Search Results (5,982)

Generative artificial intelligence (GenAI) can support engineering problem solving, but whether AI-assisted practice transfers to independent performance after the tool is removed remains unclear. This multicentre controlled trial evaluated a traceable five-stage GenAI-assisted learning configuration in agricultural engineering education. Twenty-eight second- and third-year classes from six universities in Uzbekistan were assigned within nine teacher blocks by deterministic constrained minimization to GenAI (14 classes) or structured active-control (14 classes) groups. Both groups completed the same 16-week module, tasks, software, contact time, feedback, and verification requirements. They differed in the source and adaptivity of a provisional alternative used after an independent attempt: bounded adaptive GenAI dialogue versus a version-controlled curated alternative. The full assigned cohort included 656 students; likelihood-based available-outcome analyses included 641 immediate and 589 delayed outcomes. Kenward–Roger analyses estimated an adjusted immediate difference of 2.78 points (95% confidence interval (CI) [2.02, 3.55]; p < 0.001; model-based d = 0.72) and a delayed difference of 2.34 points (95% CI [1.54, 3.14]; p < 0.001; d = 0.51). The results show a positive adjusted association for the evaluated traceable instructional configuration, but deterministic post-baseline allocation limits causal interpretation. Full article

Background and Objectives: Granulosa (GCs) and cumulus cells (CCs) play a key supportive role in oocyte developmental competence and in antioxidant defence mechanisms triggered by endogenous reactive oxygen species production. Assisted reproductive technology (ART) procedures are an additional source of oxidative stress (OS) that somatic cells must counteract to ensure healthy embryo production. In this study, we quantified the major antioxidant enzymes (CAT, GPx1, SOD-1 and 2) in GCs and CCs retrieved from human antral follicles classified as large (>18 mm, L) or small (<18 mm, S) using ELISA. We correlated their contents with ongoing pregnancy rate (OPR). Materials and Methods: Healthy women (n = 17) undergoing ART procedures were selected for this study. During pickup, antral follicles were classified by diameter as L or S, and their somatic cells were stored separately for ELISA. After intracytoplasmic sperm injection (ICSI), embryo quality was assessed morphologically, and the OPR was evaluated. Results: Results showed that embryo quality and OPR were positively correlated with higher antioxidant enzyme levels detected in both GCs and CCs independently of follicle size. Conclusions: The data suggest that adequate levels of antioxidant enzymes in GCs and CCs are necessary not only to protect the oocyte from oxidative damage, but may also help predict embryo quality and OPR. Full article

Background/Objectives: Wilson disease (WD) is difficult to diagnose in children because it can mimic other pediatric liver diseases. It lacks specific routine histologic features. Recent studies suggest that metallothionein (MT) immunohistochemistry (IHC) may aid diagnosis, but pediatric data remain limited. Methods: This is a retrospective study of 121 pediatric liver biopsies, including WD (n = 63), primary sclerosing cholangitis (n = 23), metabolic dysfunction-associated steatotic liver disease (n = 19), autoimmune hepatitis (n = 13), and multidrug resistance protein 3 deficiency (n = 3). MT IHC was assessed for extent, intensity, pattern, and distribution. Results: MT positivity was identified in 96.8% of WD cases compared with 73.9% of PSC, 38.5% of AIH, and 10.5% of MASLD cases (p < 0.001). In WD, MT IHC characteristically demonstrated diffuse non-zonal cytoplasmic staining involving ≥25% of hepatocytes, most frequently >50%, with moderate-to-strong intensity. A threshold of ≥25% positive hepatocytes achieved the highest overall diagnostic accuracy (87.6%), with 80.9% sensitivity and 94.8% specificity. Diffuse non-zonal staining demonstrated 100% specificity in this cohort. Conclusions: MT IHC is a sensitive adjunctive marker for pediatric WD. Interpretation of staining extent, intensity, and distribution improves diagnostic specificity and assists in distinguishing WD from histologic mimics and cholestatic disorders. Full article

Background: Global developmental delay (GDD) affects multiple domains of early childhood development, including gross motor, cognitive and communication skills. Early, intensive, family-centered rehabilitation is considered key to optimizing functional outcomes in affected children. Case Presentation: We report a female child with GDD who began rehabilitation at our institution at 15 months of age, presenting with generalized hypotonia with superimposed fluctuating episodes of hypertonia, poor postural control, absent independent sitting, markedly reduced spontaneous motor activity, and associated cognitive and communication delay. Brain MRI at 7 months showed no parenchymal abnormality, with mildly enlarged extracerebral cerebrospinal fluid spaces and ventricular system. The metabolic and genetic evaluation performed so far, including microarray/MLPA-based screening for common microdeletion syndromes and SMN1/SMN2 genotyping, has not identified a specific underlying etiology. Diagnostic work-up is ongoing. Rehabilitation was delivered as a comprehensive, multidomain program; this report focuses specifically on the child’s motor progression. Intervention: The child underwent the Early Intensive Stojčević-Polovina Rehabilitation Method (EIR-SPM), a high-intensity, continuous approach for children with cerebral palsy, at-risk infants, and other developmental disabilities, built on parental education enabling home-based continuity of therapy. Rehabilitation focus is selected according to the child’s optimal developmental stage—the milestone showing the least abnormal movement patterns and muscle tone—rather than chronological age, with positions progressively adjusted following the trajectory of typical motor development described by Vojta. Results: Gross motor function, monitored using the Gross Motor Function Measure–88 (GMFM-88) at four assessment points from 15 months to 6 years 6 months of age, improved progressively from 10.8% to 48.9%, 64.7%, and finally 73.9%. The child achieved independent kneeling, reciprocal crawling, independent sitting in all positions, independent standing and assisted stepping. Conclusions: In this child with GDD of undetermined etiology, more than five years of intensive, family-centered rehabilitation according to the EIR-SPM were accompanied by substantial and sustained gains in gross motor function and functional independence. This report suggests that meaningful progress remains achievable even when rehabilitation begins later than the period considered optimal within the EIR-SPM framework, and that a family-centered structure may be what makes therapy of this intensity and duration sustainable. Full article

Furfural is a pivotal indicator of the aging condition of transformer oil-paper insulation. Traditional analytical techniques such as liquid chromatography require sophisticated pretreatment and phase-separation procedures and are therefore not well suited to rapid oil-sample analysis. This study reports the direct analysis of furfural in complex oil mixtures using surface-enhanced Raman spectroscopy (SERS). The weak vibrational response of furfural in oil was enhanced using a Au-coated silicon nanowire substrate fabricated by metal-assisted chemical etching (MACE). The textured metal-coated surface enabled trace furfural at the μL/L level to be measured directly in the oil mixture without adsorption enrichment or chemical extraction, with the entire test process completed within 1 min. Peak deconvolution was used to extract the fitted area of the 1365 cm⁻¹ band, and the relationship between this Raman response and furfural concentration yielded R² = 0.9910. Temperature- and pressure-dependent measurements were also performed to examine their effects on the positions of the main Raman peaks. This work demonstrates a rapid approach for analyzing trace furfural in complex liquid mixtures and provides a basis for further spectroscopic studies of transformer oil-paper insulation aging. Full article

Online review platforms are core infrastructure of digital commerce, yet the business-level determinants of review content remain poorly understood. We examine how service participation intensity, the degree to which a business format requires active customer involvement, relates to the affective–experiential language of reviews and to rating extremity. We analyze 6,151,996 Yelp reviews (2010–2022) with two independently validated instruments: a Business Participation Index, whose 39 attribute and 1263 category weights derive from an independent coding procedure (two large-language-model raters from different model families applying written, hypothesis-blind codebooks; Krippendorff’s $\alpha =0.88$–$0.96$) and a 35-indicator text framework validated against a review sample dual-coded under the same procedure. Participation and hedonic–utilitarian positioning, measured independently for the first time, are negatively entangled across the platform ($r=-0.27$). Decomposing the two shows that positioning absorbs roughly thirty percent of the raw participation–hedonic association. The remainder survives category, price, geography, and year fixed effects with business-clustered errors ($b=-0.029$, standardized $\beta =-0.084$), inverse-probability weighting, and Oster bounds ($\delta =13.7$). Extremity analysis reveals an asymmetry: higher participation predicts one-star reviews (odds ratio of 1.13) but not five-star reviews. The hedonic–analytical co-occurrence index, the joint elevation of the two language modes, adds no information beyond its components (incremental $R^2=0.00006$) and is not advanced as a mechanism. Format-based comparisons in review analytics must condition on positioning. Full article

Edge-enabled Internet of Things (IoT) systems are rapidly becoming the operational substrate of mission-critical infrastructure spanning industrial automation, smart healthcare, vehicular ecosystems, and cyber–physical environments. The distributed, resource-constrained, and physically exposed nature of these systems makes them persistent targets for a diverse and evolving spectrum of cyber attacks. Critically, cyber attacks on edge-enabled IoT systems do not merely threaten data confidentiality; they simultaneously erode two interdependent operational objectives: sustainability, the ability of the system to maintain continuous, energy-efficient operation within its resource envelope and resilience, the ability to absorb adversarial disruptions, recover operational continuity, and adapt to prevent recurrence. The structural conflict between defending sustainability and maintaining resilience under active cyberattack conditions constitutes a research gap that prior surveys have not systematically addressed. This survey introduces a cyber attack-driven Sustainability–Resilience (S-R) framework that positions cyber threats as the primary stressor forcing a bilateral trade-off between operational efficiency and continuity in edge-enabled IoT systems. A five-layer, attack-centric taxonomy is developed spanning: network-layer attacks (DDoS, MitM, routing manipulation, jamming); device and firmware attacks (malware injection, firmware compromise, sensor spoofing); data and AI/ML attacks (adversarial inputs, data poisoning, model inversion); federated and Byzantine attacks (gradient poisoning, backdoor injection, free-riding); and advanced persistent threats (APT-class intrusions, ransomware, LLM prompt injection, zero-day exploitation). For each attack class, the survey systematically analyses the impact on sustainability and resilience objectives, the resulting S-R conflict, and the state-of-the-art defensive strategies. The framework is formalised as a maximin optimisation over the joint S-R objective surface, incorporating the adaptive, goal-directed nature of the adversary through a game-theoretic formulation. Cross-domain analysis spanning Industrial IoT, smart healthcare, Internet of Vehicles, UAV-assisted IoT, smart grids, and tactical edge networks establishes domain-specific S-R operating constraints under representative attack scenarios. The survey concludes with a structured characterisation of open research challenges and forward-looking directions, providing a prioritised research agenda for advancing simultaneously sustainable and adversarially resilient edge-enabled IoT ecosystems. Full article

Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these downstream functions are not evaluated directly here. This study reformulates UAV position prediction as a flight-wise, multi-horizon, three-dimensional forecasting problem and tests whether position, velocity, gravity-resolved acceleration, and quaternion-orientation histories improve predictive accuracy while measuring model-level edge-inference cost rather than end-to-end system latency. The dataset contains 3100 AirSim flights with high-rate kinematic, inertial, attitude, pressure, and magnetic-field measurements under variable horizontal wind. The reported generalization is flight-disjoint within one AirSim domain; route/scenario disjointness and transfer to physical UAVs are not established. Signals are converted to a common navigation frame, gravity-resolved, low-pass filtered, resampled to 50 Hz, and partitioned by flight identifier before normalization and window construction. Each learned model receives 2 s of history and predicts the complete next 1 s trajectory, with errors evaluated at 0.1, 0.5, and 1.0 s. The sensor-enriched LSTM (LSTM-PVAQ) is compared under matched conditions with persistence, constant-velocity, constant-acceleration, extended Kalman filter, reduced-feature LSTM, GRU, temporal convolutional network (TCN), and compact Transformer baselines. LSTM-PVAQ achieved 3D RMSE values of 0.043, 0.168, and 0.371 m at 0.1, 0.5, and 1.0 s, respectively. At 1 s, its RMSE was 21.7% lower than LSTM-PV, 13.1% lower than GRU-PVAQ, 9.3% lower than TCN-PVAQ, and 16.8% lower than Transformer-PVAQ. Its one-second ADE and FDE were 0.216 and 0.339 m. On a Raspberry Pi 5 CPU using one FP32 thread and batch size one, median neural forward-pass latency was 0.88 ms, well below the 20 ms model-update interval. The results show that gravity-resolved inertial and orientation histories improve multi-horizon prediction, while TCN-PVAQ remains an attractive lower-latency alternative. Full article

Nuclear magnetic resonance (NMR)-based metabolomics has emerged as a robust and reproducible analytical platform for investigating the molecular mechanisms underlying disease and identifying candidate biomarkers with potential clinical relevance. Although initially developed for biomedical research, its application in veterinary medicine has expanded rapidly during the last decade, driven by the growing demand for precision medicine approaches capable of improving disease diagnosis, prognosis and therapeutic monitoring. This review summarizes the current landscape of NMR metabolomics in veterinary medicine, highlighting its applications across major clinical areas, including oncology, infectious, respiratory, digestive, renal, endocrine and musculoskeletal diseases. Collectively, the available evidence demonstrates that NMR metabolomics consistently identifies disease-associated metabolic alterations, providing novel insights into pathophysiology while supporting biomarker discovery in naturally occurring animal diseases. Beyond current clinical applications, we discuss the major challenges that continue to limit routine implementation, including biological variability, standardization of analytical workflows, multicentre validation and clinical translation. Particular attention is given to recent technological advances, such as benchtop NMR instrumentation, artificial intelligence-assisted data analysis, multi-omics integration and the development of collaborative networks and standardized platforms. Together, these advances position NMR metabolomics as a promising technology to support precision veterinary medicine, facilitating its transition from biomarker discovery to routine clinical implementation. Full article

We develop a predictive–prescriptive decision-support framework for football squad composition under budget and positional constraints, in which mixed-integer optimization is the central methodological contribution. The framework integrates four components: (i) construction of a Sports Performance Index (SPI) via principal component analysis (PCA) of standardized per-90 performance features; (ii) leakage-free, player-wise, five-fold cross-fitted prediction of seasonal goals plus assists using ordinary least squares (OLS) as the primary input model, regularized linear models as additional baselines, and gradient boosting (XGBoost) as a nonlinear benchmark and uncertainty-estimation component, with each player-season receiving an out-of-fold prediction; (iii) a mixed-integer linear program (MILP) that selects a player-season portfolio under budget, position composition, squad-size, and duplicate-player constraints, with a tunable weight balancing predicted attacking output against the performance index; and (iv) a robust counterpart that incorporates prediction uncertainty into the optimization. The framework is implemented on English Premier League data covering the 2023–2024 and 2024–2025 seasons (N = 218 forward and midfielder player-seasons with at least 1800 min of playing time). Cross-fitted OLS attains a five-fold mean R² = 0.76 ± 0.03 with stable performance across folds. The exact MILP outperforms a benefit-to-cost greedy heuristic by up to 9.4% and the best of 1000 random feasible rosters by 41.0–53.0% in objective value, while guaranteeing feasibility of all composition and budget constraints. Sensitivity analyses across budget tightness, position bounds, squad size, and cost proxy demonstrate qualitative robustness. The robust counterpart with risk-aversion parameter κ ∈ {0, 0.5, 1.0} produces a Jaccard roster overlap of 0.75 relative to the nominal solution and reduces average selected-player prediction uncertainty from 2.65 to 2.49. The contribution is a mathematically rigorous, integer-programming-centered decision-support framework that integrates machine learning with optimization for sports business analytics and is directly applicable to club-level squad planning. Full article

This paper examines the relationship among gold production, terrorist attacks, FDI, trade openness, financial assistance, World Bank aid, and economic growth in Burkina Faso over the period of 1975–2024. To account for structural shifts and nonlinear dynamics, the analysis employs the FBARDL model together with Fourier Granger causality tests. To check the robustness of the results, the FBARDL estimates were compared with those obtained from the conventional ARDL, ARDL with structural break dummies, and OLS models. In addition, the BDS and Fourier-NL tests were performed on the residuals to assess neglected nonlinear dynamics and remaining structural breaks, while the Fourier F test was employed to verify the significance of the Fourier approximation. Finally, the Fourier Granger causality test was re-estimated using alternative lag lengths to confirm the robustness of the causality. The empirical results revealed contrasting effects of gold production and terrorism on economic growth in Burkina Faso. Gold production exerts a positive and economically meaningful effect in both the long and short run: a 1% increase in gold production increases economic growth by approximately 0.894% in the long run and 0.56% in the short run. By contrast, terrorism significantly undermined long-run economic growth, with a 1% increase in terrorist activity reducing economic growth by approximately 0.45%. The causality analysis further identified unidirectional causality from gold production to terrorism and trade openness, from trade openness and FDI to terrorism, and from terrorism to IBRD/IDA assistance. Bidirectional causality is found between FDI and trade openness, economic growth and FDI, economic growth and terrorism, FDI and gold production, economic growth and trade openness, and trade openness and financial assistance. Taken together, these results revealed a resource–security paradox. Gold production generates a clear resource blessing effect in terms of economic growth, but terrorism indicated that resource expansion may simultaneously intensify conflict-related vulnerabilities. Moreover, the growth benefits generated by gold production do not necessarily translate into broader and more inclusive development. The results therefore suggest that resource blessing in economic growth can coexist with resource curse dynamics in economic development. The policy implications emphasize strengthening resource governance and security institutions while channeling mining revenues toward productive investment and inclusive development. Such measures are essential for transforming gold-driven growth into sustainable development while mitigating the security risks associated with resource dependence. The results therefore revealed a sustainability paradox in which resource wealth generates economic benefits but also increases security and development vulnerabilities. The policy implications emphasized strengthening resource governance and security institutions and improving the allocation of mining revenues toward productive investment and inclusive development. Such policies are essential for transforming gold wealth into sustainable economic development while limiting the conflict-related risks associated with resource dependence. Full article

Early diagnosis of breast cancer (BC) remains challenging. The limited sensitivity and specificity of existing serum tumor markers for reliable clinical application highlight the need to develop a more accurate and efficient screening workflow. This study analyzed serum samples from 255 breast cancer patients and 300 healthy controls using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, identifying 58 differentially expressed peptides (37 upregulated, 21 downregulated). Combined with machine learning, peptide identification, and external validation, a complete standardized workflow was established. Nine machine learning (ML) algorithms were employed and compared, including SVM, LightGBM, XGBoost, etc. The models were interpreted using SHAP and LIME to identify key features. Peptides of interest were sequenced via mass spectrometry. Their expression and potential prognostic value were further validated in breast cancer transcriptomic datasets. Nine machine learning algorithms showed favorable discriminatory ability in the study cohort. The LightGBM model achieved an AUC of 0.97 internally and maintained an AUC of 0.88, an accuracy of 0.8543, and a precision of 0.9799 externally. However, after correcting for the markedly elevated prevalence (80.3%) in the external cohort, the positive predictive value (PPV) decreased substantially under real-world screening scenarios, warranting prospective validation in true screening populations. Model interpretation and subsequent sequencing identified six core biomarker peptides: Apolipoprotein A-IV (APOA4), Serum Deprivation Response Protein (SDPR), Alpha-1-Antitrypsin (SERPINA1), Ezrin (EZR), Serglycin (SRGN), and Fibrinogen Alpha Chain (FGA). Transcriptomic corroboration suggested that these molecules were significantly dysregulated in breast cancer tissues and showed univariate prognostic associations with patient survival. These findings demonstrated the potential of a proteomics-driven integrated machine learning pipeline as a proof-of-concept auxiliary risk-stratification tool for enhancing early breast cancer diagnosis, warranting further prospective validation in real-world screening cohorts before clinical translation. Full article

The integration of AI into higher education provides new opportunities for supporting learning in interdisciplinary contexts and fostering entrepreneurial development. However, previous research has mainly focused on AI technology adoption, with limited attention paid to its broader educational outcomes. In particular, how AI-assisted learning contributes to students’ entrepreneurial intention within an interdisciplinary course context remains underexplored. This study develops an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model by incorporating AI-assisted learning engagement as a mediating factor. A questionnaire survey was conducted among 300 undergraduate students enrolled in a food product development course within a food science and engineering program at a university in China. Using PLS-SEM, the results reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions positively influence AI-assisted learning engagement and entrepreneurial intention. Furthermore, AI-assisted learning engagement significantly mediates the relationships between UTAUT factors and entrepreneurial intention. This study contributes to AI education and entrepreneurial learning research by demonstrating how AI-assisted learning within interdisciplinary contexts can promote students’ entrepreneurial intention through learning engagement. Full article

The emergence of seekRNA- and bridgeRNA-guided recombination has introduced a distinct paradigm in genome engineering by coupling programmable RNA-directed DNA recognition with recombinase-mediated insertion, excision, inversion, and genomic rearrangement without canonical double-strand breaks. Since their discovery in 2024, these systems have progressed rapidly from bacterial mobile genetic elements and mechanistic characterization to structural elucidation and programmable genome engineering in human cells. However, these advances remain distributed across foundational and rapidly emerging studies, creating a need for an integrated molecular perspective on their mechanisms, technological development, and position within contemporary genome engineering. This review synthesizes the molecular architecture, RNA-guided recognition, strand-exchange mechanisms, programmability, and engineering of seekRNA and bridgeRNA systems, with particular emphasis on complementary human-cell advances involving ISCro4 and engineered IS621. Whereas ISCro4 systems have enabled multikilobase DNA insertion, genomic excision, and near-megabase inversion, the enIS621–tebRNA platform has enabled scarless kilobase-scale integration across multiple human cell types, including proof-of-concept functional CD19 chimeric antigen receptor and factor IX gene insertion. The review further integrates recent genome-scale bacterial rewriting and Targetable Recombinase Assisted DNA Exchange (TRADE)-mediated DNA replacement, while benchmarking RNA-guided recombination against conventional site-specific recombination, clustered regularly interspaced short palindromic repeats (CRISPR)-based editing, Programmable Addition via Site-specific Targeting Elements (PASTE), CRISPR-associated transposases, and emerging large-payload genome-writing strategies, including kilobase-scale nickase-targeting (KNIT) editing, Prime Assembly, engineered R2 retrotransposons, and TransCRISTI. This comparative framework highlights a broader transition from programmable sequence modification toward direct engineering of genomic architecture, while identifying recognition-site constraints, mismatch-tolerant recombination, unintended recombination products, delivery, and genome-wide specificity as key translational barriers. By integrating foundational mechanisms with recent mammalian engineering, genome-scale bacterial rewriting, large-payload technologies, and emerging computational design strategies, this review provides a contemporary framework for defining the distinctive capabilities, current limitations, and future development of programmable RNA-guided DNA recombination. Full article

Workplace phubbing (being ignored during an interaction because a colleague is attending to a mobile phone) is increasingly common in clinical settings and may function as a subtle interpersonal stressor relevant to nursing teamwork, occupational well-being, and cooperative functioning. This cross-sectional survey examined associations between perceived workplace phubbing and self-reported job performance (overall, in-role, and extra-role). It tested whether resilience and perceived phubbing norms were related to performance. Nurses and nurse assistants from six public hospitals in Northern Greece provided eligible questionnaires (N = 338). In the primary overall-score models, being phubbed was positively associated with total and in-role performance but not with extra-role performance. In secondary dimension-level models, perceived phubbing norms were positively associated with all three performance outcomes, whereas feeling ignored and interpersonal conflict were not independent predictors. Resilience was positively associated with total, in-role, and extra-role performance. The GSBP total × resilience interaction was statistically significant only for in-role performance; however, simple-slope interpretation indicated attenuation of a positive association at higher resilience rather than buffering of an adverse association. Overall, resilience appeared to function as an independent correlate of perceived work performance rather than as evidence of the hypothesized buffering of a negative phubbing–performance association. These findings suggest that workplace phubbing should be understood as a context-dependent interpersonal phenomenon rather than a uniform performance risk. Clarifying digital-attention norms and protecting inclusive communication during key clinical interactions may support teamwork, psychosocial climate, and occupational well-being among nurses. Full article