented solution to this problem is the Multi-armed bandit algorithm.
 Scalability: There are millions of users and products in many of the environments in which these systems make recommendations. Thus, a large amount of computation power is often necessary to calculate recommendations.
 Sparsity: The number of items sold on major e-commerce sites is extremely large. The most active users will only have rated a small subset of the overall database. Thus, even the most popular items have very few ratings.

One of the most famous examples of collaborative filtering is item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by Amazon.com's recommender system.

Many social networks originally used collaborative filtering to recommend new friends, groups, and other social connections by examining the network of connections between a user and their friends. Collaborative filtering is still used as part of hybrid systems.

Content-based filtering

Another common approach when designing recommender systems is content-based filtering. Content-based filtering methods are based on a description of the item and a profile of the user's preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.

In this system, keywords are used to describe the items, and a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items similar to those that a user liked in the past or is examining in the present. It does not rely on a user sign-in mechanism to generate this often temporary profile. In particular, various candidate items are compared with items previously rated by the user, and the best-matching items are recommended. This approach has its roots in information retrieval and information filtering research.

To create a user profile, the system mostly focuses on two types of information:

1. A model of the user's preference.

2. A history of the user's interaction with the recommender system.

Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks in order to estimate the probability that the user is going to like the item.

A key issue with content-based filtering is whether the system can learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended. For example, recommending news articles based on news browsing is useful. Still, it would be much more useful when music, videos, products, discussions, etc., from different services, can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of the hybrid system.

Content-based recommender systems can also include opinion-based recommender systems. In some cases, users are allowed to leave text reviews or feedback on the items. These user-generated texts are implicit data for the recommender system because they are potentially rich resources of both feature/aspects of the item and users' evaluation/sentiment to the item. Features extracted from the user-generated reviews are improved meta-data of items, because as they also reflect aspects of the item like meta-data, extracted features are widely concerned by the users. Sentiments extracted from the reviews can be seen as users' rating scores on the corresponding features. Popular approaches of opinion-based recommender system utilize various techniques including text mining, information retrieval, sentiment analysis (see also Multimodal sentiment analysis) and deep learning.

Hybrid recommendations approaches

Most recommender systems now use a hybrid approach, combining collaborative filtering, content-based filtering, and other approaches. There is no reason why several different techniques of the same type could not be hybridized. Hybrid approaches can be implemented in several ways: by making content-based and collaborative-based predictions separately and then combining them; by adding content-based capabilities to a collaborative-based approach (and vice versa); or by unifying the approaches into one model (see for a complete review of recommender systems). Several studies that empirically compare the performance of the hybrid with the pure collaborative and content-based methods and demonstrated that the hybrid methods can provide more accurate
recommendations than pure approaches. These methods can also be used to overcome some of the common problems in recommender systems such as cold start and the sparsity problem, as well as the knowledge engineering bottleneck in knowledge-based approaches.

Netflix is a good example of the use of hybrid recommender systems. The website makes recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering).

Some hybridization techniques include:
Weighted: Combining the score of different recommendation components numerically.
Switching: Choosing among recommendation components and applying the selected one.
Mixed: Recommendations from different recommenders are presented together to give the recommendation.
Feature Combination: Features derived from different knowledge sources are combined together and given to a single recommendation algorithm.
Feature Augmentation: Computing a feature or set of features, which is then part of the input to the next technique.
Cascade: Recommenders are given strict priority, with the lower priority ones breaking ties in the scoring of the higher ones.
Meta-level: One recommendation technique is applied and produces some sort of model, which is then the input used by the next technique.

Technologies

Session-based recommender systems
These recommender systems use the interactions of a user within a session to generate recommendations. Session-based recommender systems are used at Youtube  and Amazon. These are particularly useful when history (such as past clicks, purchases) of a user is not available or not relevant in the current user session. Domains, where session-based recommendations are particularly relevant, include video, e-commerce, travel, music and more. Most instances of session-based recommender systems rely on the sequence of recent interactions within a session without requiring any additional details (historical, demographic) of the user. Techniques for session-based recommendations are mainly based on generative sequential models such as Recurrent Neural Networks, Transformers, and other deep learning based approaches

Reinforcement learning for recommender systems

The recommendation problem can be seen as a special instance of a reinforcement learning problem whereby the user is the environment upon which the agent, the recommendation system acts upon in order to receive a reward, for instance, a click or engagement by the user. One aspect of reinforcement learning that is of particular use in the area of recommender systems is the fact that the models or policies can be learned by providing a reward to the recommendation agent. This is in contrast to traditional learning techniques which rely on supervised learning approaches that are less flexible, reinforcement learning recommendation techniques allow to potentially train models that can be optimized directly on metrics of engagement, and user interest.

Multi-criteria recommender systems
Multi-criteria recommender systems (MCRS) can be defined as recommender systems that incorporate preference information upon multiple criteria. Instead of developing recommendation techniques based on a single criterion value, the overall preference of user u for the item i, these systems try to predict a rating for unexplored items of u by exploiting preference information on multiple criteria that affect this overall preference value. Several researchers approach MCRS as a multi-criteria decision making (MCDM) problem, and apply MCDM methods and techniques to implement MCRS systems. See this chapter for an extended introduction.

Risk-aware recommender systems
The majority of existing approaches to recommender systems focus on recommending the most relevant content to users using contextual information, yet do not take into account the risk of disturbing the user with unwanted notifications. It is important to consider the risk of upsetting the user by pushing recommendations in certain circumstances, for instance, during a professional meeting, early morning, or late at night. Therefore, the performance of the recommender system depends in part on the degree to which it has incorporated the risk into the recommendation process. One option to manage this issue is DRARS, a system which models the context-aware recommendation as a bandit problem. This system combines a content-based technique and a contextual bandit algorithm.

Mobile recommender systems

Mobile recommender systems make use of internet-accessing smart phones to offer personalized, context-sensitive recommendations. This is a particularly difficult area of research as mobile data is more complex than data that recommender systems often have to deal with. It is heterogeneous, noisy, requires spatial and temporal auto-correlation, and has validation and generality problems.

There are three factors that could affect the mobile recommender systems and the accuracy of prediction results: the context, the recommendation method and privacy. Additionally, mobile recommender systems suffer from a transplantation problem – recommendations may not apply in all regions (for instance, it would be unwise to recommend a recipe in an area where all of the ingredients may not be available).

One example of a mobile recommender system are the approaches taken by companies such as Uber and Lyft to generate driving routes for taxi drivers in a city. This system uses GPS data of the routes that taxi drivers take while working, which includes location (latitude and longitude), time stamps, and operational status (with or without passengers). It uses this data to recommend a list of pickup points along a route, with the goal of optimizing occupancy times and profits.

The Netflix Prize
One of the events that energized research in recommender systems was the Netflix Prize. From 2006 to 2009, Netflix sponsored a competition, offering a grand prize of $1,000,000 to the team that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company's existing recommender system. This competition energized the search for new and more accurate algorithms. On 21 September 2009, the grand prize of US$1,000,000 was given to the BellKor's Pragmatic Chaos team using tiebreaking rules.

The most accurate algorithm in 2007 used an ensemble method of 107 different algorithmic approaches, blended into a single prediction. As stated by the winners, Bell et al.:
Predictive accuracy is substantially improved when blending multiple predictors. Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique.  Consequently, our solution is an ensemble of many methods.

Many benefits accrued to the web due to the Netflix project. Some teams have taken their technology and applied it to other markets. Some members from the team that finished second place founded Gravity R&D, a recommendation engine that's active in the  RecSys community. 4-Tell, Inc. created a Netflix project–derived solution for ecommerce websites.

A number of privacy issues arose around the dataset offered by Netflix for the Netflix Prize competition. Although the data sets were anonymized in order to preserve customer privacy, in 2007 two researchers from the University of Texas were able to identify individual users by matching the data sets with film ratings on the Internet Movie Database. As a result, in December 2009, an anonymous Netflix user sued Netflix in Doe v. Netflix, alleging that Netflix had violated United States fair trade laws and the Video Privacy Protection Act by releasing the datasets. This, as well as concerns from the Federal Trade Commission, led to the cancellation of a second Netflix Prize competition in 2010.

Evaluation

Performance measures
Evaluation is important in assessing the effectiveness of recommendation algorithms. To measure the effectiveness of recommender systems, and compare different approaches, three types of evaluations are available: user studies, online evaluations (A/B tests), and offline evaluations.

The commonly used metrics are the mean squared error and root mean squared error, the latter having been used in the Netflix Prize. The information retrieval metrics such as precision and recall or DCG are useful to assess the quality of a recommendation method. Diversity, novelty, and coverage are also considered as important aspects in evaluation. However, many of the classic evaluation measures are highly criticized.

Evaluating the performance of a recommendation algorithm on a fixed test dataset will always be extremely challenging as it is impossible to accurately predict the reactions of real users to the recommendations. Hence any metric that computes the effectiveness of an algorithm in offline data will be imprecise.

User studies are rather a small scale. A few dozens or hundreds of users are presented recommendations created by different recommendation approaches, and then the users judge which recommendations are best.

In A/B tests, recommendations are shown to typically thousands of users of a real product, and the recommender system randomly picks at least two different recommendation approaches to generate recommendations. The effectiveness is measured with implicit measures of effectiveness such as conversion rate or click-through rate.

Offline evaluations are based on historic data, e.g. a dataset that contains information about how users previously rated movies.

The effectiveness of recommendation approaches is then measured based on how well a recommendation approach can predict the users' ratings in the dataset. While a rating is an explicit expression of whether a user liked a movie, such information is not available in all domains. For instance, in the domain of citation recommender systems, users typically do not rate a citation or recommended article. In such cases, offline evaluations may use implicit measures of effectiveness. For instance, it may be assumed that a recommender system is effective that is able to recommend as many articles as possible that are contained in a research article's reference list. However, this kind of offline evaluations is seen critical by many researchers. For instance, it has been shown that results of offline evaluations have low correlation with results from user studies or A/B tests. A dataset popular for offline evaluation has been shown to contain duplicate data and thus to lead to wrong conclusions in the evaluation of algorithms. Often, results of so-called offline evaluations do not correlate with actually assessed user-satisfaction. This is probably because offline training is highly biased toward the highly reachable items, and offline testing data is highly influenced by the outputs of the online recommendation module. Researchers have concluded that the results of offline evaluations should be viewed critically.

Beyond accuracy
Typically, research on recommender systems is concerned with finding the most accurate recommendation algorithms. However, there are a number of factors that are also important.

Diversity – Users tend to be more satisfied with recommendations when there is a higher intra-list diversity, e.g. items from different artists.

Recommender persistence – In some situations, it is more effective to re-show recommendations, or let users re-rate items, than showing new items. There are several reasons for this. Users may ignore items when they are shown for the first time, for instance, because they had no time to inspect the recommendations carefully.
Privacy – Recommender systems usually have to deal with privacy concerns because users have to reveal sensitive information. Building user profiles using collaborative filtering can be problematic from a privacy point of view. Many European countries have a strong culture of data privacy, and every attempt to introduce any level of user profiling can result in a negative customer response. Much research has been conducted on ongoing privacy issues in this space. The Netflix Prize is particularly notable for the detailed personal information released in its dataset. Ramakrishnan et al. have conducted an extensive overview of the trade-offs between personalization and privacy and found that the combination of weak ties (an unexpected connection that provides serendipitous recommendations) and other data sources can be used to uncover identities of users in an anonymized dataset.

User demographics – Beel et al. found that user demographics may influence how satisfied users are with recommendations. In their paper they show that elderly users tend to be more interested in recommendations than younger users.
Robustness – When users can participate in the recommender system, the issue of fraud must be addressed.
Serendipity – Serendipity is a measure of "how surprising the recommendations are". For instance, a recommender system that recommends milk to a customer in a grocery store might be perfectly accurate, but it is not a good recommendation because it is an obvious item for the customer to buy. "[Serendipity] serves two purposes: First, the chance that users lose interest because the choice set is too uniform decreases. Second, these items are needed for algorithms to learn and improve themselves".
Trust – A recommender system is of little value for a user if the user does not trust the system. Trust can be built by a recommender system by explaining how it generates recommendations, and why it recommends an item.
Labelling – User satisfaction with recommendations may be influenced by the labeling of the recommendations. For instance, in the cited study click-through rate (CTR) for recommendations labeled as "Sponsored" were lower (CTR=5.93%) than CTR for identical recommendations labeled as "Organic" (CTR=8.86%). Recommendations with no label performed best (CTR=9.87%) in that study.

Reproducibility
Recommender systems are notoriously difficult to evaluate offline, with some researchers claiming that this has led to a reproducibility crisis in recommender systems publications. The topic of reproducibility seems to be a recurrent issue in some Machine Learning publication venues, but does not have a considerable effect beyond the world of scientific publication. In the context of recommender systems a 2019 paper surveyed a small number of hand-picked publications applying deep learning or neural methods to the top-k recommendation problem, published in top conferences (SIGIR, KDD, WWW, RecSys, IJCAI), has shown that on average less than 40% of articles could be reproduced by the authors of the survey, with as little as 14% in some conferences. The articles considers a number of potential problems in today's research scholarship and suggests improved scientific practices in that area.
More recent work on benchmarking a set of the same methods came to qualitatively very different results whereby neural methods were found to be among the best performing methods. Deep learning and neural methods for recommender systems have been used in the winning solutions in several recent recommender system challenges, WSDM, RecSys Challenge.
Moreover neural and deep learning methods are widely used in industry where they are extensively tested. The topic of reproducibility is not new in recommender systems. By 2011, Ekstrand, Konstan, et al. criticized that "it is currently difficult to reproduce and extend recommender systems research results," and that evaluations are "not handled consistently". Konstan and Adomavicius conclude that "the Recommender Systems research community is facing a crisis where a significant number of papers present results that contribute little to collective knowledge […] often because the research lacks the […] evaluation to be properly judged and, hence, to provide meaningful contributions." As a consequence, much research about recommender systems can be considered as not reproducible. Hence, operators of recommender systems find little guidance in the current research for answering the question, which recommendation approaches to use in a recommender systems. Said & Bellogín conducted a study of papers published in the field, as well as benchmarked some of the most popular frameworks for recommendation and found large inconsistencies in results, even when the same algorithms and data sets were used. Some researchers demonstrated that minor variations in the recommendation algorithms or scenarios led to strong changes in the effectiveness of a recommender system. They conclude that seven actions are necessary to improve the current situation: "(1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research."

Artificial intelligence applications in recommendation

Artificial intelligence (AI) applications in recommendation systems are the advanced methodologies that leverage AI technologies, to enhance the performance recommendation engines. The AI-based recommender can analyze complex data sets, learning from user behavior, preferences, and interactions to generate highly accurate and personalized content or product suggestions. The integration of AI in recommendation systems has marked a significant evolution from traditional recommendation methods. Traditional methods often relied on inflexible algorithms that could suggest items based on general user trends or apparent similarities in content. In comparison, AI-powered systems have the capability to detect patterns and subtle distinctions that may be overlooked by traditional methods.  These systems can adapt to specific individual preferences, thereby offering recommendations that are more aligned with individual user needs. This approach marks a shift towards more personalized, user-centric suggestions.

Recommendation systems widely adopt AI techniques such as machine learning, deep learning, and natural language processing. These advanced methods enhance system capabilities to predict user preferences and deliver personalized content more accurately. Each technique contributes uniquely. The following sections will introduce specific AI models utilized by a recommendation system by illustrating their theories and functionalities.

AI-based approaches

KNN-based collaborative filters
Collaborative filtering (CF) is one of the most commonly used recommendation system algorithms. It generates personalized suggestions for users based on explicit or implicit behavioral patterns to form predictions. Specifically, it relies on external feedback such as star ratings, purchasing history and so on to make judgments. CF make predictions about users' preference based on similarity measurements. Essentially, the underlying theory is: "if user A is similar to user B, and if A likes item C, then it is likely that B also likes item C."

There are many models available for collaborative filtering. For AI-applied collaborative filtering, a common model is called K-nearest neighbors. The ideas are as follows:
 Data Representation: Create a n-dimensional space where each axis represents a user's trait (ratings, purchases, etc.). Represent the user as a point in that space.
 Statistical Distance: 'Distance' measures how far apart users are in this space. See statistical distance for computational details
 Identifying Neighbors: Based on the computed distances, find k nearest neighbors of the user to which we want to make recommendations
 Forming Predictive Recommendations: The system will analyze the similar preference of the k neighbors. The system will make recommendations based on that similarity

Neural networks
An artificial neural network (ANN), is a deep learning model structure which aims to mimic a human brain. They comprise a series of neurons, each responsible for receiving and processing information transmitted from other interconnected neurons. Similar to a human brain, these neurons will change activation state based on incoming signals (training input and backpropagated output), allowing the system to adjust activation weights during the network learning phase. ANN is usually designed to be a black-box model. Unlike regular machine learning where the underlying theoretical components are formal and rigid, the collaborative effects of neurons are not entirely clear, but modern experiments has shown the predictive power of ANN.

ANN is widely used in recommendation systems for its power to utilize various data. Other than feedback data, ANN can incorporate non-feedback data which are too intricate for collaborative filtering to learn, and the unique structure allows ANN to identify extra signal from non-feedback data to boost user experience. Following are some examples:
 Time and Seasonality: what specify time and date or a season that a user interacts with the platform
 User Navigation Patterns: sequence of pages visited, time spent on different parts of a website, mouse movement, etc
 External Social Trends: information from outer social media

Natural language processing
Natural language processing is a series of AI algorithms to make natural human language accessible and analyzable to a machine. It is a fairly modern technique inspired by the growing amount of textual information. For application in recommendation system, a common case is the Amazon customer review. Amazon will analyze the feedbacks comments from each customer and report relevant data to other customers for reference. The recent years have witnessed the development of various text analysis models, including Latent Semantic Analysis (LSA), Singular value decomposition (SVD), Latent Dirichlet allocation (LDA), etc. Their uses have consistently aimed to provide customers with more precise and tailored recommendations.

See also

 Rating site
 Cold start
 Collaborative filtering
 Collective intelligence
 Content discovery platform
 Enterprise bookmarking
 Filter bubble
 ACM Conference on Recommender Systems
 Personalized marketing
 Preference elicitation
 Product finder
 Configurator
 Pattern recognition

References

Further reading
Books

 Kim Falk (January 2019), Practical Recommender Systems, Manning Publications,

 Seaver, Nick (2022). Computing Taste: Algorithms and the Makers of Music Recommendation. University of Chicago Press.

Scientific articles
 Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan. (2002) Content-Boosted Collaborative Filtering for Improved Recommendations.  Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187–192, Edmonton, Canada, July 2002.

.
.

External links

 Hangartner, Rick, "What is the Recommender Industry?", MSearchGroove, December 17, 2007.
 ACM Conference on Recommender Systems
 Recsys group at Politecnico di Milano
 Data Science: Data to Insights from MIT (recommendation systems)

Information systems
Mass media monitoring

Social information processing

Recommender system


La Copa Centenario de Belo Horizonte fue una competición de fútbol de carácter amistoso celebrada en agosto de 1997 en la Ciudad de Belo Horizonte, capital del Estado de Minas Gerais, Brasil. Participaron 8 clubes de 4 países diferentes.

La competición fue organizada por la Federación Mineira de Fútbol en conjunto con la Prefectura de Belo Horizonte y medios de prensa, con el objetivo de conmemorar los 100 años de la fundación de la Capital Mineira.

La final fue entre dos clubes locales, el Atlético Mineiro y el Cruzeiro.  El club Galo se proclamó vencedor tras imponerse por un marcador de 2-1.

El partido entre Atlético Mineiro y AC Milan fue el último partido del mediocampista Toninho Cerezo, a los 42 años de edad.

Participantes

  AC Milan
  América Mineiro
  Atlético Mineiro
  Benfica
  Corinthians
  Cruzeiro
  Flamengo
  Olimpia

Resultados

Grupo A

Grupo B

Final

</center>

Goleadores

Referencias

Torneos amistosos de fútbol de Brasil
Fútbol en 1997
Competiciones de fútbol celebradas en Brasil
Deporte en Brasil en 1997
Deporte en Belo Horizonte

Copa Centenario de Belo Horizonte


Die Röhrchentasche ist eine spezielle Form eines Separators für elektrochemische Elemente wie elektrolytische und galvanische Zellen. Sie finden Verwendung in industriellen Bleiakkumulatoren wie Traktionsbatterien und dienen dort als Behälter für Bleidioxid (PbO2), welches das aktive Grundmaterial für die positiven Elektroden bildet.

Aufbau
Röhrchentaschen bestehen aus einer Anzahl von tangential miteinander verbundenen runden Röhrchen aus Polyestervlies oder Polyestergewebe, die auf beiden Seiten offen sind. Die Anzahl der Röhrchen, die Länge sowie der Durchmesser können, je nach Batterietyp in dem sie verwendet werden, unterschiedlich sein.

Herstellung
Die Röhrchentaschen werden in zwei Varianten hergestellt:
  aus gewebten, mit Acrylharz imprägnierten Polyesterbahnen
  aus ungewebtem, mit Acrylharz imprägniertem Polyestervlies

Bei der Herstellung aus ungewebtem Polyestervlies werden zunächst zwei Lagen der gewünschten Breite übereinander gelegt und durch mehrfache parallele Nähte miteinander verbunden. Die Röhrchenform ergibt sich durch Hindurchdrücken von runden Metallstangen zwischen den zwei Lagen bei erhöhten Temperaturen. Durch das Harz im Vlies wird die runde Form nach dem Abkühlen beibehalten. Die Taschen werden mittels eines Glühdrahtes auf die gewünschte Länge abgeschnitten.

Für gewebte Röhrchentaschen werden die zwei Lagen bereits in der Weberei mit den passenden Abständen zusammengewebt. Die Röhrchenform ergibt sich dann genauso wie bei den ungewebten Kollegen.

Anwendung
Die mit Röhrchentaschen hergestellten positiven Elektroden werden auch als Röhrchenplatten bezeichnet. Der Vorteil der heutzutage vorwiegend verwendeten Röhrchenplatten gegenüber den früher verwendeten flachen Platten ist die größere Oberfläche der Platten, wodurch es möglich wird, Batterien mit größerer Kapazität herzustellen.

Röhrchentaschen können zur Verbesserung der positiven Röhrchenplatten gegen Kurzschlüsse mit einer Schutzschicht auf der Hälfte der beiden äußeren Röhrchen versehen werden. Dies geschieht entweder durch Auftragen eines Schmelzklebers oder im Fall der gewebten Taschen alternativ durch eine erhöhte Webdichte (größere Anzahl an Fäden pro Zentimeter).

Weblinks
 Mecondor, Hersteller von Röhrchentaschen
 Amer-Sil, Hersteller von Röhrchentaschen
 Wtorplast, Hersteller von Röhrchentaschen

Batterietechnik
Elektrochemie

Röhrchentasche


Mikael Åke Persbrandt  /ˌmiːkaˈɛl ˈoːkɛ ˌpæːʂˈbrant/ Jakobsberg, Järfälla, Estocolmo, Suecia, 25 de septiembre de 1963) es un actor sueco de teatro y cine.

Carrera
Se formó en el Teatro Real Sueco donde interpretó La muerte de un viajante de Arthur Miller, Don Juan, Rey Lear, La señorita Julia, Esperando a Godot, Julio César y otros clásicos.

EGran parte de su fama procede de su reiterada interpretación del policía Gunvald Larsson en el último reparto de la serie Beck.

Protagonizó el film de Susanne Bier ganador del Premio Óscar a la mejor película extranjera en 2010, Hævnen.

Su último proyecto de relevancia es el personaje del cambiaformas Beorn en las películas de Peter Jackson basadas en El hobbit. Más recientemente, Ha participado en la serie de Netflix "sex education" dando vida al personaje de Jakob Nyman.

Es embajador de buena voluntad de la UNICEF desde 2006.

Filmografía

Películas
 Rederiet (1992)
 Polismördaren (1994)
 Svensson, Svensson (1994)
 Sommaren (1995)
 Anna Holt (1996)
 9 millimeter (1997)
 Sista kontraktet (1998)
 Vuxna människor (1998)
 Nu (2002)
 Alla älskar Alice (2002)
 Rånarna (2003)
 Tre solar (2003)
 Dag och natt (2004)
 Medicinmannen (2005)
 Bang Bang Orangutang (2005)
 Som man bäddar (2005)
 Inga tårar (2005)
 Tjocktjuven (2005)
 Ett öga rött (2007)
 Kautokeino-opprøret (2007)
 Solstorm (2007)
 Maria Larssons eviga ögonblick (Everlasting Moments) (2007)
 Die Patin (2007)
 Oskyldigt dömd (2008)
 Himlens Hjärta (2008)
 Hævnen (2010)
 Películas de Martin Beck, 23 largometrajes de 1997 a 2010;
 Någon annanstans i Sverige (2011)
 Hamilton - I nationens intresse (2011)
 Hypnositören (2012)
 Hamilton - Men inte om det gäller din dotter (2012)
 Mig äger ingen (2013)
 El hobbit: la desolación de Smaug (2013)
 The Salvation (2014)
 El hobbit: la batalla de los Cinco Ejércitos (2014)
 Hamilton - I hennes majestäts tjänst (2016)
  Eurovision : The Story of Fire Saga  (2020)

Enlaces externos
 Página web oficial.

Actores de Suecia
Personas de Estocolmo
Reparto de la trilogía de El hobbit

Mikael Persbrandt


was a village located in Hita District, Ōita Prefecture, Japan.

As of 2003, the village had an estimated population of 1,258 and the density of 14.21 persons per km2. The total area was 88.53 km2.

On March 22, 2005, Kamitsue, along with the towns of Amagase and Ōyama, and the villages of Maetsue and Nakatsue (all from Hita District), was merged into the expanded city of Hita.

Dissolved municipalities of Ōita Prefecture

Kamitsue, Ōita


Bataklık geyiği (Blastocerus dichotomus) geyikgiller (Cervidae) familyasından bir geyik türüdür. Güney Amerika'nın en büyük geyik türü olan bataklık geyiği 2 metre boyunda ve 1,2 metre yüksekliğinde olur. Arjantin, Brezilya, Uruguay ve Paraguay'da bulunur. Bir zamanlar Güney Amerika'nın tüm tropikal bölgelerinde bulunan bu geyik türü artık yalnızca Paraná ve Paraguay havzasında bataklık ve lagün bölgelerinde küçük tecrit olmuş popülasyonlar olarak yaşar.

Geyikgiller
Brezilya'daki memeliler
Pantanal

Bataklık geyiği


Skyggebørn er en film instrueret af Louise Jappe.

Handling
Hvordan er det at være barn i en familie, hvor ens bror eller søster er alvorligt syg? Filmen vil vise, hvordan man som barn oplever det, når ens familie går gennem en krise. Ensomheden, frygten, glæden og det håb og de pludselige impulsive udbrud, der opstår, når en familie kommer gennem sådan et forløb. Hvordan bevarer man som barn sin egen identitet, og hvor finder man de lyspunkter, der gør, at hverdagen er til at klare?

Referencer

Eksterne henvisninger

Danske film fra 2015

Dokumentarfilm fra Danmark

Skyggebørn


Jamie Phillip Croft (Sídney, Nueva Gales del Sur; 4 de agosto de 1981) es un actor australiano, principalmente conocido por haber interpretado a Billy Moss en la serie A Country Practice.

Biografía
Su hermana mayor es la actriz Rebecca Croft.

En febrero de 2008 se casó con la actriz Saskia Burmeister. La pareja comenzó a salir en 2003 después de conocerse durante la película The Pact. Saskia y Jamie le dieron la bienvenida a su primer hijo, Jackson Croft, en mayo de 2012. En junio de 2014 le dieron la bienvenida a su segundo hijo, Bodhi Croft.

Carrera
En 1992 se unió al elenco de la serie A Country Practice donde interpretó a Billy Moss hasta 1993, Previamente en 1991 había aparecido por primera vez en la serie interpretando a Ashley Baker en dos episodios.

En 1995 prestó su voz para el personaje del perro labrador Napoleón en la película del mismo nombre Napoleon. Ese mismo año apareció en la película Power Rangers: la película donde interpretó a Fredd Kelman, un alumno del ranger blanco Tommy Oliver (Jason David Frank) y aliado de los Power Rangers.

En el 2001 interpretó a Rick Hawker en el episodio "Broken Englisg" de la serie Water Rats, anteriormente había interpretado a Danny Banks durante el episodio "Sympathy for the Devil" en 1998.

En el 2002 apareció en un episodio de la exitosa serie Farscape donde interpretó a John Crichton de adolescente, John de adulto fue interpretado por el actor Ben Browder.

En el 2003 Jamie fue el anfitrión del programa de niños Sarvo junto al actor Josh Quong Tart en Nickelodeon.

Filmografía
 Series de Televisión.:

 Películas.:

Teatro.:

Premios y nominaciones

Referencias

Enlaces externos

 Jamie Croft on TV.com
 Jamie Croft - Zimbio

Actores de televisión de Australia
Actores de cine de Australia
Nacidos en Sídney

Jamie Croft


Der Gerichtsbezirk Ferlach ist einer von elf Gerichtsbezirken in Kärnten und umfasst einen Teil der Gemeinden im Bezirk Klagenfurt-Land. Der übergeordnete Gerichtshof ist das Landesgericht Klagenfurt.

Gerichtssprengel
Einwohner: Stand

Städte
 Ferlach ( Ew.)

Marktgemeinden
 Feistritz im Rosental ( Ew.)

Gemeinden
 Sankt Margareten im Rosental ( Ew.)
 Zell ( Ew.)

Siehe auch
 Gerichtsorganisation in Österreich
 Liste österreichischer Gerichte

Weblinks
 Gerichtssuche in der Gerichtsdatenbank des Bundesministeriums für Justiz

Bezirk Klagenfurt-Land
Ferlach

Gerichtsbezirk Ferlach


San Vero Milis (sardinski: Santu 'èru, Santèru) je grad i općina (comune) u pokrajini Oristanu u regiji Sardiniji, Italija. Nalazi se na nadmorskoj visini od 10 metara i ima 2 504 stanovnika.

Prostire se na 72,48 km². Gustoća naseljenosti je 35 st/km².

Susjedne općine su: Baratili San Pietro, Milis, Narbolia, Riola Sardo, Tramatza i Zeddiani.

Izvori

Općine u pokrajini Oristano

San Vero Milis


Sand stargazers are blennioids; perciform marine fish of the family Dactyloscopidae. Found in temperate to tropical waters of North and South America; some may also inhabit brackish environments. The giant sand stargazer (Dactylagnus mundus) is the largest at 15 cm in length; all other species are under 10 cm.

These blennies are named well: sand stargazers have protruding eyes on the top of their heads, fixed in an upward gaze, and may be on stalks. Their large mouths are also upturned. The dorsal fin is long and may or may not be continuous, with seven to 23 spines; the pelvic fins are situated below the throat and possess one spine. The anal fin is equally long and flowing. The mouth is fringed, and like the upper edge of the operculum (the gill cover), this fringe is divided into finger-like structures. The body is greatly elongated, and coloration is generally drab.

As their name would suggest, sand stargazers spend most of their time buried in sandy substrates waiting for unsuspecting prey; only the eyes, nose and mouth are usually visible. Their mode of respiration is also unique among the blennioids, using a branchiostegal rather than opercular pump; this is thought to be an adaptation to their largely sedentary, obscured lives. Sand stargazers generally stay within shallow (< 10 m) intertidal zones in areas protected from surges. Small invertebrates and fish make up the bulk of the sand stargazer's diet.

The family name Dactyloscopidae derives from the Greek words daktylos meaning "finger" (a reference to the divided mouth and operculum fringes) and skopein meaning "to watch".

See also
List of fish families

References

Blenniiformes

Sand stargazer


Fredericia Byorkester er et amatørsymfoniorkester med basis i Fredericia.

Orkestret er dannet i 1979-80 på initiativ af Fredericia Musikforening, og kaldte sig frem til 2000 Fredericia Musikforenings Orkester. Det har haft mindst 2 forgængere. I årene efter 1920 fandtes et større ensemble ved navn Fredericia Filharmoniske Orkester og fra 1946 og ca. 10 år frem et andet orkester ved navn Fredericia Byorkester. Det vides ikke om disse to ensembler har nogen forbindelse til det nuværende byorkester.

Fredericia Byorkester har ca. 30 faste medlemmer som øver i Tøjhuset i Fredericia. Ved koncerter spilles med fuld besætning 50-56 musikere med både professionelle og amatører som assistenter. De dygtigste elever fra musikskolen deltager også i orkestret. Orkesteret spiller to årlige koncerter med professionelle solister, en forårskoncert på Fredericia Teater og en efterårskoncert i Tøjhuset i Fredericia.

Orkestrets repertoire strækker sig fra klassiske standard-værker af Beethoven, Mozart og Bach til Franz Lehar, filmmusikken til Ringenes Herre og enkelte helt nye værker. Fra 1993-2004 var trompetisten Henrik Rønnow den faste dirigent, og fra 2006 har Anne Cathrine Kielland Lund stået i spidsen.

Kilder
Orkestrets netsted
Om Fredericia Byorkester 1946
Om Fredericia Filharmoniske Orkester i 1920'erne

Amatørsymfoniorkestre fra Danmark

Fredericia Byorkester


Zooropa is the eighth studio album by Irish rock band U2. Produced by Flood, Brian Eno, and the Edge, it was released on 5 July 1993 on Island Records. Inspired by the band's experiences on the Zoo TV Tour, Zooropa expanded on many of the tour's themes of technology and media oversaturation. The record was a continuation of the group's experimentation with alternative rock, electronic dance music, and electronic sound effects that began with their previous album, Achtung Baby, in 1991.

U2 began writing and recording Zooropa in Dublin in February 1993, during a six-month break between legs of the Zoo TV Tour. The record was originally intended as an EP to promote the "Zooropa" leg of the tour that was to begin in May 1993, but during the sessions, the group decided to extend the record to a full-length album. Pressed for time, U2 wrote and recorded at a rapid pace, with songs originating from many sources, including leftover material from the Achtung Baby sessions. The album was not completed in time for the tour's resumption, forcing the band to travel between Dublin and their tour destinations in May to complete mixing and recording.

Zooropa received generally favourable reviews from critics. Despite none of its three singles—"Numb", "Lemon", and "Stay (Faraway, So Close!)"—being hits consistently across regions, the record sold well upon release, charting in the top ten of 26 countries. The album's charting duration and lifetime sales of 7 million copies, however, were less than those of Achtung Baby. In 1994, Zooropa won the Grammy Award for Best Alternative Music Album. Although the record was a success and music journalists view it as one of the group's most creative works, the band regard it with mixed feelings.

Background

U2 regained critical favour with their commercially successful 1991 album Achtung Baby and the supporting Zoo TV Tour in 1992. The record was a musical reinvention for the group, incorporating influences from alternative rock, industrial music, and electronic dance music into their sound. The tour was an elaborately staged multimedia event that satirised television and the viewing public's overstimulation by attempting to instill "sensory overload" in its audience. The band finished 1992 with one of their most successful years, selling 2.9 million concert tickets and reaching 10 million copies sold for Achtung Baby. Their 73 North American concerts from the year grossed US$67 million, easily the highest amount for any touring artist in 1992.

The group concluded the North American "Outside Broadcast" leg of the tour on 25 November 1992, leaving them with a six-month break before the tour resumed in Europe in May 1993 with the "Zooropa" leg. Rather than use the time to rest, lead vocalist Bono and guitarist the Edge were keen to record new material. Following a hectic year of touring, the two did not want to settle back into domestic life. Bono said, "We thought we could live a normal life and then go back on the road [in May 1993]. But it turns out that your whole way of thinking, your whole body has been geared toward the madness of Zoo TV... So we decided to put the madness on a record. Everybody's head was spinning, so we thought, why not keep that momentum going...?" The Edge also wished to distract himself from the emotions he was feeling after separating from his wife during the Achtung Baby sessions in 1991. The other members, bassist Adam Clayton and drummer Larry Mullen, Jr., ultimately agreed to join them for recording.

Recording and production
After handling audio engineering for the recording of Achtung Baby, Robbie Adams was invited by U2 to manage sound mixing on the Zoo TV Tour. Adams also recorded the group's tour soundchecks. In January 1993, the band asked him to compile these recordings and create loops of interesting parts that they could play to in the studio. After Adams spent a few weeks assembling loops, the group entered The Factory in Dublin that February to begin composing rough demos. Bono and the Edge were most involved during this initial demoing process, which lasted six weeks. Audio services company Audio Engineering installed recording equipment at the Factory that included a Soundcraft 6000 mixing console, an Otari MTR100 multi-track recorder, and four Neve console modules. The company also provided outboard gear such as a UREI 1176 Peak Limiter, a dbx 120X-DS subharmonic synthesizer, two Summit and two LA compressors, a Focusrite 115HD equaliser, a Yamaha SPX1000 multi-effects unit, Lexicon PCM-70 and AMS RMX-16 reverb units, and Yamaha NS-10 and EGV monitor speakers.

The group employed Brian Eno and his assisting partner Mark "Flood" Ellis—both of whom worked on Achtung Baby—to produce the sessions; long-time Eno collaborator Daniel Lanois was busy promoting his solo album and was unavailable. Similar to the Achtung Baby sessions, Eno worked two-week shifts. The group often gave him in-progress songs to adjust and to which he could add his own personality. Initially, the band did not have a clear plan for how they would release the material being written. At the time, Clayton said, "I don't know if what we're doing here is the next U2 album or a bunch of rough sketches that in two years will turn into the demos for the next U2 album." The Edge was a proponent of making an EP of new material to promote the upcoming leg of the tour, describing his mentality as thus: "We've got a bit of time off. We've got some ideas hanging around from the last record, let's do an EP, maybe four new songs to spice the next phase of the tour up a bit. It'll be a fan thing. It'll be cool."

Soon after the sessions commenced, Bono pushed for the band to work towards a full-length album. The Edge was initially hesitant, but saw the opportunity as a challenge to quickly record an album before returning to tour and prove the band had not become spoiled by the luxury of ample recording time. Additionally, Bono and the band's manager Paul McGuinness had discussed the possibility of releasing a "one-two punch" of records since the beginning of the Achtung Baby sessions. In early March, U2 reached a consensus to work towards a full-length album. Much like they had for the Achtung Baby sessions, the band split work between two studios at once; Adams operated a Soundtracs mixing console at The Factory, while Flood used an SSL console at the newly relocated Windmill Lane Studios.

Due to the time limit, U2 were forced to write and record songs at a more rapid pace. They continued their long-time practice of jamming in the studio. Eno used an eraseable whiteboard to give instructions and cues to the band while they jammed; he pointed at chords and various commands, such as "hold", "stop", "change", and "change back", to direct their performances. Flood recorded the material, while Eno alternated between performing with U2 and joining Flood in the control room. At the end of a given week, Eno and Flood compared their notes and compiled the week's best jams onto a cassette for the band to listen to. The producers edited together their favourite sections of the jams and then discussed the arrangements with the group. U2 suggested alterations and added lyrics and melodies, before performing to the edited arrangements. To record all of the band's material and test different arrangements, the engineers utilised a technique they called "fatting", which allowed them to achieve more than 48 tracks of audio by using a 24-track analogue recording, a Fostex D20 timecode-capable DAT recorder, and an Adams Smith Zeta Three synchroniser; Adams went through 180 two-hour DAT tapes during the recording sessions. The production crew faced issues with audio spill at The Factory, as all group members recorded in the same room as the mixing desk and Bono frequently sang in-progress lyrics that would need to be replaced. Gobos and wood booths were built to separate the performers' sounds as much as possible.

Songs originated from and were inspired by a variety of sources. "Zooropa" was the result of combining two separate pieces of music together, one of which the band discovered while reviewing recordings of tour soundchecks. The verse melody to "Stay (Faraway, So Close!)" and an instrumental backing track that became "Numb" were originally from the Achtung Baby sessions. "Babyface", "Dirty Day", "Lemon", and "The Wanderer" were written during the Zooropa sessions. Country singer Johnny Cash recorded vocals for "The Wanderer" during a visit to Dublin, and although Bono recorded his own vocals for the song, he preferred Cash's version. The production crew and the band debated which version to include on the record. Throughout the sessions, U2 were undecided on a unifying musical style for the release, and as a result, they maintained three potential track listings—one for the best songs, one for "vibes", and one for a soundtrack album. Bono suggested editing the best segments of songs together to create a montage.

As May's "Zooropa" tour leg approached, U2 continued to record while simultaneously rehearsing for the tour. Their time limit prevented them from working on live arrangements for any of the new songs. Despite the sessions' rapid pace, the album was not completed by the time they had to resume touring. Moreover, Flood and Eno had to begin work on other projects. The Edge remembers everyone was telling the group, "Well, it's an EP. You did good but there's a lot more work needed to finish some of these songs." However, the band did not want to shelve the project, as they believed they were on a "creative roll" and that they would be in a completely different frame of mind if they revisited the material six months later.

The group's solution was to fly back and forth between Dublin and their concert destinations for about ten days to finish recording and mixing at night and during their off-days. Clayton called the process "about the craziest thing you could do to yourself", while Mullen said of it, "It was mad, but it was mad good, as opposed to mad bad." McGuinness later said the band had nearly wrecked themselves in the process. The group simultaneously used three separate rooms at Windmill Lane to mix, overdub, and edit. Adams said the hectic approach meant "there was never anybody sitting around waiting or doing nothing". Flood called the period one of "absolute lunacy". Eschewing console automation, the engineers adopted a "live performance" attitude to mixing, based on past experiences with Lanois. The band and production crew sat in on the mixing and offered encouragement, creating, as Adams put it, "a kind of cheerleader thing. It all induces a nervous energy in you and creates a lot of pressure, and gives the whole thing a performance feel." Flood had to depart about a week prior to the completion of mixing due to prior obligations to produce Nine Inch Nails in Los Angeles. The recording of Zooropa concluded on 14 May 1993.

In the final weeks, the band decided to exclude the traditional rock songs and guitar-driven tracks they had written in favour of an "album of disjointed, experimental pop". The Edge received a production credit—his first on a U2 record—for the extra level of responsibility he assumed for the album. Twenty songs were recorded during the sessions, but ultimately 10 were chosen for the final track listing. One piece that was left off the record was "In Cold Blood", which featured somber lyrics written by Bono in response to the Bosnian War and was previewed prior to the album's release. Other tracks that were left off the album included "Hold Me, Thrill Me, Kiss Me, Kill Me", "If God Will Send His Angels", "If You Wear That Velvet Dress", and "Wake Up Dead Man". The first was later released as a single from the Batman Forever soundtrack in 1995, and the latter three were included on the band's following studio album, Pop, in 1997.

Composition

Music

With an even more "European" musical aesthetic than Achtung Baby, Zooropa is a further departure from the group's "rootsy" sound of the late 1980s. Much like how the group embraced technology for the Zoo TV Tour, they utilized technology as a musical resource to a greater extent on Zooropa. The record exhibits additional influences from alternative rock, electronic dance music, and industrial music—it is more synthesised than U2's past work, featuring various sound effects, audio loops, and use of synthesisers. In addition to the Edge playing synthesiser, Brian Eno received credit for the instrument on six tracks.

The Edge's guitar playing on Zooropa marks a further shift away from his trademark style, highlighted by a heavier reliance on guitar effects and the songs' reduced emphasis on his guitar parts.  The danceable "Lemon", called a "space-age German disco" by Stephen Thomas Erlewine, features a gated guitar part. The distorted "Daddy's Gonna Pay For Your Crashed Car" was described by Bono as "industrial blues". The instrumentation of the closing song, "The Wanderer", consists primarily of a synthesised bassline and was described by the group as resembling the "ultimate Holiday Inn band from hell". The song was sequenced as the final track because U2 wanted to end the album on a "musical joke".

Similar to how the Zoo TV Tour display screens sampled video footage from television programming, a number of songs from Zooropa sample audio. The introduction to the title track, "Zooropa", contains a noisy collage of indecipherable human voices from radio signals—credited to the "advertising world"—played over sustained synthesiser chords. The industrial-influenced "Numb" features a noisy backdrop of sampled, rhythmic noises, including "arcade sounds", a Walkman rewinding, and a Hitler Youth boy banging a bass drum in the 1935 propaganda film Triumph of the Will.  "Daddy's Gonna Pay For Your Crashed Car" begins with a snippet of fanfare from Lenin's Favourite Songs and samples MC 900 Ft. Jesus' song "The City Sleeps".

The vocals on Zooropa are a further departure from U2's previous style. As Jon Pareles described, Bono "underplays his lung power" throughout the record, in contrast to his impassioned, belting vocals from past work. Additionally, in songs such as "Lemon" and "Numb", Bono sings in an operatic falsetto he calls the "Fat Lady" voice. Two tracks feature other people on lead vocals: for "Numb", the Edge provides lead vocals in the form of a droning, monotonous list of "don't" commands; for "The Wanderer", country musician Johnny Cash sings lead vocals, juxtaposing the electronic nature of the song with his haggard voice.

Lyrics

Bono is credited as the sole lyricist for eight of the ten songs, while the Edge received sole credit for "Numb". The duo share credits for the lyrics to "Dirty Day". Technology is a common theme on Zooropa, inspired by the group's experiences on the Zoo TV Tour. Jon Pareles wrote that the songs are about how "media messages infect characters' souls", while music journalist David Browne said the songs are concerned with "emotional fracturing in the techno-tronic age". Critic Robert Hilburn interpreted the album as U2 probing into what they saw as the "disillusionment of the modern age".

"Zooropa" is set amongst neon signs of a brightly lit futuristic city. In the song's introduction, background voices ask, "What do you want?" In response to the question, the lyrics in the first three verses consist of various advertising slogans, including, "Better by design", "Be all that you can be", and "Vorsprung durch technik". Critic Parry Gettelman interpreted these lines as meaning to "signify the emptiness of modern, godless life". In the song's second half, the theme of moral confusion and uncertainty is introduced, particularly in the lines "I have no compass / And I have no map".

"Babyface" is about a man practicing his obsessive love for a celebrity by manipulating her image on a TV recording. "Lemon", inspired by an old video of Bono's late mother in a lemon-coloured dress, describes man's attempts to preserve time through technology. This is reflected in lines such as, "A man makes a picture / A moving picture / Through the light projected he can see himself up close". The lyrics to "Numb" are a series of "don't" commands, amidst a noisy backdrop of sounds. The Edge notes that the song was inspired by one of the themes of Zoo TV, "that sense that you were getting bombarded with so much that you actually were finding yourself shutting down and unable to respond because there was so much imagery and information being thrown at you".

In contrast to the technology-inspired lyrics of many songs, others had more domestic themes. "The First Time" was Bono's interpretation of the story of the Prodigal son, but in his version, the son decides not to return home. Similarly, "Dirty Day" was written about a character who abandons his family and returns years later to meet his son. Many of the track's lyrics are taken from phrases that Bono's father commonly used, such as "No blood is thicker than ink" and "It won't last kissing time". "Stay (Faraway, So Close!)" is a love song written for an abused woman.

Bono based his lyrics to "The Wanderer" on the Old Testament's Book of Ecclesiastes, and he modeled the song's character after the book's narrator, "The Preacher". In the song, the narrator wanders through a post-apocalyptic world "in search of experience", sampling all facets of human culture and hoping to find meaning in life. Bono described the song as an "antidote to the Zooropa manifesto of uncertainty", and he believes it presents a possible solution to the uncertainty expressed earlier on the album.

Packaging and title
The sleeve was designed by Works Associates of Dublin under the direction of Steve Averill, who had created the majority of U2's album covers. Brian Williams was the graphic designer and created the digital images and layout. Inspired by the Zoo TV Tour's "highly charged electronic TV images in all of their saturated colours and fizz", Works Associates conceived a "kind of electronic flag" for Zooropa. The cover features a sketch of the circle of stars from the Flag of Europe with an "astrobaby" drawing in the center. The illustration, created by Shaughn McGrath, was an alteration of the "graffiti babyface" by Charlie Whisker that was originally on the face of the Achtung Baby compact disc/vinyl record. The cover's drawing was meant to represent an urban legend of a Soviet cosmonaut supposedly left floating in orbit for weeks after the collapse of the Soviet Union. In the background is a montage of blurred images, similar to the arrangement of images on Achtung Babys sleeve. The images include shots of a woman's face and mouth, as well as photographs of European leaders, including Vladimir Lenin, Benito Mussolini, and Nicolae Ceauşescu. These images are obscured by distorted purple text comprising the names of songs planned for the record that were provided to Works Associates during the sleeve design process. However, the album's track listing was eventually changed and the titles of several songs withheld from the album were accidentally left in the cover image; the songs include "Wake Up Dead Man", "Hold Me, Thrill Me, Kiss Me, Kill Me", and "If You Wear That Velvet Dress". Author Višnja Cogan described this text as giving the impression of a "torn veil".

Zooropa was named for the "Zooropa" leg of the Zoo TV Tour, which began in May 1993 while the band completed the record. The name is a portmanteau of "zoo" (from Zoo TV Tour and "Zoo Station") and "Europa". During the album's production, one of the proposed titles was Squeaky.

Release
Zooropa completed U2's contractual obligation to Island Records, and to PolyGram, the multinational that purchased Island in 1989. Although the group were free to sign a new contract elsewhere, their strong relationship with the label and its founder Chris Blackwell prompted the band to remain with Island/Polygram by signing a long-term, six-album deal in June 1993. The Los Angeles Times estimated that the deal was worth US$60 million to U2, making them the highest-paid rock group ever. At the time, the group were cognizant of several emerging technologies that would potentially impact the delivery and transmission of music to consumers in the following years. Author Bill Flanagan speculated, "Record stores could become obsolete as music is delivered over cable, telephone wires, or satellite transmissions directly into consumers' homes." With uncertainty over the future of these technologies and the implications of entertainment and telecommunications companies merging, the band negotiated with Island that the division of their earnings from future transmission systems would be flexible and decided upon at a relevant time. U2 toyed with the idea of releasing Zooropa as an interactive audio-video presentation in lieu of conventional physical formats, but the deadline imposed by the Zoo TV Tour prevented the band from realising this idea.

U2's delivery of Zooropa in late May caught PolyGram somewhat off-guard, because they were not expecting a new album by the group for several years. With Achtung Baby, PolyGram had approximately six months to market the record and plan its release strategy, but the sudden completion of Zooropa necessitated a more hurried promotional plan. PolyGram president/CEO Rick Dobbis explained: "For the last one, we prepared for six months. It was like a marathon. But this is like a sprint, and that is the spirit it was made in. The band was so excited about it, they sprinted to complete the album before the ... tour. We want to bring it to the street with that same spirit." Island/PolyGram's and U2's marketing for Zooropa was intended to focus less on singles and more on the record as a whole, and ultimately, only three singles were released, compared to Achtung Babys five singles. The first single "Numb" was released in June 1993 exclusively on VHS as a "video single". The music video was directed by Kevin Godley. The song peaked at number seven in Australia and number nine in Canada, while reaching number two on the US Billboard Modern Rock Tracks chart. However, it failed to chart on the singles charts in the UK or US.

Zooropa was released on 5 July 1993, during the Zooropa leg of the Zoo TV Tour. An initial shipment of 1.6 million copies was made available in stores at the time of release. Two additional commercial singles were released from the album. "Lemon" received a limited commercial release in North America, Australia, and Japan in September 1993. The single peaked at number six in Australia and number three on the Modern Rock Tracks chart. The final commercial single was "Stay (Faraway, So Close!)", released worldwide on 22 November 1993. It was the album's most successful single, topping the Irish Singles Chart and peaking at number five in Australia, number six in New Zealand, number four in the UK, and number 61 in the US—making it the record's only single to chart on the UK Singles Chart and Hot 100. "Zooropa" was released as a promotional single in Mexico and the United States.

Reissues
In October 2011, Achtung Baby was reissued to commemorate its 20th anniversary; CD copies of Zooropa were included in the "Super Deluxe" and "Über Deluxe" editions of the release. Continuing a campaign by U2 to reissue all of their records on vinyl, Zooropa was re-released on two 180-gram vinyl records on 27 July 2018. Remastered under the Edge's direction, the reissue included two remixes to commemorate the album's 25th anniversary: "Lemon (The Perfecto Mix)" and "Numb (Gimme Some More Dignity Mix)".
 Each copy includes a download card that can be used to redeem a digital copy of the album. To commemorate the album's 30th anniversary, in October 2023 the group will release a limited-edition yellow vinyl pressing of the album that contains a new photo from 1993 on the inner gatefold.

Reception

Critical reaction

Zooropa received generally favourable reviews from critics. Anthony DeCurtis of Rolling Stone wrote in his four-star review that the album was "a daring, imaginative coda to Achtung Baby" and that "it is varied and vigorously experimental, but its charged mood of giddy anarchy suffused with barely suppressed dread provides a compelling, unifying thread". Spin wrote a positive review, commenting that the record "sounds mostly like a band shedding its skin, trying on different selves for size". The review said the album "has the feel of real collectivity", praising the cohesiveness of the individual band members' playing. The review concluded by saying Zooropa "indicates U2 might be worthy of whatever absurd mutations the '90s throw our way". Jon Pareles of The New York Times praised the group for transforming themselves and becoming "raucous, playful and ready to kick its old habits". Pareles enjoyed the sonics and electronic effects that made the "sound of a straightforward four-man band ... hard to find", and he commented that "The new songs seem destined not for stadiums ... but for late-night radio shows and private listenings through earphones." The Orlando Sentinel gave the record a rating of three-out-of-five stars, commenting, "Although U2 leans heavily on the electronic sound of contemporary dance music, the rhythm tracks on Zooropa are less than propulsive." The review said that Eno's production and the electronic flourishes made the album interesting, but that ultimately, "there's nothing especially hummable" and "the songs are not very memorable".

David Browne of Entertainment Weekly gave Zooropa an "A", calling it "harried, spontaneous-sounding, and ultimately exhilarating album". Browne judged it to sound "messy" and "disconnected", but clarified "that sense of incoherence is the point" in the context of the record's technology themes. He concluded, "For an album that wasn't meant to be an album, it's quite an album." Robert Hilburn of the Los Angeles Times gave the record a maximum score of four stars. In two separate articles, he said that it "captured the anxious, even paranoid tone of the Zoo TV Tour" so much so that "it stands as the first tour album that doesn't include any of the songs from the tour" and yet sounds like a "souvenir" of Zoo TV. In a positive review, Jim Sullivan of The Boston Globe called the album a "creative stretch", noting that the band experimented more yet retained their recognizable sound. He commented that the group's "yearning anthemic reach" and "obvious, slinky pop charm" were replaced with "darker corners, more disruptive interjections, more moodiness". Paul Du Noyer of Q gave Zooropa a score of four-out-of-five stars, finding a "freewheeling feel of going with the flow" throughout the album and calling it "rootless and loose, restless and unsettled". For Du Noyer, U2 sounded "monstrously tight as a performing unit and fluidly inventive as composers, so the results transcend the merely experimental".

A review from The New Zealand Herald was more critical, saying that the album started as an EP and "just got longer but not necessarily better". The publication called it "more perplexing than challenging" and commented that it "sounds like the biggest band in the world having one of the biggest, strangest mid-life crises". Jim DeRogatis of the Chicago Sun Times gave the record a three-and-a-half star review, calling it "inconsistent", but admitting "it's satisfying and surprising to hear a band of U2's status being so playful, experimental, and downright weird". Robert Christgau gave the album a B−, calling it "half an Eno album" in the same manner that David Bowie's Eno-produced albums Low and "Heroes" were, but saying, "The difference is that Bowie and Eno were fresher in 1977 than Bono and Eno are today." The Irish media were more critical in their reviews of the album; George Byrne of the Irish Independent said, "The songs sound like they were knocked up in double-quick time and with about as much thought put into the lyrics as goes into a DJ's timecheck". Byrne remarked that the record resembles "a lot of mickey-taking over a variety of drum patterns". In a retrospective, four-star review, Stephen Thomas Erlewine of AllMusic stated that "most of the record is far more daring than its predecessor". For him, although there were moments that the album was "unfocused and meandering ... the best moments of Zooropa rank among U2's most inspired and rewarding music".

Accolades
Zooropa finished in 9th place on the "Best Albums" list from The Village Voices 1993 Pazz & Jop critics' poll. At the 36th Annual Grammy Awards, it won the award for Best Alternative Music Album. In his acceptance speech, Bono sarcastically mocked the "alternative" characterisation the album received and used a profanity on live television: "I think I'd like to give a message to the young people of America. And that is: We shall continue to abuse our position and fuck up the mainstream." Zooropa was also nominated for Album of the Year at the 1993 GAFFA Awards in Denmark.

Commercial performance
The album performed very well commercially, debuting at number one in the United States, United Kingdom, Canada, Australia, New Zealand, France, Germany, Austria, Sweden, and Switzerland. It also reached number one in the Netherlands, Italy, Japan, Norway, Denmark, Ireland, and Iceland. In the US, the album spent its first two weeks on the Billboard 200 at the top spot, staying in the top 10 for seven weeks. In its first week on sale, Zooropa sold 377,000 copies in the US, the group's best debut in the country to that point. The album reached the top 10 in 26 countries.

Despite reaching impressive peak positions, Zooropa had a shorter stay on the music charts than Achtung Baby did. In total, Zooropa spent 40 weeks on the Billboard 200, 61 fewer weeks than Achtung Baby. Zooropa spent 34 weeks on the UK Albums Chart, nine of which were in the top ten, but it charted in the UK for 59 fewer weeks than Achtung Baby.

According to Nielsen Soundscan, Zooropa had sold 1.8 million copies in the US by the end of 1993, the 22nd-highest total in the country that year, and by February 1997 sales in the US had reached 2.1 million copies. The album has been certified 2× Platinum certification in the US by the Recording Industry Association of America, 3× Platinum in Australia, Platinum in the UK, and 4× Platinum in both New Zealand and Canada. To date, it has sold more than 7 million copies.

Zoo TV Tour

The band began the Zoo TV Tour in February 1992 in support of Achtung Baby. In contrast to the austere stage setups of previous U2 tours, Zoo TV was an elaborate multimedia event. It satirised television and the viewing public's overstimulation by attempting to instill "sensory overload" in its audience. The stage featured large video screens that showed visual effects, random video clips from pop culture, and flashing text phrases. Live satellite link-ups, channel surfing, crank calls, and video confessionals were incorporated into the shows.

The Zooropa album was released in July 1993, halfway through the Zooropa leg of the tour. Of the 157 shows the band played during the Zoo TV Tour, approximately 30 of them were after the release of Zooropa. Many of the album's songs found permanent places in the shows' set lists. "Lemon" and "Daddy's Gonna Pay for Your Crashed Car" were performed with Bono in his MacPhisto persona, during encores of the Zoomerang Leg of the tour. "Dirty Day" was also played on this leg after the acoustic set. "Numb" was performed with the Edge playing guitar and on lead vocals, with Mullen performing backing vocals while drumming. "Zooropa" was played only three times and "Babyface" twice more at the same shows on the Zooropa leg, but they were cut out of the set list after the band were displeased with how they sounded live. "Stay (Faraway, So Close!)" was performed acoustically for the Zooropa and Zoomerang legs.

Legacy
Although the record was a success, in the years following its release, the group have regarded it with mixed feelings and rarely play its material in live performances. Bono said, "I thought of Zooropa at the time as a work of genius. I really thought our pop discipline was matching our experimentation and this was our Sgt. Pepper. I was a little wrong about that. The truth is our pop disciplines were letting us down. We didn't create hits. We didn't quite deliver the songs. And what would Sgt. Pepper be without the pop songs?" The Edge said that he did not think the songs were "potent", further stating, "I never thought of Zooropa as anything more than an interlude... but a great one, as interludes go. By far our most interesting." Clayton said, "It's an odd record and a favourite of mine." In 2005, Bono claimed that the album's track "Stay (Faraway, So Close!)" is "perhaps the greatest U2 song".

After the release of Zooropa, David Bowie praised the band, writing, "[U2] might be all shamrocks and deutsche marks to some, but I feel that they are one of the few rock bands even attempting to hint at a world which will continue past the next great wall—the year 2000." In 2023, Steven Hyden of Uproxx echoed Bowie's sentiments in a 30th anniversary retrospective on Zooropa: "U2 dared to imagine something that in the present moment seems to be of little common interest: the future... I mean the future as it stood in the '90s, when people looked beyond the 20th century and envisioned a radically different world emerging from a period of political and cultural uncertainty." Hyden felt that U2 had been guided by uncertainty for the record, calling it "artistically successful in that it set out to evoke an increasingly incoherent world by making anyone who heard it also feel incoherent". He believed that unlike other alternative rock albums from 1993, Zooropa was even more relevant in 2023 than when first released, and that it had not become dated: "And that's because the world U2 thought they were commenting on in 1993 was in reality just coming into existence, and it's the world we're living in now." He added, "Above all, Zooropa summons the modern desire to unplug from the grid and reconnect with something 'real' or 'authentic.'"

Edna Gundersen of USA Today said in 2002, "the alien territory of Achtung Baby and Zooropa cemented U2's relevance and enhanced its cachet as intrepid explorers". Neil McCormick wrote about Zooropa, "It feels like a minor work, and generally U2 don't do minor. But if you're not going to make the Big Statement, you're maybe going to come up with something that has the oxygen of pop music." In 1997,  Ann Powers of Spin wrote, "Zooropa took U2 as far from the monastic mysticism of The Joshua Tree as they could go. It freed U2 from itself." In 2013, the magazine published an article by Rob Harvilla that called Zooropa the album that almost killed U2's career. Harvilla referred to the album as "a weird blip best understood as a portent of the burps that followed, a mega-band dipping a big toe into murky art-rock waters before belly-flopping completely with Pop and its subsequent crass, costly, cred-depleting tour misadventures." While lamenting the band's latter-career creative output, he added: "Mark this record, then, as a celebration of a time when U2 was still musically daring; give 'Lemon' credit at least for successfully trolling you. It is the maddening, befuddling, discomfiting, somewhat ill-advised, occasionally inspired sound of very famous, very difficult men trying on some ill-fitting clothes." In 2011, Rolling Stone ranked the record at number 61 on its list of "100 Best Albums of the Nineties".

Track listing

Notes
After "The Wanderer" fades out at 4:41, a "hidden track", consisting of a ringing alarm used to alert disc jockeys of "dead air", starts at 5:13 and plays for 30 seconds.
The 2018 vinyl reissue splits the original album's ten tracks over sides 1–3, with the bonus tracks appearing on side 4.

Personnel
Adapted from the liner notes.

U2
Bono – vocals, guitar
The Edge – guitar, piano, synthesisers, vocals
Adam Clayton – bass guitar
Larry Mullen Jr. – drums, percussion, backing vocals

Additional musicians
Brian Eno – synthesisers, piano, arcade sounds, backing vocals, loops, strings, harmonium
Des Broadbery – loops
Flood – loops
Johnny Cash – lead vocals

Production

Flood – production, mixing, engineering
Brian Eno – production
The Edge – production
Anne-Louise Kelly – album production manager
Robbie Adams – engineering
Willie Mannion – engineering and mixing assistance
Rob Kirwan – engineering and mixing assistance
Mary McShane – engineering and mixing assistance
Suzanne Doyle – studio production manager

Arnie Acosta – mastering
Stewart Whitmore – digital editing
Cheryl Engels – post production coordinator
Terry Cromer – additional recording facilities (Audio Engineering)
Julian Douglas – additional recording facilities (Audio Engineering)
Steve Averill – art direction
Brian Williams – design, computer treatments
Shaughn McGrath – baby illustration

Charts

Certifications and sales

References
Footnotes

Bibliography

External links
Zooropa on U2.com

1993 albums
Albums produced by Brian Eno
Albums produced by the Edge
Albums produced by Flood (producer)
Grammy Award for Best Alternative Music Album
Island Records albums
U2 albums
Dance-rock albums

Zooropa