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Copyright of Annales Françaises de Médecine d'Urgence is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Referring to Paul Ricoeur’s work on narrativity, this article addresses digital subjectivity and the influence of algorithms.

Computer-aided detection software based on artificial intelligence (CAD-IA), combined with digital chest X-rays, has recently been touted as an easy solution to a complex problem: “ending tuberculosis by 2030”. The WHO recommended the use of these devices in 2021, and numerous public/private partnerships have helped to evaluate them, creating a market for these first artificial intelligence-based global health tools. This article explores the stages in the creation of value and a market for these detection algorithms, thanks to an “accelerationist” regime that is well entrenched within the field of global health. By following the social life of the data used to develop, validate, sell, and profit from CAD-IA devices, we analyze how a new form of technological TB detection used in global health, despite its disruptive claims, remains caught up in processes of inequality that drive TB epidemics in many contexts and are indirectly valued scientifically, economically, and politically, by the technology itself.

What is a financial crash, and why does it happen? The answers to these fundamental questions require an investigation of the ontological and epistemic state of the financial markets which will identify the causes of a financial crash, the entities involved, and the relations between them.To this end, I examine several theories on financial systems which have conceptualized financial crashes. I analyze how these theories: a) identify different causes of a crash; b) deal with the basic entities and units of analysis; c) define what we can know about those causes and entities, and what they can reveal about an impending crash. On this basis, I outline some ontological and epistemic preconditions that are necessary for a financial crash to occur. I also examine an example of a price drop on the stock market and the role played by the epistemic and ontic states of the market in that event.

Following a sociological perspective, this article offers a documented overview of issues of racism and discrimination in the digital context, particularly since the emergence in the 2010s of a series of advanced technologies related to artificial intelligence. The functioning of certain algorithms (those using machine learning) is questioned, and a reflection on the training data, the algorithmic biases and the social choices behind the development of these technical tools is carried out. Particular attention is paid to the content recommendation algorithms of social media platforms, considered as the tools of artificial intelligence posing the most important social challenges today. This work was carried out in light of the productions and definitions established by the sociologies of racism, discrimination and race. We also exploit the technical literature in security and reliability of machine learning.

Popüler kültür geniş kitlelerin içerisinde yaşadıkları toplumu anlamlandırmaları noktasında yer aldığı konumu nedeniyle sosyolojik bir olgu olarak öne çıkmaktadır. Bu önemli sosyolojik olgu, kitle iletişim araçlarının yükselişi ile birlikte kapsamını ve ağırlığını arttırmış, bu yönüyle iletişim teknolojileri ile zorunlu ilişkisi bağlamında ele alınması gerekmektedir. Bir iletişim teknolojisi devrimi olarak sunulan dijitalleşme bu bağlamda önem kazanmaktadır. Dijitalleşme yaşantımızın gerçekleştiği yeni bir kültürel uzam olması yönüyle de başlı başına toplumsal bir süreçtir. Bu görüşlerden hareketle çalışma, popüler kültür olgusunun dijitalleşme bağlamında dönüşmekte olduğu ön kabulüyle bu dönüşümü teorik bir tartışmaya açmak ve kavramsal analizini gerçekleştirmek amacındadır. Popüler kültür, birden çok aktörün sürece dahil olduğu karmaşık, ilişkisel bir alan görünümündedir. Gerçekleştirilen kavramsal analizde bu alana ilişkin yapısal özellikler ve alanda gerçekleşen faillik öncelikli odak noktalarını oluşturmaktadır. Yapısal özellikler alanın endüstriyel boyutunun bir uzantısı olarak sunduğu medya ekosistemi ile, gerçekleşen faillik ise bu alan içerisindeki failliğin iki boyutu olarak kitlenin konumu ve algoritma teknolojilerinin analizi ile temellendirilmiştir. Dijitalleşen medya platformları ile geleneksel medyanın ortadan kaybolduğu yönündeki görüşlerin aksine dijital medya ve geleneksel medya giderek birbirine yakınsanan yeni, hızlı ve devasa boyutta bir medya ekosistemi oluşturmaktadır. Bu yeni ekosistem demokratik bir ortam sunma vaadiyle öne çıkıyormuş gibi görünse de ancak hızla ve sıklıkla üretilebilen ve kitle tarafından paylaşılabilecek türde içerikler popüler kültüre mal olmaktadırlar. Buradan hareketle “popüler olmanın” dijitalleşme diskurunun üst ideolojisi olarak konumlandığı düşünülmektedir. Faillik bağlamında ise bu devasa ekosistemde artık pasif, yalnızca alıcı konumundaki bir kitle kavrayışından söz etmek mümkün görünmemektedir. Katılımcı kültür etrafında gerçekleşen ilişkiler popüler kültürün şekillenmesinde büyük önem taşımakla birlikte, sürece dahil bir diğer aktör olarak algoritmalar kitle davranışının şekillenmesinde en az özgür tercihler kadar rol sahibidir. Günümüz popüler kültürü, tek bir aktörün diğer aktörler üzerinde belirleyici olduğu doğrusal modelde değil, aktörler arası ilişkisel bir sürecin sonucu olarak şekillenmekte, gerçekleştirilecek çalışmalarda bu ilişkiselliğin göz önünde tutulması gerekliliği önerilmektedir.

Copyright of Revue du Financier is the property of Societe Cybel and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Vie et Sciences de l'Entreprise is the property of ANDESE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Annales Françaises de Médecine d'Urgence is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article explores the historical and philosophical dimensions of the relationship between machines, particularly artificial intelligence, and politics. It highlights the concern of a loss of human agency and decision-making to machines and algorithms. It discusses the anxieties surrounding the application of cybernetics and the fear of a "machine to govern" that could potentially override human decisions.

This article presents an ethnography of alternative currencies that foregrounds the notion of “circulation”. Building upon a long legacy wherein money is equated with a primary life force—being either water or blood—that is contained within a body, “circulation” became a dominant metaphor for the use of money from the mid-seventeenth century onwards. Imagining money as a liquid that flows and circulates means that remedying economic inequalities and injustice is often reduced to a matter of redistribution. Instead, money is itself an institutional project engineered to distribute resources and authority based on a philosophy of growth and accumulation. Alternative currency initiatives aim to re-design, rather than re-distribute, money. Importantly, they believe the technological fix of a circular software system effectively does away with the inequalities of the capitalist mode of production. What happens when on-going practices towards systemic change converge on money and the economic “body” of a local community is imagined as software?

(Series Information) European Papers - A Journal on Law and Integration, 2021 6(2), 1193-1228 | Article | (Table of Contents) I. Preliminary remarks. - II. The increasing attractiveness of cartels in the age of big data analytics. - III. Collusion between rational algorithms: the relation between market transparency and artificial intelligence in a world of big data. - IV. Existing vs created market conditions: tacit collusion or algorithmic concerted practices? - V. The limited role played by intent and imputability in the antitrust realm: the case of parent company liability and its applicability by analogy to the relation undertakings vis-à-vis algorithms. - VI. Big data analytics does create competitive concerns: should the Commission and national competition authorities commit to tackling algorithmic collusion? - VII. Conclusions. | (Abstract) Due to digital markets' transparency, algorithmic collusion may occur even if algorithms are designed to maximize profits rather than to conspire. The literature suggests that competition rules may not cover algorithmic collusion, being the latter an example of tacit collusion: by monitoring market conditions, each algorithm unilaterally and rationally decides to maintain supra-competitive prices. Data analytics ease the monitoring and reaction to competitors' behaviours, increasing the number of markets subject to tacit collusion. Yet, intention to conspire seems absent. In this Article, it is submitted that algorithmic collusion is different from tacit collusion and can be tackled under EU competition law. In the traditional scenario, undertakings base their rational decisions on the existing markets conditions. While designing their algorithms to maximize profits, undertakings are contributing to create the conditions allowing "tacit" collusion to occur. Moreover, a quasi-strict liability regime applies to antitrust offences, so that intention and imputability play limited roles. As a consequence, if algorithms programmed to maximize profits end up colluding, a rebuttable presumption of the existence of a concerted practice should apply. The practice should be prohibited unless undertakings can prove that, in the specific case, a concerted practice did not occur or that art. 101(3) TFEU applies. Moreover, competition rules may be enforced even without ascertaining any antitrust infringement. Competitive concerns are enough to adopt commitment decisions. Here, the Commission (or a National Competition Authority) may negotiate with the concerned undertakings technical remedies to prevent algorithmic collusion by intervening on the way the algorithms work.

Introduction. The advertising of food supplements on various media channels or in specialty stores with natural products and other places, as well as the lack of informative and educational programs for the population upon side effects and the interaction of food supplements with food and drugs, have led to the development of uncontrolled marketing of these products. Material and methods. PubMed, ResearchGate and EUR-Lex databases were analyzed during 2015-2021, based on search criteria based on: adverse reactions, ingredient new food, food supplements, algorithms. Results. Certain dietary supplements can cause multiple side effects, such as: impaired platelet function by decreased platelet aggregation, gastrointestinal side effects (diarrhea, vomiting), decreased wound healing/epithelialization, bacterial or fungal sepsis, most common in patients older. Herbal resources such as Aloe vera, Matricaria recutita, Taraxacum officinale and others, can cause dermatological side effects and inhibit the elimination of dermatological drugs metabolised by cytochrome P-450 (e.g. terbinafine). Matricaria recutita, Allium sativum, Mentha piperita L. and others, inhibit the enzyme CYP2C9. Another enzyme with a role in the metabolism of dermatological drugs and which is inhibited by plant resources is CYP3A4. Conclusions. It is imperative to legislate the reporting of adverse reactions caused by food supplements, including their interaction with food or drugs.

Whether they are used by private or public entities, facial recognition technologies are subject to debate and controversy, notably around the lack of specific legislation on their use. Although artificial intelligence (AI) and automated decision-making systems have raised questions and sparked debate in Canada and the U.S., police departments have used such technologies in various contexts. Literature demonstrates the challenges brought by facial recognition algorithms range from biases related to errors of identification and matching to challenges of values and ethics, including environmental issues. Despite an increasing trend in the use of leading-edge automated biometric identification technologies, existing legal and policy instruments have not kept pace with the highly evolving field of high-tech. This note provides an overview of the policies and legal frameworks in Canada and in the U.S. with respect to the acquisition, use and integration of facial recognition technologies. In particular, we show that despite an increasing integration of such technologies across various entities, the policy, legal and regulatory frameworks are yet to be adapted to the new reality of AI biometric identification. Issues related to human rights, privacy and equity call for further examination of the instruments in place. [ABSTRACT FROM AUTHOR]

Macro-amputations are extremely serious traumas and represent one of the rare extreme emergencies in hand and upper limb surgery. Their rarity, especially in our developed countries, makes their treatment relatively unknown by surgical and anesthetic teams. However, the action plan to use during a macro-reimplantation, the decisive elements of pre- and post-operative management, and the key stages of the surgery, must be perfectly known, because they determine the success of a major reimplantation, for the limb survival and the future functional result. After a brief historical overview, the literature review proposed here provides an opportunity for an update on these formidable injuries and propose a treatment algorithm to guide the medical team in the management of these complex patients., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Artificial intelligence (AI) is a rapidly advancing technology in our society. The emergency radiology is an area facing an increase of the number of imaging studies and associated to the necessity to promptly deliver an accurate interpretation. The integration of AI algorithms to assist the clinician in providing analyses of the imaging studies while maintaining adequate diagnostic quality opens up new perspectives. There are numerous potential advantages of the implementation of AI in emergency radiology. However, the use of AI faces new challenges, as the algorithms reliability, data security, responsibility issues, and financial, human and material resources., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

The rapidly growing academic and public attention to algorithmic-selection applications such as search engines and social media is indicative of their alleged great social relevance and impact on daily life in digital societies. To substantiate these claims, this paper investigates the hitherto little explored subjective relevance that Internet users assign to algorithmic-selection applications in everyday life. A representative online survey of Internet users comparatively reveals the relevance that users ascribe to algorithmic-selection applications and to their online and offline alternatives in five selected life domains: political and social orientation, entertainment, commercial transactions, socializing and health. The results show that people assign a relatively low relevance to algorithmic-selection applications compared to offline alternatives across the five life domains. The findings vary greatly by age and education. Altogether, such outcomes complement and qualify assessments of the social impact of algorithms that are primarily and often solely based on usage data and theoretical considerations.

Social accessibility involves the capacity of a person to be reached by other people, typically through established social networks. Locative media provide new complications to the issue of social accessibility in urban spaces. They do not connect people with their existing social networks, but rather with people – often strangers – immediately around them. Locative media apps provide users with the ability to manage these anonymous connections through filtering functionalities. This filtering out and filtering in functionality has profound implications for the social relations of strangers in urban spaces. At the heart of the matter is the question of who is made visible and who is made invisible. Many studies of locative media use demonstrate troubling directions in the ways in which they are developed and used. Often, locative media apps reinforce and reify existing racisms and other forms of prejudice, and a general tendency to try to simplify or eliminate differences.

The mediation of love and sexuality has often relied on dating advice. This paper analyses a corpus of dating guides concerning the popular app Tinder. Our analysis suggests that the practice of algorithmic blackboxing, that is, characterizing algorithms as black boxes, is central to these guides that rely on the alleged opacity of Tinder’s algorithms to praise certain products, advice, or services that aim to facilitate matchmaking. The practice of algorithmic blackboxing primarily encourages users to hack Tinder. In this context, the notion of hack stresses the importance for users to modulate their behaviours in ways that make them more algorithmically recognisable, especially in relation to their gender. Thus, we suggest that Tinder’s algorithms participate in reproducing a speculative dating economy.

With the ever-growing application of data science and machine learning in the fourth industrial revolution era, many challenges faced within the energy sector in past decades have now been receiving timely interventions through the proper application of programming and machine learning tools coupled with the implementation and utilization of modern technology. In recent years, balancing in real-time the demand and supply of energy generated from renewable sources such as wind and solar has gained much improvement because of its ability to forecast the quantity of energy that could be produced from the renewable sources using historical data. Likewise, the application of model and algorithms has also helped to predict accurately, the amount of energy that could be produced from a batch of anaerobic digestion process to produce biogas or biomethane of acceptable quality. In this research work, a set of data was collected from an industrial biogas plant and based on the variables from the data set, Design Expert Software version 11 was used to develop mathematical models and algorithms to optimize the production process of the plant based on the feedstock fed into the digesters. The result of the optimization proves that the biogas currently produced from the post-digester tank with methane (CH4) content of about 68.8% can be upgraded to biomethane with methane content of 78.22% without any adjustment to the digesters or production process.

Continuous advances in computer technology have provided good support for the expansion of agricultural research using machine learning. This article considered the current problem of yield forecasting using methods and algorithms of machine learning to support management decision-making in the agricultural sector. For a set of data collected from five districts of the Issyk-Kul region, such as weather conditions, soil characteristics and pre-processing of the sowing area, a study of the yield of various crops using advanced machine learning algorithms, such as the support vector method, k-nearest neighbors, variants of gradient boosting and random forest, etc., is demonstrated. To assess the accuracy of the models, a comparative analysis with the results of multiple regression was carried out. It is shown that powerful regression machine learning algorithms like k-nearest neighbors (KNN), random forest (RF), support vector method (SVR) and gradient boosting (GBR) give tangible results in prediction compared to other machine learning methods (MAPE=10%). The calculation results showed the effectiveness of using algorithms with ensemble methods to solve the problems of yield forecasting, and that environmental factors (weather conditions) have a greater impact on yield than soil genotype.

The operation of Bradley's algorithm is simple. After that, we divide the image into several areas alongside d, take the average value of the sum of the values of Im pixels in this area, add the value, and compare the value of each pixel with the obtained result. 1/8 of the image width and 15% of the average pixel brightness across the area. In general, both of these parameters can and should be changed according to the specific situation. Thus, if the size of the object is larger than the area of a square with a side equal to d, then the center of this object can be taken as the background by the algorithm, and the problem is solved by reducing the value of d, but fine details of the image may be lost. This article provides an overview of some research and image processing analysis on the problem of crop and vegetation image recognition. Remote identification of crops and plants and their cultivation, assessment of horticulture status startup projects are presented. A computer vision system for obtaining images from a drone and assessing the condition of a garden is provided. Digitization methods for data processing are considered.

Copyright of Finance & Business Economies Review is the property of Finance & Business Economies Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article focuses on digital dictionary is a hypertext navigation system. It mentions digital dictionary is a hypertext navigation system and also a lexicographic corpus from which one can simulate the semantic paths of a speaker in the form of a network. It also mentions algorithmic approach to reveal recursive semantic mechanisms and thus to develop a formal approach to the meaning.

Que ce soit par des organismes privés ou publics, l’adoption des technologies de reconnaissance faciale est sujet de controverses, en particulier en raison du manque de lois spécifiques réglementant leur usage. Malgré les questionnements et débats qu’engendrent l’utilisation de l’intelligence artificielle (IA) et la prise de décision automatique dans les pays comme le Canada et les États-Unis, des services de police, entre autres, reconnaissent avoir eu recours à de telles technologies dans divers contextes. La littérature existante montre que les défis qu’apporte l’adoption des technologies ayant recours aux algorithmes de reconnaissance faciale vont des biais liés, notamment, aux erreurs d’identification et d’appariement aux questions d’éthique et de valeurs, en passant par des enjeux environnementaux. Alors qu’une prolifération de ces systèmes de haute technologie permettant l’identification biométrique automatique est de plus en plus remarquable, les outils juridiques et politiques en la matière tardent à s’adapter à un domaine en constante évolution. Cette note fait un tour d’horizon des politiques, des instruments juridiques et des cadres légaux en place au Canada et aux États-Unis vis-à-vis de l’adoption et de l’utilisation des technologies de reconnaissance faciale. En particulier, nous montrons que bien que cette adoption soit chose courante à travers différentes entités, les contextes politiques et les cadres légaux et réglementaires ne permettent pas de faire face à un système d’identification biométrique par l’IA. Les préoccupations en matière de droits de la personne, de vie privée et d’équité invitent à un examen plus approfondi de l’état des instruments en place.

Résumé: Les cibles des lésions anales méritent d'être clarifiées parce qu'elles ne peuvent être évaluées avec les mêmes outils que la maladie luminale. Dans l'état actuel de la pratique ou des publications scientifiques, l'évaluation repose sur une approche pragmatique (nécessité d'une (ré-)intervention chirurgicale), sur des scores mixtes anciens (Perineal Disease Activity Index), des caractéristiques cliniques sommaires (fistule ouverte ou fermée) ou des scores radiologiques plus subtils mais non validés. Les stratégies thérapeutiques médicochirurgicales récentes ont été définies sans que l'on connaisse la quantité d'effet des biothérapies seules ou en combothérapie, et le résultat des stratégies innovantes telles que l'injection in situ de cellules souches. Cette dernière technique bénéficie d'une autorisation de mise sur le marché (AMM) et d'un remboursement alors qu'on ne dispose que d'un nombre encore restreint d'études. Trois catégories de cibles thérapeutiques proposées ont été retenues dans le consensus : symptômes, données d'examen clinique et données IRM. De façon consensuelle, le temps nécessaire pour évaluer cliniquement l'efficacité du traitement était de six mois. La réponse IRM devait comprendre l'absence de collection de 10 mm ou plus à six mois et une diminution franche ou une disparition de l'hyper-intensité du trajet principal à douze mois. The targets of anal lesions need to be clarified because they cannot be assessed with the same tools as luminal disease. In the current state of practice or scientific publications, evaluation is based on a pragmatic approach (need for surgical (re-)intervention), on old mixed scores (Perineal Disease Activity Index), summary clinical characteristics (open or closed fistula) or more subtle but unvalidated radiological scores. Recent medical-surgical treatment strategies have been defined without knowing the effect of biotherapies alone or in combination, and the outcome of innovative strategies such as in situ stem cell injection. The latter technique has a marketing authorisation and reimbursement, although only a limited number of studies are available. Three categories of proposed therapeutic targets were retained in the consensus: symptoms, clinical examination data and MRI data. The consensus was that the time to clinically assess treatment efficacy was 6 months. MRI response should include the absence of a collection of 10 mm or more at 6 months and a clear reduction or disappearance of the hyperintensity of the main pathway at 12 months. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of the application of artificial intelligence on administrative decision theory. Algorithmic software has become increasingly employed at the public administration in choosing between multiple alternatives, allowing the emergence of a new type of decision called "algorithmic administrative decision". We concluded that this new type of decision imposes on the judiciary and legislation a rapid adaptation with the use of algorithmic processing systems for data on the traditional theory of administrative decisions through a conscious update of its legal framework to ensure effective judicial control of their legitimacy on the one hand, and real protection of citizens ’rights on the other hand.

In the beginning, the use of computer programs was limited to drawing the idea that was designed by the designer (sketch) before, and then to the digital design to show it But with the development of the impact of digital techniques and The appearance of parametric design and the term- algorithms have affected the design process itself, now most of the architects and internal- designers are using computer programs to develop ideas and not only drew, but the work of their own prototypes, and these programs can connect between several different types of information that provides the program to be forms streamlines.As we know, the characteristic of the parametric model is not the output but the need to build and maintain the relationships associated with the model. Some researchers develop this approach, such as creating a continuous interactive process of design instead of developing the basic product. All this led to Ideas Generate ideas and new patterns of designs that never existed before, such as Digital design, Folding, Parametric, and Topology.There are many computer programs that have been used to control design, development and modification to obtain multiple results and to obtain unexpected and unpredictable results such as 3D Max and 3D Rhinoceros plug in Grasshopper.It is possible to rely on the digital and Parametric design in the modern architectural design through which the design of the architectural units, partitions and glass partitions can be drawn from the stage of the first sketch until the execution and the work of the first model and then the final product whether the final product is a unit or a glass partition or a glass separator full of The tiles are not typical but can be moved and calculated method of installation and theoretical experiment and ensure the validity of their implementation in practice on the final glass product.

The first orientation test for proteinuria typing is electrophoresis. However, this technique has several drawbacks, such as delayed turnaround time and subjective readings. Some laboratories therefore use quantitative assays of glomerular markers combined with tubular markers. However, the cost of reagents and the instability of certain markers are significant drawbacks for some peripheral laboratories. The aim of this study is to evaluate the implementation of an algorithm based on parameters that can be used by all laboratories for proteinuria typing within a timeframe compatible with the urgency of the situation. Albuminuria and urinary IgG were determined on 161 urines. ROC curves were produced, using urine electrophoresis read by an expert center as the reference method. The decision thresholds used are: glomerular proteinuria is defined by a Albumin+IgGproteinsratio greater than 75.4% (100% specificity), and tubular or overload proteinuria is defined by by a Albuminproteinsratio less than 37.3% (100% sensitivity). Agreement between the results of the algorithm selected and the reference method used in our study was 88 %, with a kappa value of 0.807 (95% CI [0.729 to 0.885]). The algorithm's performance suggests that it can find its place in the diagnostic strategy for clinically significant proteinuria, despite its limited indications. It is up to each biologist to assess the value of this algorithm in relation to the recruitment, habits and needs of clinicians.

Infectious Endocarditis: STRATEGY FOR DIAGNOSIS. The diagnosis of infective endocarditis is often difficult because the clinical presentations are very heterogeneous. Epidemiology has evolved with more acute forms, different microorganisms, and an increase in prevalence in patients with cardiac prosthetic or electronic devices. Diagnosis is based on a clinical suspicion, associated with microbiological data and imaging evidence of lesions of the endocardium. Echocardiography plays a key role, but advanced imaging techniques provide additional information. The 2023 European Society of cardiology (ESC) recommendations like those of 2015 confirmed the essential role of multimodal imaging, integrating lesions highlighted by any imaging technique as major criteria. The diagnostic criteria have thus been modified to consider new epidemiological and imaging data. Different diagnostic strategy algorithms are proposed depending on whether the patient has prosthetic material or not. The endocarditis team is the keystone in this diagnostic approach to improve patient management., Competing Interests: A. Bernard déclare avoir participé à des interventions ponctuelles pour General Electric, Abbott, Boston, BMS-Pfizer, Boehringer Ingelheim, Novartis, Medtronic et avoir été prise en charge, à l’occasion de déplacements pour congrès, par Abbott. N. Chane-Sone, F Dion et A Lemaignen déclarent n’avoir aucun lien d’intérêts.

Chemotherapy-induced nausea and vomiting (CINV) are frequent and dreaded side effects in cancer treatments. CINV has a major impact on patient's condition and quality of life. Prophylaxis is tailored to patient's profile and the emetogenic level of their chemotherapy. The aim of this study is to update the recommendations for CINV prevention and management in pediatric onco-hematology for use in France, by adapting the guidelines of the Pediatric Oncology Group of Ontario (POGO). Clinical practice guideline adaptation is a recognized method for tailoring existing clinical practice guidelines to local context. A multidisciplinary French-speaking panel was formed to discuss about POGO guideline recommendations for the acute and delayed phases, breakthrough, refractory and anticipatory CINV and the evidence supporting them. Panel members were asked whether they wanted to adopt, modify or reject each of the POGO guideline recommendations. Panel members translated each recommendation and adapted recommendations for an implementation in France. Their acceptance required agreement at least 80 % of panel members. Algorithms and tables were created, listing all the recommendations and providing a better overview for decision-making process adapted to the patient's profile. These recommendations should be reviewed for implementation at French institutions caring for pediatric cancer patients and once implemented, the rates of adherence to recommendations and CINV control should be reported., (Copyright © 2024 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.)

The aim of the psychotrauma prevention algorithm is to limit the occurrence of psychotrauma in a subject who has experienced a serious life event, by carrying out an initial assessment to define the severity criterion and the monitoring modality best suited to his or her clinical condition. This approach is in line with the philosophy of outreach and the ethics of concern. Recontacting the patient during the course of treatment helps to maintain the therapeutic link and prevent any deterioration in his condition, thus limiting the risk of his traumatic state becoming chronic., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Introduction: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of an artificial intelligence driven tool (text mining, machine learning, classification…) for the screening and detection of patients, potentially eligible for recruitment in one of the clinical trials open at the "Institut de Cancérologie de Lorraine"., Methods: Computerized clinical data during the first medical consultation among patients managed in an anticancer center over the 2019-2023 period were used to study the performances of an artificial intelligence tool (SAS® Viya). Recall, precision and F1-score were used to determine the artificial intelligence algorithm effectiveness. Time saved on screening was determined by the difference between the time taken using the artificial intelligence-assisted method and that taken using the standard method in clinical trial participant screening., Results: Out of 9876 patients included in the study, the artificial intelligence algorithm obtained the following scores: precision of 96 %, recall of 94 % and a 0.95 F1-score to detect patients with breast cancer (n=2039) and potentially eligible for inclusion in a clinical trial. The screening of 258 potentially eligible patient's files took 20s per file vs. 5min and 6s with standard method., Discussion: This study suggests that artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient screening process, as well as in approaches to feasibility, site selection, and trial selection., (Copyright © 2024 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.)

Medical research uses increasingly massive, complex and interdependent data, the analysis of which requires the use of specialized algorithms. In order to independently reproduce and validate the results of a scientific study, it is no longer sufficient to share the text of the article as an open-access document, together with the raw research data according to the open-data approach. It is now also needed to share the algorithms used to analyze the data with other research teams. Free and open-source software precisely responds to this need to disseminate technical knowledge at a large scale. In this paper, we present several examples of free software used in medicine, with a particular focus on medical imaging.

This article aims to present a debate over the increasingly complex and widespread use of measurement indicators and performance of digital content in the journalistic industry, considering the case study of La Nación, one of the leading news institutions in Argentina. The paper reconstructs the introduction and architecture of metrics measurement in the newsroom of La Nación, and the journalists’ perceptions and experiences regarding these organizational changes. The focus will be on the adoption of the Score, an algorithmic metric developed in-house at La Nación, designed with journalistic input and eventually modified to include economic factors. The findings confirming the tensions between professional and commercial logics produced by adopting digital metrics in the newsroom; and suggesting that journalists experiences metrics as strong disciplining influence. All this is involved in an uncertain context implicating the financing of Argentinean digital media along with the decline in the traditional journalistic business model.

Editorial of the nº10, vol. 4, of 2021.

The impact of artificial intelligence algorithms is widely felt on many different aspects of life, and because of the efficiency and speed of such algorithms, decisions can be taken as well as their implementation on behalf of humans in the field of criminal justice. Despite the progress that has been made, there are still great concerns on the issue of rapid mechanization of the justice sector in general, and the judiciary in particular. Therefore, discussion and analysis of the impact of such algorithms on the justice system in this paper is of high importance. Such discussions will be carried out through three chapters; while chapter one reviews the concept of algorithms and their importance in criminal justice, chapters two and three are devoted to discuss the use of artificial intelligence algorithms in pre-trail and during trail stages respectively. This work, however, ended with a conclusion that contained the main results of the research and recommendations

The impact of artificial intelligence algorithms is widely felt on many different aspects of life, and because of the efficiency and speed of such algorithms, decisions can be taken as well as their implementation on behalf of humans in the field of criminal justice. Despite the progress that has been made, there are still great concerns on the issue of rapid mechanization of the justice sector in general, and the judiciary in particular. Therefore, discussion and analysis of the impact of such algorithms on the justice system in this paper is of high importance. Such discussions will be carried out through three chapters; while chapter one reviews the concept of algorithms and their importance in criminal justice, chapters two and three are devoted to discuss the use of artificial intelligence algorithms in pre-trail and during trail stages respectively. This work, however, ended with a conclusion that contained the main results of the research and recommendations

In the era of the data revolution (Kitchin, 2014), new kinds of data and data sources allow researchers to share innovative ways of studying society and its dynamics to comprehend the peculiarities related to the territory. In fact, the analysis of space-time data extracted from social media can support the comprehension of complex social phenomena such as, for example, mobility (Noulas, Scellato, Lathia, & Mascolo, 2012; Yuan et al., 2017; Zhang et al., 2016) or where and when certain social activities are carried out, such e.g., the participation in big events, tourist experiences or the monitoring and management of emergencies and security. Following the actual dissemination of use of social media in the cities, the analysis of the spatial dimension is considered particularly promising in the field of urban analysis (Singleton et al., 2018) and in the emerging line of digital studies. Nowadays, the intentional action of many people is increasingly contributing to the production and sharing of spatial data with a relevant impact on the construction of Territorial Information Systems (Zupi, 2017). This new category of spatial data is commonly known "Volunteered Geographic Information" (VGI) (Goodchild, 2007). The analysis of this kind of data leads to an innovative scenario for the collection and diffusion of geographic information from millions of users all over the world. It provides valuable insight into their perceptions and needs, opinions on places, events, and daily routes, which allow researchers to better follow traces about the knowledge of the local identities of a given place (Campaign, 2014), information flows, and social networks within society (Stefanidis et al., 2013). However, a clear awareness of the limits of these tools of investigation is needed. For instance, problems may arise regarding representativeness of population, or even technical issues, such as for scarping systems which are not able to offer all the effective amount of data available to the researcher. Despite this, these kinds of studies are taking hold in the social sciences which are paying particular attention to both data and source contextualisation. As a result, they are interesting and valid tools to study issues that classic instruments are not able to perform. Among the different kinds of digital platforms, Twitter allows the combination of textual and geo-referenced data, making this platform an interesting choice, for instance, to detect or monitor different kinds of events (Korkmaz et al.,2015), or it could be used to study the dissemination of influence (Generous et al., 2014). Algorithms are, in this way, extraordinary tools for organising and spreading information. According to these claims, our work aims to understand, both theoretically and methodologically, how the research about territory, as a field of study, has been adapted to prolifically use data coming from algorithms of geo-location or geoparsing (Middleton et al, 2018).Specifically, this work aims to critically discuss the elements and the steps that characterize this emerging approach in order to highlight how the use of geo-localized data can be advantageous for territorial analysis and for which kinds of phenomena..Thus, after showing the most commonly used ways of collecting information about territory using algorithms, we will discuss strategies and techniques of analysis used to combine online (social media) and offline (territorial) sources and try to identify the possible applications for each type of technique. Moreover, a third step allows us to identify the different meanings and operational processes made regarding the concept of territory. That will allow us to recognize the different conceptions of sociological space emerging from the analysis and suggest how to avoid the use of geo-localized data with a simple data-driven perspective (Kitchin, 2014) that does not consider the sociological peculiarities of the space. We will try to answer these questions by examining papers that used Twitter as the source of data. In particular, the analysis of articles will be led thanks to a third kind of content analysis following Rositi’s categorisation (Rositi, 1988). Also, the extraction of the articles will be made using PRISMA methodology (Moher et. Al, 2018) for systematic review.

Video games have been contemporary to most technological advances in the field of digital images. Even more than other media, they have long embraced scientific techniques in the representation of bodies and spaces. The video game Spacewar! (1962), developed by a team of engineers from Massachusetts Institute of Technology, and often considered as one of the first in history, gives us an example of this process. Developed in the context of experimental research and driven by the technological race against USSR, Spacewar! plays a key role in defining a visual regime where images function as a manifestation of the power of algorithms. A semiotic analysis of the game will lead us to the hypothesis that scientific visual strategies have an influence on how video games are designed.

The Atlas cedar (Cedrus atlantica) is an emblematic species of Morocco, classified by the International Union for the Conservation of Nature (IUCN) in the red list of endangered species. Determining its potential area under current climatic conditions is an essential step in planning and ensuring its conservation. In this paper, we applied the maximum-entropy algorithm (MaxEnt) to predict the current and potential distribution of the Atlas cedar in order to provide a basis for its conservation. The adjusted model had an excellent predictive quality (AUC = 0.967). The most significant variables influencing the distribution of Atlas cedar, under current climatic conditions, are the precipitation in the driest quarter, precipitation in the driest month, average annual temperature and relative humidity. The model developed made it possible to establish the first map of the suitable potential area of the Atlas cedar in Morocco. The results obtained will guide managers to identify favorable sites for the rehabilitation of the Atlas cedar threatened with extinction. [ABSTRACT FROM AUTHOR]

This article is an overview of tensor networks and is intended for beginners in this field. We focus on the tools required for the concrete implementation of algorithms. Four basic operations (remodelling, permutation of indices, contraction, and decomposition) commonly used in tensor network algorithms are described. This study also covers diagrammatic notation, entanglement, matrix product states (MPS), matrix product operators (MPO), projected entangled pair state (PEPS), multi-scale entanglement renormalization ansatz (MERA), time evolving block decimation (TEBD), and tensor renormalization group (TRG). [ABSTRACT FROM AUTHOR]