Search Results (1,437)

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article

A modular strategy for the molecular design of silicone-based antifoaming agents was developed by precisely controlling the architecture of poly (methylhydrosiloxane) (PMHS). Sixteen PMHS variants were synthesized by systematically varying the siloxane chain length (L1–L4), backbone composition (D₃T₁ vs. D₃₀T₁), and terminal group chemistry (H- vs. M-type). These structural modifications resulted in a broad range of Si-H functionalities, which were quantitatively analyzed and correlated with defoaming performance. The PMHS matrices were integrated with high-viscosity PDMS, a nonionic surfactant, and covalently grafted fumed silica—which was chemically matched to each PMHS backbone—to construct formulation-specific defoaming systems with enhanced interfacial compatibility and colloidal stability. Comprehensive physicochemical characterization via FT-IR, ¹H NMR, GPC, TGA, and surface tension analysis revealed a nonmonotonic relationship between Si-H content and defoaming efficiency. Formulations containing 0.1–0.3 wt% Si-H achieved peak performance, with suppression efficiencies up to 96.6% and surface tensions as low as 18.9 mN/m. Deviations from this optimal range impaired performance due to interfacial over-reactivity or reduced mobility. Furthermore, thermal stability and molecular weight distribution were found to be governed by repeat unit architecture and terminal group selection. Compared with conventional EO/PO-modified commercial defoamers, the PMHS-based systems exhibited markedly improved suppression durability and formulation stability in high-viscosity environments. These results establish a predictive structure–property framework for tailoring antifoaming agents and highlight PMHS-based formulations as advanced foam suppressors with improved functionality. This study provides actionable design criteria for high-performance silicone materials with strong potential for application in thermally and mechanically demanding environments such as coating, bioprocessing, and polymer manufacturing. Full article

With the objective of identifying superior Vernicia montana trees grounded in phenotypic and agronomic traits, this study sought to develop and implement a comprehensive evaluation method which would provide a practical foundation for future clonal breeding initiatives. Using the Vernicia montana propagated from seedling forests grown in the Suxian District of Chenzhou City in southern Hunan Province, we conducted pre-selection, primary selection, and re-selection of Vernicia montana forest stands and took the nine trait indices of single-plant fruiting quantity, single-plant fruit yield, disease and pest resistance, fruit ripening consistency, fruit aggregation, fresh fruit single-fruit weight, fresh fruit seed rate, dry seed kernel rate, and seed kernel oil content rate as the optimal evaluation indexes and carried out cluster analysis and a comprehensive evaluation in order to establish a comprehensive evaluation system for superior Vernicia montana trees. The results demonstrated that a three-stage selection process—consisting of pre-selection, primary selection, and re-selection—was conducted using a comprehensive analytical approach. The pre-selection phase relied primarily on sensory evaluation criteria, including fruit count per plant, tree size, tree morphology, and fruit clustering characteristics. Through this rigorous screening process, 60 elite plants were selected. The primary selection was based on phenotypic traits, including single-plant fruit yield, pest and disease resistance, and uniformity of fruit ripening. From this stage, 36 plants were selected. Twenty plants were then selected for re-selection based on key performance indicators, such as fresh fruit weight, fresh fruit seed yield, dry seed kernel yield, and oil content of the seed kernel. Then the re-selected optimal trees were clustered and analyzed into three classes, with 10 plants in class I, 7 plants in class II, and 3 plants in class III. In class I, the top three superior plants exhibited outstanding performance across key traits: their fresh fruit weight per fruit, fresh fruit seed yield, dry seed yield, and seed kernel oil content reached 41.61 g, 42.80%, 62.42%, and 57.72%, respectively. Compared with other groups, these figures showed significant advantages: 1.17, 1.09, 1.12, and 1.02 times the average values of the 20 reselected superior trees; 1.22, 1.19, 1.20, and 1.08 times those of the 36 primary-selected superior trees; and 1.24, 1.25, 1.26, and 1.19 times those of the 60 pre-selected trees. Fruits counts per plant and the number of fruits produced per plant of the best three plants in class I were 885 and 23.38 kg, respectively, which were 1.13 and 1.18 times higher than the average of 20 re-selected superior trees, 1.25 and 1.30 times higher than the average of 36 first-selected superior trees, and 1.51 and 1.58 times higher than the average of 60 pre-selected superior trees. Class I superior trees, especially the top three genotypes, are suitable for use as mother trees for scion collection in grafting. The findings of this study provide a crucial foundation for developing superior clonal varieties of Vernicia montana through selective breeding. Full article

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Consumers are increasingly exposed to environmental claims on food products. These claims often lack scientific validation and there are different methodologies that can be used for grounding these claims, which can lead to misleading results. The European Union’s (EU) Environmental Footprint methodology excludes the aggregation of environmental impacts, including damage to human health. This fact reduces transparency and limits the consumers’ ability to make information-based sustainable choices. This study aims to address this issue by calculating aggregated impacts on human health via life cycle assessment (LCA) in the agriculture and food-production sectors. In the study the IMPACT World+ method was used, including trustworthy databases and proper functional unit definition. The assessment encompassed three types of vegetables, four types of fruit, and four types of ready meals. The study also attempts to assess the impact of different farming systems (organic and conventional) on human health. Two standardised functional units, i.e., the unit based on product weight and product energy value were considered for each group of products. Our findings showed significant differences in results when different functional units were used. Additionally, no conclusion could be drawn regarding which farming system is more sustainable. Therefore, it is essential that the regulator clearly defines the criteria for selecting the appropriate functional unit in LCA within the agriculture and food-production sectors. In the absence of these criteria, results should be presented for all alternatives. Although not required by EU regulation, the authors suggest that companies should nevertheless disclose information regarding the environmental impact of agriculture and food production on human health, as this is important for consumers. Full article

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

Huntington’s disease (HD) is a severe and progressive neurodegenerative disease for which therapeutic options have so far been confined to symptomatic treatment. Currently, the diagnosis relies on the signs and symptoms shown by patients; however, by that stage, the psychomotor issues have progressed to a point where reversal of the condition is unattainable. Although numerous clinical trials have been actively investigating therapeutic agents aimed at preventing the onset of disease or slowing down the disease progression, there has been a constant need for reliable biomarkers to assess neurodegeneration, monitor disease progression, and assess the efficacy of treatments accurately. Therefore, to discover the key biomarkers associated with the progression of HD, we employed bioinformatics and machine learning (ML) to create a robust pipeline that integrated differentially expressed gene (DEG) analysis with ML to select potential biomarkers. We performed a meta-analysis to identify DEGs using three Gene Expression Omnibus (GEO) microarray datasets from different platforms related to HD-affected brain tissue, applying both relaxed and strict criteria to identify differentially expressed genes. Subsequently, focusing only on genes identified through the inclusive threshold, we employed 19 diverse ML techniques to explore the common genes that contributed to the top three selected ML algorithms and the shared genes that had an impact on the ML algorithms and were observed in the meta-analysis using the stringent condition were selected. Additionally, a receiver operating characteristic (ROC) analysis was conducted on external datasets to validate the discriminatory power of the identified genes. Based on the results of an inverse variance weighted meta-analysis of the AUCs across both human and mouse cohorts, GABRD and PHACTR1 were identified as the most robust candidates and were selected as key biomarkers for HD. Our comprehensive methodology, which integrates DEG meta-analysis with ML techniques, enabled a systematic prioritization of these biomarkers, providing valuable insights into their biological significance and potential for further validation in clinical research. Full article

Remanufacturing design is a green design model that considers remanufacturability during the design process to improve the reuse of components. However, traditional remanufacturing design scheme decision making focuses on the remanufacturability indicator and does not fully consider the carbon emissions of the remanufacturing process, which will take away the energy-saving and emission reduction benefits of remanufacturing. In addition, remanufacturing design schemes rarely consider the human ergonomics of the product, which leads to uncomfortable handling of the product by the customer. To reduce the remanufacturing carbon emission and improve customer comfort, it is necessary to select a reasonable design scheme to satisfy the carbon emission reduction and ergonomics demand; therefore, this paper proposes an integrated multi-criteria decision-making method for remanufacturing design that considers the carbon emission and human ergonomics. Firstly, an evaluation system of remanufacturing design schemes is constructed to consider the remanufacturability, cost, carbon emission, and human ergonomics of the product, and the evaluation indicators are quantified by the normalization method and the Kansei engineering (KE) method; meanwhile, the hierarchical analysis method (AHP) and entropy weight method (EW) are used for the calculation of the subjective and objective weights. Then, a multi-attribute decision-making method based on the combination of an assignment approximation of ideal solution ranking (TOPSIS) and gray correlation analysis (GRA) is proposed to complete the design scheme selection. Finally, the feasibility of the scheme is verified by taking a household coffee machine as an example. This method has been implemented as an application using Visual Studio 2022 and Microsoft SQL Server 2022. The research results indicate that this decision-making method can quickly and accurately generate reasonable remanufacturing design schemes. Full article

Echocardiography is the primary imaging modality for diagnosing cardiac disease in children, with quantitation largely based on nomograms. Over the past decade, significant efforts have been made to address the numerical and methodological limitations of earlier nomograms. As a result, robust and reliable pediatric echocardiographic nomograms are now available for most two-dimensional anatomical measurements, three-dimensional volumes, and strain parameters. These more recent nomograms are based on adequate sample sizes, strict inclusion and exclusion criteria, and rigorous statistical methodologies. They have demonstrated good reproducibility with minimal differences across different authors, establishing them as reliable diagnostic tools. Despite these advances, some limitations persist. Certain ethnic groups remain underrepresented, and data for preterm and low-weight infants are still limited. Most existing nomograms are derived from European and North American populations, with sparse data from Asia and very limited data from Africa and South America. Nomograms for preterm and low-weight infants are few and cover only selected cardiac structures. Although diastolic parameter nomograms are available, the data remain heterogeneous due to challenges in normalizing functional parameters according to age and body size. The accessibility of current nomograms has greatly improved with the development of online calculators and mobile applications. Ideally, integration of nomograms into echocardiographic machines and reporting systems should be pursued. Future studies are needed to develop broader, more comprehensive, and multi-ethnic nomograms, with better representation of preterm and low-weight populations, and to validate new parameters derived from emerging three- and four-dimensional echocardiographic techniques. Full article

Background: Cyclic vomiting syndrome (CVS) is a functional gastrointestinal disorder characterized by recurrent episodes of intense nausea and vomiting. Despite increasing awareness, a standardized treatment approach remains lacking in pediatric populations. Lifestyle factors and anxiety are common triggers, yet their systematic management has not been fully incorporated into therapeutic strategies. Objective: To evaluate the effectiveness of lifestyle modifications and selective serotonin reuptake inhibitors (SSRIs) in the management of pediatric CVS and to compare their outcomes with standard cyproheptadine prophylaxis. Methods: This retrospective study included 119 patients aged 1.2–17.5 years who were diagnosed with CVS according to Rome IV criteria between September 2021 and January 2025. Clinical, psychiatric, and lifestyle data were retrieved from the university’s digital medical records. Patients were grouped according to treatment modality: cyproheptadine, SSRI, or acute attack management alone. Treatment success at 12 weeks was defined as complete cessation of vomiting episodes or absence of hospitalization, prolonged attacks, and school/work absenteeism. Results: Anxiety symptoms were present in 78.2% of patients. SSRIs were prescribed to 34 patients with moderate to severe anxiety, all of whom achieved treatment success. Lifestyle adherence was observed in 73.9% and was found to be a predictor of treatment success. Cyproheptadine was administered to 66 patients but did not provide additional benefit over effective lifestyle modification. Six patients discontinued cyproheptadine due to drowsiness or weight gain. Conclusions: Lifestyle interventions significantly improve outcomes in pediatric CVS. SSRIs represent a safe and effective prophylactic option for patients with comorbid anxiety or poor adherence to behavioral recommendations. These findings support the integration of psychosocial and lifestyle-based strategies into standard CVS treatment protocols. Full article

Background: Semaglutide and Liraglutide are medications in the Glucagon-like peptide-1 agonists (GLP-1 RAs) class used to manage type 2 diabetes mellitus and obesity in Saudi Arabia. Although the 1.0 mg once weekly dosage of Semaglutide does not have a labeled indication for the management of obesity, many believe that this dosage is more effective than the 3.0 mg once daily Liraglutide dosage for the management of both diabetes and obesity. Objective: To compare the effectiveness of the dosage of 1.0 mg of Semaglutide administered once weekly versus 3.0 mg of Liraglutide administered once daily in controlling HbA1c levels, promoting weight loss, and evaluating their financial implications among obese patients in Saudi Arabia using real-world data. Methods: A retrospective review of Electronic Medical Records (EMRs) from January 2021 to June 2024 was conducted on patients prescribed Semaglutide or Liraglutide for at least 12 months. Exclusion criteria included pre-existing severe conditions (e.g., cardiovascular disease, stroke, or cancer) and missing baseline data. The primary outcomes assessed were changes in HbA1c, weight, and direct medical costs. Results: Two hundred patients (100 patients on the 1.0 mg once weekly dose of Semaglutide and 100 patients on the 3.0 mg once daily dose of Liraglutide) of those randomly selected from the EMRs met the inclusion criteria and were included in the analysis. Of the 200 eligible patients (65.5% female, mean age 48.54 years), weight loss was greater with Semaglutide (−8.09 kg) than Liraglutide (−5.884 kg). HbA1c reduction was also greater with Semaglutide (−1.073%) than Liraglutide (−0.298%). The use of Semaglutide resulted in lower costs of USD −1264.76 (95% CI: −1826.82 to 33.76) and greater reductions in weight of −2.22 KG (95% CI: −7.68 to −2.784), as well as lower costs of USD −1264.76 (95% CI: (−2368.16 to −239.686) and greater reductions in HbA1c of −0.77% (95% CI: −0.923 to −0.0971) in more than 95% of the cost effectiveness bootstrap distributions. Conclusions: Semaglutide 1.0 mg weekly seems to be more effective and cost-saving in managing prediabetes, diabetes, and obesity compared to Liraglutide 3.0 mg daily. Future studies should examine these findings using a more representative sample and a robust study design. Full article

Supplier selection is one of the critical processes that entail multiple complex deliberations. The selection of an appropriate alternative supplier is a highly intricate process, primarily due to there being multiple criteria which are exceptionally subjective. This paper aims to develop a practical framework for choosing a suitable precast supplier by integrating the Value Engineering (VE) concept, Stepwise Weight Assessment Ratio Analysis (SWARA), and the Weighted Aggregated Sum Product Assessment (WASPAS) technique. This paper introduces a novel method to estimate the quality weights of alternative suppliers’ criteria (CQW) by linking factory capacity with the coefficients of the nine significant criteria, computed using principal component analysis (PCA). None of the formal studies make this link directly. The framework’s findings were validated by comparing its results with an expert assessment of five Saudi supplier alternatives. The results revealed that the framework’s results agree with the expert’s judgment. The method of payment criterion received the highest weight, indicating that it was considered the most important of the nine criteria identified. Combining PCA and VE with the WASPAS technique resulted in an unprecedentedly effective selection tool for precast suppliers. Full article

Background/Objectives: Developmental dysplasia of the hip (DDH) affects 1–3% of newborns and requires early detection for optimal outcomes. DDH involves abnormal acetabular–femoral congruence between the acetabulum and femoral head, resulting from either shallow acetabular development or delayed femoral ossification of the femoral head. We evaluated the ossification nucleus of the femoral head (ONFH) to determine prevalence, radiographic timing, and associations with perinatal factors. Methods: We analyzed 100 pelvic radiographs of infants between 90 and 150 days of age. Participants were selected by convenience sampling, based on inclusion criteria. We identified the presence of ONFH and measured biometric parameters, morphology, and anatomical location. Sociodemographic and perinatal data were collected from the participating infants. Results: The prevalence of ONFH was 33%, and the mean age at visualization was 104 days. The presence of ONFH was correlated with birth weight (p = 0.011), discharge weight (p = 0.005), and weight at 1 month (p = 0.034). In our study, female sex (p = 0.004) was associated with a 4.966-fold higher odds of ONFH prevalence compared to males. Conclusions: This study provides relevant evidence regarding the prevalence, morphology, and characteristics of ONFH. Few studies report this information on ONFH in different populations. The optimal timing for radiographic visualization of ONFH in infants remains undefined, but the appearance of the ONFH was concentrated around 104 days of life. The novel association between weight and ONFH provides new insights into DDH. This provides new insights for DDH screening. This association warrants further research for the early detection of DDH. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

The development of technology has influenced agricultural production and the establishment of the Agriculture 4.0 system in practice. This research is focused on the selection of equipment and machinery suppliers for the needs of the MAMEX Company. When selecting suppliers, an approach based on the application of an intuitionistic fuzzy set for decision-making was used. This approach allows the uncertainty present in decision-making to be incorporated, considered, and, hopefully, reduced in order to make a final decision on which of the observed suppliers is the most suitable for this company. Ten criteria were used that enable the application of sustainability in the supply chain. Eight local suppliers of equipment and machinery were observed with these criteria. The results obtained by applying the SWARA (Step-wise Weight Assessment Ratio Analysis) method showed that the most important criterion for selecting suppliers is the reliability and quality of equipment and machinery, while the results of the CORASO (COmpromise Ranking from Alternative Solutions) method showed that the SUP2 supplier is the best choice for establishing partnership relations with the MAMEX company. This supplier should help the MAMEX company improve its business and achieve better results in the market. The contribution of this research is to improve the application of intuitionistic fuzzy sets in decision-making, and to emphasize the importance of equipment and machinery in agricultural production in the Agriculture 4.0 system. Full article