Search Results (286,327)

The CPace protocol (Internet-Draft:draft-irtf-cfrg-cpace-14) is a password-authenticated key exchange optimized for simplicity. In particular, it involves only two messages exchanged in an arbitrary order. CPace combines a simple and elegant design with privacy guarantees obtained via strict mathematical proofs. In this paper, we go further and analyze its resilience against malicious cryptography implementations. While the clever design of CPace immediately eliminates many kleptographic techniques applicable to many other protocols of this kind, we point to the remaining risks related to kleptographic setups. We show that such attacks can break the security and privacy features of CPace. Thereby, we point to the necessity of very careful certification of the devices running CPace, focusing in particular on critical threats related to random number generators. Full article

The biotechnological conversion of CO₂ to biomethane represents an energy-efficient, environmentally friendly, and sustainable approach within the waste-to-energy cycle. This process, in which CO₂ and H₂ are converted to biomethane in anaerobic bioreactors, is referred to as hydrogenotrophic biomethane production. While several studies have investigated hydrogenotrophic biomethane production, there is a lack of research comparing flocculent and granular sludge inoculum in continuously operated systems fed with a gas substrate. Both granular and flocculent sludge possess distinct advantages: granular sludge offers higher density, stronger microbial cohesion, and superior settling performance, whereas flocculent sludge provides faster substrate accessibility and more rapid initial microbial activity. In this study, two UASB (Upflow Anaerobic Sludge Blanket) reactors operated under mesophilic conditions were continuously fed with synthetic off-gas composed of pure H₂ and CO₂ in a 4:1 ratio and were compared in terms of microbial community shifts and their effects on hydrogenotrophic biomethane production. Biomethane production reached 75 ± 2% in the granular sludge reactor, significantly higher than the 64 ± 1.3% obtained with flocculent sludge. Although hydrogen consumption did not differ significantly, the granular sludge reactor exhibited higher CO₂ removal efficiency. Microbial analyses further revealed that granular sludge was more effective in supporting methanogenic archaea under conditions of gas substrate feeding. These findings offer advantageous suggestions for improving biogas production, enhancing waste gas management, and advancing sustainable energy generation. Full article

Spigelian hernia (SH) is an infrequent aponeurotic defect in Spiegel’s semilunar line. The literature on pediatric SH is scarce. A comprehensive review of the previous literature was conducted. Eligible studies were identified by searching primary medical bibliography databases, and a pooled analysis of published case-level data was performed. Medians and interquartile ranges were used to describe the quantitative variables and proportions for categorical variables. The Kruskal–Wallis, Mann–Whitney U, and Fisher’s exact tests were used to compare group variables. Spearman’s and Pearson’s correlation analyses were used to assess the degree of correlation between variables, while Cramér’s V was applied to evaluate the degree of association among the variables. A p-value < 0.05 (two-tailed) was considered statistically significant. Our search identified 82 publications reporting on 123 patients (106 male, 86.2%), with an age range of 0–21 years. Forty-seven patients (38.2%) had a left-sided SH, fifty-six (45.5%) had a right-sided SH, and thirteen (10.6%) had a bilateral SH. Traumatic SH, mostly from bicycle injuries, accounted for 45 cases (36.6%), while 41 (33.3%) were associated with undescended testis (UDT). In this series of published cases, hernia incarceration/strangulation (I/S) was reported in 15 patients (12.2%), who were significantly younger (p = 0.02). Surgical correction was performed in 95 cases (77.2%), 14 of them laparoscopically, with a 35.7% conversion rate. Eight cases (6.5%) were managed conservatively. Overall, outcomes were favorable. SH is an infrequent pediatric condition that, based on the synthesized literature, predominantly affects males. The published cases suggest two main clinical phenotypes: a congenital form, often linked to ipsilateral UDT, and an acquired form, typically resulting from trauma. Analysis of the reported data indicates a higher risk of incarceration in early childhood. Surgical treatment is the most frequently reported approach with generally favorable outcomes, whereas the evidence for conservative management remains limited. This comprehensive review highlights the dual nature of pediatric SH and underscores the need for a high index of suspicion in relevant clinical scenarios. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

Neonatal calf diarrhea (NCD) represents a major cause of economic loss in dairy cattle herds worldwide. The condition is primarily associated with several key pathogens, including enterotoxigenic Escherichia coli (ETEC), viral agents such as bovine rotavirus (BRV) and bovine coronavirus (BCoV), and the protozoan Cryptosporidium parvum. This study aimed to assess the prevalence of NCD-associated pathogens in Italian dairy farms over the period 2020–2022. Among the 598 farms affected by NCD and included in the investigation, ETEC strains were detected in 17.2% of cases. The prevalence of BRV, BCoV, and Cryptosporidium spp. was 22.2%, 20.2%, and 32.3%, respectively. Co-infections were also frequently observed and are considered to significantly exacerbate the clinical severity of the disease. Ongoing surveillance of NCD pathogens is essential to generate reliable and updated epidemiological data, which are critical for guiding effective control and prevention strategies. Full article

Mining and metallurgical activities negatively impact ecosystems due to the release of potentially toxic elements (PTEs). This study assesses PTE pollution and accumulation in native plant species that have spontaneously colonized a historical mining site (Michaly, site A) and a nearby metallurgical smelter site (Varvara, site B) on the Lavreotiki Peninsula, Attika, Greece. Soils were analyzed for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, and Zn. A total of 89 native plant taxa across 28 families were identified. The aerial parts from dominant species were analyzed for PTE concentrations, and bioconcentration factors (BCFs) were calculated. One-way ANOVA and principal component analysis (PCA) using R were used for statistical evaluation. Soils at both sites showed elevated As, Cd, Cr, Cu, Ni, Pb, Sb, and Zn; Mn was high only at site B, while Co and Fe remained at background levels. Several plant species, especially at Michaly, had elevated concentrations of As, Cd, Co, Cr, Fe, Pb, Sb, and Zn in their aerial parts. BCFs indicated general PTE exclusion from aerial parts, particularly at site B. Native vegetation on these contaminated sites shows resilience and PTE exclusion, highlighting their potential for phytoremediation, especially phytostabilization, and ecological restoration in similarly polluted Mediterranean environments. Full article

Autonomous ship control faces significant challenges due to the diversity of ship types, the complexity of task scenarios, and the uncertainty of dynamic marine environments. These factors limit the effectiveness of traditional control approaches that rely on explicit dynamics modeling and handcrafted control laws. With the rapid advancement of computing and artificial intelligence, imitation learning offers a promising alternative by directly learning expert behaviors from data. This paper proposes a Generative Adversarial Imitation Learning (GAIL) method for heading and path-following control of autonomous surface ships. It employs an adversarial learning structure, in which a generator learns control policies that reproduce expert behavior while a discriminator distinguishes between expert and learned trajectories. In this way, the control strategies can be learned from expert demonstrations without requiring explicit reward design. The proposed method is validated through simulations on a model-scale tug. Compared with a behavioral cloning (BC) baseline controller, the GAIL-based controller achieves superior performance in terms of path-following accuracy, heading stability, and control smoothness, confirming its effectiveness and potential for real-world deployment. Full article

Nuclear factor erythroid 2-related factor 2 (NRF2) serves as a master transcriptional regulator of cellular antioxidant responses through orchestration of cytoprotective gene expression, establishing its significance as a therapeutic target in cerebral pathophysiology. Classical electrophilic NRF2 activators, despite potent activation potential, exhibit paradoxically reduced therapeutic efficacy relative to single antioxidants, attributable to concurrent oxidative stress generation, glutathione depletion, mitochondrial impairment, and systemic toxicity. Although emerging non-electrophilic pharmacological activators offer therapeutic potential, their utility remains limited by bioavailability and suboptimal potency, underscoring the imperative for innovative therapeutic strategies to harness this cytoprotective pathway. Non-pharmacological interventions, including neuromodulation, physical exercise, and lifestyle modifications, activate NRF2 through non-canonical, non-electrophilic pathways involving protein–protein interaction inhibition, KEAP1 degradation, post-translational and transcriptional modulation, and protein stabilization, though mechanistic characterization remains incomplete. Such interventions utilize multi-mechanistic approaches that synergistically integrate multiple non-electrophilic NRF2 pathways or judiciously combine electrophilic and non-electrophilic mechanisms while mitigating electrophile-induced toxicity. This strategy confers neuroprotective effects without the contraindications characteristic of classical electrophilic activators. This review comprehensively examines the mechanistic underpinnings of non-pharmacological NRF2 modulation, highlighting non-electrophilic activation pathways that bypass the limitations inherent to electrophilic activators. The evidence presented herein positions non-pharmacological interventions as viable therapeutic approaches for achieving non-electrophilic NRF2 activation in the treatment of cerebrovascular and neurodegenerative pathologies. Full article

Schmallenberg virus (SBV) is a negative-sense RNA virus transmitted by insect vectors, causing arthrogryposis-hydranencephaly syndrome in newborn ruminants. Since its discovery in Germany and the Netherlands in 2011, SBV has rapidly spread across multiple European countries, resulting in significant economic losses in the livestock industry. With the increasing global animal trade and the expanded range of insect transmission, the risk of SBV introduction into non-endemic regions is also rising. As the gold standard for serological testing, the virus neutralization test (VNT) is crucial for tracking the spread of SBV and evaluating the efficacy of vaccines. However, in non-endemic regions, the lack of local viral strains and the biosafety risks associated with introducing foreign strains pose challenges to the implementation of VNT. In this study, we employed reverse genetics techniques using vesicular stomatitis virus (VSV) to substitute the VSV G protein with the envelope glycoproteins of SBV, thereby successfully generating and rescuing the recombinant virus rVSVΔG-eGFP-SBVGPC. The recombinant virus was then thoroughly characterized in terms of SBV Gc protein expression, viral morphology, and growth kinetics. Importantly, rVSVΔG-eGFP-SBVGPC exhibited SBV-specific cell tropism and was capable of reacting with SBV-positive serum, enabling the measurement of neutralizing antibody titers. The results suggest that this recombinant virus can serve as a feasible alternative for SBV neutralization tests, with promising potential for application in serological screening and vaccine evaluation. Full article

Establishing precise welding parameters is essential to achieving the desired bead geometry and ensuring consistent quality in manufacturing processes. However, determining the optimal configuration of parameters remains a challenge, particularly when relying on limited experimental data. This study proposes the use of artificial neural networks (ANNs), with their architecture optimized via differential evolution (DE), to predict key MAG welding parameters based on target bead geometry. To address data limitations, cross-validation and data augmentation techniques were employed to enhance model generalization. In addition to the ANN model, machine learning algorithms commonly recommended for small datasets, such as K-nearest neighbors (KNNs) and support vector machines (SVMs), were implemented for comparative evaluation. The results demonstrate that all models achieved good predictive performance, with SVM showing the highest accuracy among the techniques tested, reinforcing the value of integrating traditional ML models for benchmarking purposes in low-data scenarios. Full article

Livestock plays a crucial role in the global food system, not only as an important source of nutrients but also as a means of economic and social well-being. It constitutes a critical parameter of agricultural production in Mediterranean countries, with the majority of farms still having a relatively small herd size and depending largely on family labor. The purpose of this study is to record and evaluate the perceptions of livestock farmers in the Region of Western Macedonia, Greece (which represents a typical paradigm of an agricultural region), regarding the future prospects and the actions taken to ensure the sustainability of their farms. The research is based on a survey carried out from May to October, 2024, on ruminant farmers. Selective breeding and crossbreeding with higher-productivity breeds are some of the genetic improvements that are generally applied to increase productivity and were, therefore, investigated in this study. Through gradual crossbreeding, farmers attempt to improve the composition of their initial herds by incorporating high-productivity traits—although without officially participating in any recognized improvement program. This increases the risk of extinction for indigenous breeds, which are abandoned for use by the farmers. Our results also showed that most livestock farms derive from inheritances, with many livestock farmers practicing grazing mainly in mountainous areas and still rearing indigenous breeds. From the farmers’ point of view, more information and education regarding market conditions are needed. Furthermore, the sustainability of farms largely depends on subsidies, which are crucial due to difficulties in economic viability, particularly in mountainous areas. Encouraging the support of market differentiation and public awareness for the nutritional value of products derived from local breeds may serve as a promising agrobiodiversity conservation strategy. Full article

Inverse problems have numerous important applications in science, engineering, medicine, and other disciplines. In this study, we present a numerical solution for a one-dimensional parabolic inverse problem with energy overspecification at a fixed spatial point, using the radial basis function (RBF) method. The collocation matrix arising in RBF-based approaches is typically highly ill-conditioned, and the method’s performance is strongly influenced by the choice of the radial basis function and its shape parameter. Unlike previous studies that focused primarily on Gaussian radial basis functions, this work investigates and compares the performance of three RBF types—Gaussian (GRBF), Multiquadrics (MQRBF), and Inverse Multiquadrics (IMQRBF). By transforming the inverse problem into an equivalent direct problem, we apply the RBF collocation method in both space and time. Numerical experiments on two test problems with known analytical solutions are conducted to evaluate the approximation error, optimal shape parameters, and matrix conditioning. Results indicate that both MQRBF and IMQRBF generally provide better accuracy than GRBF. Furthermore, IMQRBF enhances numerical stability due to its lower condition number, making it a more robust choice for solving ill-posed inverse problems where both stability and accuracy are critical. Full article

Background: Meniere’s disease (MD) is a rare inner ear disorder characterized by endolymphatic hydrops and symptoms such as vertigo and hearing loss, with no curative treatment currently available. XuanYunNing tablets (XYN) have been clinically used to treat MD, but their molecular mechanisms remain unclear. Objective: This study aimed to systematically evaluate the pharmacological effects of XYN in a guinea pig model of MD and to elucidate the underlying molecular mechanisms of both MD pathogenesis and XYN intervention through integrated multi-omics analyses, including transcriptomics, proteomics, and bioinformatics. Methods: A guinea pig model of endolymphatic hydrops was induced by intraperitoneal injection of desmopressin acetate (dDAVP). Pharmacodynamic efficacy was evaluated via behavioral scoring and histopathological analysis. The differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) modulated by XYN treatment were identified using high-throughput transcriptomic and proteomic sequencing. These data were integrated through multi-omics bioinformatic analysis. Key molecular targets and signaling pathways were further validated using RT-qPCR and Western blotting. Results: Pharmacological evaluations showed that guinea pigs in the model group exhibited a 26% increase in endolymphatic hydrops area, while high-dose XYN treatment reduced this area by 19% and significantly improved functional parameters, including overall physiological condition (e.g., weight and general appearance), auricular reflexes to low-, medium-, and high-frequency sound stimuli, nystagmus, and the righting reflex. High-throughput sequencing combined with integrative omics analysis identified 513 potential molecular targets of XYN. Subsequent network and module analyses pinpointed the JAK-STAT signaling pathway as the central axis. Mendelian randomization (MR) analysis further supported a causal relationship between MD and metabolic, immune, and inflammatory traits, reinforcing the central role of JAK-STAT signaling in both MD progression and XYN-mediated intervention. Mechanistic studies confirmed that XYN downregulated IFNG, IFNGR1, JAK1, p-STAT3/STAT3, and AOX at both mRNA and protein levels, thereby inhibiting aberrant JAK-STAT pathway activation in MD model animals. In addition, a total of 125 chemical constituents were identified in XYN by UHPLC-MS analysis. ZBTB20 and other molecules were identified as potential blood-based biomarkers for MD. Conclusions: This study reveals that XYN alleviates MD symptoms by disrupting a pathological cycle driven by JAK-STAT signaling, inflammation, and metabolic dysfunction. These findings support the clinical potential of XYN in the treatment of Meniere’s disease and may inform the development of novel therapeutic strategies. Full article

In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic practice of musicians and music practitioners. This paper aims to address this question via Prompt Audio Generation User Research Investigation (PAGURI), a user experience study where we leverage recent text-to-music developments to study how musicians and practitioners interact with these systems, evaluating their satisfaction levels. We developed an online tool through which users can generate music samples and/or apply recently proposed personalization techniques based on fine-tuning to allow the text-to-music model to generate sounds closer to their needs and preferences. Using semi-structured interviews, we analyzed different aspects related to how participants interacted with the proposed tool to understand the current effectiveness and limitations of text-to-music models in enhancing users’ creativity. Our research centers on user experiences to uncover insights that can guide the future development of TTM models and their role in AI-driven music creation. Additionally, they offered insightful perspectives on potential system improvements and their integration into their music practices. The results obtained through the study reveal the pros and cons of the use of TTMs for creative endeavors. Participants recognized the system’s creative potential and appreciated the usefulness of its personalization features. However, they also identified several challenges that must be addressed before TTMs are ready for real-world music creation, particularly issues of prompt ambiguity, limited controllability, and integration into existing workflows. Full article

Coronavirus disease 2019 (COVID-19) in adults is well characterized and associated with multisystem dysfunction. A subset of patients develop post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID), marked by persistent and fluctuating organ system abnormalities. In children, distinct clinical and pathophysiological features of COVID-19 and long COVID are increasingly recognized, though knowledge remains limited relative to adults. The exponential expansion of the COVID-19 literature has made comprehensive appraisal by individual researchers increasingly unfeasible, highlighting the need for new approaches to evidence synthesis. Large language models (LLMs) such as the Generative Pre-trained Transformer (GPT) can process vast amounts of text, offering potential utility in this domain. Earlier versions of GPT, however, have been prone to generating fabricated references or misrepresentations of primary data. To evaluate the potential of more advanced models, we systematically applied GPT-4 to summarize studies on pediatric long COVID published between January 2022 and January 2025. Articles were identified in PubMed, and full-text PDFs were retrieved from publishers. GPT-4-generated summaries were cross-checked against the results sections of the original reports to ensure accuracy before incorporation into a structured review framework. This methodology demonstrates how LLMs may augment traditional literature review by improving efficiency and coverage in rapidly evolving fields, provided that outputs are subjected to rigorous human verification. Full article