Abstract Background Shoulder dystocia is a peracute mechanical dystocia and a prepartum, usually unpredictable, life-threatening entity with significant forensic implications due to significantly poor perinatal outcome, especially permanent disability or perinatal death. Content To better objectify the graduation and to include other important clinical parameters, we believe it is appropriate to present a proposal for a complete perinatal weighted graduation of shoulder dystocia, based on several years of numerous other and our own clinical and forensic studies and thematic biobibliography. Obstetric maneuvers, neonatal outcome, and maternal outcome are three components, which are evaluated according to the severity of 0–4 proposed components. Thus, the gradation is ultimately in four degrees according to the total score: I. degreee, score 0–3: slightly shoulder dystocia with simple obstetric interventions, but without birth injuries; II. degree, score 4–7: mild shoulder dystocia resolved by external, secondary interventions and minor injuries; III. degree, score 8–10: severe shoulder dystocia with severe peripartum injuries; IV. degree, score 11–12: extremely difficult, severe shoulder dystocia with ultima ratio interventions applied and resulting extremely severe injuries with chronic disability, including perinatal death. Summary As a clinically evaluated graduation, it certainly has an applicable long-term anamnestic and prognostic component for subsequent pregnancies and access to subsequent births, as it includes all relevant components of clinical forensic objectification.

This paper explores the “lost language” of monuments erected in the former Yugoslavia from the 1960s to the 1980s—more precisely, the 25 national monuments captured by the lens of photographer Jan Kempenaers over the span of three years (2006−2009), and published in the monograph Spomenik [Monument] (2010). By combining the approach of cognitive linguistics and cultural studies, in particular that of Forceville (“Identifi cation”, “Metaphor”, “Agendas”), Kövecses (Culture, Context), Ortiz, and Kirn and Burghardt, this paper aims to explore the conceptual metaphors embedded in these monuments as part of a specifi c symbolic landscape, immanent to the countries of the former Yugoslavia at a historical point of their four-decades-long political, social, and cultural merger, as well as the current possibilities and limitations of the visual/multimodal decodifi cation of the memorials.

The proliferation of Internet of Things (IoT) devices has led to exponential data growth that can be harnessed for personalized services, cost savings, and environmental benefits. However, collecting and sharing this data comes with significant risks, including hacking attacks, breaches of sensitive data, and non-compliance with privacy regulations. This paper proposes a comprehensive, end-to-end secure system, MOZAIK, for privacy-preserving data collection, analysis, and sharing to address these challenges. We perform a requirements analysis from the perspectives of security, privacy, legal, and functionality, highlighting the various mechanisms employed to safeguard sensitive data throughout the entire data cycle. This includes the use of lightweight encryption, distributed computation, and anonymous communication mechanisms to reduce security and privacy risks and to protect against single points of failure. MOZAIK provides a trusted and secure platform for data sharing and processing that can enable the creation of a data market and data economy.

The integration of technology in education has become indispensable in acquiring new skills, knowledge, and competencies. This paper addresses the issue of analyzing and predicting the learning behavior of Computer Science students. Specifically, we present a dataset of compiler errors made by students during the first semester of an Introduction to Programming course where they learn the C programming language. We approach the problem of predicting the number of student errors as a missing data imputation problem, utilizing several prediction methods including Singular Value Decomposition, Polynomial Regression via Latent Tensor Reconstruction, Neural Network-based method, and Gradient Boosting. Our experimental results demonstrate high accuracy in predicting student learning behaviors over time, which can be leveraged to enhance personalized learning for individual students.

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients’ hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient’s career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2–6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

Nationalism as an idea, movement and ideology denotes tendency of members of an ethnic group towards the establishment of an ethnically pure territory. Achieving this political goal, with superiority in relation to others as a guiding idea, often leads to jeopardizing sovereignty and territorial integrity and oppression and exploitation of other people. Nationalism has been a significant feature of the Russian society for centuries. Traditionally, the focus of Russian nationalism has been the preservation and strengthening of a large and powerful multi-ethnic state. Opposed to this imperial nationalism stands ethno-nationalism focused on the struggle for the interests of ethnic Russians. One of the main drivers of the Russian nationalism in the past couple of decades has been the country’s economic situation and the political tensions with the West. The Russian nationalist sentiment grew stronger after the annexation of Crimea and the beginning of war in Ukraine, being justified with a strong nationalist political narrative focused on ethnic Russians and the protection of their rights. Contrary to nationalists, realists advocate a more pragmatic approach to international relations, focusing on economic and social development. The aim of this paper is to research to what extent nationalism and realism have shaped political thought in Russia.

The ATLAS experiment relies on real-time hadronic jet reconstruction and b-tagging to record fully hadronic events containing b-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅, a key signature relying on b-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.

Group recommender systems (GRSs) are tools that support a group to find items that the whole group would enjoy experiencing jointly. There are two main lines of research in this field. The first line of research focuses on methods that combine the preferences of individual group members to obtain a group preference model and generate appropriate recommendations. The second line of research is more holistic and aims to support groups in all the phases of their decision-making process. The majority of the approaches of the second type use a simple conversational approach, which is critiquing. However, nowadays people heavily rely on social and chat platforms to make group decisions, and we believe that these platforms could be a valuable mean for building more effective GRSs. To this end, we have designed a framework tool that extends standard chat platforms by augmenting it with a chat-bot. The chat-bot enables the communication between the users on one side and the group recommender agent on the other. Our goal is a new holistic approach to group recommendations that would be the more beneficial than previous proposed conversational approaches. We aim to provide the proposed framework as an open environment for researchers to prototype their own GRSs.

Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems’ modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work.

Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.

We often make choices that involve a group of people, such as selecting a movie to watch with friends or choosing a travel destination to visit with the family. Sometimes, a single member of the group may be in charge of making the decision for the group, by playing the role of “organizer”. Although some tools for supporting Group Decision-Making have been proposed, none of them have considered the case where a single group member is autonomously making such a decision, hence entering the preferences of the group members, interacting with the system, and finally selecting a proper recommendation. In this paper, we introduce MyFoodGRS, a web application for a single user to find a proper restaurant for their group, that supports the previously mentioned tasks. We introduce an interaction design to follow the Attribute and Socially-based group decision patterns, and we report the positive result of the conducted system usability evaluation.

O sistema circadiano tem papel fundamental na saúde humana, podendo influenciá-la tanto de forma positiva, quanto negativa, visto que governa os ritmos biológicos diários. Disrupções nesse sistema acabam impactando o metabolismo e provocando desordens que vão desde o ganho de peso corporal ao desenvolvimento de resistência à insulina, doenças crônicas, cardiovasculares e câncer. A crononutrição tem despontado como uma opção terapêutica interessante, pois tem como foco a relação entre padrões alimentares, ritmo circadiano e saúde metabólica. Sendo assim, o objetivo deste trabalho foi avaliar o impacto do ritmo biológico nos processos fisiológicos do corpo, bem como averiguar o potencial da crononutrição na prevenção e tratamento de indivíduos em risco. Como metodologia para desenvolvimento deste trabalho foi feita uma revisão de literatura em que foram selecionados artigos científicos publicados em periódicos internacionais de 2012 a 2023. Utilizou-se para consultas às bases de dados PubMed e MDPI. Em conclusão, as pesquisas, em geral, salientam ser possível conter os efeitos da cronodisrupção e a evolução de doenças crônicas por meio de estratégias baseadas na estimulação dos sincronizadores circadianos, bem como na supressão dos fatores que desregulam o ritmo biológico. Para tanto, diversas estratégias são propostas com foco na alimentação, sono, atividade física e exposição à luz artificial.