Search Results (4,088)

Automated legacy code modernization using Large Language Models lacks rigorous evaluation frameworks and multi-objective quality assessment methodologies. Existing research suffers from three critical deficiencies: single-metric evaluation paradigms creating pathological optimization incentives, statistical validation limited to p-values without effect size analysis, and absence of systematic transformation pattern taxonomies explaining what works and why. We present a novel multi-objective statistical framework that jointly assesses Cyclomatic Complexity (CC) and Maintainability Index (MI) while providing comprehensive effect size analysis addressing software engineering research gaps. Applied to 47 legacy Java samples from Apache Ant (version 1.10.x, commit rel/1.10.14), our framework achieves 97.9% metric-level improvement with very large practical effects (Cohen’s $d=1.86$, 95% CI [1.36, 2.35], $p<0.0001$) for maintainability—substantially exceeding prior work and conventional significance thresholds. We note that this success rate reflects quality metric improvement; functional equivalence was verified through syntactic validation and manual inspection of a 20% random sample, while comprehensive automated test-based verification remains a limitation addressed in future work. We contribute: (1) first multi-objective quality assessment framework for code modernization with weighted composite scoring and sensitivity analysis, (2) rigorous statistical methodology with effect size analysis beyond p-values, (3) systematic transformation pattern taxonomy identifying four successful patterns and three failure modes with predictive value (inter-rater agreement $\kappa =0.82$), and (4) negative result showing iterative refinement provides no benefit ($d=0.08$, $p=0.179$), saving community resources. Our transformation taxonomy enables practitioners to predict success likelihood from code characteristics, while our statistical framework provides replicable methodology for evaluating LLM-based software engineering tools. The very large effect size indicates metric-level improvements are materially meaningful for real-world software maintenance, not merely statistically detectable. Full article

Background: Remimazolam tosilate is a novel, ultra-short-acting benzodiazepine. To address the unmet clinical need for safe and controllable general anesthetic options in children and adolescents, both top-down (i.e., population pharmacokinetics—PopPK) and bottom-up (i.e., physiologically based PK—PBPK) modeling approaches were combined to leverage their respective strengths for dose selection in children and adolescents aged 3–18 years. Methods: Pooled PK data from adult studies were used to develop and verify the adult PopPK and PBPK models. The PopPK model included allometric scaling to describe body weight effects, while the PBPK modeling incorporated the age-dependent physiological and metabolic ontogeny. Potential covariates and intrinsic factors influencing remimazolam exposure were assessed. Both models were then applied to simulate PK and derive exposure metrics in 3–18-year-old children and adolescents. The predictions from both approaches were used to support pediatric dose selection using an adult-matching exposure approach. Results: The PopPK and PBPK model simulations yielded consistent exposure predictions and converged on the same recommended dosing regimens for the pediatric population, providing mutual confirmation of model reliability. Both models indicated that the proposed regimens of remimazolam would achieve systemic exposures in children and adolescents (3–18 years) comparable to those in adults receiving an induction dose of 0.3 mg/kg followed by maintenance infusions of 1.0 or 3.0 mg/kg/h. Two pediatric dosing regimens were recommended: 1. Lower dose group: induction 0.2 mg/kg, initial maintenance 1.0 mg/kg/h, titratable as needed, with a maximum rate of 3.0 mg/kg/h (up to 4.0 mg/kg/h for individuals ≤ 30 kg). 2. Higher dose group: induction 0.3 mg/kg, initial maintenance 2.0 mg/kg/h, titratable as needed, with a maximum rate of 3.0 mg/kg/h (up to 4.0 mg/kg/h for individuals ≤ 30 kg). The model-informed dosing regimens have received regulatory approval from the Center for Drug Evaluation (CDE) in China and are currently being evaluated in an ongoing clinical trial. Conclusions: The integrated PopPK–PBPK approach supports evidence-based dosing recommendations of remimazolam for general anesthesia in children and adolescents aged 3–18 years and provides a reference for dose selection in future clinical studies. Full article

Membrane lipid remodeling plays a pivotal role in regulating plant growth, reproductive development, and adaptive responses to environmental stress. However, several lipid-modifying enzymes remain uncharacterized in Arabidopsis thaliana. Here, we provide the first comprehensive in silico functional characterization of the unannotated gene AT5G35460, integrating domain architecture, AlphaFold-supported structural validation, and phylogenetic, expression, and regulatory analyses. Domain architecture and conserved DUF2838 signatures, together with transmembrane topology and validation using AlphaFold-predicted structural data, support its identity as a glycerophosphocholine acyltransferase (GPCAT1). Phylogenetic reconstruction showed that GPCAT1 clustered closely with its orthologs of major angiosperms, suggesting deep evolutionary preservation. Expression profiling revealed over a tenfold higher transcript abundance in mature pollen, detected 6–8 times more than during leaf senescence, indicating strong developmental control. Co-expression network analysis revealed links to the lipid metabolism genes (CDS2, LACS8, and SBH1) as well as factors involved in response to stress, indicating that AT5G35460 may act at the level of phosphatidylcholine remodeling, membrane resistance and stress response. Analysis of the promoter sequences showed AACTAAA, ABRE and G-box elements (pollen-specific, ABA-responsive and stress-inducible motif respectively), suggesting appropriate transcriptional regulation consistent with its expression profile. As a whole, the findings revealed that AT5G35460 is an unexplored membrane-localized acyltransferase involved in lipid maintenance during reproductive development and environmental responses. This study serves as a basis for subsequent functional characterization and identifies AT5G35460 as a potential target for modifying pollen viability, senescence kinetics and stress tolerance in plants. Full article

This study used finite element simulation and theoretical analysis to predict the crack distribution patterns that may occur during the shrinkage cracking process of rectangular timber beams. Based on the predictions, experimental specimens with six typical crack distribution patterns (I–VI) were designed. Subsequently, a four-point bending test method was employed to conduct large-sample size fracture tests on a total of 1200small-sized Pinus sylvestris var. mongolica specimens, quantifying the effects of the crack depth, location, and distribution patterns on the specimens’ load-bearing capacity. The results indicate that when multiple cracks exist in a timber beam, their collective effect is not a simple superposition of individual cracks but a spatial distribution coupling effect. Both the depth and location of the cracks play crucial roles in their interaction. This study introduces three coefficients for evaluating the influence of cracks on timber beams, namely the load-bearing capacity coefficient (R), the decline ratio of load-bearing capacity (D), and the comprehensive crack-influence coefficient (β), which can effectively quantitatively evaluate crack damage effects. The framework established in this study, which links shrinkage crack characteristics with the load-bearing capacity of timber beams, along with the experimental data provided, can serve as a reference for the safety evaluation and scientific maintenance of historical timber components and modern timber structures with shrinkage cracks. Full article

Abiotic stresses such as drought, salinity, extreme temperatures, and heavy metal contamination severely limit global crop productivity and threaten food security. Plants have evolved epigenetic strategies, particularly DNA methylation, to perceive, adapt to, and memorize environmental challenges. This review systematically elucidates the dynamic regulatory mechanisms of DNA methylation—including establishment via RNA-directed DNA methylation (RdDM), maintenance by methyltransferases (MET1, CMT), and active removal by demethylases (ROS1)—in plant responses to diverse abiotic stresses. We highlight how stress-induced methylation reprogramming modulates gene expression, chromatin states, and physiological adaptations, contributing to both somatic and transgenerational stress memory. Furthermore, we discuss advanced detection technologies for profiling methylation patterns and evaluate their applications in epigenetic breeding, such as exploiting heritable epialleles, RdDM-based gene silencing, and methylation markers for heterosis prediction. Despite significant progress, translating epigenetic insights into predictable breeding tools remains challenging. Future efforts should focus on establishing causal links between methylation changes and stress phenotypes, improving epigenome editing precision, and integrating multi-omics approaches for the development of climate-resilient crops. This work provides a comprehensive epigenetic perspective for enhancing crop adaptability and sustainable agriculture. Full article

This study presents a systematic review of Explainable Artificial Intelligence (XAI) applications in Transportation Infrastructure Management (TIM), focusing on predictive maintenance of safety-critical assets such as railways and bridges. A predefined review protocol was implemented, and peer-reviewed literature was systematically retrieved from Web of Science and Scopus covering the period 2015 to March 2025. Using structured Boolean search logic and clearly defined inclusion and exclusion criteria—requiring explicit integration of explainability within AI-driven infrastructure maintenance—450 records were initially identified, screened in multiple stages, and refined to 163 eligible studies for detailed analysis. Through structured data extraction and thematic synthesis, the review develops a taxonomy of model-specific, model-agnostic, hybrid, and human-centered XAI approaches while identifying recurring challenges including heterogeneous multi-modal data environments, lack of standardized interpretability metrics, computational constraints in real-time deployment, limited robustness validation under field conditions, and unresolved performance–interpretability trade-offs. The findings demonstrate systematic growth in XAI-driven predictive maintenance research and highlight the need for domain-specific benchmarks, hybrid interpretable architectures, digital twin-assisted validation, and edge-enabled explainable systems to enable scalable, transparent, and regulation-ready infrastructure management aligned with Industry 5.0. Full article

High-strength low-alloy (HSLA) steels in marine environments suffer from microbiologically influenced corrosion (MIC) and hydrogen-assisted degradation. This study investigates the synergistic effects of sulfate-reducing bacterial biofilms, mechanical stress, and seawater chemistry on HSLA AH36 steel using electrochemical, microstructural, and magnetic Barkhausen noise (MBN) monitoring. Under multiparametric exposure (80% yield strength tensile stress, Desulfovibrio vulgaris, 28 days), biotic samples exhibited sustained 1.88× corrosion acceleration despite 86% sulfate depletion. Magnetic Barkhausen noise RMS amplitude (MBN_RMS) peaked at day 7 (612 ± 38 mV/mm) at pit depths of only 20–50 μm, detecting subsurface hydrogen damage before macroscopic failure. Quantitative correlations (R² ≥ 0.99) between MBN_RMS and cumulative mass loss revealed distinctive linear relationships in abiotic conditions and nonlinear cubic polynomials in biotic conditions, providing a non-destructive signature diagnostic of hydrogen-assisted MIC. Directional anisotropy analysis (parallel vs. perpendicular fields) showed that hydrogen-induced damage produces isotropic magnetic signatures (anisotropy ratio: 1.27 → 1.15), enabling discrimination between hydrogen embrittlement and stress-controlled degradation. The integration of portable MBN measurements with electrochemical monitoring establishes a quantitative framework for real-time structural health assessment and predictive maintenance of HSLA steels in maritime applications. Full article

Background and Objectives: Anemia management in maintenance hemodialysis patients with erythropoiesis-stimulating agent (ESA) hyporesponsiveness remains challenging. Roxadustat, a hypoxia-inducible factor prolyl hydroxylase inhibitor, offers a mechanistically distinct alternative. Materials and Methods: This multicenter retrospective study analyzed 110 hemodialysis patients with persistent anemia (Hemoglobin (Hb) < 10 g/dL) despite ≥ 3 months of maximum-reimbursable-dose ESA therapy in Türkiye. Outcomes were evaluated between patients who switched to Roxadustat (n = 80) and those who continued ESA therapy (n = 30) over 6 months in a non-randomized, observational comparison. Results: At baseline, median Hb levels were significantly lower in the Roxadustat-group than in the ESA-group (8.70 vs. 9.50 g/dL; p < 0.001), while weight-adjusted ESA doses were comparable (p = 0.332). By Month 6, the Roxadustat group achieved a significant Hb increase (from 8.70 to 9.95 g/dL), whereas the ESA-group showed no significant change (9.50 to 9.65 g/dL), and end-of-treatment Hb did not differ significantly between groups. The unadjusted mean Hb rise was greater in the Roxadustat cohort than in the ESA cohort (+1.40 ± 1.55 vs. +0.65 ± 1.93 g/dL; p = 0.037). However, after adjustment for baseline Hb (ANCOVA), baseline Hb predicted final Hb, while treatment group was not independently associated with final Hb. Transfusion requirements declined over follow-up in both groups. No new short-term safety signal was identified based on available clinical documentation. Conclusions: Roxadustat improved Hb in ESA-hyporesponsive patients with lower baseline Hb, but adjusted analyses indicated that baseline severity influenced response. Targets were not consistently achieved; these findings are hypothesis-generating regarding dose optimization, treatment duration, and earlier initiation. Full article

International shipping contributes approximately 3% of global carbon dioxide emissions while serving as the circulatory system of global commerce. The International Maritime Organization’s 2023 GHG Strategy mandates net-zero emissions by or around 2050, with indicative targets requiring a 20–30% reduction by 2030 and a 70–80% reduction by 2040. From a coastal and ocean conservation perspective, these targets represent more than climate mitigation—they offer an opportunity to reduce the maritime sector’s broader ecological footprint, including underwater noise pollution, chemical contamination from antifouling coatings, and the transfer of invasive species through biofouling. This article examines the role of artificial intelligence in supporting maritime decarbonization across multiple domains: voyage optimization, wind-assisted propulsion management, vessel automation, port coordination, predictive maintenance, ship design optimization, and hull maintenance robotics. Critically, the analysis also addresses AI’s own environmental footprint—the substantial energy demands of data centers that power these technologies—and emphasizes the importance of transparent accounting of AI-related emissions. The article proposes research directions that advance both climate objectives and marine ecosystem protection. Full article

Background: Resting energy expenditure (REE) represents 60–75% of total daily energy expenditure and is mainly determined by fat-free mass (FFM). Indeed, the predictive equations vary according to FFM techniques and population characteristics. Therefore, this study aimed to explore the influence of dual-energy X-ray absorptiometry (DXA)-derived FFM on REE prediction by different predictive equations in a large and diverse cohort. Methods: A total of 1987 active and sedentary participants of both sexes (43.8 ± 19.4 years) underwent body composition assessment by DXA. REE was predicted using the Harris–Benedict, Schofield, Mifflin–St Jeor (weight- and height-based), and Mifflin (FFM-based) equations. Statistical analyses included Kruskal–Wallis, Spearman correlations, and linear regression. Results: Men presented higher absolute FFM, whereas women exhibited higher relative fat mass (FM) (p < 0.01). Across age groups, FFM declined progressively, while FM increased (p < 0.01). The REE differed significantly (p < 0.001) between equations, with the lowest values predicted from the FFM-based model, while the Harris–Benedict and Schofield equations showed the highest REE, especially in women. Strong correlations were observed between FFM and REE (r = 0.77–0.98; p < 0.01) for all age groups and equations, whereas FM showed strong correlations (r = 0.77–0.85; p < 0.01) only for the ≥60 years group. REE tended to be higher in active than sedentary participants, with the correlations to FFM and FM exhibiting a similar profile to that observed for the whole group. Conclusions: FFM showed a strong association with the estimate of REE in active and sedentary participants from both sexes and different age groups, but FM showed a similar trend in older participants only. Therefore, the increase or the maintenance of FFM with an active lifestyle is important to keep REE at high and efficient levels regardless of sex and age. Full article

To protect the environment, the R1234yf refrigerant was introduced into the air conditioning systems of modern vehicles. Its price is much higher than that of previous refrigerants, and the gas is slightly flammable, making the prompt detection and repair of even small leaks even more critical. This research aimed to develop a simple, dashboard-based method for serially monitoring and visualizing anomalies in cars after production and before and shortly after delivery. It is possible to infer the presence of minor leaks through online or frequent pressure monitoring after the system has been “resting” (last ignition off for at least 5 h to allow system stabilization: air conditioner vs. outer or engine coolant temperature). Using this method, it can be determined whether the given pressure losses fall within the normal operating range. The essence of the technique is to detect a possible small amount of leakage by monitoring the pressure change (Δp) of the air conditioning system, supported by dashboard(s). The results on the test fleet with 500 cars show that the procedure can be suitable for detecting defects that cause micro-leaks immediately after production. The false-negative detection rate was 0.2, and the false-positive rate was 1.2 at a threshold of ±0.5 bar. Based on a practical example, the method can also be applied to offline cars until the first factory-related claim occurs. Full article

Unexpected degradation in servomotor-driven multi-mode industrial systems such as CNC feed drives and robotic machining cells compromises positioning accuracy, availability and operational safety, rendering early fault diagnosis and predictive maintenance essential in smart manufacturing environments. In this study, a predictive maintenance framework based on multi-sensor data fusion was developed to support condition monitoring, fault classification, and remaining useful life estimation of robot servomotors. Time- and frequency-domain features were extracted from synchronized electrical current, vibration, acoustic, and temperature signals using fixed-length sliding windows. Feature-level fusion was applied to combine complementary information from different sensor modalities. A data-driven health assessment approach was employed in which an autoencoder model trained on healthy operating data was used to generate a scalar Servomotor Health Score representing degradation progression. Fault types were identified using a Random Forest classifier, while remaining useful life was estimated in terms of operational cycles using a Gradient Boosting regression model. Experimental evaluations were carried out under repeated reference motion profiles, and representative mechanical and electrical fault conditions were introduced in a controlled manner. The results demonstrated that the proposed health score provided a smooth and monotonic degradation trend, enabling early fault detection without false alarms under healthy conditions. High classification performance was achieved for fault identification, and remaining useful life predictions showed low estimation error on previously unseen faulty servomotors. Feature contribution analysis indicated that electrical current and temperature signals provided the most robust indicators of degradation, while vibration and acoustic measurements offered complementary diagnostic information. The proposed framework was shown to be an effective and practical solution for predictive maintenance of servomotor-driven manufacturing systems such as CNC axes and robotic machining platforms operating under low-speed and variable-load conditions. Full article

Stress corrosion cracking (SCC) is a critical failure mechanism that arises from the synergistic interaction between tensile stress and corrosive environments, leading to sudden and often catastrophic failures in structural components across various industries, including aerospace, nuclear energy, oil and gas, and marine engineering. This review synthesizes current understanding of SCC mechanisms, including film rupture and anodic dissolution, hydrogen embrittlement, and adsorption-induced cleavage, and evaluates material susceptibility across steels, aluminum alloys, nickel-based alloys, titanium, and emerging high-entropy alloys. Environmental factors such as aqueous chemistry, temperature, pressure, pH, and dissolved gases are examined for their roles in SCC initiation and propagation. Advanced testing methodologies, including slow strain rate testing, bent-beam configurations, electrochemical monitoring, and high-resolution microscopy, are discussed for characterizing SCC behavior. Engineering mitigation strategies are presented, encompassing material selection, stress reduction, surface treatments, and environmental control. Case studies illustrate real-world SCC failures and inform best practices. Emerging trends highlight the potential of machine learning for predictive maintenance and the development of SCC-resistant materials through additive manufacturing and microstructural engineering. This comprehensive review provides mechanical engineers with actionable insights for designing, maintaining, and safeguarding components against SCC in demanding service environments. Full article

The rapid evolution of Artificial Intelligence (AI) has significantly transformed the capabilities, performance, and autonomy of modern mechatronic systems. As industries transition toward intelligent and interconnected manufacturing environments, AI has emerged as a powerful enabler of real-time decision-making, adaptive control, predictive maintenance, and autonomous operation. This review provides a comprehensive analysis of AI integration within mechatronic systems, examining its influence on system performance, autonomy, and manufacturing efficiency. Key AI techniques including machine learning, deep learning, reinforcement learning, evolutionary optimization, and computer vision are evaluated in terms of their applications in control, sensing, diagnostics, and robotics. The paper also highlights advancements in AI-driven motion control, autonomous navigation, sensor fusion, and smart factory operations. Critical challenges such as data requirements, computational constraints, system interoperability, and safety concerns are discussed to identify research gaps. Finally, emerging trends and future directions, such as edge AI, digital twins, explainable AI, and fully autonomous mechatronic cells, are explored. This review consolidates current knowledge and provides insights to guide researchers and practitioners in developing next-generation intelligent mechatronic systems capable of supporting the demands of Industry 4.0 and beyond. Full article

Objectives: Horizontal ridge augmentation remains a clinical challenge due to limitations in terms of spatial maintenance, graft stability and predictability of new bone formation. The umbrella-screw tent technique provides mechanical stability for particulate grafts, while adjuvants such as hyaluronic acid (HA) and polynucleotides (PN) may enhance biological remodeling. Evidence for this compound in implant-related bone augmentation is still scarce. Material and methods: In a single patient with a knife-edge alveolar ridge, augmentation was performed in regions 34 to 36 using the umbrella-screw tent technique. The defect was grafted with deproteinized bovine bone mineral (DBBM) mixed with hyaluronic acid (HA) and polynucleotides (PN), supplemented with platelet-rich fibrin (PFR) and covered with a resorbable collagen membrane. After six months, two implants were installed, and a biopsy was obtained by trepanation for histological and histomorphometric analysis. Results: Healing occurred without compromise, with no signs of infection or graft exposure. Horizontal bone gain averaged 4.5 mm, corresponding to a relative Target Performance Index (TPI-h) of 75%. Histomorphometric analysis revealed a total mineralized fraction of 76.4%, consisting of 36.1% newly formed bone and 40.3% residual DBBM particles. The xenogeneic granules were completely integrated into mature bone, with no signs of inflammation or foreign body reaction. Conclusion: The case report illustrates that the combination of DBBM with HA and PN, stabilized by the umbrella-screw tent technique, can lead to significant new bone formation and favorable graft integration. Although limited by its single-case design, the case report provides preliminary insights into the synergistic potential of HA and PN as biological enhancers in bone augmentation, warranting further controlled studies. Full article