Background: Cricket has gradually drifted away from intuition led coaching toward a discipline governed by measurement and computation, propelled largely by artificial intelligence and wearable sensing. Yet the scholarship charting this shift stays scattered across engineering, biomechanics, and sports science, leaving little clarity on how these strands connect. Methods: This study mapped that terrain through a bibliometric and science mapping exercise. A total of 688 documents indexed in Scopus between 1981 and March 2025 were retrieved using a Boolean title–abstract–keyword strategy, screened under PRISMA guidance, and examined with the R-based Bibliometrix package. Results: The field showed a modest annual growth of 4.35%, drew contributions from 2,199 authors, and saw roughly a fifth of its output emerge through cross border partnerships. Three phases surfaced long dormancy, gradual momentum after 2005, and a steep climb from 2022. India dominated in volume while the United States commanded citation influence, and the University of Twente anchored institutional productivity. A dual thematic character blended acoustic sensor biology with performance-oriented wearable and machine learning research. Conclusion: The evidence sketches a maturing yet fragmented field, offering scientists, engineers, and sporting bodies a grounded reference point for where smart cricket inquiry has travelled and where it seems headed.

Background: Stature estimation is a fundamental component of forensic identification and anthropological investigations. Although long-bone measurements provide the highest accuracy, they are not always available in cases involving fragmented or incomplete human remains. Anthropometric measurements of the hand and fingers offer practical alternatives because they are simple, inexpensive, and non-invasive. The little finger has been comparatively underexplored as a predictor of stature, particularly in South Indian populations. Objectives: To evaluate the relationship between little finger length and stature and to develop regression models for anthropometric estimation. Materials and Methods: A cross-sectional observational study was conducted among 105 healthy adults aged 18–40 years at Yenepoya Medical College. Standing height was measured using a stadiometer, while right and left little finger lengths were measured using a Vernier caliper following standard anthropometric techniques. Each measurement was recorded twice, and the average value was used for analysis. Pearson correlation and linear regression analyses were performed using Microsoft Excel. Statistical significance was set at p < 0.05. Results; Stature demonstrated a significant positive correlation with right little finger length (r = 0.484, p < 0.001) and left little finger length (r = 0.390, p < 0.001). Weight showed a significant correlation only with the right little finger (r = 0.328, p = 0.001), whereas BMI was not significantly associated with either measurement. Regression analysis identified height and gender as significant predictors of right little finger length (R² = 27.3%), while height alone significantly predicted left little finger length (R² = 15.2%). Discriminant analysis based on bilateral little finger lengths correctly classified 67.61% of participants according to sex. Conclusion: Little finger length demonstrates a statistically significant association with stature and may serve as a practical anthropometric parameter in forensic and anthropological applications. Population-specific regression models may improve the accuracy of stature estimation in situations where conventional skeletal measurements are unavailable.

Background: Ventilator-associated events (VAEs) are major healthcare-associated complications among mechanically ventilated patients, contributing to increased morbidity, prolonged intensive care unit (ICU) stay, and higher healthcare costs. Critical care nurses play a pivotal role in implementing evidence-based strategies to prevent VAEs; however, evidence regarding their knowledge, attitudes, and practices (KAP) remains limited, particularly in Saudi Arabia. Objective: To assess critical care nurses' knowledge, attitudes, and practices regarding VAE prevention, examine the relationships among these domains, and determine differences according to demographic and professional characteristics. Methods: A descriptive cross-sectional correlational study was conducted among 89 critical care nurses working in the adult ICU, pediatric ICU, and neonatal ICU at King Abdulaziz Hospital, Al Ahsa in Saudi Arabia. Participants were recruited through convenience sampling and completed a validated self-administered questionnaire assessing demographic characteristics, knowledge, attitudes, and practices related to VAE prevention. Descriptive statistics summarized participant characteristics and domain scores. Pearson's correlation examined relationships among KAP domains, while Mann–Whitney U and Kruskal–Wallis tests compared scores across participant characteristics. Statistical significance was set at p < .05. Results: Participants demonstrated highly positive attitudes and high self-reported adherence to recommended VAE prevention practices, with median scores of 20 for both domains, whereas knowledge scores were more variable (median = 6). Knowledge was positively associated with practice (r = 0.257, p = .015). Specialized training was associated with higher knowledge scores (p = .010), while age was associated with attitude (p = .038). No significant differences in practice were observed across demographic or professional characteristics. Conclusion: Critical care nurses demonstrated positive attitudes and high adherence to evidence-based VAE prevention practices despite variability in knowledge. The association between knowledge and practice highlights the importance of structured education and competency-based training to strengthen evidence-based care. These findings provide baseline evidence to support nursing education, leadership, and quality improvement initiatives aimed at enhancing patient safety in Saudi critical care settings.

Capsule retention is a rare but clinically significant complication of video capsule endoscopy (VCE), particularly in patients with refractory celiac disease. We report the case of a 42-year-old man with type II refractory celiac disease who developed jejunal capsule retention after VCE despite a negative magnetic resonance enterography (MRE). Conservative treatment with prednisone and laxatives resulted in spontaneous capsule passage after 30 days, avoiding endoscopic or surgical retrieval. This case highlights the importance of careful patient selection, pre-procedural risk assessment, and individualized management of capsule retention in inflammatory small bowel disease.

Losses of data are a widespread issue of the real-world tabular data, utilized in machine learning (ML). Missing values may dramatically hamper the quality of the model, be biased, and result in incorrect inferences unless addressed correctly. This is a systematic literature review (SLR) that explores and syntheses 52 research articles published 2020-2026 in high-impact peer review journals. The review is done under the guidelines of PRISMA (Preferred Reporting Items to Systematic Reviews and Meta-Analyses). Methods of imputation can be divided into 5 broad categories: statistical and conventional imputation methods (mean, median, mode, and Last Observation Carried Forward), machine learning-based methods (k-Nearest Neighbors, Random Forest, Decision Trees and Support Vector Machines), multiple imputation methods (including MICE, missForest and missRanger), deep learning-based methods (including Autoencoders, Vari There is a systematic comparison between methods based on type of dataset, missing data mechanism (MCAR, MAR, MNAR), evaluation measures (RMSE, MAE, accuracy, AUC), computational complexity and scalability. Using our results, it appears that, up to low missingness rates, conventional approaches are equally competitive, but that deep generative models (with GAN-based models or diffusion-based models being two different approaches to the same task) are matched when applied to high-dimensional and heterogeneous tabular data. However, there is no one particular approach that prevails in all situations. This review finds the overall gaps in research, such as the absence of standardized benchmarks, the relative dearth of interest in MNAR mechanisms, and the lack of research on imputation in federated learning. The results give practical advice to practitioners and researchers to use the right imputation techniques when using tabular ML tasks.

The Regional Security Complex Theory (RSCT) developed by Buzan and Wæver (2003) has conventionally treated the Indian Ocean Region (IOR) and West Asia as analytically distinct security complexes, connected only loosely through great-power overlay. The 2024 enlargement of BRICS which brought Egypt, Ethiopia, Iran, and the United Arab Emirates into full membership alongside founding member India, with Saudi Arabia occupying an unresolved associate status has drawn actors from both complexes into a single multilateral institutional framework. This paper asks whether this development constitutes a genuine merger of the two regional security complexes, an instance of institutional overlay, or a distinct pathway of regional change that RSCT has not yet theorized. Using a qualitative, historical-comparative method that examines BRICS summit declarations, energy and connectivity linkages, and the persistence of intra-bloc security frictions, the paper argues that BRICS enlargement has produced institutional convergence without corresponding security convergence. This decoupling carries direct implications for India, which now occupies the institutional seam between both complexes and must manage this convergence while preserving its strategic autonomy.

Artificial intelligence [AI] has become an indispensable part of scientific and medical research, however, the exponential growth in model complexity and computational requirements has made AI adoption challenging for resource-limited settings. The concept of Frugal AI has emerged to address this gap by designing efficient, accessible, and sustainable AI systems that achieve comparable accuracy with reduced computational, financial, and environmental costs. In the context of medical research, Frugal AI enables data-driven discovery, diagnostics, and decision-making through low-cost, scalable, and adaptable methodologies. This review explores the evolution and influence of Frugal AI on research methodology in biomedical sciences over the past decade. It discusses its principles, technological innovations, and integration into experimental design, data management, and analytical frameworks. Furthermore, the paper critically evaluates the benefits and limitations of Frugal AI in medical research, highlighting its transformative role in improving accessibility, reducing bias, and fostering innovation in low-resource environments. Finally, it underscores the future perspectives of Frugal AI in achieving equitable, reproducible, and sustainable medical research worldwide.

Background: Thin gingival phenotype is associated with increased susceptibility to gingival recession, soft-tissue instability, and compromised esthetic outcomes. The present study evaluated the effectiveness of microneedling (MN) with and without concentrated platelet-rich fibrin (C-PRF) for gingival phenotype modification. Methods: This prospective split-mouth comparative clinical study included 42 sites, with 21 sites each in the group A and group B. Ground A sites received MN with adjunctive C-PRF, whereas Group B sites received MN alone. Gingival thickness (GT) and keratinized tissue width (KTW) were assessed at baseline, 3 months, and 6 months. Postoperative pain was assessed using a visual analogue scale (VAS). Patient-level mean values were used for statistical analysis. Results: Both groups showed significant improvement in GT and KTW from baseline to follow-up. Group A demonstrated significantly greater GT than Group B at 3 and 6 months (P <0.05). Intergroup differences in KTW were not statistically significant. Pain scores decreased progressively in both groups without significant intergroup differences. Conclusion: Adjunctive use of C-PRF with microneedling resulted in superior improvement in gingival thickness and may represent an effective minimally invasive option for gingival phenotype enhancement.

Background: Abnormal uterine bleeding (AUB) in perimenopausal women may indicate a spectrum of endometrial pathology ranging from atrophic change to carcinoma, and transvaginal sonographic measurement of endometrial thickness (ET) has been proposed as a non-invasive tool to triage women who require histopathological sampling. This study aimed to determine the distribution of ET and histopathological diagnoses among perimenopausal women with AUB and to evaluate the diagnostic performance of ET cut-off values in predicting endometrial pathology. Materials & Methods: This cross-sectional study was conducted in the Department of Radiology and Imaging, Sylhet MAG Osmani Medical College Hospital, Bangladesh, in collaboration with the Departments of Obstetrics and Gynaecology and Pathology, from July 2021 to June 2023. Ninety-seven perimenopausal women aged 39 years and above presenting with AUB underwent transvaginal sonography followed by histopathological evaluation of endometrial samples. ET was correlated with histopathological findings using the chi-square test. Results: The mean age was 43.04±2.54 years; menorrhagia was the commonest symptom (51.5%). The overall mean ET was 7.98±3.32 mm, ranging from 5.83±1.06 mm in atrophic endometrium to 12.15±2.36 mm in endometrial polyps. ET was significantly associated with histopathological diagnosis (χ²=21.74, p<0.001). An ET cut-off of >4.9 mm gave 100% sensitivity and negative predictive value but 24.0% specificity; >7.9 mm gave the best balance (66.0% sensitivity, 76.0% specificity, 71.1% accuracy); >11.9 mm gave 100% specificity and positive predictive value but 29.8% sensitivity. Conclusion: Endometrial thickness correlates significantly with histopathological diagnosis in perimenopausal women with AUB. No single cut-off simultaneously optimises both rule-in and rule-out performance; a graded diagnostic approach combining ET with clinical judgement is recommended before proceeding to invasive sampling.

Chemotherapy remains a cornerstone in cancer management; however, its non-selective cytotoxic effects frequently result in significant oral mucosal complications that adversely affect patient wellbeing and treatment outcomes. The oral mucosa is particularly susceptible to injury because of its rapid epithelial turnover, rich vascularity, and constant exposure to mechanical and microbial insults. Chemotherapy-induced oral changes commonly include oral mucositis, xerostomia, opportunistic infections, dysgeusia, ulcerative lesions, mucosal pigmentation, and bleeding manifestations. Among these, oral mucositis is the most common and debilitating complication, often leading to pain, dysphagia, impaired nutrition, reduced quality of life, and interruption of cancer therapy. The pathogenesis involves direct epithelial toxicity, oxidative stress, inflammatory cytokine activation, and breakdown of the mucosal barrier, predisposing patients to secondary infections and delayed healing. Early diagnosis, preventive oral care, and multidisciplinary management are essential for minimizing morbidity and improving therapeutic outcomes. This review summarizes the pathogenesis, clinical manifestations, diagnosis, prevention, and management of chemotherapy-induced oral mucosal changes.

Mercuric chloride is highly toxic and known to cause significant liver and kidney damage through degeneration and inflammation of the tissues. Sarcocephalus latifolius is a medicinal plant traditionally used for its antioxidant and therapeutic properties. This study investigates the protective effects of Sarcocephalus latifolius on mercuric chloride-induced hepatorenal toxicity in albino Wistar rats. The animals were divided into six groups (A-F); A. control group, B. mercuric chloride-induced group, three treatment groups (C, D, E) with two receiving Sarcocephalus latifolius extract and one vitamin C, in addition to mercuric Chloride. The last group (F) received Sarcocephalus latifolius extract only. The result showed that mercuric chloride caused disruption and degeneration of hepatocytes around the central vein while the groups that received Sarcocephalus latifolius showed structural organization relative to that of the control group. Significant improvement was observed in kidney function test (urea and creatinine) when compared to the mercuric chloride treated group. AST and ALT activity showed significant increase but the group (s) treatment with Sarcocephalus latifolius and vitamin C showed significant reduction AST and ALT activity when compared with the mercuric chloride treated group. Histological examination revealed hepatic and glomerular degeneration and disruption in the mercuric chloride treatment group while Sarcocephalus latifolius exhibits potent hepatorenal effects against mercuric chloride-induced hepatorenal damages which could be due to its anti-inflammatory and anti-oxidant properties

Advanced multifunctional nanomaterials are emerging as versatile platforms for addressing interconnected energy and environmental challenges through tunable composition, high surface area, engineered interfaces, and nanoscale reactivity. This review critically examines the design, synthesis, functionalization, and application of metal, metal oxide, carbon-based, polymeric, porous, two-dimensional, and hybrid nanomaterials. Particular emphasis is placed on green synthesis, renewable precursors, low-impact fabrication, waste-derived feedstocks, scalability, and life-cycle sustainability. The role of surface functionalization, heteroatom doping, defect engineering, heterostructure formation, and interfacial charge transfer in controlling catalytic, optical, electrochemical, and adsorption properties is systematically discussed. Applications in heterogeneous catalysis, photocatalytic hydrogen production, carbon dioxide conversion, pollutant degradation, rechargeable batteries, supercapacitors, and emerging flexible storage devices are evaluated using structure–property–performance relationships. The review also explores nanomaterial-enabled water purification, membrane separation, antimicrobial treatment, and chemical, electrochemical, optical, and gas sensing, including integrated remediation–sensing platforms for real-time monitoring. Current barriers involving toxicity, agglomeration, instability, poor selectivity, recovery, reproducibility, standardization, and industrial scale-up are identified. Finally, future directions highlight artificial-intelligence-guided discovery, operando characterization, circular material design, self-healing systems, and safe, economical deployment, establishing a unified roadmap for sustainable multifunctional nanotechnology across energy conversion, storage, environmental remediation, and chemical detection under realistic operating conditions and across diverse environmental matrices worldwide.

Micro- and nanoplastics have emerged as biologically active contaminants that connect environmental pollution with ecosystem dysfunction, microbial imbalance, animal toxicity, and human health uncertainty. This One Health review critically synthesizes current evidence on the sources, physicochemical characteristics, environmental fate, biological toxicity, microbial interactions, and medical relevance of micro- and nanoplastics in biological systems. These particles originate from primary manufactured materials and secondary fragmentation of plastic waste, synthetic textiles, agricultural plastics, tyre wear, packaging, wastewater, and atmospheric deposition. Their toxicity is strongly influenced by particle size, shape, polymer type, surface chemistry, aging, biofilm formation, and contaminant adsorption. In plants and ecosystems, micro- and nanoplastics can impair seed germination, root architecture, photosynthesis, nutrient cycling, soil microbial activity, crop productivity, and ecological stability. Animal studies further demonstrate oxidative stress, inflammation, neurotoxicity, reproductive injury, developmental disruption, gut damage, and metabolic disturbance, although many experiments still rely on high doses and pristine laboratory particles. Microplastics also create plastisphere biofilms that alter soil, aquatic, and gut microbiomes and may facilitate pathogen survival and antimicrobial resistance gene dissemination. Human exposure occurs through food, drinking water, air, dust, and consumer products, with increasing detection in blood, lung, placenta, breast milk, vascular tissues, and other biological samples. However, causal links with chronic disease remain uncertain due to limitations in detection, dose standardization, contamination control, and long-term epidemiological evidence. Overall, micro- and nanoplastics represent an emerging cross-sectoral biological threat requiring standardized monitoring, realistic toxicology, biosensor development, biotechnology-based remediation, safer material design, and integrated One Health risk assessment.

This article examines Ling Ma’s short story “G” from Bliss Montage by focusing on invisibility as a literary device through which female bodily freedom, friendship, and self-erasure are simultaneously imagined. In the story, G is a drug that makes its users invisible. At first, invisibility appears as a fantasy of bodily release: the narrator feels light, unburdened, and temporarily free from the visual discipline attached to femininity. Yet the story gradually complicates this fantasy. The ritual of taking G requires the removal of makeup, clothing, and personal objects, revealing how the female body is normally organized through visibility, surfaces, and social evaluation. As the narrator and Bonnie move through restaurants, streets, stores, and Central Park, invisibility allows them to disturb consumer spaces and ordinary social order, but it also produces anxiety about disconnection and disappearance. Their need to hold hands shows that the invisible body still requires relational anchoring. The final movement from “gravity” to “ghost” transforms G from a tool of escape into a sign of self-erasure. Through close reading informed by feminist theories of the body and visibility, this article argues that “G” presents invisibility as an ambivalent condition: it grants a temporary freedom from being seen, but also exposes the risk of becoming socially, physically, and affectively ungraspable.

The transition toward carbon-neutral chemical manufacturing requires catalytic systems that can convert abundant waste molecules into value-added chemicals under mild, renewable-energy-driven conditions. Single-atom electrocatalysts (SAECs), featuring isolated metal centers anchored on tailored supports, offer maximum atom utilization, tunable coordination environments, and well-defined active sites for complex multielectron reactions. This review critically examines the emerging role of artificial intelligence in accelerating the discovery, optimization, and mechanistic understanding of SAECs for carbon dioxide electroreduction and green ammonia synthesis through nitrate conversion. First, fundamental design principles are discussed, including metal–support interactions, coordination engineering, electronic-structure modulation, and stability limitations. Next, machine learning, density functional theory integration, high-throughput screening, explainable descriptors, and self-driving laboratory concepts are evaluated as tools for rational catalyst development. Particular emphasis is placed on CO₂ valorization pathways toward CO, formate, hydrocarbons, and alcohols, together with nitrate-to-ammonia conversion as a sustainable nitrogen-recycling strategy. Advanced in situ/operando characterization, performance benchmarking, selectivity control, and degradation mechanisms are also assessed. Finally, this review highlights current barriers related to data quality, catalyst durability, reactor design, product separation, techno-economic feasibility, and industrial scale-up. The article provides a forward-looking framework for integrating AI, atomically precise catalysis, and renewable electrosynthesis in future sustainable chemical manufacturing.