Search Results (2,626)

During humanitarian crises, social media generates over 30 million multimodal tweets daily, but 20% textual noise, 40% cross-modal misalignment, and severe class imbalance (4.1% rare classes) hinder effective classification. This study presents CLIP-BCA-Gated, a dynamic multimodal framework that integrates bidirectional cross-attention (Bi-Cross-Attention) and adaptive gating within the CLIP architecture to address these challenges. The Bi-Cross-Attention module enables fine-grained cross-modal semantic alignment, while the adaptive gating mechanism dynamically weights modalities to suppress noise. Hierarchical learning rate scheduling and multidimensional data augmentation further optimize feature fusion for real-time multiclass classification. On the CrisisMMD benchmark, CLIP-BCA-Gated achieves 91.77% classification accuracy (1.55% higher than baseline CLIP and 2.33% over state-of-the-art ALIGN), with exceptional recall for critical categories: infrastructure damage (93.42%) and rescue efforts (92.15%). The model processes tweets at 0.083 s per instance, meeting real-time deployment requirements for emergency response systems. Ablation studies show Bi-Cross-Attention contributes 2.54% accuracy improvement, and adaptive gating contributes 1.12%. This work demonstrates that dynamic multimodal fusion enhances resilience to noisy social media data, directly supporting SDG 11 through scalable real-time disaster information triage. The framework’s noise-robust design and sub-second inference make it a practical solution for humanitarian organizations requiring rapid crisis categorization. Full article

This study aimed at proposing a predictive model for real-time monitoring of epidemic dynamics at the municipal scale in Lombardy region, in northern Italy, leveraging Emergency Medical Services (EMS) dispatch data and Geographic Information Systems (GIS) methodologies. Unlike traditional epidemiological models that rely on official diagnoses and offer limited spatial granularity, our approach uses EMS call data (rapidly collected, geo-referenced, and unbiased by institutional delays) as an early proxy for outbreak detection. The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. Developed in collaboration with the Lombardy EMS agency (AREU), the system is designed for operational applicability, emphasizing simplicity, speed, and interpretability. Despite the complexity of the phenomenon and the use of a five-class output, the model shows promising predictive capacity, particularly for identifying outbreak-free areas. Performance is affected by changing epidemic dynamics, such as those induced by widespread vaccination, yet remains informative for early warning. The framework supports health decision-makers with timely, localized insights, offering a scalable tool for epidemic preparedness and response. Full article

Background: Type 2 diabetes mellitus (T2DM) increases sepsis risk due to immune dysfunction and chronic inflammation. Antidiabetic medications, while primarily used for glycemic control, may modulate sepsis susceptibility through immune and inflammatory pathways. This study investigates the association between antidiabetic medication use and sepsis risk in T2DM patients. Methods: A longitudinal cohort study was conducted using clinical registry data from 5009 T2DM patients at the University Hospital, Debrecen, Hungary (2016–2020). Sepsis cases were identified via ICD-10 code A41, and antidiabetic medication use was categorized using ATC codes. Baseline comorbidities and laboratory parameters were extracted. Chi-square and Wilcoxon rank–sum tests assessed associations between sepsis and categorical/numerical variables, respectively. Time-adjusted multivariate logistic regression evaluated predictors of sepsis risk, with odds ratios (ORs) and 95% confidence intervals (CIs) reported. Results: Age, hypertension, ischemic heart disease, nephropathy, elevated blood glucose, C-reactive protein, and creatinine also independently increased sepsis risk. Insulin use was associated with a 2.6-fold increased sepsis risk (OR = 2.6, 95% CI: 2.09–3.34, p < 0.001), while SGLT2 inhibitors (OR = 0.56, 95% CI: 0.34–0.91, p = 0.02) and GLP-1 receptor agonists (OR = 0.39, 95% CI: 0.19–0.79, p = 0.009) were protective. Conclusions: Insulin-treated patients may require closer infection monitoring, while SGLT2 inhibitors and GLP-1 RAs could be prioritized in high-risk individuals. These findings highlight the potential to inform risk stratification and guide personalized antidiabetic therapy to reduce sepsis risk in T2DM. Full article

A principal factor of the fourth Industrial Revolution is the Internet of Things (IoT), a network of “smart” objects that communicate by exchanging helpful information about themselves and their environment. Our research aims to address the gaps in the existing literature regarding the educational and technological affordances of IoT applications in learning environments in secondary education. Our systematic review using the PRISMA method allowed us to extract 25 empirical studies from the last 10 years. We present the categorization of educational and technological affordances, as well as the devices used in these environments. Moreover, our findings indicate widespread adoption of organized educational activities and design-based learning, often incorporating tangible interfaces, smart objects, and IoT applications, which enhance student engagement and interaction. Additionally, we identify the impact of IoT-based learning on knowledge building, autonomous learning, student attitude, and motivation. The results suggest that the IoT can facilitate personalized and experiential learning, fostering a more immersive and adaptive educational experience. Based on these findings, we discuss key recommendations for educators, policymakers, and researchers, while also addressing this study’s limitations and potential directions for future research. Full article

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

Rabies remains a significant global public health concern, causing an estimated 59,000–69,000 human fatalities annually. Despite being entirely preventable through vaccination, rabies continues to impose substantial economic burdens worldwide. This study presents a scoping review of the economic research on rabies to determine overlaps and gaps in knowledge and inform future research strategies. We selected 150 studies (1973–2024) to analyze. The review categorizes the literature based on geographic distribution, species focus, and type of study. Findings indicate that economic studies are disproportionately concentrated in developed countries, such as the United States and parts of Europe, where rabies risk is low, while high-risk regions, particularly in Africa and Asia, remain underrepresented. Most studies focus on dog-mediated rabies, reflecting its dominant role in human transmission, while fewer studies assess the economic impacts of wildlife and livestock-mediated rabies. Case studies and modeling approaches dominate the literature, whereas cost–benefit and cost–effectiveness analyses—critical for informing resource allocation—are limited. The review highlights the need for more economic evaluations in rabies-endemic regions, expanded research on non-dog reservoirs, and broader use of economic methods. Addressing these gaps will be crucial for optimizing rabies control and supporting global initiatives to eliminate dog-mediated rabies by 2030. Full article

Background/Objectives: Rapid eye movements (REMs) during sleep were initially associated with dreaming, suggesting a relationship between REMs and dream content; however, this hypothesis was questioned by their differences with the REMs during wakefulness and the evidence that REMs are also present in blind individuals with no visual dreaming. Successive studies have focused on the phenomenology and physiological significance of REMs during sleep. REMs are categorized as expressions of the phasic REM component, which is characterized by bursts of eye movements, whereas the tonic REM component is characterized by quiescent periods without eye movements. Methods: The study is a retrospective analysis of 105 sleep records from 15 subjects. We analyzed the two components, tonic and phasic REM, across the sleep period, the REM activity in the first 5 min and in the last 5 min of each REM period were also assessed. Results: Phasic epochs were more represented than tonic epochs across the whole night period. REM activity in the first and last five minutes of an REM period presented different, although non-significant, patterns across the night. REM activity in the first 5 min showed a curvilinear profile, whereas REM activity in the last 5 min showed a linear increasing trend. A significant correlation was found between the REM activity in the first 5 min of the REM period and the total duration of the REM period. Conclusions: According to our results, the analysis of REM activity and the focus on segments of an REM period could provide more information both on the temporal evolution of REM activity within an REM period and on the possible role of REMs in REM sleep regulation and its significance in psychiatric and neurological disorders. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

In marketing science, product categorization using cannibalization relationship data is an emerging but still underdeveloped area, where clustering using price effect information is a novel direction that is worth further exploration. In this study, by assuming a realistic modeling of the cross-price effect, we developed and experimentally validated with simulations an agglomerative clustering algorithm that outputs clustering results closer to the ground truth compared with other agglomerative algorithms based on traditional cluster linkages. Full article

Background/Objective: Understanding how chronic conditions such as knee osteoarthritis (OA) and chronic obstructive pulmonary disease (COPD) influence long-term physical activity (PA) is essential for developing condition-specific rehabilitation strategies. This study aimed to examine whether baseline diagnoses of knee OA and COPD are independently associated with the trajectories of PA decline over five years in U.S. adults, informed by the disablement process model. Methods: We analyzed data from 855 adults aged ≥45 years enrolled in the Osteoarthritis Initiative (OAI). The participants were categorized into three baseline groups, control (n = 122), knee OA (n = 646), and COPD (n = 87), based on self-reports and prior clinical assessments. PA was measured annually for five years using the Physical Activity Scale for the Elderly (PASE). General linear mixed models assessed changes in PA over time, adjusting for demographic, behavioral, and clinical covariates. Results: Compared to the controls, participants with knee OA had a significant decline in PA over time (β = −6.62; 95% CI: −15.4 to −2.19; p = 0.014). Those with COPD experienced an even greater decline compared to the knee OA group (β = −11.2; 95% CI: −21.7 to −0.67; p = 0.037). These associations persisted after adjusting for age, sex, body mass index, comorbidities, and smoking. Conclusions: Baseline knee OA and COPD were independently associated with long-term reductions in PA. These findings underscore the importance of early, tailored rehabilitation strategies, particularly pulmonary rehabilitation, in preserving functional independence among older adults with chronic conditions. Full article

Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. Synthetic table data generation has emerged as a promising solution to enable data analysis while mitigating privacy risks. While Generative Adversarial Networks (GANs) are widely used for this purpose, they exhibit limitations in modeling table data due to challenges in handling mixed data types (numerical/categorical), non-Gaussian distributions, and imbalanced variables. To address these limitations, this study proposes a novel adversarial learning framework integrating gradient boosting trees for synthesizing table data, called Adversarial Gradient Boosting Decision Tree (AGBDT). Experimental evaluations on several datasets demonstrate that our method outperforms representative baseline models regarding statistical similarity and machine learning utility. Furthermore, we introduce a privacy-aware adaptation of the framework by incorporating k-anonymization constraints, effectively reducing overfitting to source data while maintaining practical usability. The results validate the balance between data utility and privacy preservation achieved by our approach. Full article

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article

Introduction: Cystic fibrosis (CF) is a genetic condition affecting several organs and systems, including the pancreas, colon, respiratory system, and reproductive system. The detection of a growing number of CFTR variants and genotypes has contributed to an increase in the CF population which, in turn, has had an impact on the overall statistics regarding the prognosis and outcome of the condition. Given the increase in life expectancy, it is critical to better predict outcomes and prognosticate in CF. Thus, each person’s choice to aggressively treat specific disease components can be more appropriate and tailored, further increasing survival. The objective of our narrative review is to summarize the most recent information concerning the value and significance of clinical parameters in predicting outcomes, such as gender, diabetes, liver and pancreatic status, lung function, radiography, bacteriology, and blood and sputum biomarkers of inflammation and disease, and how variations in these parameters affect prognosis from the prenatal stage to maturity. Materials and methods: A methodological search of the available data was performed with regard to prognostic factors in the evolution of CF in children and young adults. We evaluated articles from the PubMed academic search engine using the following search terms: prognostic factors AND children AND cystic fibrosis OR mucoviscidosis. Results: We found that it is crucial to customize CF patients’ care based on their unique clinical and biological parameters, genetics, and related comorbidities. Conclusions: The predictive significance of more dynamic clinical condition markers provides more realistic future objectives to center treatment and targets for each patient. Over the past ten years, improvements in care, diagnostics, and treatment have impacted the prognosis for CF. Although genotyping offers a way to categorize CF to direct research and treatment, it is crucial to understand that a variety of other factors, such as epigenetics, genetic modifiers, environmental factors, and socioeconomic status, can affect CF outcomes. The long-term management of this complicated multisystem condition has been made easier for patients, their families, and physicians by earlier and more accurate identification techniques, evidence-based research, and centralized expert multidisciplinary care. Full article

This article describes the design, methods, and baseline characteristics of the social needs assessment (SNA) of participants enrolled in an ongoing randomized clinical trial implementing a comprehensive approach to improving diabetes self-management and providing an intensive Diabetes Self-Management Education and Support (iDSMES) Program at St. Vincent’s House Clinic, a primary care practice serving resource-challenged diverse populations in Galveston, Texas. Standardized SNA was conducted to collect information on financial needs, psychosocial well-being, and other chronic health conditions. Based on their identified needs, participants were referred to non-medical existing community resources. A series of in-depth interviews were conducted with a subset of participants. A team member independently categorized these SNA narratives and aggregated them into two overarching groups: medical and social needs. Fifty-nine participants (with a mean age of 53 years and equal representation of men and women) completed an SNA. Most (71%) did not have health insurance. Among 12 potential social needs surveyed, the most frequently requested resources were occupational therapy (78%), utility assistance (73%), and food pantry services (71%). SNA provided data with the potential to address barriers that may hinder participation, retention, and outcomes in diabetes self-management. SNA findings may serve as tertiary prevention to mitigate diabetes-related complications and disparities. Full article

Super-resolution microscopy (SRM) has revolutionized our understanding of subcellular structures, including cell organelles and viruses. For human immunodeficiency virus (HIV), SRM has significantly advanced knowledge of viral structural biology and assembly dynamics. This review analyzes how SRM techniques (particularly PALM, STORM, STED, and SIM) have been applied over the past decade to study HIV structural components and assembly. By categorizing and comparing studies based on SRM methods, HIV components, and labeling strategies, we assess the strengths and limitations of each approach. Our analysis shows that PALM is most commonly used for live-cell imaging of HIV Gag, while STED is primarily used to study the viral envelope (Env). STORM and SIM have been applied to visualize various components, including Env, capsid, and matrix. Antibody labeling is prevalent in PALM and STORM studies, targeting Env and capsid, whereas fluorescent protein labeling is mainly associated with PALM and focused on Gag. A recent emphasis on Gag and Env points to deeper investigation into HIV assembly and viral membrane dynamics. Insights from SRM studies of HIV not only enhance virological understanding but also inform future research in therapeutic strategies and delivery systems, including extracellular vesicles. Full article