Browse Articles

Fuzzy decision making for cross domain sentiment analysis and MABAC approach with complex T-spherical fuzzy operators

To treat or not to treat: Experiences and considerations of veterinarians in management of cats with diabetes mellitus

Veterinarians’ daily work involves navigating ethical, medical, and emotional considerations while balancing diverse perceptions of responsible care within the triadic relationship between veterinarian, animal, and owner. These challenges are particularly evident in the management of chronic diseases. Diabetes mellitus serves as an example in our study, where we explore Swedish veterinarians’ experiences of managing it in cats. Individual in-depth interviews were conducted with ten veterinarians, and the data were systematically analysed using reflexive thematic analysis. One overarching theme, the perceived prioritisation of the cat’s wellbeing, was identified as central to how veterinarians approached the clinical situation. Three themes are addressed: the role of the owner in disease management, the negotiation between medical ideals and practical realities, and the perception of euthanasia as being in the best interest of the cat. Taken together, the three themes explore how veterinarians continually negotiate what it means to act responsibly within the constraints of real-life practice. This ongoing negotiation between ideals, pragmatism, and compassion underscores the ethical depth and emotional labour embedded in everyday veterinary work. The results contribute in-depth insights into how clinical reasoning and decision-making is shaped by both moral positions and pragmatic adaptations, and the importance of veterinary-owner communication in finding the optimal management plan in each individual case. Awareness of the challenges associated with diabetes care in cats may facilitate ethical reflexivity in veterinary practice, supporting decisions that impact both animal welfare and veterinarians’ professional wellbeing.

Exploring sectoral energy structures for decarbonization: an analysis of leading global emitting countries

Economic shock and the erosion of COVID-19 precautionary behavior in Canada during the early pandemic

Maintaining voluntary adherence to public health guidelines during a pandemic is fundamentally a collective action problem. We argue that one challenge is the economic costs these behaviors impose on individuals and society. As their costs are revealed to citizens, adherence declines, in part by changing people’s expectations of the behavior of fellow citizens. We leverage the case of the April 2020 Canadian jobs report and use an Unexpected Event during Survey Design (N ~ 4,910) and an Interrupted Time Series to show that the release of this report corresponded with reduced public health adherence, particularly among young panel respondents, and increased aggregate-level mobility. We also use two survey experiments (N ~ 2,500) on national samples of Canadians to show that information about the economic consequences of public health guidelines reduces expectations of adherence by other citizens and by oneself, especially among young respondents. Further, expectations of adherence by others causes expectations of one’s own adherence in the future. The implication is that we need to develop policies that can facilitate pandemic containment without requiring as much costly voluntary behavior on the part of citizens, particularly when the costs of the crisis, and of adherence, are inequitably distributed through society.

Knock-down of the long isoform of the WNK1 kinase mitigates the anti-glomerular basement membrane glomerulonephritis in mice

Abstract The roles of the long isoform of the With No Lysine (K) 1 (L-WNK1) kinase have been mainly studied in the distal renal tubule, where it participates in sodium and chloride homeostasis. Yet, little is known about its role in its predominant expression site within the kidney, i.e. the glomerulus. We chose to study the consequences of L-WNK1 inhibition in a mouse model of rapidly progressive glomerulonephritis (RPGN), combining podocyte injury and parietal epithelial cell (PEC) activation. We show that L-WNK1 expression is upregulated in glomerular cells during RPGN in mice. A 50% reduction in L-WNK1 expression mitigates experimental nephrotoxic serum-induced RPGN in mice. Given that L-WNK1 is strongly expressed in podocytes, which play an important role in RPGN pathogenesis, we then studied a model of podocyte-specific knockdown of WNK1 . Even if we could observe some changes in NTS-induced RPGN following this knock-down, the effects are not as marked as in the global inhibition model. These data suggest that L-WNK1 plays an important role in other glomerular cell types. Accordingly, we show in vitro that the inhibition of L-WNK1 activity impairs PECs migration. These results suggest that L-WNK1 could represent a new player in the pathophysiology of RPGN.

Research on the performance degradation and failure mechanism of exhaust Systems for explosion proof diesel engines in different mining applications

Mine explosion-proof diesel engines are critical for underground coal mining, yet the long-term performance degradation of their exhaust systems poses substantial risks to operational safety and environmental compliance—a gap not addressed in short-term performance-focused studies. This study investigates the degradation and failure mechanisms of dry, wet, and combined exhaust systems under simulated mining conditions using a JHP4105DZDFB-G engine. Experimental results show that the wet system exhibits the fastest degradation, with exhaust backpressure increasing by 15.2% within 500 hours of continuous operation under simulated mining conditions due to sludge accumulation, leading to a 12–15% power loss. In contrast, the dry system maintains lower emissions (NOₓ, HC, CO, and PM were reduced by 68.36%, 71.71%, 55.39%, and 82.28% compared to the wet system) but suffers thermal fatigue in condensers after 1000 hours. The combined system shows hybrid failures, with PM emissions exceeding regulatory limits (0.4 g/kWh) at 300 hours. Mechanistic analysis reveals that wet systems fail primarily due to mechanical blockage and corrosion, while dry systems succumb to thermal-mechanical fatigue. A multivariable regression model and machine-learning algorithms are developed to predict degradation thresholds, enabling proactive maintenance. Thermal management optimization for dry systems is proposed.

Building compositional tasks with shared neural subspaces

Abstract Cognition is highly flexible—we perform many different tasks 1 and continually adapt our behaviour to changing demands 2,3 . Artificial neural networks trained to perform multiple tasks will reuse representations 4 and computational components 5 across tasks. By composing tasks from these subcomponents, an agent can flexibly switch between tasks and rapidly learn new tasks 6,7 . Yet, whether such compositionality is found in the brain is unclear. Here we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task flexibly engaging these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. In neural recordings, we found that task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Monkeys adapted to changes in the task by iteratively updating their internal belief about the current task and then, based on this belief, flexibly engaging the shared sensory and motor subspaces relevant to the task. In summary, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations.

Development of Eu-doped CdS photodetectors with enhanced photodetection performance

Entropy-based analysis of Rift Valley fever transmission dynamics using delay differential equations

Rift Valley Fever (RVF) is a vector-borne, transmissible zoonotic disease in animals and humans caused by RVF virus and transmitted primarily by mosquitoes and biting flies. According to the World Health Organization (WHO), RVF outbreaks have great public health and economic impact, with up to 90% mortality in lambs and approximately 10% in adult sheep. Besides small ruminants, RVF affects cattle, camels, and goats and induces abortion storms and neonatal mortality at high rates, whereas humans develop severe febrile illness, hemorrhagic fever, or encephalitis in extreme cases. In this study, a deterministic epidemic model is developed and studied to investigate the RVF transmission dynamics under inclusion of delay differential equations and an entropy-based global stability approach to capture the time-dependent effect of disease progression. The model separates the ruminant population into susceptible, vaccinated, infected, and recovered, and the mosquito population into susceptible and infected. Positivity, and boundedness are demonstrated as basic dynamical properties. Two equilibria are found and compared: Rift Valley Fever–Free Equilibrium (RVFFE) and Rift Valley Fever Endemic Equilibrium (RVFEE). The basic reproduction number ( R 0 ) is derived and local and entropy-based global stability are examined by the Routh–Hurwitz criteria and LaSalle’s invariance principle. Sensitivity analysis reveals the contribution of critical parameters on disease transmission and persistence. Numerical simulations performed using MATLAB’s built-in DDE23 solver assist in revealing the effect of delay on infection dynamics and susceptibility with time. The implications provide tremendous insight into the role of temporal considerations and parameter uncertainty in the transmission dynamics of RVF and enable better understanding as well as potential control strategies for this emerging zoonotic disease.

Miniaturized dual-band MIMO antenna with high gain and isolation for mm-wave applications

An analysis of farmers’ experiences with deterrence methods and investment in mitigation of agricultural crop damage caused by geese

The number of geese foraging in agricultural fields and causing damage to crops is increasing. Farmers attempt to reduce damage using passive, active, auditory, and combined deterrent measures, accommodation fields and, increasingly, derogation shooting. For protected geese like the barnacle goose Branta leucopsis and huntable species outside the hunting season, it is a legal requirement within the EU that other deterrent measures have proven insufficient before a derogation permit can be granted. However, there is a lack of guidance regarding the effectiveness of different measures. Via in-person interviews with 54 Danish farmers experiencing problems with wintering barnacle geese we analyse farmers’ experiences with deterrence methods to provide an overview of their effectiveness, defined by duration and area coverage. The information obtained is far more extensive than what could realistically be achieved through scientific experiments. We check the validity of responses by comparing reports with existing scientific evidence. Passive deterrents (e.g., scarecrows) cover a few hectares and have a duration effect of 4–6 days, but only until the geese habituate. Active measures (e.g., a person walking into the field) and auditory deterrents (firing scare shots) have high area effect but short duration. Largest area/duration effects are achieved using gas cannons, sound deterrents and derogation shooting. Intensified active deterrence or increased density of passive deterrents enhance effectiveness but require greater investments of time and resources. Effective derogation shooting requires that hunters can respond quickly when needed. Hunting lease agreements regarding hunters’ contributions to deterrence and derogation can enhance cooperation and problem-solving. In addition, cooperation between neighboring farmers, including accommodation areas, furthers effectiveness. The cost of geese (yield loss plus time/materials) can have a sizeable impact on the farmers’ operation profits. Lack of effort may be due to farmers either coping with the problem, having given up deterrence, or unawareness of more effective deterrence.

The utilization of CEUS and SWE for predicting pathological complete response to neoadjuvant chemotherapy for invasive breast cancer

Forecasting crude oil futures price with energy uncertainty: Evidence from machine learning methods

Energy related uncertainty has significant influence on crude oil market. To explore the influence, this paper investigates the predictive ability of the Energy-Related Uncertainty Index (EUI), over and above standard macroeconomic predictors, in forecasting crude oil prices using an array of machine learning methods. We find that EUI has a significant impact on crude oil prices. Moreover, machine learning methods combined with EUI performed better than the linear regression method due to a lower rate of prediction errors. Among these methods, the Random Forest (RF) model with EUI performs better in the short term, while the Attention-enhanced Long Short-Term Memory (Attention-LSTM) model with EUI has more substantial predictive power in the long term. These empirical results pass a series of robustness tests. Our findings have important implications for both regulators and investors in the crude oil market.

Functional properties of skeletal myotube-derived extracellular vesicles based on microRNA profiles: a comparative analysis with mesenchymal stem cell-derived extracellular vesicles

Abstract Skeletal muscle-derived extracellular vesicles (SkM-EVs) are promising candidates for non-invasive, systemically delivered therapies, but their functional specificity relative to clinically advanced mesenchymal stem cell-derived EVs (MSC-EVs) remains unclear. We reanalyzed public miRNA-seq datasets of SkM-EVs and MSC-EVs and integrated validated miRNA–mRNA interactions to infer pathway-level repression potential of EV miRNA cargo. Two complementary approaches were used: a differential-expression-based relative evaluation with RBiomirGS, and an abundance-weighted absolute evaluation that converts miRNA profiles into gene-level Impact Scores followed by preranked KEGG enrichment. Despite their different formulations, both approaches converged on a shared pattern. SkM-EV miRNAs showed a predicted repression bias in FoxO, TGF-β and ErbB signaling pathways linked to muscle atrophy, metabolic homeostasis and pro-proliferative signaling. By contrast, MSC-EV miRNAs showed a predicted repression bias in immune signaling pathways. These source-dependent pathway signatures provide hypothesis-generating evidence that SkM-EVs may be better suited for muscle-, metabolic-, and cancer-related indications, whereas MSC-EVs may be more appropriate for immunomodulatory indications, pending experimental validation. Our miRNA–target-based framework provides a general strategy to benchmark EV sources at the pathway level directly from miRNA profiles.

Water, sanitation, and depressive symptoms in Indonesia: The mediating role of life satisfaction

Access to clean water and adequate sanitation is vital for public health. However, the effects of water and sanitation on depressive symptoms remain insufficiently explored among the general population in low- and middle-income countries (LMICs). This study aims to examine the links between sanitation conditions and depressive symptoms, and to evaluate the mediating role of life satisfaction (LS) among adults in Indonesia. We used the fifth wave of the Indonesian Family Life Survey (IFLS-5) dataset with participants aged 15 and above (mean: 37.32 years; SD: 14.93), collected in September 2014/March 2015. Depressive symptoms were measured using the CES-D-10 scale. Five sanitation indicators were included: drinking water, water source, toilet facilities, liquid waste disposal and safe waste disposal methods. Structural equation modelling (SEM) was employed to estimate the direct and indirect pathways linking sanitation factors and depressive symptoms through LS, adjusting for age and sex. The study involved 31,446 participants, of whom 7,312 (23%) were classified as having depressive symptoms. In the first SEM model, drinking water (β = 0.023, p < 0.001), water source (β = 0.033, p < 0.001), toilet facilities (β = 0.039, p < 0.001), sewage disposal (β = 0.027, p < 0.001) and waste disposal method (β = 0.021, p < 0.005) were directly associated with depressive symptoms. In the second SEM model, which included LS as a mediator, the direct effects remained consistent: drinking water (β = 0.013, p < 0.05), water source (β = 0.054, p < 0.001), toilet facilities (β = 0.068, p < 0.001), sewage disposal (β = 0.022, p < 0.001) and waste disposal method (β = 0.046, p < 0.001). Additionally, unimproved sanitation was significantly linked to lower LS and LS was strongly associated with depressive symptoms across all sanitation factors (β ≈ 0.124–0.126, p < 0.001). Poor water and sanitation are associated with a higher risk of depressive symptoms among Indonesian adults, with LS as a partial mediator of this relationship. These findings highlight the need to incorporate water and sanitation improvements into national mental health and public health policies.

Wafer-scale formation of MoS2 with controlled thickness and high uniformity via conversion of MoOx using H2S sulfurization and subsequent crystallization

Repeated pattern detection on fabric: A survey and novel approach

Modern textile industries frequently apply patterns, such as brand logos or motifs, in near-regular arrangements to create visually appealing products. Consequently, the application of computer vision for pattern recognition is highly valuable for automating production chains and reducing waste. In this work, we address the challenging task of automatically detecting repeating patterns on fabric images, accounting for real-world complexities such as variable lighting and intentional pattern variance. We begin with an in-depth literature review on repeated pattern detection, highlighting current trends, organizing them into a hierarchy of sub-tasks, and discussing the novelty of each paper. Subsequently, we propose a novel method to solve our specific instance of this problem, focusing on detecting patterns with sub-pixel accuracy. We conduct extensive experiments to compare its performance against several baselines from the literature. Our method can be applied with high precision to real-world problems without requiring training data, instead using an automatic calibration procedure with limited human supervision. On a small synthetic dataset, our method detects repeated patterns with a 96% recall rate and an average alignment error of less than 0.5 pixels in just a few seconds, making it competitive with all tested baselines. Finally, we release our dataset and the code for its generation to encourage further research in this area.

Publisher Correction: Polyamine-dependent metabolic shielding regulates alternative splicing

Integrated metagenomic and 16S rRNA analysis reveals temporal associations between resistance genes and microbial communities during dairy manure composting

Abstract Dairy manure composting is widely applied to stabilize organic waste and reduce environmental pollution, yet the behavior of resistance determinants during this process remains insufficiently resolved. In this study, shotgun metagenomic sequencing was used to characterize temporal changes in antibiotic resistance genes (ARGs), metal resistance genes (MRGs), biocide resistance genes (BRGs), mobile genetic elements (MGEs), and microbial community composition during dairy manure composting. Rather than inferring direct mechanistic causation, our analyses focused on identifying statistically supported trends, associations, and co-occurrence patterns across composting stages. We observed a rapid decline in the relative abundance of ARGs compared with MRGs and BRGs during the thermophilic phase, coinciding with increasing temperature, while specific genes such as sul2 persisted throughout the process. Shifts in microbial community composition, particularly changes in the relative dominance of Actinobacteria and Proteobacteria , were significantly associated with variations in resistome profiles. Correlation and network analyses further revealed strong associations among ARGs, MRGs, BRGs, and MGEs, suggesting potential co-selection and horizontal gene transfer linkages without implying direct causal mechanisms. In addition, several opportunistic bacterial genera showed positive associations with aminoglycoside- and macrolide–lincosamide–streptogramin-type ARGs, indicating possible dissemination risks following compost application. Overall, this study provides an integrated, association-based overview of resistome and microbial community dynamics during dairy manure composting and highlights the importance of considering multiple resistance determinants when evaluating composting as a manure management strategy.

Barriers and facilitators to the diagnosis of HIV and other STIs in primary care within publicly funded healthcare systems: A systematic review of qualitative studies

Background Human immunodeficiency virus (HIV) and other sexually transmitted infections (STI) are a major public health concern. Primary care (PC) is the ideal setting for their diagnosis. Designing effective strategies requires a thorough understanding of the problems these patients face when seeking care. Objectives To identify barriers and facilitators to the diagnosis of HIV and STIs in PC and classify them according to the theoretical Capability, Opportunity, and Motivation model of Behaviour (COM-B model). The review’s findings will inform the development of specific intervention strategies. Methods We conducted a systematic review (SR) of qualitative studies of barriers and facilitators to the diagnosis of HIV/STI. A systematic literature search, without publication date limitation, was performed in Embase, Cochrane Library, and Medline via PubMed. The Joanna Briggs Institute (JBI) scale was used to assess the risk of bias of the included studies. The results were synthesised and presented according to the dimensions of the COM-B model. Results Of the 1,366 records from the initial search strategy, 70 articles met all inclusion criteria, covering all HIV/STIs and different risk groups. Barriers and facilitators to diagnosis were identified at three levels: patients, professionals, and health system. Patient barriers included confidentiality issues, lack of risk perception, shame and stigma, and access. The barriers at the level of professionals were lack of training, time constraints, and scarcity of resources. Among the facilitators, the most notable were minimally invasive sampling methods, ensuring confidentiality, standardisation of tests, fast and efficient entry points, and professional training. Discussion This SR provides information on barriers and facilitators to HIV/STI diagnosis in PC and across the different stakeholders involved (patients, providers, system). The results are grouped into the different dimensions of a behavioural model (capability, opportunity, and motivation). A thorough understanding of the complexity of the behaviour of people diagnosed with HIV/STIs and all those involved in their management will enable the development of effective intervention strategies to diagnose and prevent these processes, thereby reducing the associated disease burden. Registration: International Prospective Register of Systematic Reviews (PROSPERO: CRD42023404578).