Search Results (1,659)

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

Background/Objectives: Feigning spectrum behavior (FSB) is the exaggeration, fabrication, or false imputation of symptoms. It occurs in compensable injury with great cost to society by way of loss of productivity and excessive costs. The aim of this study is to identify feigning by developing cut scores on the long and short forms (SF) of the Orebro Musculoskeletal Pain Screening Questionnaire (OMPSQ and OMPSQ-SF) and the Perceived Stress Scale (PSS and PSS-4). Methods: As part of pre-screening for a support program, 40 injured workers who had been certified unfit for work for more than 2 weeks were screened once with the OMPSQ and PSS by telephone by a mental health professional. A control sample comprised of 40 non-injured community members were screened by a mental health professional on four occasions under different aliases, twice responding genuinely and twice simulating an injury. Results: Differences between the workplace injured people and the community sample were compared using ANCOVA with age and gender as covariates, and then receiver operator characteristics (ROCs) were calculated. The OMPSQ and OMPSQ-SF discriminated (ρ < 0.001) between all conditions. All measures discriminated between the simulation condition and workplace injured people (ρ < 0.001). Intraclass correlation demonstrated the PSS, PSS-4, OMPSQ, and OMPSQ-SF were reliable (ρ < 0.001). Area Under the Curve (AUC) was 0.750 for OMPSQ and 0.835 for OMPSQ-SF for work-injured versus simulators. Conclusions: The measures discriminated between injured and non-injured people and non-injured people instructed to simulate injury. Non-injured simulators produced similar scores when they had multiple exposures to the test materials, showing the uniformity of feigning spectrum behavior on these measures. The OMPSQ-SF has adequate discriminant validity and sensitivity to feigning spectrum behavior, making it optimal for telephone screening in clinical practice. 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

The precursor-derived ceramic route is recognized as an advanced and efficient technique for fabricating ceramic matrix composites, particularly suitable for the development and microstructural tailoring of continuous fiber-reinforced ceramic matrix composites. In this work, octamethylcyclotetrasiloxane and tetravinylcyclotetrasiloxane were employed as monomers to synthesize a branched siloxane via ring-opening polymerization. A subsequent hydrosilylation reaction led to the formation of polyvinylsiloxane with a three-dimensional crosslinked structure. The precursor exhibited excellent fluidity, adjustable viscosity, and superior thermosetting characteristics, enabling efficient impregnation and densification of reinforcements through the polymer infiltration and pyrolysis process. Upon pyrolysis, the polyvinylsiloxane gradually converted from an organic polymer to an amorphous inorganic ceramic phase, yielding silicon oxycarbide ceramics with a high ceramic yield of 81.3%. Elemental analysis indicated that the resulting ceramic mainly comprised silicon and oxygen, with a low carbon content. Furthermore, the material demonstrated a stable dielectric constant (~2.5) and low dielectric loss (<0.01), which are beneficial for enhanced thermal stability and dielectric performance. These findings offer a promising precursor system and process reference for the low-cost production of high-performance, multifunctional ceramic matrix composites with strong potential for engineering applications. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. 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

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

In recent decades, the rapid expansion of the food processing industry has led to significant losses and waste, with the fruit and vegetable sector among the most affected. According to the Food and Agriculture Organization of the United Nations (FAO), losses in this category can reach up to 60%. Vegetable waste includes edible parts discarded during processing, packaging, distribution, and consumption, often comprising by-products rich in bioactive compounds such as polyphenols, carotenoids, dietary fibers, vitamins, and enzymes. The underutilization of these resources constitutes both an economic drawback and an environmental and ethical concern. Current recovery practices, including their use in animal feed or bioenergy production, contribute to a circular economy but are often limited by high operational costs. In this context, fermentation has emerged as a promising, sustainable approach for converting vegetable by-products into value-added food ingredients. This process improves digestibility, reduces undesirable compounds, and introduces probiotics beneficial to human health. The present review examines how fermentation can improve the nutritional, sensory, and functional properties of plant-based foods. By presenting several case studies, it illustrates how fermentation can effectively valorize vegetable processing by-products, supporting the development of novel, health-promoting food products with improved technological qualities. Full article

Natural polymer materials are increasingly utilized in drilling fluid additives. Starch has come to be applied extensively due to its low cost and favorable fluid loss reduction properties. However, its poor temperature resistance and high viscosity limit its application in high-temperature wells. This study innovatively introduces for the first time diatomite as an inorganic material in the modification process of starch-based fluid loss additives. Through synergistic modification with acrylamide and acrylic acid, we successfully resolved the longstanding challenge of balancing temperature resistance with viscosity control in existing modification methods. The newly developed fluid loss additive demonstrates remarkable performance: It remains effective at 160 °C when used independently. When added to a 4% sodium bentonite base mud, it achieves an 80% fluid loss reduction rate—significantly higher than the 18.95% observed in conventional starch-based products. The resultant filter cake exhibits thin and compact characteristics. Moreover, this additive shows superior contamination resistance, tolerating 30% NaCl and 0.6% calcium contamination, outperforming other starch-based treatments. With starch content exceeding 75%, the product not only demonstrates enhanced performance but also achieves significant cost reduction compared to conventional starch products (typically containing < 50% starch content). Full article

Extensive beef systems in the tropics are the cheapest but require more land and longer rearing times with environmental impact. This study was carried out to evaluate three beef bull’s production systems in tropics: pasture-based system (PASTU), feedlot system immediately after weaning (FELOT) and a system with the combination of rearing in pasture and finishing in feedlot (PRIME) on animal performance and carcass characteristics of 30 bulls crossbred Angus x Nellore. The final weight, average daily gain and carcass weight (hot and cold) were higher (p < 0.050) for the FELOT system, intermediate for the PRIME system and lowest for the PASTU system. The carcass dressing (hot and cold), dripping losses, ratio (Longissimus dorsi) and degree of finishing were similar (p > 0.050). The carcass pH_24h was higher for the PRIME system (p < 0.010). Subcutaneous fat thickness (mm) was lower for the PASTU system (p < 0.050). Marbling was better for the PRIME system. The tissular composition was similar among systems related to muscle percentage but PASTU showed the highest bone percentage (p < 0.050) and lowest of adipose (p < 0.050). PRIME enable cost-effective, fast beef production with less environmental impact. Full article

Background/Objectives: Porcine circovirus type 2 (PCV2) infection causes porcine circovirus disease (PCVD), a global immunosuppressive disease in pigs. Its clinical manifestations include post-weaning multisystemic wasting syndrome (PMWS) and porcine dermatitis and nephropathy syndrome (PDNS), which cause significant economic losses to the swine industry. The Cap protein, which is the major protective antigen of PCV2, can self-assemble to form virus-like particles (VLPs) in the insect baculovirus expression system. Few studies have compared the expression of Cap proteins in different baculovirus expression systems. Methods: In this study, we compared two commonly commercialized baculovirus construction systems with the Cap protein expression in various insect cells. Results: The results demonstrate that the flashBAC system expressed the Cap protein at higher levels than the Bac-to-Bac system. Notably, when expressing four copies of the Cap protein, the flashBAC system achieved the highest protein yield in High Five cells, where it reached 432 μg/mL at 5 days post-infection (dpi) with 27 °C cultivation. Animal experiments confirmed that the purified Cap protein effectively induced specific antibody production in mice and swine. Conclusions: This study provides critical data for optimizing the production of the PCV2 Cap protein, which is of great significance for reducing the production cost of PCV2 vaccines and improving the industrial production efficiency. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems—costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance. Full article

The impact of five different nets—conventional black, grey, white, and photoselective red and yellow—on the performance of “Gala Redlum” apples was evaluated over a five-year period (2020–2024) and compared to an uncovered control. The cumulative production over this period, ranked from highest to lowest, was as follows: white net (182.4 t/ha), grey net (178.5 t/ha), yellow net (175.8 t/ha), black net (175.5 t/ha), red net (169.5 t/ha), and uncovered control (138.8 t/ha). Vegetative growth results were inconsistent among the studied years. The cumulative photosynthetic rate (An) was slightly higher under the white net (57.9 µmol m⁻² s⁻¹). Fv/Fm values remained closest to optimal levels under the black and grey nets. Netting effectively protected fruits from elevated temperatures, particularly under the grey net, and reduced sunburn damage, with the grey, black, and yellow nets performing best in this regard. Overall profitability was increased by netting: the black net provided the highest cumulative income per hectare over a five-year period (EUR 72,315) alongside the second-lowest sunburn loss (0.69%), while the yellow net also showed strong economic performance (€64,742) with a moderate sunburn loss (1.26%) compared to the red net. Fruit dry matter and soluble solids content (SSC) were generally higher in the uncovered control, whereas °Hue values tended to be higher under the red and yellow nets. In summary, the black and yellow nets provided more balanced microclimatic conditions that enhanced tree performance, particularly under heat stress, leading to improved yield and profitability. However, the economic feasibility of each net type should be evaluated in relation to its installation and maintenance costs. Full article

In petroleum exploration and production, lost circulation not only significantly increases exploration and development costs and operational cycles but may also lead to major incidents such as wellbore instability or even project abandonment. This paper constructs a polymer gel plugging system by optimizing high-molecular-weight polymers, crosslinker systems, and resin hardeners. The optimized system composition was determined as 1% polymer J-1, 0.3% catechol, 0.6% hexamethylenetetramine (HMTA), and 15% urea–formaldehyde resin. Experimental studies demonstrated that during the initial stage (0–3 days) at 120 °C, the optimized gel system maintained a storage modulus (G′) of 17.5 Pa and a loss modulus (G″) of 4.3 Pa. When the aging period was extended to 9 days, G′ and G″ decreased to 16 Pa and 4 Pa, respectively. The insignificant reduction in gel strength indicates excellent thermal stability of the gel system. The gel exhibited superior self-filling capacity during migration, enabling complete filling of fractures of varying sizes. After aging for 1 day at 120 °C, the plugging capacity of the gel system under water flooding and gas flooding conditions was 166 kPa/m and 122 kPa/m, respectively. Furthermore, a complete gel barrier layer formed within a 6 mm wide vertical fracture, demonstrating a pressure-bearing capacity of 105.6 kPa. This system shows good effectiveness for wellbore isolation and fracture plugging. The polymer gel plugging system studied in this paper can simplify lost circulation treatment procedures while enhancing plugging strength, providing theoretical support and technical solutions for addressing lost circulation challenges. Full article