Search Results (15,488)

Background/Objectives: Varicocele repair can improve semen parameters and pregnancy rates in appropriately selected men; however, persistence or recurrence remains a common cause of treatment failure with ongoing infertility or scrotal pain. Because mechanisms and definitions vary across studies, counseling and salvage selection can be challenging. This review synthesizes contemporary evidence on why varicocele recur and provides an anatomy-informed approach to evaluation and retreatment. Methods: A narrative evidence synthesis was performed using PubMed/MEDLINE, prioritizing clinical practice guidelines, systematic reviews, meta-analyses, and contemporary adult and adolescent clinical series addressing mechanisms of failure, diagnostic workup, and outcomes of salvage microsurgery and endovascular therapy. Results: Recurrence rates vary by technique and follow-up, with the lowest rates reported in contemporary microsurgical subinguinal series. The dominant drivers of failure are incomplete venous control and complex reflux pathways, including duplicated internal spermatic veins and missed collaterals such as cremasteric, external spermatic, gubernacular, and deferential veins. Clinical examination remains central; Doppler ultrasonography is most useful when pain persists or semen parameters and testicular growth do not improve. Venography can define culprit channels in complex or multiply treated cases and enables targeted embolization. Retreatment achieves high anatomic success with consistent improvements in semen parameters and meaningful pregnancy rates in available series, with modality-specific complication profiles. Conclusions: Recurrent varicocele should be managed with structured reassessment that links venous anatomy and the index procedure to the salvage option. Microsurgical redo is generally favored after non-microscopic repairs, whereas endovascular occlusion is often preferred after prior surgery or when venographic mapping is needed. Full article

Objectives: Anticipated telehealth use is an important determinant of whether telehealth can function as a durable component of hybrid care models. However, there are limited practical tools to identify patients at risk of discontinuing telehealth. We aim to (1) identify factors associated with anticipated telehealth use; (2) develop a risk stratification tool (CONTEST); (3) compare its performance with machine learning (ML) models; and (4) evaluate model fairness across sex and race/ethnicity. Methods: We conducted a retrospective cross-sectional analysis of the 2024 Health Information National Trends Survey 7 (HINTS 7), including U.S. adults with ≥1 telehealth visit in the prior 12 months. The primary outcome was anticipated telehealth use. Survey-weighted multivariable logistic regression informed a Framingham-style point score (CONTEST). ML models (XGBoost, random forest, logistic regression) were trained and evaluated using the area under the receiver operating characteristic curve (AUROC), precision, and recall. Global interpretation used SHAP values. Fairness was assessed using group metrics (Disparate Impact, Equal Opportunity) and individual counterfactual-flip rates (CFR). Results: Approximately one-third of adults reported at least one telehealth visit in the prior year. Among these users, nearly one in ten expressed an unwillingness to continue using telehealth in the future. Four telehealth experience factors were independently associated with unwillingness to continue: lower perceived convenience, technical problems, lower perceived quality compared to in-person care, and unwillingness to recommend telehealth. CONTEST demonstrated strong discrimination for identifying individuals with lower anticipated telehealth use (AUROC 0.876; 95% CI, 0.843–0.908). XGBoost performed best among the ML models (AUROC 0.902 with all features). With the same four top features, an ML-informed point score achieved an AUROC of 0.872 (95% CI, 0.839–0.904), and a four-feature XGBoost model yielded an AUROC of 0.893 (95% CI, 0.821–0.948, p > 0.05). Group fairness metrics revealed disparities across sex and race/ethnicity, whereas individual counterfactual analyses indicated low flip rates (sex CFR: 0.024; race/ethnicity CFR: 0.013). Conclusions: A parsimonious, interpretable score (CONTEST) and feature-matched ML models provide comparable discrimination for stratifying risk of lower anticipated telehealth use. Sustained engagement hinges on convenience, technical reliability, perceived quality, and patient advocacy. Implementation should pair prediction with operational support and routine fairness monitoring to mitigate subgroup disparities. Full article

Brucellosis continues to pose a substantial zoonotic risk in Kazakhstan; however, evidence describing the knowledge, attitudes, and practices (KAP) of cattle farmers and veterinary personnel remains limited. A cross-sectional study was undertaken between May and October 2024 across twelve administrative locations nationwide. Structured questionnaires were administered to 506 cattle farmers and 33 veterinary professionals, and the data were evaluated using descriptive analyses and univariable logistic regression. Awareness of brucellosis in cattle was relatively high among farmers, yet understanding of its implications for human health was markedly lower. In contrast, animal health workers demonstrated consistently higher levels of knowledge (OR: 12.6; 95% CI: 9.88–16.34; p < 0.001). Several practices associated with zoonotic transmission were commonly reported by farmers, including handling aborted materials without protective gloves, consumption of unpasteurised milk, and leaving reproductive tissues in grazing areas. Nevertheless, most farmers expressed readiness to adopt preventive measures, particularly cattle vaccination and the use of basic protective practices. These findings reveal important gaps between awareness and behavior that may contribute to continued transmission of brucellosis. Strengthening farmer education through locally tailored, One Health-based interventions offers a practical pathway to improving brucellosis control and enhancing cattle productivity in Kazakhstan. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

Natural antibacterial food packaging materials endowed with environmental responsiveness are garnering substantial research interest in sustainable food preservation. This study reports the development of a pH-responsive antimicrobial composite film through encapsulation of eugenol—a natural phenolic compound—within zeolitic imidazolate framework-8 (ZIF-8). The engineered eugenol@ZIF-8 system demonstrated pH-dependent release characteristics, with cumulative release reaching 32.2% at pH 6 versus merely 0.61% at pH 7 over 4 h. Subsequent integration of this nanocarrier into a polyvinyl alcohol (PVA)/hydroxypropyltrimethyl ammonium chloride chitosan (HACC) matrix yielded a multifunctional composite film for active food packaging applications. The characterization of film revealed that while eugenol@ZIF-8 incorporation slightly compromised mechanical strength (tensile resistance decreased by 18.7%) and flexibility (elongation at break reduced to 54.3% of control), it significantly enhanced hydrophobicity (water contact angle increased to 92.5°) and thermal stability (decomposition temperature elevated by 34 °C). The composite film demonstrated synergistic antibacterial efficacy through the combined action of Zn²⁺ ions, ZIF-8 nanostructures, and eugenol, achieving 88% inhibition against E. coli. Practical validation through fresh noodle preservation trials confirmed the material’s effectiveness, with the optimized formulation (PVA-HACC-2% eugenol@ZIF-8, PHEZ2) extending shelf life by >5 days compared to conventional packaging. This work establishes a novel strategy for engineering intelligent ZIF-based packaging systems that respond to food spoilage microenvironments, offering significant potential for reducing food loss. Full article

Mental health problems are increasing among young people in South Africa due to diverse determinants such as, poverty, social shame, and lack of proper access to health services. Although physical activity is a low-cost and non-medical way to help improve mental health, its effects in rural areas are still not clearly understood. A scoping review was carried out following the PRISMA-ScR guidelines. We systematically searched three online databases (PubMed, CINAHL, and Google Scholar) for studies published from 2014 through 2025. Studies met eligibility criteria if they targeted youth aged 14–35 years living in rural South Africa and reported on physical activity interventions designed for mental health. Two reviewers independently carried out data extraction and came up with the overall result. Overall, 42,384 records were identified, of which only 12 studies met all the specified criteria. The findings of the study were that participation in organised forms of physical activity (including school-based aerobic sessions, community-based walking groups, and charity training programmes) was associated with reductions in mental health issues. Even with these issues, the findings show that physical activity can be a useful, efficient, and practical way to support mental health among rural South African youth. To improve the evidence, strong trials, community-based plans, and sessions at schools and clinics are needed. In addition, policy cooperation across health, education, and sports sectors is essential for lasting impact. Full article

Polymer modification is a well-established strategy for improving the performance and extending the service life of cementitious and other construction materials, with direct implications for environmental sustainability and infrastructure resilience. Among these polymers, polyvinylpyrrolidone (PVP), a non-ionic, water-soluble, and highly compatible polymer, has emerged as a uniquely versatile additive for mitigating degradation in aggressive environments. This review provides a critical and comprehensive synthesis of the state-of-the-art research on PVP’s roles in cement, mortar, concrete, and asphalt systems. The novelty of this work lies in its mechanistic integration and system-level interpretation, which consolidate fragmented knowledge across multiple domains—ranging from rheology and durability to nanotechnology and interfacial engineering—into a unified and coherent framework. Through cross-study comparison, this approach establishes a comprehensive understanding of PVP’s role in cementitious systems while outlining clear pathways for future research and practical implementation. This review provides the first integrated framework that connects PVP’s molecular structure, adsorption behavior, and ion-coordination mechanisms to its macroscopic influence on rheology, hydration, microstructure, and long-term durability. The review critically analyzes the underlying mechanisms, including physical pore-filling and crack-bridging, as well as chemical ion-coordination, which collectively govern PVP’s performance. Key quantitative findings are consolidated, showing that optimal PVP addition can reduce water absorption by over 35%, increase fracture toughness by ~47%, and, when used as an interfacial modifier, enhance the strain capacity of fiber-reinforced composites by over 100%. Reported benefits include improved workability, enhanced mechanical performance and toughness, superior durability under chemical and frost exposure, and the development of functional materials such as self-sensing concretes and photocatalytic coatings that support structural health monitoring and pollution mitigation. Overall, this review synthesizes current knowledge, consolidates experimental evidence in tabular form, and identifies future opportunities for leveraging PVP in the design of sustainable, low-impact, and environmentally resilient construction materials and infrastructures. Full article

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge this gap, this quasi-experimental study (N = 259) evaluated a triadic instructional design synergizing the intelligent technological, pedagogical, and content knowledge (Intelligent-TPACK) framework, Synthesis of Qualitative Data model, and curated AI tools. Pre-service English as a foreign language (EFL) teachers were assigned to an experimental group (n = 137) receiving the structured intervention or a control group (n = 122) engaging in self-directed AI exploration. Results reveal that the experimental group achieved greater gains across all Intelligent-TPACK dimensions and demonstrated higher-order AI applications in lesson planning. Furthermore, the experimental group experienced a significant reduction in perceived pressure and reported higher perceived usefulness regarding AI integration. Qualitative data revealed that hands-on AI tasks enhanced participants’ confidence, yet challenges with prompts and critical adaptation persisted. The findings demonstrate that systematic training is essential for transforming pre-service teachers’ passive awareness into competent AI integration. Finally, this paper proposes practical implications for integrating this triadic framework into teacher education curricula to facilitate sustainable AI adoption. Full article

Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. The literature suggests numerous potential causes of cost overrun but the clustering of cause variables and relative importance of clusters has not been researched. The research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. Fifty-three completed questionnaires contributed by knowledge experts and experienced managers from Canada, the United States, the Middle East, and Australia met the sample requirements of statistical methods. Starting from 53 variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights into the causes of cost overruns obtained from this research suggest mitigation measures (e.g., improved qualification and experience of personnel, enhanced planning and design practices, risk analysis of inputs to cost estimation process) that are within reach of managers. The results can enhance infrastructure planning and management practice including a reduction in claims and disputes. Full article

Since the Brundtland Report (1987), its definition has anchored sustainable development. An EBSCOhost co-mention scan (1987–2025) finds 259,112 records linking “sustainable development” with the Brundtland Report—used only as a descriptive attention proxy, sensitive to coverage, indexing, keywords, and residual duplicates. We then analyze concept-to-implementation barriers in building governance and propose an update pathway: explicit boundaries, minimum disclosures, and assurance logic. Yet in the built environment—characterized by long-lived assets, carbon lock-in, and net-zero commitments—the definition is difficult to operationalize without explicit boundaries, measurable indicators, and auditable trade-offs. We identify two concept-level weaknesses: (1) the definition reflects late-twentieth-century socio-technical conditions and offers limited guidance for practice shaped by digitalized delivery and operations, accelerated climate policy, and whole-life carbon accounting; and (2) its openness around “needs,” “harm,” and trade-offs enables boundary ambiguity (e.g., operational versus embodied emissions), fragmented standards and certifications, and greenwashing risks. We propose a built-environment update pathway that (i) operationalizes “needs” and “harm” through a minimum life-cycle indicator set linking affordability and occupant well-being with operational energy performance and whole-life carbon outcomes; and (ii) strengthens concept-consistent implementation via harmonized boundary declarations and verification principles across existing net-zero and green building tools, supported by targeted AEC capacity building. Full article

The increasing coexistence of oncological disease and cardiovascular implantable technology poses complex clinical challenges that require close collaboration among cardiologists, electrophysiologists, radiation oncologists, and medical physicists. A structured, systematic, and multidisciplinary approach is essential for the safe management of cancer patients with a cardiac implantable electronic device (CIED) undergoing radiation therapy. This document reports a consensus statement issued by the Italian Association of Hospital Cardiologists (ANMCO), aiming to provide practical guidance for clinicians involved across the entire care continuum of this high-risk patient population. A comprehensive pre-treatment evaluation is strongly recommended, including detailed assessment of the type of radiotherapy, treatment planning parameters, device characteristics, and patient-specific cardiac conditions, to minimize the risk of CIED malfunction. Particular emphasis is placed on risk stratification before radiation therapy, as well as on the appropriate timing and modality of cardiological assessments during treatment and in the post-therapy follow-up phase. The document offers an overview of oncological electrophysiology and the mechanisms of radiation-induced damage to cardiac devices with the goal of supporting standardized, safe, and effective clinical practice in this evolving field. Full article

Aboveground storage tanks are used to store various fluids and chemicals for many industrial purposes. According to API standard 653, the structural integrity of these tanks must be regularly assessed. The U.S. EPA requires each operator to have a Spill Prevention, Control and Countermeasure Plan (SPCC) for aboveground storage containers. The accepted practice for inspection of these tanks, particularly the tank bottoms, requires removing the tank from service, emptying the tank, and interior entry for direct inspection of the structure. The required inspection operations are hazardous due to the chemicals themselves as well as the requirement to operate within confined spaces. An inspection from outside the tank would have significant cost and time benefits and would provide a large reduction in the risks faced by inspection personnel. Guided wave (GW) testing is a promising candidate for screening of storage tank walls and bottoms from the tank exterior due to the ability of GWs to propagate over long distances from a fixed probe location. The lowest-order transverse-motion guided wave modes (e.g., torsional vibrations in pipes) are a good choice for long-range inspection because this mode is not dispersive; therefore, the wave packets do not spread out in time. A common weakness of guided wave inspection is the complexity of report generation in the presence of multiple geometry features in the structure, such as welds, welded plate corners, attachments and so on. In some cases, these features cause generation of non-relevant indications caused by mode conversion. Another significant challenge in applying GW testing is development of probes with high-enough signal amplitudes and relatively small footprints to allow them to be mounted on short tank bottom extensions. In this paper, a new generation of magnetostrictive transducers will be presented. The transducers are based on the reversed Wiedemann effect and can generate shear horizontal mode guided waves over a wide frequency range (20–150 kHz) with SNRs in excess of 50 dB. The recently developed SwRI MST 8 × 8 probe contains an array of eight pairs of individual magnetostrictive transducers (MsTs). The data acquisition hardware allows acquisition using Full Matrix Capture (FMC) and analysis software reporting of anomalies based on Total Focusing Method (TFM) image reconstruction. This novel inspection package allows generation of reports that map out corrosion locations and provide estimates of defect widths. Case studies of this technology on actual storage tank walls and bottoms will be presented together with validation of processing methods on mockups with known anomalies and geometry features. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

Vulnerability severity assessment plays a critical role in cybersecurity risk management by quantifying risk based on vulnerability disclosure reports. However, interpreting these reports and assigning reliable risk levels remains challenging in Internet of Things (IoT) environments. This paper proposes an IoT vulnerability severity prediction framework aligned with the Common Vulnerability Scoring System (CVSS). The framework is based on a lightweight transformer architecture. It uses a distilled version of Bidirectional Encoder Representations from Transformers (BERT). The model is fine-tuned using transfer learning to capture contextual semantic information from vulnerability descriptions. The lightweight design preserves computational efficiency. Experimental evaluation on an IoT vulnerability dataset shows strong and consistent performance across all severity classes. The proposed model achieves double-digit improvements across key evaluation metrics. In most cases, the improvement exceeds 20% compared with traditional machine learning and baseline deep learning approaches. These results show that lightweight transformer models are well suited for IoT security. They provide a practical and effective solution for automated vulnerability severity classification in resource- and data-constrained environments. Full article

Background/objectives: Structural joint damage remains a major determinant of long-term disability in psoriatic arthritis (PsA). However, its relationship with current disease activity and patient-reported impact in routine clinical practice is not fully understood. We aimed to assess the prevalence and burden of structural joint damage in PsA and to examine its associations with disease activity, patient-reported impact, and clinical characteristics using complementary analytical approaches. Methods: This cross-sectional real-world study included 165 patients with PsA. Structural damage was assessed on conventional radiographs and defined as the presence of at least one joint with erosion, deformity/ankylosis, or joint space narrowing. Damage was analyzed as a binary outcome and as an ordinal burden (0, 1–2, ≥3 affected joints). Disease activity was evaluated using DAPSA, and patient-reported impact using PsAID and the ASAS Health Index (ASAS HI). Multivariable logistics and ordinal regression models were applied. Sensitivity analyses included alternative damage definitions, exclusion of joint space narrowing, restriction to longer disease duration, and adjustment for treatment exposure. Results: Structural damage was present in 26.7% of patients. Disease duration was consistently associated with the presence (OR 1.10 per year; 95% CI 1.05–1.15) and increasing burden of structural damage across all analyses. Distal interphalangeal involvement at presentation was strongly associated with higher damage burden (OR 4.29; 95% CI 1.88–9.78). No significant association was observed between structural damage and current disease activity as assessed by DAPSA, while PsAID showed only a non-significant trend. In contrast, ASAS HI scores were significantly higher in patients with structural damage and increased progressively with greater damage burden (ρ = 0.172; p = 0.027). Findings remained robust across sensitivity analyses, including restriction to erosive damage and exclusion of joint space narrowing. Conclusions: In PsA, structural joint damage is primarily driven by cumulative disease exposure rather than current inflammatory activity. Disease duration and distal interphalangeal involvement identify patients at higher structural risk, while health impact measured by ASAS HI reflects accumulated damage more closely than conventional activity indices. Full article