Zagovornici nekritičkog korištenja informatičke tehnologije u obrazovanju tvrde da je budućnost obrazovanja u kibernetskome prostoru i digitalizaciji. No, digitalna tehnologija nije alternativa za poželjnu interakciju. Zato se danas u suvremenoj pedagogiji i govori o socijalnome biću škole kao polazištu humane škole. Kritičkomu mišljenju treba posvetiti više pozornosti u odgoju i obrazovanju, a pretpostavka za razvoj kritičkoga mišljenja jest uvođenje toga kolegija na svim društveno-humanističkim fakultetima, a onda i kao izborni predmet u osnovnim školama. U tome kontekst predlažemo znatno šire od sadašnjih aspekte sadržaja medijske kulture u čitankama za hrvatski jezik i književnost, osobito u osnovnim školama. Ključne riječi: mediji; manipulacija; indoktrinacija; odgoj za kritičko mišljenje; medijska kultura.

The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pruning. A new frontier in this area is given by \emph{dynamic, non-uniform} compression methods, which adjust the compression levels (e.g., sparsity) per-block or even per-layer in order to minimize accuracy loss, while guaranteeing a global compression threshold. Yet, current methods rely on heuristics for identifying the"importance"of a given layer towards the loss, based on assumptions such as \emph{error monotonicity}, i.e. that the end-to-end model compression error is proportional to the sum of layer-wise errors. In this paper, we revisit this area, and propose a new and general approach for dynamic compression that is provably optimal in a given input range. We begin from the motivating observation that, in general, \emph{error monotonicity does not hold for LLMs}: compressed models with lower sum of per-layer errors can perform \emph{worse} than models with higher error sums. To address this, we propose a new general evolutionary framework for dynamic LLM compression called EvoPress, which has provable convergence, and low sample and evaluation complexity. We show that these theoretical guarantees lead to highly competitive practical performance for dynamic compression of Llama, Mistral and Phi models. Via EvoPress, we set new state-of-the-art results across all compression approaches: structural pruning (block/layer dropping), unstructured sparsity, as well as quantization with dynamic bitwidths. Our code is available at https://github.com/IST-DASLab/EvoPress.

Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, is a chronic and relapsing condition characterized by persistent inflammation of the gastrointestinal tract. The complex pathogenesis of IBD involves a combination of genetic, environmental, and immune factors, which complicates the achievement of long-term remission. Lower abdominal pain, stomach cramps, blood in stool, chronic diarrhea, fatigue, and unexpected weight loss are common presenting symptoms. Despite the range of therapies and medications, including anti-inflammatory and anti-diarrheal drugs, immunosuppressants, antibiotics, and analgesics aimed at managing symptoms and controlling inflammation, a definitive cure for IBD remains elusive. Current therapy targets inflammation, mainly cytokines, inflammatory receptors, and immune cells, however, there is a need for novel targets to improve clinical outcomes. To identify novel targets and interactions among various factors, we performed a network analysis using various cytokines, TLRs, and NLRP3 inflammasome as inputs. This analysis revealed orosomucoid-like protein 3/ORMDL sphingolipid biosynthesis regulator 3 (ORMDL3) as a central hub gene interacting with multiple factors. While the role of ORMDL3 in IBD pathogenesis is not well-established, our findings and existing literature suggest that ORMDL3 plays a role in inflammation, impaired mitochondrial function, and disrupted autophagy, all contributing to the disease progression. Given its central role in these pathogenic processes, targeting ORMDL3 presents a promising therapeutic target. Modulating ORMDL3 activity could alleviate inflammation, restore mitochondrial function, and enhance autophagy, potentially leading to more effective treatments and improved outcomes for IBD patients.

Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations. These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

Effective real-time monitoring technique is crucial for detecting material degradation and maintaining the structural integrity of nuclear systems to ensure both safety and operational efficiency. Traditional physical sensor systems face limitations such as installation challenges, high costs, and difficulties in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a promising solution by enhancing physical sensor capabilities to monitor critical degradation indicators like pressure, velocity, and turbulence. However, conventional machine learning models struggle with real-time monitoring due to the high-dimensional nature of reactor data and the need for frequent retraining. This paper explores the use of Deep Operator Networks (DeepONet) within a digital twin (DT) framework to predict key thermal-hydraulic parameters in the hot leg of an AP-1000 Pressurized Water Reactor (PWR). In this study, DeepONet is trained with different operational conditions, which relaxes the requirement of continuous retraining, making it suitable for online and real-time prediction components for DT. Our results show that DeepONet achieves accurate predictions with low mean squared error and relative L2 error and can make predictions on unknown data 160,000 times faster than traditional finite element (FE) simulations. This speed and accuracy make DeepONet a powerful tool for tracking conditions that contribute to material degradation in real-time, enhancing reactor safety and longevity.

Effective real-time monitoring technique is crucial for detecting material degradation and maintaining the structural integrity of nuclear systems to ensure both safety and operational efficiency. Traditional physical sensor systems face limitations such as installation challenges, high costs, and difficulties in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a promising solution by enhancing physical sensor capabilities to monitor critical degradation indicators like pressure, velocity, and turbulence. However, conventional machine learning models struggle with real-time monitoring due to the high-dimensional nature of reactor data and the need for frequent retraining. This paper explores the use of Deep Operator Networks (DeepONet) within a digital twin (DT) framework to predict key thermal-hydraulic parameters in the hot leg of an AP-1000 Pressurized Water Reactor (PWR). In this study, DeepONet is trained with different operational conditions, which relaxes the requirement of continuous retraining, making it suitable for online and real-time prediction components for DT. Our results show that DeepONet achieves accurate predictions with low mean squared error and relative L2 error and can make predictions on unknown data 160,000 times faster than traditional finite element (FE) simulations. This speed and accuracy make DeepONet a powerful tool for tracking conditions that contribute to material degradation in real-time, enhancing reactor safety and longevity.

The personality traits of top judo referees are crucial for fair decision-making in elite competitions, shaping the experience for athletes, coaches, and spectators. This study examines potential differences in personality traits among 63 referees from the World Judo Tour between 1 January 2018 and 31 December 2022. Factors analyzed include completing the IJF Academy course Level 1, elite athlete status, number of officiated events, performance ratings, and participation in the Olympic or Paralympic Games. Our research shows that older referees tend to exhibit greater extraversion, whereas less experienced officials show lower levels of this trait. Referees with limited experience generally demonstrate higher agreeableness than their more experienced counterparts. Female referees and those with top performance ratings display greater conscientiousness than male referees. Completing the IJF Academy course is associated with lower neuroticism, while lower performance ratings are linked to higher neuroticism. Openness tends to decrease with increased officiating experience, with less experienced referees showing higher levels of this trait. In conclusion, competitive experience, training completion, and officiating tenure are associated with specific personality traits among judo referees, highlighting the importance of continuous training for effective officiating. The analysis of personality traits revealed no statistically significant differences between male and female referees in the dimensions measured by the BFI (Big Five Inventory). This indicates that the levels of extraversion, agreeableness, conscientiousness, neuroticism, and openness were similar for both genders, with no significant variation in how these traits were expressed.

The social potential of Urban Air Mobility (UAM) as a greener and faster transportation system in and around urban environments is indisputable. Nevertheless, the success of UAM introduction and its wide use will strongly depend on acceptance by the citizens and future UAM users. The impact on overall quality of life, as a multidimensional concept that encompasses physical health, mental and emotional well-being, economic status, education, and the environment, is becoming a significant issue. This paper aims to describe the performance framework for the assessment of the social and environmental impact of UAM. The specific objectives are to identify the full range of UAM’s impacts on citizens’ quality of life and to propose a set of indicators that enables the quantification and assessment of the identified impacts. Firstly, the main issues (focus areas) were identified, namely, noise, visual pollution, and privacy concerns, followed by access and equity, economic aspect, emissions, public safety, and impact on wildlife. In the next step, for each identified focus area, performance indicators were defined along with the several cross-cutting areas for a geographical, temporal, demographic, socioeconomic, and behavioral resolution. The proposed performance framework could enable more efficient mitigation measures and possibly contribute to wider adoption of the UAM operations.

This research work presents a comprehensive overview of four traits related to the head, with the aim of assessing the statistical phenotypic association among them. The traits examined in this study encompass earlobe type, tongue rugosity, cleft chin and tongue rolling. The primary objective was to investigate the potential associations between these traits and understand their interrelationships. The study focused on examining specific traits in a diverse group of 7431 unrelated individuals, where the genders were almost evenly distributed. To facilitate a comprehensive analysis, three distinct groups were created for each characteristic, comprising the total population, as well as male and female subsets. The selection of subjects was carefully done to ensure a fair representation across different geographical regions within Bosnia and Herzegovina, thereby accurately reflecting the nation's national and ethnic diversity. The association among these traits was assessed for statistical significance using the Chi-squared test, with Fisher's exact test used as a supplementary method to examine the connection between each pair of observed traits. Additionally, the Chi-squared test was applied to examine gender-based differences in the frequencies of the phenotypic characteristics of the head. Following traits were shown to have a statistically significant association: tongue rugosity - tongue rolling, tongue rugosity – earlobe type, cleft chin – earlobe type, cleft chin – tongue rolling and earlobe type – tongue rolling. Investigation into the variations in the frequencies of observed phenotypic traits of the head, with respect to gender, revealed statistically significant results for every trait examined.