Search Results (4,169)

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks. Full article

The integration of Educational Robotics (ER) into teaching and learning has increased in recent years. While several studies have described the design and use of simulators, no content analysis has systematically documented the features and contribution of simulators in ER. Therefore, a systematic review of eight databases was conducted. From 1200 retrieved articles, 89 met the inclusion criteria. The emerged articles comprised two distinct categories: (a) simulator framework studies—describing tools or platforms (54 articles)—and (b) simulator-based intervention studies—reporting empirical implementations (35 articles). Each article was analyzed by two independent researchers who recorded the design features of simulators, their domain of use, the educational level at which the implementation occurred, intervention characteristics, teacher involvement in the studies, and the skills they tried to promote. Findings showed that simulators were primarily designed for STEM education. Most operated in coding environments, used 3D visualization, and were freely available. Interventions were more frequent at the tertiary level, with fewer at primary and secondary levels. Many empirical studies that used simulators employed small samples in short durations, limiting the generalizability of the findings. Simulator-based practices were mainly linked to programming, problem solving, and computational thinking. Higher-order competences such as collaboration or metacognition were rarely addressed. Finally, most intervention studies reported either no or only moderate teacher involvement. This article aims to be a basis for researchers who study ER implementation and simultaneously serve as a basis for choosing, designing, and adopting ER simulators as teaching tools. Full article

Viable but non-culturable (VBNC) cells represent a reversible, metabolically active state that promotes the survival of bacteria under stressful conditions and their persistence in healthcare facilities and food industry. We conducted a systematic review following PRISMA 2020 guidelines to identify in vitro methodologies for inducing and resuscitating VBNC Enterobacteriaceae and Pseudomonas aeruginosa, and to determine key influencing factors. Eligible studies reported in vitro resuscitation of these species. Searches were performed in MEDLINE (PubMed), Scopus, and Google Scholar up to July 2025. Two independent reviewers screened and extracted data. Exclusion criteria included absence of original experimental data, focus on other species, or lack of clear VBNC definition. Risk of bias was qualitatively assessed. Analyses were descriptive without meta-analysis. Of the 1041 records, 24 articles (27 studies) were included. Resuscitation protocols typically employed standard culture media with additives and moderate incubation temperatures, with most successful recoveries occurring after 24–48 h. P. aeruginosa generally required less supplementation than Enterobacteriaceae. Reported mechanisms involved metabolic reactivation, oxidative stress modulation, nutrient sensing, and ribosome reactivation. The limitations of our study include protocol heterogeneity, lack of standardization, and selective reporting. While simple resuscitation methods were often effective, tailoring conditions to species-specific ecological preferences appears critical. Standardized approaches of VBNC cells will improve detection, risk assessment, and infection control. Full article

Background: Lung cancer is the leading cause of cancer-related mortality globally. Early detection via low-dose computed tomography (LDCT) can reduce mortality, but its implementation is challenged by the absence of objective diagnostic criteria and the necessity for extensive manual interpretation. Public datasets like the Lung Image Database Consortium often lack pathology-confirmed diagnoses, which can lead to inaccuracies in ground truth labels. Variability in voxel sizes across these datasets also complicates feature extraction, undermining model reliability. Many existing methods for integrating nodule boundary annotations use deep learning models such as generative adversarial networks, which often lack interpretability. Methods: This study assesses the effect of voxel normalization on pulmonary nodule classification and introduces a Fast Fourier Transform-based contour fusion method as a more interpretable alternative. Utilizing pathology-confirmed LDCT data from 415 patients across two medical centers, both machine learning and deep learning models were developed using voxel-normalized images and attention mechanisms, including transformers. Results: The results demonstrated that voxel normalization significantly improved the overlap of features between datasets from two different centers by 64%, resulting in enhanced selection stability. In the ROI-based radiomics analysis, the top-performing machine-learning model achieved an accuracy of 92.6%, whereas the patch-based deep-learning models reached 98.5%. Notably, the FFT-based method provided a clinically interpretable integration of expert annotations, effectively addressing a major limitation of generative adversarial networks. Conclusions: Voxel normalization enhances reliability in pulmonary nodule classification while the FFT-based method offers a viable path toward interpretability in deep learning applications. Future research should explore its implications further in multi-center contexts. Full article

Objectives: This systematic review and meta-analysis aimed to evaluate microbial adhesion and biofilm formation on additively manufactured composite-based orthodontic clear aligners compared with thermoformed aligners and other conventional polymeric materials. The influence of material composition, surface roughness, post-processing parameters, and cleaning protocols on microbial colonization was also assessed. Methods: A comprehensive search of PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Library was conducted up to September 2025. Only in vitro studies investigating microbial adhesion, biofilm biomass, or microbiome changes on three-dimensional (3D)-printed aligner composites were included. Primary outcomes consisted of colony-forming units (CFU), optical density (OD) from crystal violet assays, viable microbial counts, and surface roughness. Risk of bias was assessed using the RoBDEMAT tool. Data were narratively synthesized, and a random-effects meta-analysis was performed for comparable datasets. Results: Five studies fulfilled the inclusion criteria, of which two in vitro studies were eligible for meta-analysis. Microbial adhesion and biofilm accumulation were influenced by the manufacturing technique, composite resin formulation, and surface characteristics. Certain additively manufactured aligners exhibited smoother surfaces and reduced bacterial adhesion compared with thermoformed controls, whereas others with increased surface roughness showed higher biofilm accumulation. Incorporating bioactive additives such as chitosan nanoparticles reduced Streptococcus mutans biofilm formation without compromising material properties. The meta-analysis, based on two in vitro studies, demonstrated higher OD values for bacterial biofilm on 3D-printed aligners compared with thermoformed aligners, indicating increased biofilm biomass (p < 0.05), but not necessarily viable bacterial load. Conclusions: Microbial adhesion and biofilm formation on 3D-printed composite clear aligners are governed by resin composition, additive manufacturing parameters, post-curing processes, and surface finishing. Although certain 3D-printed materials display antibacterial potential, the limited number of studies restricts the generalizability of these findings. Clinical Significance: Optimizing composite formulations for 3D printing, alongside careful post-curing and surface finishing, may help reduce microbial colonization. Further research is required before translating these findings into definitive clinical recommendations for clear aligner therapy. Full article

This study presents a systematic review of ontology–AI integration for construction image understanding, aiming to clarify how ontologies enhance semantic consistency, interpretability, and reasoning in AI-based visual analysis. Construction sites involve highly dynamic and unstructured conditions, making image-based hazard detection and situation assessment both essential and challenging. Ontology-based frameworks offer a structured semantic layer that can complement deep learning models; however, most existing studies adopt ontologies only as post-processing mechanisms rather than embedding them within model training or inference workflows. Following PRISMA 2020 guidelines, a comprehensive search of the Web of Science Core Collection (2014–2025) identified 587 publications, of which 152 met the eligibility criteria, and 16 explicitly addressed construction image data. Topic modeling revealed five functional objectives—regulatory compliance, hazard reasoning, decision support, knowledge reuse, and sustainability—and four primary data modalities: BIM, text, image, and sensor data. Two dominant integration patterns were observed: training-stage and output-stage enhancement. While quantitative performance improvements were modest, qualitative gains were consistent across studies, including reduced false positives, improved interpretability, and enhanced situational understanding. Persistent gaps were identified in standardization, scalability, and real-world validation. This review provides the first structured synthesis of ontology–AI research for construction image understanding and offers an evidence-based research agenda that links observed limitations to actionable directions for semantic AI in construction. Full article

Background: Benzodiazepines (BZDs) are commonly prescribed for anxiety, insomnia, and muscle relaxation, but concerns remain regarding their potential long-term cognitive effects. Prior reviews have reported inconsistent associations between BZD use and cognitive risk, often limited by methodological heterogeneity and unresolved biases. Objective: This systematic review critically evaluates the evidence on BZD exposure and cognitive outcomes, with a specific focus on study design, potential biases, and other factors of methodological heterogeneity. Methods: Following PRISMA 2020 guidelines, we searched PubMed and Google Scholar for studies published between 2010 and 2025. The final search was conducted on 1 July 2025. Eligible studies assessed cognitive performance or incident dementia in community-dwelling adults with regard to BZD use. One reviewer independently screened titles, abstracts, and full texts to determine the eligibility of each study. The other two reviewers participated in the inclusion and exclusion decisions. Any disagreements were resolved by consensus among all three reviewers to minimize bias. No statistical data handling, data conversions, or missing imputation was conducted, as this review did not include quantitative synthesis or meta-analysis. Results: Of 79 references screened by titles, abstracts, and full texts, seventeen articles met the inclusion criteria for this review. Participants in the cohort studies were mostly older adults and cognitively healthy at baseline, while those from case–control studies were dementia patients and their matched controls. Overall, findings remain inconsistent, with five studies reporting an association between BZD use and cognition in the entire cohort, six reporting effects only in specific contexts or subgroups, and six finding no evidence at all. Potential contributing factors to this variability include protopathic bias, operational definitions of BZD exposure, and whether analyses were stratified by factors such as sex, drug half-life, or dose. Conclusions: Due to inconsistencies among findings and limitations in study design rigor, the current review cannot determine whether BZDs independently raise cognitive risk. Future research should adopt more rigorous study designs, with particular attention to addressing protopathic bias, and clarify limitations related to the operational definition of BZD. In addition, our findings support the need for further investigation of BZD association with vulnerable populations, including those with sex-specific susceptibility, low socioeconomic status, and exposure to high-risk prescribing practices. Full article

Background: Sarcopenia is highly prevalent among hospitalized older adults and is associated with poor clinical outcomes. Multiple diagnostic criteria exist, but the comparative implications of different handgrip strength (HGS) thresholds and measurement tools are less explored. Objectives: This study aimed to assess the prevalence of sarcopenia, comparing the diagnostic yield of different HGS thresholds using two measurement instruments (dynamometer and vigorimeter) in hospitalized older adults. Design: This was a cross-sectional observational study. Setting: A tertiary geriatric hospital with acute, rehabilitation, and long-term care wards was included. Participants: A total of 376 hospitalized older adults with complete HGS and bioelectrical impedance analysis (BIA) data were recruited. Measurements: HGS was measured using both a hydraulic dynamometer and a pneumatic vigorimeter. Sarcopenia was defined using cut-offs from EWGSOP2, SDOC, and two DO-HEALTH-derived thresholds. Low muscle mass was identified using the fat-free mass index (FFMI) by BIA. Multivariate logistic regression was used to identify predictors of sarcopenia. Results: The prevalence of probable sarcopenia ranged from 68.1% to 89.4%, and confirmed sarcopenia from 39.6% to 50.3%, depending on the thresholds applied. Sarcopenic patients were older (86.1 ± 9.8 vs. 80.4 ± 11.0 years; p < 0.001), had lower BMI (20.7 ± 2.9 vs. 26.1 ± 4.8 kg/m²; p < 0.001), and were more frequently in long-term care (p = 0.014–0.043). Older age (OR 1.03–1.07 per year; p < 0.05) and lower BMI (OR 0.59–0.68 per kg/m²; p < 0.001) were independently associated with sarcopenia; sex and fall history were not. Conclusions: Sarcopenia prevalence was high and varied widely across diagnostic definitions and measurement tools, reflecting both methodological variability and the high vulnerability of hospitalized older adults. These findings highlight the need for standardized, context-adapted diagnostic strategies to guide timely intervention in high-risk hospitalized older adults. Full article

Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties of Kazakhstani potatoes were selected as objects of study: Alians, Alians mini, Astana, Astana mini, Edem, Edem mini, Nerli, Nerli mini, Zhanaisan, and Zhanaisan mini. Two convolutional neural network (CNN) models, SqueezeNet and GoogLeNet, were refined via transfer learning employing three optimization approaches. Then, they were used to classify the potato images. A comparison of the two neural networks’ classification performance was conducted using common evaluation criteria—accuracy, precision, F1 score, and recall—alongside a confusion matrix to highlight misclassified samples. The comparative analysis demonstrated that both CNN architectures—SqueezeNet and GoogLeNet—achieve high classification accuracy for Kazakhstani potato varieties, with the best performance on Astana and Zhanaisan (>97%). The study confirms the applicability of lightweight CNNs for digital varietal identification and automated quality assessment of seed potatoes under controlled imaging conditions. The developed approach is the first comparative CNN-based varietal identification of Kazakhstani potato tubers using transfer learning and contributes to the digitalization of potato breeding, and provides a baseline for future real-time sorting systems using deep learning. Full article

We present a theoretical and numerical framework for computing asymmetric two-dimensional droplet shapes on surfaces with a sharp wetting boundary separating regions of distinct contact angles. Through Lagrange multiplier analysis of the constrained Gibbs free energy functional, we derive a simplified spreading condition that relates the contact line position ratio to the ratio of spreading functions encoding unbalanced Young stress at each contact line, reducing to an explicit algebraic relation that eliminates iterative computation. Gravitational effects substantially modify droplet height and curvature distribution across Bond number regimes, yet the contact line position ratio remains invariant, confirming that horizontal partitioning depends exclusively on interfacial energy ratios rather than body forces. Hydrophilic surfaces exhibit intuitive spreading toward regions with better wettability, producing flattened asymmetric profiles, while hydrophobic surfaces display counterintuitive behavior where droplets preferentially occupy regions with poorer wettability, maintaining tall compact geometries. Mixed hydrophilic–hydrophobic boundaries violate equilibrium conditions and drive spontaneous droplet migration. We develop an efficient two-stage computational strategy decoupling shape computation from equilibrium position determination, reducing computational cost by orders of magnitude. These findings provide quantitative design criteria for controlled droplet positioning on patterned substrates, with implications for microfluidic devices and droplet-based technologies. Full article

Background/Objectives: Leishmaniasis constitutes one of the most fatal parasitic diseases globally, adversely impacting the health of individuals residing in both intertropical and temperate zones. In these geographical areas, the administration of treatment is often inconsistent and largely ineffective with the available pharmaceuticals, as these exhibit more pronounced side effects than the therapeutic advantages they purport to provide. Methods: Consequently, the current investigation seeks to engage in molecular modeling of novel pharmacological candidates incorporating 1,2,3 disubstituted triazole moieties, coordinated with Cu^II metal centers, in pursuit of promising bioactive properties. Results: Two complexes were prepared and X-ray analysis revealed a comparable structural configuration surrounding the copper (II) atom. The planar square coordination geometry was elucidated through the assessment of the ${\tau }_4=0$ (tau four) parameters. The comprehensive characterization encompasses HRMS-ESI (+), NMR, elemental analyses, mid-infrared, and UV-vis spectroscopic techniques. Time-dependent density functional theory (TD-DFT) analyses will substantiate the findings obtained through UV-vis spectroscopy. Crucially, the biological assays against Leishmania (L.) amazonensis revealed that Complex 1 exhibited outstanding potency against the intracellular amastigote form, demonstrating a half-maximal inhibitory concentration (IC₅₀) of 0.4 µM. This activity was 6-fold higher than that of amphotericin B (IC₅₀ = 2.5 µM) and 33-fold higher than pentamidine (IC₅₀ = 13.3 µM). Furthermore, Complex 1 showed a promising selectivity index (SI = 9.7) against amastigotes, surpassing the reference drugs and meeting the criteria for a lead compound. While less active on promastigotes, both complexes demonstrated high stability in DMSO solution, a prerequisite for biological testing. Conclusions: These results unequivocally identify Complex 1 as a highly promising candidate for the development of new antileishmanial therapies, warranting further in vivo studies. Full article

Background/Objectives: Varicocele is a common cause of testicular hypotrophy and impaired semen quality in adolescents. Laparoscopic varicocelectomy (LV) and microsurgical varicocelectomy (MV) with internal spermatic vein-superficial epigastric vein bypass are established treatment options. This study aimed to compare clinical outcomes, complication rates, and functional recovery between LV and MV in adolescents. Methods: A retrospective two-center analysis was conducted on adolescents who underwent LV or MV between 2019 and 2024. Primary outcomes included postoperative complications, recurrence, testicular volume recovery, and semen parameter improvement. Secondary outcomes included operative time, hospital stay, and return to full activity. Statistical significance was set at p < 0.05. Results: A total of 430 patients met the inclusion criteria (270 LV, 160 MV). LV had a significantly shorter operative time (15 ± 5.1 min vs. 55.5 ± 6.4 min; p < 0.0001). There were no significant differences in hospital stay (p = 0.28), postoperative hematoma (p = 0.06), hydrocele (p = 0.06), or recurrence rates (p = 0.20). Full recovery of testicular volume occurred in 75.0% after LV vs. 70.6% after MV (p = 0.40). Overall semen improvement was 89.5% in LV vs. 100% in MV (p = 0.07). Normalization of oligospermia was significantly higher in the MV group (92.8% vs. 65.3%; p = 0.0048). Conclusions: Both LV and MV are safe and effective techniques for adolescent varicocele repair, with comparable complication and recurrence rates. LV offers significantly shorter operative time, whereas MV provides a superior improvement in semen parameters, suggesting a potential advantage of microsurgical repair in adolescents presenting with abnormal semen analysis. Full article

The selection of a final operating point from a Pareto front set of marine diesel engine configurations relies on the critical task of translating operator priorities into quantitative criterion weights. This study isolates this pivotal weighting step and introduces an operator-defined fuzzy weighting module that maps linguistic importance ratings to normalized weights. This module systematically maps important ratings for Specific Fuel Consumption (SFC), Nitrogen Oxides (NOx), and Particulate Matter (PM) into a set of normalized weights for the Multi-Criteria Decision-Making method. The module’s core is a Mamdani-type fuzzy logic module that utilizes triangular membership functions and centroid defuzzification. These fuzzy weights are integrated with the TriMetric Fusion algorithm to generate a robust consensus ranking. Validation on a Pareto front from a two-stroke diesel engine demonstrates the framework’s efficacy: a Fuel-Economy priority selected a configuration with SFC advantage, while a Strict Environmental Compliance priority correctly identified dual emissions strengths. Furthermore, the system effectively mediated trade-offs in a high-competition scenario. Rank correlation analysis confirmed that while the Pareto front nature of the alternatives leads to inherent similarities in rankings, the fuzzy weights induce significant and logical divergences. Future work will focus on validation with real operator feedback and comparative studies with traditional weighting methods. Full article

Background and Objectives: Retinopathy of prematurity (ROP) remains a leading cause of preventable childhood blindness, with its severity influenced by a complex interaction between nutritional status, metabolic maturation, and systemic vulnerability. This study aimed to evaluate whether early nutritional patterns and serum metabolic parameters, including hepatic and renal biomarkers, are associated with ROP severity and whether they may serve as potential predictors of disease progression. Materials and Methods: We conducted a retrospective study on 140 preterm infants, totaling 280 eyes, admitted between 2021 and 2024 in two neonatal intensive care units (NICU). Each eye was analyzed independently according to International Classification of Retinopathy of Prematurity (ICROP) criteria. Data on the timing of enteral feeding, duration and type of nutrition, and serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein, blood glucose, urea and creatinine were collected throughout the first 28 days of life. Statistical analysis included Kruskal–Wallis and Chi-square tests, with a significance threshold of p < 0.05. Results: ROP was identified in 53.57% of cases. Enteral feeding began earlier in infants without ROP, whereas delayed initiation and prolonged parenteral nutrition were associated with more advanced stages. Natural feeding decreased with increasing severity and was absent in aggressive retinopathy of prematurity (A-ROP). Severe disease stages showed higher AST, ALT, urea and creatinine levels, along with lower early total protein values. Glycemic instability was observed more frequently in stage 2 and stage 3. Conclusions: Early nutritional support, especially early enteral feeding and natural feeding, appears protective against ROP progression. Hepatic, renal and glycemic metabolic changes are closely correlated with disease severity, indicating that metabolic balance reflects overall vulnerability in preterm infants. Incorporating nutritional and metabolic assessment into routine screening may enhance early risk identification and optimize clinical monitoring. Full article

Background: The role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in detecting soft-tissue sarcoma (STS) local recurrence (LR) following therapeutic intervention was evaluated. Method: PubMed, Embase, and Scopus were systematically searched from January 1990 to 1 February 2024 for studies evaluating DCE-MRI for LR detection in histologically confirmed STS following surgery. Two independent reviewers screened studies and extracted data, and a bivariate diagnostic test accuracy meta-analysis was performed to estimate pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve. Results: Six studies, including 309 patients (110 with LR and 199 without LR), met the inclusion criteria. Across studies, DCE-MRI qualitative features (such as early rapid arterial enhancement and malignant time–intensity curves) and quantitative or semiquantitative parameters (such as volume transfer constants [Ktrans and Kep], initial area under the curve [iAUC], and relative plasma flow [rPF]) consistently differentiated LR from post-treatment change. When DCE-MRI parameters were added to conventional MRI, the pooled sensitivity and specificity for LR detection were 98% and 83%, respectively, with an SROC area under the curve of 0.94, indicating high overall diagnostic accuracy. Conclusions: DCE-MRI increases the accuracy of LR detection when combined with conventional MRI and offers a higher specificity and sensitivity in distinguishing LR from post-surgical changes, which support consideration of adding DCE-MRI when LR is suspected; prospective standardized studies are warranted. Full article