Search Results (6,522)

The present study evaluated the effect of adding 0% (control), 30%, 40% and 50% SPMs (small-peptide chelating trace minerals) to replace ITMs (inorganic trace minerals) in the diets of Litopenaeus vannamei; 720 shrimp were randomly assigned to four treatments (six replicates per group, 30 shrimp per replicate) in a 42-day feeding trial. There were no significant differences (p > 0.05) among the control, 40% SPM and 50% SPM groups in terms of the survival rate, weight gain rate, specific growth rate, hepatosomatic index, condition factor, feed intake, feed conversion ratio, or protein efficiency ratio; however, protein efficiency ratio was reduced in the 30% SPM group (p < 0.05). Glucose, triglyceride, and aspartate aminotransferase levels in the hemolymph of the 30% SPM group were significantly increased (p < 0.05), while the glucose and aspartate aminotransferase levels were also significantly increased in the 40% SPM group (p < 0.05). In the 50% SPM group, the glucose and triglyceride levels were also significantly increased (p < 0.05). Hepatopancreatic alkaline phosphatase activity was elevated at 40% SPM, and alkaline phosphatase, acid phosphatase, glutathione peroxidase, and total antioxidant capacity activities were significantly increased in the 50% SPM group (p < 0.05). The moisture content and drip loss were reduced in both the 40% and 50% SPM groups (p < 0.05). Therefore, replacing 40–50% ITMs with SPMs can maintain growth performance while enhancing physiological functions. In conclusion, the results of this study demonstrate that the incorporation of 30–50% SPMs into one’s diet constitutes a viable alternative to 100% ITMs. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed under three sowing dates—1 May (Early), 15 June (Mid), and 1 August (Late)—within the recommended sowing window for the region. In a parallel growth chamber study, the plants were exposed to two nighttime temperature regimes, of 15 °C (normal) and 20 °C (high), with consistent daytime conditions from booting to maturity. Late sowing resulted in shortened vegetative growth and grain filling periods and increased exposure to HNT during the reproductive phase. This resulted in elevated floret sterility, lower grain weight, and up to 40% yield loss. AVT#6 exhibited greater sensitivity to HNT despite maturing earlier. Leaf gas exchange analysis revealed increased nighttime respiration (Rn) and reduced assimilation (A), resulting in higher Rn/A ratio for late-sown crops. The results from controlled environment chambers resembled trends of the field experiment, producing lower grain yield and biomass under HNT. Cumulative nighttime hours above 20 °C correlated more strongly with yield losses than daytime heat. These findings highlight the need for HNT-tolerant genotypes and optimized sowing schedules under future climate scenarios. Full article

This study investigated the effects of reducing organic trace minerals below commercial inclusion levels and compared them with both low-dose and commercial levels of inorganic trace minerals, focusing on growth performance, carcass traits, tibia characteristics, oxidative stress (superoxide dismutase [SOD] and malondialdehyde [MDA]), and immune response (serum IgG) in broilers. A total of 384 one-day-old Ross 308 chicks were randomly assigned to three dietary treatments: (1) commercial-level inorganic trace minerals (ILI; Zn 100 ppm; Cu 15 ppm; Fe 100 ppm; Mn 80 ppm; Se 0.2 ppm; I 3 ppm); (2) low-level organic trace minerals (LLO; Zn 30 ppm; Cu 4 ppm; Fe 11 ppm; Mn 30 ppm; Se 0.225 ppm; I 3 ppm), and (3) low-level inorganic trace minerals (LLI; Zn 30 ppm; Cu 4 ppm; Fe 11 ppm; Mn 30 ppm; Se 0.2 ppm; I 3 ppm). Each treatment consisted of eight replicates with 16 birds per replicate, and diets were provided in two phases: starter (days 1–21) and grower (days 22–35). The results showed that the LLO group demonstrated a significantly improved feed conversion ratio (FCR) during the starter phase, 2.4% better than that of the ILI and LLI groups (p = 0.02). Additionally, filet and thigh muscle yields in the LLO group were higher by 11.9% (p = 0.03) and 13.9% (p = 0.02), respectively, compared to the ILI group. Other carcass traits, as well as pH and drip loss, were not significantly affected. However, tibia breaking strength at day 35 was 15.1% lower in the LLO group compared to the ILI group (p = 0.02). No significant differences were observed in oxidative stress markers or IgG levels among groups. This study demonstrated that reducing the inclusion level of inorganic trace minerals did not negatively affect broiler growth performance, whereas supplementation with low levels of organic trace minerals improved both growth performance and carcass quality. Full article

This study optimizes the production of activated carbons from hydrothermally carbonized (HTC) biomass using potassium hydroxide (KOH) and phosphoric acid (H₃PO₄) as activating agents. A 2³ factorial experimental design evaluated the effects of agent-to-precursor ratio, dry impregnation time, and activation duration on mass yield and iodine adsorption capacity. KOH-activated carbons achieved superior iodine numbers (up to 1289 mg/g) but lower mass yields (18–35%), reflecting enhanced porosity at the cost of material loss. Conversely, H₃PO₄ activation yielded higher mass retention (up to 54.86%) with moderate iodine numbers (up to 1117.3 mg/g), balancing porosity and yield. HTC pretreatment at 190 °C reduced the ash content, thereby enhancing the stability of hydrochar. These findings highlight the trade-offs between adsorption performance and process efficiency, with KOH suited for high-porosity applications (e.g., water purification) and H₃PO₄ for industrial scalability. The study advances biomass waste valorization, aligning with circular economy principles and offering sustainable solutions for environmental and industrial applications, such as water purification and energy storage. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

This study proposes an innovative Thermodynamic Activity Controlled Homogeneous Charge Compression Ignition (TAC-HCCI) strategy for diesel–natural gas dual-fuel engines, aiming to achieve high thermal efficiency while maintaining low emissions. By employing numerical simulation methods, the effects of the intake pressure, intake temperature, EGR rate, intake valve closing timing, diesel injection timing, diesel injection pressure, and diesel injection quantity on engine combustion, energy distribution, and emission characteristics were systematically investigated. Through a comprehensive analysis of optimized operating conditions, a high-efficiency and low-emission TAC-HCCI combustion technology for dual-fuel engines was developed. The core mechanism of TAC-HCCI combustion control was elucidated through an analysis of the equivalence ratio and temperature distribution of the in-cylinder mixture. The results indicate that under the constraints of PCP ≤ 30 ± 1 MPa and RI ≤ 5 ± 0.5 MW/m², the TAC-HCCI technology achieves a gross indicated mean effective pressure (IMEPg) of 24.0 bar, a gross indicated thermal efficiency (ITEg) of up to 52.0%, and indicated specific NOx emissions (ISNOx) as low as 1.0 g/kW∙h. To achieve low combustion loss, reduced heat transfer loss, and high thermal efficiency, it is essential to ensure the complete combustion of the mixture while maintaining low combustion temperatures. Moreover, a reduced diesel injection quantity combined with a high injection pressure can effectively suppress NOx emissions. Full article

Recurrent pregnancy loss (RPL) is defined as the occurrence of two or more pregnancy losses before 20 weeks of gestation. RPL is a common medical condition among reproductive-age women, with approximately 23 million cases reported annually worldwide. Up to 5% of pregnant women may experience two or more consecutive pregnancy losses. Previous studies have investigated risk factors for RPL, including maternal age, uterine pathology, genetic anomalies, infectious agents, endocrine disorders, thrombophilia, and immune dysfunction. However, RPL is a disease caused by a complex interaction of genetic factors, environmental factors (e.g., diet, lifestyle, and stress), epigenetic factors, and the immune system. In addition, due to the lack of research on genetics research related to RPL, the etiology remains unclear in up to 50% of cases. Platelets play a critical role in pregnancy maintenance. This study examined the associations of platelet receptor and ligand gene variants, including integrin subunit beta 3 (ITGB3) rs2317676 A > G, rs3809865 A > T; fibrinogen gamma chain (FGG) rs1049636 T > C, rs2066865 T > C; glycoprotein 1b subunit alpha (GP1BA) rs2243093 T > C, rs6065 C > T; platelet endothelial cell adhesion molecule 1 (PECAM1) rs2812 C > T; and platelet endothelial aggregation receptor 1 (PEAR1) rs822442 C > A, rs12137505 G > A, with RPL prevalence. In total, 389 RPL patients and 375 healthy controls (all Korean women) were enrolled. Genotyping of each single nucleotide polymorphism was performed using polymerase chain reaction–restriction fragment length polymorphism and the TaqMan genotyping assay. All samples were collected with approval from the Institutional Review Board at Bundang CHA Medical Center. The ITGB3 rs3809865 A > T genotype was strongly associated with RPL prevalence (pregnancy loss [PL] ≥ 2: adjusted odds ratio [AOR] = 2.505, 95% confidence interval [CI] = 1.262–4.969, p = 0.009; PL ≥ 3: AOR = 3.255, 95% CI = 1.551–6.830, p = 0.002; PL ≥ 4: AOR = 3.613, 95% CI = 1.403–9.307, p = 0.008). The FGG rs1049636 T > C polymorphism was associated with a decreased risk in women who had three or more pregnancy losses (PL ≥ 3: AOR = 0.673, 95% CI = 0.460–0.987, p = 0.043; PL ≥ 4: AOR = 0.556, 95% CI = 0.310–0.997, p = 0.049). These findings indicate significant associations of the ITGB3 rs3809865 A > T and FGG rs1049636 T > C polymorphisms with RPL, suggesting that platelet function influences RPL in Korean women. Full article

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

Background/Objectives: Senecio scandens Buch.-Ham. is a flavonoid-rich traditional medicinal plant with established immunomodulatory properties. However, the mechanisms underlying the immunoregulatory and intestinal protective effects of its flavonoid extract (Senecio scandens flavonoids—SSF) remain unclear. This study characterized SSF’s bioactive components and evaluated its efficacy against cyclophosphamide (CTX)-induced immunosuppression and intestinal injury. Methods: The constituents of SSF were identified using UHPLC/Q-Orbitrap/HRMS. Mice with CTX-induced immunosuppression were treated with SSF (80, 160, 320 mg/kg) for seven days. Immune parameters (organ indices, lymphocyte proliferation, cytokine, and immunoglobulin levels) and gut barrier integrity markers (ZO-1, Occludin, Claudin-1 protein expression; sIgA secretion; microbiota composition) were assessed. Network pharmacology combined with functional assays elucidated the underlying regulatory mechanisms. Results: Twenty flavonoids were identified in SSF, with six prototype compounds detectable in the blood. The SSF treatment significantly ameliorated CTX-induced weight loss and atrophy of the thymus and spleen. It enhanced splenic T- and B-lymphocyte proliferation by 43.6% and 29.7%, respectively; normalized the CD4⁺/CD8⁺ ratio (1.57-fold increase); and elevated levels of IL-2, IL-6, IL-10, TNF-α, IFN-γ, IgM, and IgG. Moreover, SSF reinforced the intestinal barrier by upregulating tight junction protein expression and sIgA levels while modulating the gut microbiota, enriching beneficial taxa (e.g., the Lachnospiraceae_NK4A136_group, Akkermansia) and suppressing pathogenic Alistipes. Mechanistically, SSF activated the TLR/MyD88/NF-κB pathway, with isoquercitrin identified as a pivotal bioactive constituent. Conclusions: SSF effectively mitigates CTX-induced immunosuppression and intestinal damage. These findings highlight SSF’s potential as a dual-functional natural agent for immunomodulation and intestinal protection. Subsequent research should validate isoquercitrin’s molecular targets and assess SSF’s clinical efficacy. Full article

Modular multilevel converters (MMCs) are widely used in power-conversion applications, including distributed energy storage integration, because of their scalability, high efficiency, and reduced harmonic distortion. Integrating battery storage systems into MMC submodules using dual active bridge (DAB) converters provides electrical isolation and reduces voltage stress, harmonics, and common-mode issues. However, voltage fluctuations due to the battery state of charge can compromise the zero-voltage switching (ZVS) operation of a DAB and increase the reactive power circulation, leading to higher losses and reduced system performance. To address these challenges, this study investigated an active control strategy for submodule voltage regulation in an MMC with DAB-based battery integration. Assuming single-phase-shift modulation, two control strategies were evaluated. The first strategy regulated the DAB voltage on one side to match the battery voltage on the other, scaled by the high-frequency transformer turns ratio, which facilitated the ZVS operation and reduced the reactive power. The second strategy optimized this voltage to minimize the total power-conversion losses. The proposed control strategies improved the efficiency, particularly at low power levels, achieving several percentage points of improvement compared to maintaining a constant voltage. Full article

The difficulty and manual nature of procuring human evaluators for ranking the quality of images affected by various types of degradations, and of those cleaned up by developed algorithms, has lead to the widespread adoption of automated metrics, like the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM). However, disparities between rankings given by these metrics and those given by human evaluators have encouraged the development of improved image quality assessment (IQA) metrics that are a better fit for this purpose. These methods have been previously used solely for quality assessments and not as objectives in the training of neural networks for high-level vision tasks, despite the potential improvements that may come about by directly optimizing for desired metrics. This paper examines the adequacy of ten recent IQA metrics, compared with standard loss functions, within two trained dehazing neural networks, with observed broad improvement in their performance. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article