Search Results (4,202)

Background/Objectives: The PB family of “cut-and-paste” DNA transposons shows great promise as genetic manipulation tools while significantly impacting eukaryotic genome evolution. However, their evolutionary profile in beetles (Coleoptera), the most species-rich animal order, remains poorly characterized. Methods: A local tBLASTN search was conducted to mine PiggyBac (PB) transposons across 136 coleopteran insect genomes, using the DDE domain of the PB transposase as the query. Multiple sequence alignment was performed with MAFFT, and a maximum likelihood phylogenetic tree of the transposase DDE domains was constructed using IQ-TREE. Evolutionary dynamics were analyzed by means of K-divergence. Results: Our study reveals PB transposons are widely distributed, highly diverse, and remarkably active across beetles. We detected PB elements in 62 of 136 examined species (45%), classifying them into six distinct clades. A total of 62 PB-containing species harbored intact copies, with most showing recent insertions (K divergence ≈ 0), indicating ongoing transpositional activity. Notably, PB elements from Harmonia axyridis, Apoderus coryli, and Diabrotica balteata exhibit exceptional potential for genetic tool development. Structurally, intact PB elements ranged from 2074 to 3465 bp, each containing a single transposase ORF (500–725 aa). All were flanked by terminal inverted repeats and generated TTAA target site duplications. Conclusions: These findings demonstrate PB transposons have not only shaped historical beetle genome evolution but continue to drive genomic diversification, underscoring their dual significance as natural genome architects and promising biotechnological tools. Full article

In animals and humans, nutrients influence signaling cascades, transcriptional programs, chromatin dynamics, and mitochondrial function, collectively shaping traits related to growth, immunity, reproduction, and stress resilience. This review synthesizes evidence supporting nutrient-mediated regulation of DNA methylation, histone modifications, non-coding RNAs, and mitochondrial biogenesis, and emphasizes their integration within metabolic and developmental pathways. Recent advances in epigenome-wide association studies (EWAS), single-cell multi-omics, and systems biology approaches have revealed how diet composition and timing can reprogram gene networks, sometimes across generations. Particular attention is given to central metabolic regulators (e.g., PPARs, mTOR) and to interactions among methyl donors, fatty acids, vitamins, and trace elements that maintain genomic stability and metabolic homeostasis. Nutrigenetic evidence further shows how genetic polymorphisms (SNPs) in loci such as IGF-1, MSTN, PPARs, and FASN alter nutrient responsiveness and influence traits like feed efficiency, body composition, and egg quality, information that can be exploited via marker-assisted or genomic selection. Mitochondrial DNA integrity and oxidative capacity are key determinants of feed conversion and energy efficiency, while dietary antioxidants and mitochondria-targeted nutrients help preserve bioenergetic function. The gut microbiome acts as a co-regulator of host gene expression through metabolite-mediated epigenetic effects, linking diet, microbial metabolites (e.g., SCFAs), and host genomic responses via the gut–liver axis. Emerging tools such as whole-genome and transcriptome sequencing, EWAS, integrated multi-omics, and CRISPR-based functional studies are transforming the field and enabling DNA-informed precision nutrition. Integrating genetic, epigenetic, and molecular data will enable genotype-specific feeding strategies, maternal and early-life programming, and predictive models that enhance productivity, health, and sustainability in poultry production. Translating these molecular insights into practice offers pathways to enhance animal welfare, reduce environmental impact, and shift nutrition from empirical feeding toward mechanistically informed precision approaches. Full article

This study develops a comprehensive competitiveness model for Indonesia’s WealthTech ecosystem by integrating Interpretive Structural Modeling (ISM) and MICMAC analysis. The research identifies and classifies 23 interrelated variables derived from SEM-PLS and NVivo analysis, of which 17 passed expert validation and were subsequently retained in the ISM–MICMAC structural model, including innovation capabilities, regulatory support, digital infrastructure, capital readiness, and customer trust, to evaluate their systemic roles in shaping competitive advantage. Through expert interviews, bibliometric analysis, and a structured modeling process, key independent drivers such as innovation capabilities, geopolitical events, and economic shocks were identified as foundational enablers. Linkage variables including digital transformation, strategic alliances, and cost leadership connect these enablers to dependent outcomes such as customer satisfaction and platform personalization. The resulting hierarchical framework and strategic roadmap offer actionable insights for policymakers, fintech stakeholders, and investors to enhance resilience, regulatory alignment, and ecosystem integration. This research not only fills a critical gap in the digital finance literature but also provides a strategic tool for advancing Indonesia’s WealthTech sector within the global financial landscape. Full article

Microtopography regulates soil erosion by shaping surface heterogeneity, but the mechanism of loess slope soil loss remains insufficiently quantified. This study combined laboratory rainfall simulations and machine learning to investigate how tillage-induced microtopography modulates soil loss through surface heterogeneity and hydrodynamic processes. Simulations used loess soil (silty loam) with a 5° slope, 60 mm/h rainfall intensity, and 5–30 min rainfall durations (RD). Results indicated that the mean weight diameter (MWD) and aggregate stability index (ASI) of structural, transition, and depositional crusts under micro-terrain decreased by 36~65% and 41~60%, respectively, while the fractal dimension (D) increased by 10~19%. Negative relationships were observed between ASI/MWD and D (R² = 0.83~0.98). Horizontal cultivation (T_HC, surface roughness [SR] = 1.76, average depression storage [ADS] = 2.34 × 10⁻² m³) delayed runoff connectivity and reduced cumulative soil loss (LS) by 42–58% compared to hoeing cultivation (T_HE, SR = 1.47, ADS = 3.23 × 10⁻⁴ m³). Abrupt hydrodynamic transitions occurred at 10 min RD (T_HE) and 15 min RD (artificial digging [T_AD]), driven by trench connectivity and depression overflow. LS exhibited a significant positive correlation with D and RD and was inversely correlated with ASI, MWD, and SR. A three-hidden-layer BPNN exhibited high predictive accuracy for LS (mean square error = 0.07), verifying applicability in complex scenarios with significant microtopographic heterogeneity and multi-factor coupling. This study demonstrated that surface roughness and depression storage were the dominant microtopographic controls on loess slope soil loss. BPNN provided a reliable tool for soil loss prediction in heterogeneous microtopographic systems. The findings provide critical insights into optimizing tillage-based soil conservation strategies for sloping loess farmlands. Full article

Intergovernmental transfers play a crucial role in shaping the fiscal position of local governments, especially in countries where municipalities, such as those in Poland, exhibit a high dependence on central funding. Recent reforms and the increasing reliance on discretionary revenues transferred from the central budget have motivated a closer examination of how these instruments influence local fiscal sustainability. This article analyses how different types of transfers—general subsidies and targeted grants—affect the fiscal sustainability of Polish municipalities across several dimensions, including autonomy, solvency, efficiency and economic resilience. Using panel data, five sets of models test the crowding-out effect, developmental impact, pro-cyclicality, fiscal discipline, and fiscal replacement mechanisms. Results show that general subsidies crowd out local tax revenues, particularly in less developed municipalities, while targeted grants strengthen the tax base in rural areas. Transfers have mixed effects: targeted grants strongly stimulate investment and support local development but tend to increase debt; general subsidies weaken local tax capacity and reduce fiscal autonomy, although they improve short-term fiscal discipline. In municipalities with limited fiscal independence, transfers act as short-term compensatory tools, fostering dependence on state aid rather than self-reliance. A macroeconomic crowding-out effect also appears, as higher transfers reduce private sector resources. Regarding fiscal discipline, equalization and compensatory subsidies decrease debt levels, whereas targeted grants can raise debt in urban municipalities with co-financing obligations. General subsidies show fiscal replacement effects, substituting local revenue sources. The findings provide insights for designing transfer systems that balance financial support with incentives for local autonomy and sustainable development. Full article

The coastal environment is a dynamic system shaped by both natural processes and human activities. In recent decades, increasing anthropogenic pressure and climate change—manifested through sea-level rise and more frequent extreme events—have accelerated coastal retreat, highlighting the need for improved management strategies and standardized tools for coastal risk assessment. Existing approaches remain highly heterogeneous, differing in structure, input data, and the range of factors considered. To address this gap, this study proposes an index-based methodology of general validity designed to quantify coastal erosion risk through the combined analysis of hazard, vulnerability, and exposure factors. The approach was developed for multi-scale and multi-risk applications and implemented across 54 representative sites along the Calabrian coast in southern Italy, demonstrating strong adaptability and robustness for regional-scale assessments. Results reveal marked spatial variability in coastal risk, with the Tyrrhenian sector exhibiting the highest values due to the combined effects of energetic wave conditions and intense anthropogenic pressure. The proposed framework can be easily integrated into open-access GIS platforms to support evidence-based planning and decision-making, offering practical value for public administrations and stakeholders, and providing a flexible, accessible tool for integrated coastal risk management. Full article

Background and Objectives: Thyroid nodules are common, and distinguishing benign from malignant lesions is essential for clinical decision-making. While EU-TIRADS provides ultrasound-based risk stratification, fine-needle aspiration biopsy (FNAB) and the Bethesda System remain central diagnostic tools. This study aimed to compare the diagnostic performance of EU-TIRADS and Bethesda classifications and to identify ultrasonographic features independently associated with malignancy. Materials and Methods: This retrospective single-center study included 824 patients (1132 nodules) who underwent FNAB between August 2021 and June 2024. All ultrasound examinations and FNAB procedures were performed by the same endocrinologist. Sonographic features, EU-TIRADS categories, Bethesda classes, surgical indications, and histopathology were analyzed. Diagnostic accuracy was assessed using ROC curves, and multivariable logistic regression was applied to determine independent predictors of malignancy. Results: Among all nodules, 51.0% were EU-TIRADS 3, 28.6% were EU-TIRADS 4, and 19.2% were EU-TIRADS 5. Bethesda class II constituted 62.7% of FNAB results. Of the 289 surgically treated nodules, 53.3% were malignant. Malignant nodules were smaller, more often solitary and unilateral, and more frequently located in the upper pole (p < 0.05). Irregular margins (OR = 8.15, p < 0.001) and microcalcifications (OR = 10.01, p = 0.003) were independent predictors of malignancy. Taller-than-wide shape also showed significant association. ROC analyses demonstrated that EU-TIRADS (AUC = 0.808) and Bethesda (AUC = 0.869) were both significant predictors, with Bethesda showing higher specificity. Malignancy rates were 0% in EU-TIRADS II, 4.3% in III, 14.5% in IV, and 37.8% in V. Conclusions: EU-TIRADS is a practical and sensitive non-invasive tool for malignancy risk stratification; however, Bethesda classification remains superior in overall diagnostic accuracy. Microcalcification and irregular margins were the strongest ultrasonographic predictors of malignancy, while macrocalcification, parenchymal heterogeneity, and thyroiditis showed no significant association. These findings support the complementary roles of EU-TIRADS and FNAB and highlight key sonographic markers that enhance malignancy prediction in thyroid nodule evaluation. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

Objective: Ongoing debates focus on the role of human leukocyte antigen (HLA) class II expression in shaping clinical phenotypes of chronic inflammatory airway diseases. This study seeks to clarify the impact of class II HLA on chronic obstructive pulmonary disease (COPD) and asthma–COPD overlap (ACO). Method: The expression levels of HLA-DQ/DR in blood immune cells were analyzed in 116 participants: 41 with COPD, 37 with ACO, 20 with pure asthma, and 18 healthy subjects (HS). Results: In the COPD group, HLA-DR protein expression levels were significantly elevated on blood M2a monocytes (7695 ± 3743 vs. 5391 ± 3153 MFI, p = 0.026), helper T cells (2551 ± 956 vs. 1836 ± 531 MFI, adjusted p = 0.018), cytotoxic T cells (1591 ± 531 vs. 1360 ± 477 MFI, adjusted p = 0.036), and B cells (20,667 ± 7985 vs. 15,694 ± 2003 MFI, adjusted p = 0.031) compared to the HS group. Conversely, no significant changes were observed in the asthma group. In ACO patients, helper T cells showed increased HLA-DR protein expression (2416 ± 914 MFI; adjusted p = 0.016) compared with the HS group. Higher levels of HLA-DR expression correlated with reduced pulmonary function, frequent exacerbations, and more severe symptoms. Following one year of treatment in 14 COPD and 16 ACO patients, HLA-DR protein expression on blood helper T cells, cytotoxic T cells, M2a monocytes, and neutrophils significantly declined (all p < 0.05). In vitro experiments demonstrated that exposure of M2- or M1-polarized THP-1 cells to a stimulus mix containing cigarette smoke extract, house dust mite antigens, and lipopolysaccharide led to up-regulation of HLA-DR expression. This response was linked to increased apoptosis and reduced production of reactive oxygen species. Conclusions: Up-regulation of HLA-DR in COPD and ACO patients may represent a novel biomarker for assessing disease severity and treatment response. Additionally, it could serve as a useful tool to distinguish COPD and ACO from asthma. Full article

This study conducts a comparative content analysis of media coverage of the Russia–Ukraine war by China Global Television Network (CGTN) and Voice of America (VOA), focusing on emotional content and framing strategies. Analyzing 4997 articles from CGTN and 4975 articles from VOA, the study examines how each outlet emphasizes emotions such as neutrality, anger, fear, and hope. The findings reveal that CGTN predominantly adopts a neutral and analytical tone, prioritizing geopolitical implications; in contrast, VOA employs a more emotionally charged approach, highlighting the humanitarian crisis and expressing solidarity with Ukraine. While CGTN emphasizes hope and diplomatic solutions, VOA underscores anger and fear to justify international intervention and support for Ukraine. The contrasting framing strategies reflect the geopolitical interests of China and the U.S., with CGTN positioning China as a mediator advocating for peace and stability, and VOA framing Russia as the aggressor to bolster Western democratic values. By leveraging divergent emotional narratives, both media outlets serve the strategic objectives of their countries, shape global perceptions, and garner public support for their respective policies. This study contributes to understanding how emotional framing functions as a strategic tool in international media coverage during geopolitical conflicts. Full article

Generative AI technologies are rapidly permeating higher education as innovative tools that support teaching and learning processes. This study investigates the integration of GenAI tools into academic learning and examines their influence on students’ learning effectiveness, attitudes, and satisfaction. A quantitative survey was administered to 485 college students. The findings indicate that students’ attitudes, satisfaction, and accumulated experience with GenAI constitute the most influential factors in promoting effective learning. Perceived advantages and disadvantages also play a substantial role in shaping students’ attitudes, satisfaction, and learning outcomes. Ethical knowledge demonstrates only modest positive effects, whereas institutional training shows no meaningful impact, largely due to its limited availability. The results suggest that higher education institutions should not focus solely on tool accessibility and technical training, but should prioritize fostering positive perceptions, maximizing the perceived benefits of GenAI, offering applied instruction and practical ethical guidance, and reducing concerns and negative perceptions among students. Full article

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

This study addresses the fabrication challenges associated with producing diverse geometries for concrete dry connections, particularly regarding cost, time, and geometric limitations. The research investigates methods for fabricating precise, rebar-free dry connections in concrete, focusing on stamping and green-state computer numerical control (CNC) milling. These methods are evaluated using metrics such as dimensional accuracy, tool abrasion, and energy consumption. In the stamping process, a design of experiments (DOE) approach varied water content, concrete age, stamping load, and operational factors (vibration and formwork) across cone, truncated cone, truncated pyramid, and pyramid geometries. An optimal age range of 90 to 105 min, within a broader operational window of 90 to 120 min, was identified. Geometry-specific exceptions, such as approximately 68 min for the truncated cone and 130 min for the pyramid, were attributed to interactions between shape and age rather than deviations from general guidance. Within the tested parameters, water fraction primarily influenced lateral geometric error (diameter or width), while age most significantly affected vertical error. For green-state milling, both extrusion- and shotcrete-printed stock were machined at 90 min, 1 day, and 1 week. From 90 min to 1 week, the total milling energy increased on average by about 35%, and at one week end-face (head) passes caused substantially higher tool wear, with mean circumference losses of about 3.2 mm for head engagement and about 1.0 mm for side passes. Tool abrasion and energy demand increased with curing time, and extrusion required marginally more energy at equivalent ages. Milling was conducted in two engagement modes: side (flank) and end-face (head), which were evaluated separately. End-face engagement resulted in substantially greater tool abrasion than side passes, providing a clear explanation for tolerance drift in final joint geometries. Additionally, soil-based forming, which involves imprinting the stamp into soft, oil-treated fine sand to create a reversible mold, produced high-fidelity replicas with clean release for intricate patterns. This approach offers a practical alternative where friction and demolding constraints limit the effectiveness of direct stamping. Full article

Violence against women remains one of the most persistent social problems in Türkiye, often reinforced by patriarchal interpretations of religion and cultural traditions. This study investigates the role of religious institutions and values-based education in preventing such violence by analyzing national curricula, mosque sermons, policy documents, and reports of the Presidency of Religious Affairs. Using a qualitative design based on document analysis and literature review, it examines how religious education reflects or omits gender-related themes and how institutional practices shape public awareness. The findings reveal that while formal and non-formal types of religious education promote moral values such as compassion, justice, and respect, they rarely address gender-based violence explicitly. Religious discourse tends to emphasize general moral development rather than specific strategies for preventing violence against women. The study concludes that integrating gender-sensitive content into religious curricula, promoting authentic Qur’anic teachings on equality and mercy, and providing professional training for religious personnel are essential to transforming societal attitudes. Strengthening cooperation between educational institutions, religious authorities, and policymakers will ensure that religion functions as a constructive moral resource rather than a tool for legitimizing inequality. Full article

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions. Full article