Neutron stars (NSs) are powerful factories for new particles with masses up to the 100 keV range. These compact stars contain significant populations of charged particles, notably protons, electrons and muons. We calculate the emission rates for new scalar, vector, and pseudoscalar bosons that predominantly couple to electrons and muons. For vector bosons, the in-medium renormalization of the effective couplings strongly modifies the emission rates, e.g., purely muon-philic vectors are predominantly emitted by ultra-relativistic electrons. We focus on bremsstrahlung in electromagnetic lepton-lepton or lepton-proton collisions in the ultradegenerate limit. When protons are superconducting, the scalar and vector energy loss rates scale as $T^4$, the pseudoscalar one as $T^6$, to be compared with $T^8$ for neutrino losses by the modified Urca process. For normal-conducting protons, the screening of transverse photons implies instead scalings with a power reduced by $1/3$ and thus $T^{11/3}$ for scalars and vectors, and $T^{17/3}$ for pseudoscalars. As the NS cools, such new particle losses would become important at late times, when surface photon emission begins to take over, which itself scales roughly as $T^2$ in terms of the internal temperature. Our results can be used to constrain the leptophilic coupling strengths through observed NS cooling ages.

Multimorbidity refers to the presence of multiple co-existing conditions, but is often underappreciated in the context of severe asthma (SA) management. We sought to identify differences in approaches to multimorbidity management in SA, variability in access to multidisciplinary team (MDT) resources, and whether physician perspectives on multimorbidity differ between SA specialists and general respiratory physicians. The Severe Heterogeneous Asthma Registry, Patient-centred (SHARP) Clinical Research Collaboration circulated an online physician survey via European national respiratory societies to assess a) available resources to address multimorbidity and b) physician perspectives on multimorbidity in SA. 495 responses from 25 European countries included 48% SA specialists and 52% general respiratory physicians. SA specialists had more experience with SA patients (20% seeing>60 patients/month) compared to general respiratory physicians. SA specialists had greater access to multidisciplinary care – including better access to MDT, allied health professionals and referrals to external specialists and therefore more routinely assessed comorbidities and considered them greater influences on their practice. They also considered multimorbidity to a greater degree, and rated its impact on their patients’ asthma outcomes (and general health outcomes) as more substantial. Alongside more experience of treating SA, SA specialists have increased awareness of multimorbidity and better resources to manage it. However, access to MDTs remains a significant gap for both SA specialists and general respiratory physicians. Furthermore, both groups identified a high need for further education and training about multimorbidity. These findings highlight key areas for improvement in clinical practice, resources and training.

Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and control, it also increases vulnerability to cyber-physical disruptions. Operators must distinguish physical incidents, such as faults or line disturbances, from malicious actions, such as false data injection or unauthorized command execution. This chapter investigates this problem using the well-known MSU/ORNL Power System Attack Dataset. The proposed method combines machine learning with genetic-algorithm-based feature selection. The objective is twofold: to classify attack and natural events accurately, and to determine whether a reduced set of physically informative PMU/IED measurements can support reliable detection. Several baseline models are evaluated, including logistic regression, RBF-SVM, XGBoost, Random Forest, and Extra Trees. The results show that tree-based ensemble models are the most effective for the considered dataset, with Extra Trees providing the strongest full-feature baseline. After feature selection, the GA + Extra Trees model reduces the clean PMU feature space from 112 attributes to an average of 27.4 attributes over five runs, while increasing macro-F1 from 0.9118 to 0.9212 and ROC-AUC from 0.9791 to 0.9837. These results indicate that many synchronized electrical measurements are redundant. A compact subset of phasor-based features can still provide accurate and interpretable anomaly detection in smart grids.

Explainable AI (XAI) is crucial for fostering human trust in deep neural network (DNN) predictions, particularly in tasks like image classification. Multiple surveys exist on XAI methodologies, however, the practical usability and reproducibility of these methods remain largely unexplored. This paper addresses this gap by conducting a systematic survey of recent XAI papers published in leading computer vision and AI conferences and journals. We categorize these works, identify prevalent datasets and evaluation metrics, and analyse the associated code repositories. Our analysis reveals that almost 95% of the surveyed codebases are research prototypes rather than published releases, and a concerning majority of two‐thirds of them exhibit inconsistencies with their corresponding publications. These findings highlight the challenges in benchmarking new XAI methods against existing ones and explain the slow adoption of state‐of‐the‐art research in real‐world applications. This paper aims to underscore the importance of releasing well‐documented, readily usable code alongside XAI research to foster a more robust and reproducible ecosystem, ultimately facilitating the development and deployment of trustworthy AI systems. The results of this study are presented on an interactive website Interactive‐XAI.

[This corrects the article DOI: 10.1371/journal.pone.0315011.].

Clinical pharmacists enhance safe and high-quality patient care through effective interprofessional collaboration. This study aimed to evaluate pharmacotherapy counseling services provided by clinical pharmacists, assess physician acceptance of recommendations, and determine their impact on patients and the healthcare system. A retrospective observational study was conducted at the University Hospital’s Pharmacotherapy Counseling Unit over a 15-month period. Pharmacotherapy plans of 61 ambulatory patients were analyzed, and therapy modifications were classified according to PCNE V9.1. After clinical pharmacist intervention, the median (IQR) number of prescribed medications significantly decreased from 7.5 (8) to 3 (9) ( p < 0.05) and drug-related problems (DRPs) from 2 (4) to 0 (4) ( p < 0.05). Among patients aged ≥65 years ( n = 22), potentially inappropriate medications were significantly reduced ( p < 0.05). Most DRPs were related to inappropriate drug selection. This study demonstrates the positive impact of clinical pharmacists in improving pharmacotherapy quality in ambulatory care in Bosnia and Herzegovina.

This study aimed to provide a more in-depth analysis of the fruit quality of three table grape varieties: 'Moldova', 'Lasta', and 'Italia', cultivated in the Žepče area (Bosnia and Herzegovina). The findings from the comparative analysis indicated substantial variations in grape quality among the studied varieties. 'Moldova' grapes exhibited significantly higher total soluble solids and pH values than those of the other table grape varieties. 'Moldova' also had higher total phenolic and flavonoid contents in the grape skin relative to 'Italia' and 'Lasta'. On the other hand, 'Italia' variety showed the highest titratable acidity, followed by 'Lasta' and 'Moldova'. Total phenolic and flavonoid contents were highly positively correlated with the antioxidant capacities of all analyzed grape samples, suggesting that phenolic compounds contribute significantly to the antioxidant properties of grapes. Study results also indicated that all heavy metal levels tested in grapes were below the threshold limits, which was expected considering that the experimental soil was not contaminated with the heavy metals being assessed. Overall, the results from the study have shown that all grape varieties studied hereby displayed a satisfactory level of quality based on key chemical parameters, and that the experimental area is quite favorable for their cultivation.

The semiconductor industry is foundational to modern technology, yet its complex global multi-relational firm network remains poorly understood, posing challenges to scientists, firms, and policymakers. Traditional analysis relies on proprietary databases that are often expensive, incomplete, and slowly updated, limiting their ability to capture rapidly evolving dependencies. Here, we demonstrate that a novel, generalizable methodology combining Large Language Models (LLMs) with open web data can reconstruct this network and its structural dynamics at scale. We identify and classify supply-chain, partnership, and ownership links from 170 million semiconductor firm webpages, yielding a temporal network of over 1,300 linked firms. We validate link-extraction quality (Precision: 0.884; F1-score: 0.784), network overlap and complementarity with a proprietary database, and consistency with aggregate economic data. Our network reveals a temporary 9% decline in edges during the 2022 chip shortage, rapid increases in the centrality of AI supply-chain bottleneck firms such as NVIDIA, and geographic realignment of interfirm relations amid geopolitical turbulence. This generalizable framework overcomes barriers to transparency and provides essential, up-to-date maps for assessing resilience and informing policy across strategically relevant sectors.

We develop a framework in which Yukawa hierarchies arise from powers of fully anarchic spurions transforming in higher representations of the flavor symmetry group $SU(2)^{n_2}\times SU(3)^{n_3}$. The core mechanism is the progressive lifting of Yukawa ranks through successive outer products of composite doublets and triplets. We formulate the general construction in detail and build explicit models realizing it. We then investigate whether renormalizable scalar potentials for higher $SU(2)$ representations can dynamically generate anarchic spurions with non-vanishing composites. The framework predicts distinctive patterns in flavor-changing neutral currents and potentially observable stochastic gravitational-wave backgrounds.

Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learning framework for next-day prediction of urban thermal conditions and for heat risk assessment. The study was carried out in Sarajevo by using MODIS land surface temperature data and Open-Meteo forecast data. We tested several models, including convolutional models and spatiotemporal models. Among them, ConvLSTM with a mixed loss function gave the best results. The obtained values were MAE = 0.2293, RMSE = 0.3089, and R2 = 0.8877. The experiments also showed that results can be improved by using longer temporal series and additional meteorological variables. Since the framework was implemented on a GPU and trained with mixed precision, the execution time was reduced. Based on the predicted temperature fields, it was also possible to combine hazard information with exposure and vulnerability data in order to generate city heat risk maps. The proposed framework can be used as a practical basis for city heat analysis.