Search Results (3,142)

Sperm cryopreservation is important for livestock breeding and germplasm conservation, but freeze–thaw injury can impair ram sperm quality through oxidative stress, membrane damage, and metabolic disturbance. This study evaluated the concentration-dependent effects of kaempferol supplementation on the cryopreservation quality of semen from Yuansheng Aite dairy rams. Qualified ejaculates were pooled and randomly allocated to five equally spaced kaempferol treatment groups: 0, 25, 50, 75, and 100 μg/mL. Post-thaw sperm motility, oxidative stress status, ATP-related energy metabolism, acrosome integrity, and multi-omics profiles were evaluated. Data were analyzed using appropriate parametric or non-parametric tests after assessment of normality and homogeneity of variance. Orthogonal polynomial analysis was performed to evaluate linear and nonlinear dose–response patterns across the tested kaempferol concentrations. Kaempferol supplementation significantly affected PM, VCL, and VAP, while RPM, LIN, WOB, and VSL were not significantly affected. No significant linear effect was observed for the motility parameters, whereas VCL exhibited a significant quadratic response to kaempferol concentration. Based on the observed overall responses of sperm motility, antioxidant capacity, oxidative stress markers, ATP content, and acrosome integrity, 25 μg/mL kaempferol showed the most favorable overall profile among the tested concentrations and was selected for subsequent mechanistic analyses. Proteomic and metabolomic analyses suggested that the protective effects of kaempferol may be associated with pathways related to focal adhesion, cytoskeletal organization, oxidative phosphorylation-related energy metabolism, and central carbon metabolism. These findings indicate that moderate kaempferol supplementation may improve the post-thaw quality of Yuansheng Aite dairy ram semen, although further fertility-oriented studies are needed to confirm its practical reproductive benefits. Full article

Mitochondrial dysfunction has been increasingly implicated in the pathobiology of neurodevelopmental conditions, particularly autism and attention-deficit/hyperactivity disorder (ADHD). Because the developing brain is critically dependent on sustained ATP production, impairments in oxidative phosphorylation, mitochondrial dynamics, and redox balance may disrupt neuronal maturation, synaptic development, and neural circuit refinement during sensitive developmental periods. This review examines evidence from postmortem neurochemistry, genomics, magnetic resonance spectroscopy, and biomarker research to characterize mitochondrial impairment across autism and ADHD. Studies in autism report an elevated burden of heteroplasmic mitochondrial DNA (mtDNA) variants, along with alterations in mtDNA copy number, respiratory chain capacity, fission–fusion dynamics, and antioxidant defenses. Postmortem data demonstrate reduced activity of electron transport chain Complexes I, III, and V in the frontal cortex, temporal lobe, and cerebellum. These bioenergetic abnormalities are accompanied by elevated oxidative stress markers alongside mitochondria-mediated immune activation. In vivo neuroimaging corroborates these findings through elevated cerebral lactate and reduced phosphocreatine-to-ATP ratios. Evidence in ADHD is limited, but similarly implicates mitochondrial dysfunction, consistent with the frequent co-occurrence of these conditions and their partially shared architecture. The available literature supports mitochondrial dysfunction as a transdiagnostic biological feature of neurodevelopmental conditions, with relevance to mechanistic biomarker identification and targeted therapeutic development. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Ensuring functional correctness in digital circuitry is arguably the most labor-intensive stage of hardware creation, routinely accounting for upwards of 70% of a project’s total resource allocation. While traditional coverage-driven verification (CDV) attempts to validate every operational state, reaching full coverage closure via manual intervention or constrained–random techniques requires significant engineering time and domain knowledge. To overcome this bottleneck, this study introduces an automated testing architecture that leverages the Advantage Actor–Critic (A2C) Reinforcement Learning (RL) algorithm. This agent intelligently navigates functional coverage closure across five diverse hardware designs: an Advanced Peripheral Bus Universal Asynchronous Receiver-Transmitter (APB UART), an Serial Peripheral Interface (SPI) Memory unit, a synchronous First-In First-Out (FIFO) queue, an APB RAM, and an Advanced High-performance Bus (AHB) Slave interface. By interfacing QuestaSim 2024.1 with a Python-based intelligent agent via a SystemVerilog DPI-C socket, the system dynamically produces test vectors informed by real-time coverage metrics. Based on evaluations across five distinct random seeds, the methodology successfully attains 95.1% to 100% coverage across all testbenches, with three designs achieving 100% and two reaching 95–98%. Notably, the RL-guided system achieved target coverage using approximately 35% fewer simulation cycles than an unguided random baseline, and 22% fewer cycles compared to a traditional constrained–random setup utilizing expert-defined rules. Ultimately, this framework bypasses the necessity for manual constraint formulation and seamlessly scales to novel hardware environments with negligible setup overhead. Full article

Manual compliance auditing in cloud environments consumes up to 40% of IT security budgets annually, yet existing approaches verify control presence rather than effectiveness, leaving institutions vulnerable to adversarial evasion. This paper presents an AI-augmented hybrid ML–LLM compliance auditing system evaluated on Rwanda’s National Cyber Security Authority (NCSA) Minimum Cybersecurity Standards (169 controls across 14 families). The system combines leakage-free XGBoost multi-label classification with GPT-4o-mini semantic log analysis, grounded in a formal effectiveness model. Key findings: (1) XGBoost v2 achieves 85.45% macro-F1 on leakage-free synthetic data (Wilson 95% CI = [84.9%, 86.0%]); an initial 86.3% data-leakage rate artificially inflated prior results to 99.99% and was identified and corrected in this revision; (2) GPT-4o-mini achieves 92.3% macro accuracy across four log types (n = 628, 37.5% real enterprise data, Wilson CI = [89.9%, 94.3%]); (3) adversarial validation across five MITRE ATT&CK scenarios yields 92.8% macro detection with 0.0% false-positive rate on real SSH/PAM compliant logs (n = 75); (4) a cross-dataset generalization analysis confirms 87.6% F1 on real SSH logs but identifies a 37.8-percentage-point out-of-vocabulary gap for Windows and HTTP log types, motivating the hybrid architecture; (5) the combined hybrid system (XGBoost for in-vocabulary logs, GPT-4o-mini for out-of-vocabulary) achieves 85.1% F1 with 6.4% false-positive rate on 180 real-world logs. The system runs at 2.0 CPU cores, 2.66 GB RAM, on $50/month cloud hosting (Apple M1 Pro baseline; storage and maintenance excluded), producing audit reports in 2–5 s depending on log volume and policy document size, demonstrating that effectiveness-based compliance auditing is accessible without enterprise-grade infrastructure. Full article

The use of agricultural by-products as feed is essential for sustainable animal husbandry. This study assessed the effects of substituting whole-plant corn silage with a mixed silage of cotton stalks and grape pomace on growth, serum biochemistry, and jejunal metabolomics in Suffolk rams. In this experiment, 135 rams (6-mo, 30.55 kg BW) were allocated to 0%, 50%, or 100% replacement (CG, EG50, EG100) and fed for 120 d after a 15-d adaptation. Compared with the CG, average daily gain improved by 27.3% and 17.5%, and feed conversion improved by 30.8% and 15.4% in EG50 and EG100 (p < 0.01). Compared with CG, the levels of BUN, TNF-α and IL-1β in serum of EG50 and EG100 were significantly decreased. The levels of IgG, IgM, IL-4, antioxidant enzymes and total antioxidant capacity were significantly increased (p < 0.05). Subsequently, the slaughter performance and jejunal content metabolome of CG and EG50 were further detected and analyzed. The results indicated that the live weight, eye area and muscle crude protein content of EG50 were extremely significantly higher than those of CG (p < 0.01). In jejunal contents, 31 differential metabolites (EG50 vs. CG) were enriched in ABC transporters, branched-chain amino acid biosynthesis, mineral absorption, purine and biotin metabolism, and glucagon signaling. In conclusion, substituting corn silage with the mixed silage promotes growth, improves antioxidant and immune status, reduces serum urea nitrogen, enhances muscle protein deposition (EG50), modulates intestinal nitrogen, purine, lipid, and carbohydrate metabolism (EG50), and supports sustainable meat sheep production. Full article

Microplastic (MP) contamination is an emerging threat to aquatic ecosystems. However, species-specific bioaccumulation patterns across trophic guilds in tropical river ecosystems remain scarcely understood. This study assessed the occurrence, organ-level distribution, polymer composition, and ecological risk of MPs in 220 fish representing 12 species, spanning across multiple trophic guilds, sampled from four sites along a pollution gradient of the river Yamuna, India. MPs were detected in all examined species, confirming extensive distribution across the river ecosystem. A total 1678 MPs were recovered, with significantly higher abundance in fish from the highly urban Delhi stretch than in those from upstream regions (Kruskal–Wallis, H = 11.03, p = 0.011). The highest species-specific MP load was recorded in omnivorous Oreochromis niloticus from Sonia Vihar (436 MPs), whereas the carnivorous species Xenentodon cancila exhibited the lowest accumulation (37 MPs). Surface- and mid-water herbivores and omnivores accumulated more MPs than benthic carnivores and detritivores. Nonetheless, spatial pollution gradients exerted a stronger influence on MP accumulation, compared to trophic guilds. The gastrointestinal tract exhibited the highest MP abundance (751 MP particles), followed by gills (605) and muscle tissues (322), confirming ingestion as primary uptake route, and suggesting possible tissue translocation. Fibers dominated in the assemblage (77.8%), while transparent (44%) and blue (19.5%) were most abundant colors. ATR–FTIR analysis confirmed 10 diverse polymers, with polyethylene (≈24%) and polypropylene (≈21%) together accounting for nearly half of the identified particles. The Polymer Hazard Index analysis classified the recovered MP mix as Category IV (high ecological hazard). These findings identify the Delhi stretch of the Yamuna as a high MP contamination zone and highlight the combined influence of urban pollution and fish ecology on MP bioaccumulation. Full article

The mechanical response of unstabilized rammed earth (URE) depends on a chain of factors spanning from soil composition to compaction conditions and specimen geometry and manufacturing conditions. This paper proposes a multiscale experimental framework for the physical and mechanical characterization of URE, structured around three hierarchical scales—soil, fabric and specimen—and demonstrates it on a single soil sample used consistently across more than a decade of experimental campaigns. At the soil scale, mineralogical composition, particle size distribution, Atterberg limits and linear shrinkage are determined. At the fabric scale, Proctor compaction tests establish the optimum moisture content and maximum dry density, and cohesion tests quantify the tensile cohesion of the material. At the specimen scale, monotonic and cyclic uniaxial compression tests reveal that compressive strength is essentially isotropic with respect to loading direction, while stiffness exhibits a pronounced anisotropy, with an anisotropy coefficient of 2.6. A Proctor-based specimen manufacturing procedure is used to reduce the coefficient of variation of compressive strength from 11.8% to 1.8%, demonstrating the critical role of compaction control in result reproducibility. Diagonal compression tests yield a shear strength of approximately 10% of the compressive strength, consistent with the tensile-to-compressive strength ratio commonly reported for URE. The proposed framework highlights the limitations of single-parameter characterization and provides methodological guidance applicable from soil evaluation to full mechanical characterization of URE. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Bio-based polyurethane (PU) coatings offer sustainable alternatives to petrochemical coatings but often suffer from inferior mechanical performance, durability, and chemical resistance. This work addresses that challenge by integrating a trifunctional bio-based crosslinker (glycerol) and a silane-based additive (hexamethyldisilane (HMDS)) to simultaneously enhance structural robustness and hydrophobicity. Coatings were synthesized using a renewable soybean oil polyol (SOP), glycerol (5, 10, 15 and 20 wt.%), and methylene diphenyl diisocyanate (MDI), followed by the addition of HMDS (10, 20, 30, 40 and 50 wt.%). Mechanical tests identified 10 wt.% glycerol as the optimal content, yielding a maximum tensile strength of 47.18 MPa. Incorporating 10 wt.% HMDS into the optimized formulation greatly increased water contact angle (WCA, 95.76°) and chemical resistance with minimal loss of mechanical performance (38.19 MPa, tensile strength); higher HMDS loadings caused network disruption and reduced strength. Calorimetry and thermogravimetric analyses confirmed that the modified coatings retained high thermal stability. This synergistic crosslinker additive strategy produced a structurally robust, water-resistant bio-based coating, demonstrating a viable high-performance sustainable coating solution for industrial applications. Full article

Hot forging of Ti-6Al-4V is extensively utilized in the manufacture of orthopedic implants; however, the coupled influence of strain rate and temperature on ductile damage evolution during the forging of femoral stems remains insufficiently quantified. In this study, a finite element framework is developed to analyze and optimize the hot forging process, incorporating strain rate- and temperature-dependent plasticity, as well as the Johnson–Cook damage criterion. Mesh convergence is established, and the assumption of quasi-adiabatic conditions is substantiated via Péclet number analysis. A full factorial design is implemented by varying the ram velocity (0.1–0.5 m/s) and initial billet temperature (850–950 °C) to evaluate the forging load, stress triaxiality, equivalent plastic strain, and damage accumulation. Results indicate that process kinetics govern the mechanical response: increasing the ram velocity enhances strain-rate hardening, resulting in higher peak loads, while explicitly reducing stress triaxiality and suppressing ductile damage evolution. Conversely, temperature exhibits a secondary influence within the investigated domain. Validation of the damage criterion confirms safe operating windows, identifying low-velocity forging as a high-risk condition for localized defect formation. These findings provide practical guidelines for the strain-rate-based optimization of thermomechanical processing parameters for Ti-6Al-4V femoral stems. Full article

Future Internet applications such as intelligent transportation, immersive services, and edge-assisted artificial intelligence require latency-sensitive service provisioning at the network edge. In containerized mobile edge computing (MEC), service orchestration is not only a task-offloading problem, but also a task–container–image constrained decision problem: an offloaded task can be executed only when the required runtime container is active, and a newly activated container must be supported by a locally cached service image. This dependency couples task placement, runtime container caching, and persistent image caching under limited RAM and ROM resources. To address this challenge, this paper proposes HAM-MADDPG, a dependency-aware hierarchical action-masked multi-agent reinforcement learning algorithm for joint task offloading and image–container caching in containerized MEC networks. HAM-MADDPG decomposes the monolithic orchestration decision into three causally ordered policy layers: task offloading, runtime container caching, and persistent image caching. Each layer learns a structured subproblem conditioned on upstream realized decisions, while dynamic action masking and feasibility-aware action realization guide the learned policies toward executable decisions satisfying task–container and container–image constraints. Extensive simulations under dynamic service demands and heterogeneous edge resources show that HAM-MADDPG achieves more stable convergence than non-hierarchical reinforcement learning baselines and reduces long-term system latency by approximately 14–25% compared with representative heuristic and flat DRL baselines. Full article

Automotive roof racks are important lightweight accessories for vehicles, and their extrusion performance is affected by the coupled effects of material hot deformation behavior, die flow resistance and billet surface layer transport. In this study, Al-0.9Mg-0.6Si alloy samples were subjected to hot compression tests at 350–500 °C and strain rates of 0.01–10 s⁻¹. The corrected true stress–true strain data were used to establish and validate an Arrhenius-type constitutive model, which was then implemented in HyperXtrude to simulate the hot extrusion of an automotive roof rack profile. The hot working map showed that the main rheological instability region was located at high strain rates, and the preferred processing window was 437–500 °C and 0.01–0.6 s⁻¹. EBSD analysis showed that hot compression refined the microstructure relative to the initial average grain size of 173.147 μm, and the most uniform grain size distribution was obtained at 500 °C and 0.1 s⁻¹. The ODF results indicated strengthened {111}<121> and <110>//TD texture components after compression. The finite-element results showed that the standard deviation of outlet velocity (SDV), used here as an index of outlet flow uniformity, increased with ram speed, billet preheating temperature and die preheating temperature, but decreased with increasing container temperature. Finally, grain size and texture measurements from butt discard samples were compared with simulated surface layer flow paths, supporting the predicted difference between simple axial flow and complex recirculating flow near the die. Full article

The Successive Self-Nucleation and Annealing (SSA) technique is a thermal fractionation method that involves subjecting a polymer sample to sequential self-nucleation and annealing steps at progressively decreasing temperatures, using differential scanning calorimetry (DSC). Since its introduction in the late 1990s, SSA has been widely applied to study the molecular structure of polymers with structural irregularities, including highly branched or crosslinked polyethylenes and random copolymers. However, the use of SSA for medical-grade ultra-high-molecular-weight polyethylene (UHMWPE), a highly linear homopolymer with minimal defects, has not yet been explored. This study aims to evaluate both its applicability to biomedical UHMWPE and its ability to reveal morphological differences among commercially available formulations. Several biomedical UHMWPE formulations, including conventional, highly cross-linked, and α-tocopherol-stabilized materials, were characterized by micro-FTIR, gel fraction and cross-link density measurements and subsequently subjected to SSA thermal fractionation. The results show that ram extrusion induces entanglements that act as interruptions in the otherwise linear chain structure, thereby enabling thermal fractionation: more than 80% of the crystalline fraction of ram-extruded UHMWPE is composed of three crystal populations melting at approximately 135, 132, and 126 °C, accompanied by four additional minor fractions at progressively lower melting temperatures. Gamma irradiation followed by thermal treatments significantly modifies the fractionation behavior, leading to the formation of an additional population of high-melting crystallites as evidenced by an increase in the number of melting peaks from 7 to 8. Oxidative degradation of highly crosslinked and annealed UHMWPE increases crystallinity by approximately 11% relative to its unoxidized counterpart but reduces the ability of the material to undergo thermal fractionation, decreasing the number of melting peaks. In contrast, the addition of low concentrations of α-tocopherol does not significantly influence the fractionation behavior. These findings demonstrate that thermal fractionation of medical-grade UHMWPE is feasible and that SSA is an effective tool for detecting morphological differences among formulations. Full article

Physical damage to smartphones creates a persistent bottleneck in mobile forensic practice: once a device can no longer be accessed through its operating system, conventional logical acquisition fails, and investigators face a choice between accepting data loss and escalating to hardware-level intervention. This paper describes an integrated forensic workflow that addresses this gap by combining In-System Programming (ISP) and Chip-Off memory extraction with an AI-assisted artifact localization and prioritization layer. The workflow was evaluated on 18 physically damaged Android smartphones for which all standard acquisition paths were unavailable. Hardware extraction produced verified binary memory images from all 18 devices. A 1D-CNN localization classifier subsequently screened those images, achieving F1-score = 0.88 and ROC-AUC = 0.94 on the synthetic test partition. Prioritization of candidate windows reduced manual review volume by 78%, cut total expert review time by 63%, and shortened the time to first relevant artifact from 42 to 14 min relative to unassisted examination (indicative estimates based on three examiner sessions; no inferential statistical test was performed). The study contributes a formalized, criteria-driven decision model for selecting between ISP and Chip-Off, which are experimentally validated thermal extraction profiles for eMMC, UFS, and PoP/RAM memory. Full article