Search Results (40,170)

Background: Recent studies suggest that erythroferrone (ERFE), an iron-regulating protein whose primary role is to inhibit hepcidin synthesis, may affect glucose and lipid metabolism, and its serum concentration is reduced in obese and diabetic individuals. The aim of this study was to evaluate the association of ERFE concentration with selected cardiometabolic risk factors in apparently healthy young adults. Methods: This preliminary study consisted of 122 (63 females, 59 males) normoglycemic, non-smoking subjects aged 25–40 years. In all participants, anthropometric measurements and the following laboratory tests were performed: fasting plasma glucose, glycated hemoglobin (HbA1c) and serum iron, lipid profile, insulin, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), C-reactive protein (CRP), ERFE and hepcidin. Results: The serum ERFE concentration was significantly lower in men compared to women (p = 0.009) and in subjects who were overweight (p < 0.001) and had abdominal obesity (p < 0.001). ERFE showed significant negative correlations with body mass index, waist–hip ratio, HbA1c, CRP, insulin, HOMA-IR and triglycerides. In the logistic regression analysis, ERFE was significantly associated with being overweight (OR = 0.051; p = 0.004), abdominal obesity (OR = 0.372; p < 0.001), HOMA-IR ≥2.0 (OR = 0.584; p = 0.013), CRP >1 mg/L (OR = 0.648; p = 0.020) and triglycerides (OR = 0.521; p = 0.033). A relevant predominance in the prevalence of cardiometabolic risk factors was observed in subjects with ERFE levels in the first tertile (<1.35 ng/mL), compared to the third tertile (>2.19 ng/mL). Conclusions: Serum ERFE is inversely associated with being overweight, increased waist circumference, CRP, and markers of insulin resistance and lipid abnormalities, suggesting its potential relevance as a marker of early cardiometabolic risk in apparently healthy young adults. Full article

Facing the diversity of the market, the industry has to move towards Industry 4.0, and smart manufacturing based on cyber-physical systems is the only way to move towards Industry 4.0. However, there are two key concepts in Industry 4.0: cyber-physical systems (CPSs) and digital twins (DTs). In the paper, we propose a smart manufacturing system suitable for stamping press processes based on the CPS concept and use DT to establish a manufacturing-end robot guidance generation model. In the smart manufacturing system of stamping press processes, fog nodes are used to connect three major architectures, including device health diagnosis, manufacturing device, and material traceability. In addition, a special hook end point is designed, and its lightweight visual guidance generation model is established to improve the production efficiency of the manufacturing end in product manufacturing. Full article

The marine environment emerges as a key provider of food and sustainable products. However, these benefits are accompanied by numerous challenges owing to harmful algal blooms (HAB) and their associated biotoxins, which accumulate in organisms, like bivalves, threatening seafood quality. Among the various biotoxins, paralytic shellfish toxins (PST), the causative agents of paralytic shellfish poisoning (PSP), are among the most potent, lethal, and frequently reported instances of human intoxication. Removing PST from marine system is particularly challenging because of their hydrophilicity, susceptibility to biotransformation and the potential influence of other substances naturally present in the environment. Although there are several methods applied to mitigate HAB, to the best of our knowledge there are no proven effective methods for removing PST in marine environments. Consequently, there is a need to develop efficient removal technologies, especially envisaging fast, environmentally safe, inexpensive, and readily available solutions. Having examined several proposed methods for removing PST (e.g., thermal and industrial procedures, adsorption using different materials, photodegradation, AOPs) and comparing their efficacy, this study aims to streamline the current knowledge on PST removal, identify knowledge gaps, and provide valuable insights for researchers, environmental managers, and policymakers engaged in mitigating the risks associated with PST. Full article

To address the challenge of feature extraction and reconstruction in weak-texture environments, and to provide data support for environmental perception in mobile robots operating in such environments, a Feature Extraction and Reconstruction in Weak-Texture Environments solution is proposed. The solution enhances environmental features through laser-assisted marking and employs a two-step feature extraction strategy in conjunction with binocular vision. First, an improved SURF algorithm for feature point fast localization method (FLM) based on multi-constraints is proposed to quickly locate the initial positions of feature points. Then, the robust correction method (RCM) for feature points based on light strip grayscale consistency is proposed to calibrate and obtain the precise positions of the feature points. Finally, a sparse 3D (three-dimensional) point cloud is generated through feature matching and reconstruction. At a working distance of 1 m, the spatial modeling achieves an accuracy of ±0.5 mm, a relative error of 2‰, and an effective extraction rate exceeding 97%. While ensuring both efficiency and accuracy, the solution demonstrates strong robustness against interference. It effectively supports robots in performing tasks such as precise positioning, object grasping, and posture adjustment in dynamic, weak-texture environments. Full article

Background/Objectives: Ketogenic diets (KDs), defined by very low carbohydrate and high fat content, are widely studied for obesity and metabolic disease. However, KD formulations vary from 60–95% fat, leading to inconsistent induction of ketogenesis and variable outcomes. The fat threshold required for sustained ketosis, and the tissue-specific programs that mediate KD efficacy, remain unclear. Methods: We evaluated multiple KD formulations (80–95% fat) in C57BL/6J wild-type (WT) and diet-induced obese (DIO) mice. Plasma, hepatic, and intestinal β-hydroxybutyrate (BHB) were measured together with expression of ketogenesis and fatty acid oxidation genes. Body weight, adipose distribution, and liver morphology were assessed under both direct feeding and therapeutic settings. Results: In WT mice, only diets exceeding 85% fat induced robust ketogenesis, reflected by elevated BHB and hepatic upregulation of Cd36, Cpt1a, Acat1, and Hmgcs2. Moderate KDs (80–85%) failed to trigger ketosis and resembled high-fat feeding. In obese mice, an 80% KD lowered fasting glucose without reducing body weight, whereas a 90% KD promoted systemic ketosis, weight loss, and adipose reduction. Interestingly, hepatic transcriptional programs for fatty acid oxidation and ketogenesis were suppressed under 90% KD despite elevated BHB, suggesting reliance on substrate availability and peripheral utilization. In contrast, intestinal Hmgcs2 was strongly induced in both WT and DIO mice, with Oxct1 upregulated only in obesity, indicating local ketone production and consumption. Conclusions: These findings identify > 85% dietary fat as a threshold for sustained ketosis and highlight distinct liver–intestine contributions, underscoring ketogenesis as the central driver of KD’s anti-obesity benefits. Full article

Disassembly is a crucial step in the remanufacturing of end-of-life (EoL) electronic products. Disassembly depth refers to the disassembly stop point determined by the disassembly sequence. For the disassembly depth optimization of EoL electronic products, a feasibility model with a fast convergence and low mean squared error (MSE) is needed to improve optimization accuracy. However, the use of a backpropagation neural network (BPNN) model or mathematical model often results in a slow convergence and high MSE due to the randomness of the initial weights and biases. In this study, an improved method for the disassembly depth optimization of smartphones based on a Particle Swarm Optimization-BPNN (PSO-BPNN) predictive model is proposed. Compared with the traditional BPNN optimization method, the proposed method in this study is that the BPNN predictive model is optimized by using PSO, which shows a superior predictive performance and reduces the MSE. The case of ‘Huawei P7’ is used to verify the feasibility of the method. The results show that the method maintains disassembly profit while reducing the disassembly time and carbon emissions by 17.1% and 7.8%, respectively. Compared with the BPNN model, the PSO-BPNN model converges 18.6%, 32.8%, and 16.6% faster in predicting the disassembly time, profit, and carbon emissions, respectively, with MSE reductions of 92.95%, 96.51%, and 92.74%, respectively. Full article

Differential tracked robots play a crucial role in various modernized work scenarios such as smart industry, agriculture, and transportation. However, these robots frequently encounter substantial challenges in trajectory tracking, attributable to substantial initial errors and dynamic environments, which result in slow convergence rates, cumulative errors, and diminished tracking precision. To address these challenges, this paper proposes a fuzzy adaptive virtual steering coefficient model predictive control (FVSMPC) algorithm. The FVSMPC algorithm introduces a virtual steering coefficient into the robot’s kinematic model, which is adaptively adjusted using fuzzy logic based on real-time positional error and velocity. This approach not only enhances the robot’s ability to quickly correct large errors but also maintains stability during tracking.The nonlinear kinematic model undergoes linearization via a Taylor expansion and is subsequently formulated as a quadratic programming problem to facilitate efficient iterative solutions. To validate the proposed control algorithm, a simulation environment was constructed and deployed on a real prototype for testing. Results demonstrate that compared to the baseline algorithm, the proposed algorithm performs excellently in trajectory tracking tasks, avoids complex parameter tuning, and exhibits high accuracy, fast convergence, and good stability. This work provides a practical and effective solution for improving the trajectory tracking performance of differential tracked robots in complex environments. Full article

Category management (CM) is crucial for optimising retailer–supplier partnerships via technological integration and data sharing. However, the role of CM in Saudi Arabia’s unique fast-moving consumer goods sector (FMCG) remains underexplored. This study aimed to answer the following research question: How do cloud-based inventory platforms and real-time data sharing improve forecasting accuracy and inventory turnover for retailer–supplier CM partnerships in Saudi Arabia’s FMCG sector? A systematic review of 87 studies from the Web of Science and Scopus databases was conducted, followed by thematic analysis. The findings indicate that CM improves demand forecasting, inventory optimisation, and collaborative decision-making. Key implementation barriers include cultural resistance to data sharing, high technology costs for small and medium-sized enterprises, and infrastructural limitations. Success relies on phased technology adoption, relational data governance, and trust building that aligns with Saudi cultural norms. The study concludes that CM is essential for leveraging technology and data capabilities, and it offers a contextualised framework to overcome local barriers and support the achievement of Vision 2030 objectives. This study provides practical strategies for sector stakeholders to adopt high-impact, low-cost technology and a basis for future comparative studies in Gulf Cooperation Council markets. Full article

Background: Isorhamnetin (ISO), a dietary O-methylated flavonol, was evaluated for hypoglycemic activity and mechanism in a streptozotocin (STZ) model of type 1 diabetes. Methods: We conducted untargeted plasma metabolomics (ESI±), network integration and docking, and measured pancreatic PI3K, phosphorylated AKT, and COX-2; INS-1 β cells challenged with the PI3K inhibitor LY294002 were used to assess viability, intracellular ROS, and PI3K phosphorylation. Results: ISO lowered fasting glycemia, increased circulating insulin, improved dyslipidemia by reducing low-density lipoprotein cholesterol (LDL-C), and preserved islet architecture. Untargeted plasma metabolomics (ESI±) indicated broad remodeling with enrichment of arachidonic-, linoleic-, starch/sucrose- and glycerophospholipid pathways. Network integration and docking prioritized targets converging on PI3K/AKT and COX-2/eicosanoid signaling. Consistently, in pancreatic tissue, ISO increased PI3K, phosphorylated AKT, and reduced COX-2. In INS-1 beta cells challenged with the PI3K inhibitor LY294002, ISO improved viability, decreased intracellular ROS, and partially restored PI3K phosphorylation at 4 µM. Conclusions: Together, these data indicate that ISO exerts hypoglycemic effects while supporting β-cell integrity through activation of PI3K/AKT and tempering of COX-2–linked lipid-mediator pathways. ISO therefore emerges as a food-derived adjunct candidate for autoimmune diabetes, and the work motivates targeted lipidomics and in vivo pathway interrogation in future studies. Full article

Accurate recognition of basic facial emotions is well documented, yet the mechanisms of misclassification and their relation to gaze allocation remain under-reported. The present study utilized a within-subjects eye-tracking design to examine both accurate and inaccurate recognition of five basic emotions (anger, disgust, fear, happiness, and sadness) in healthy young adults. Fifty participants (twenty-four women) completed a forced-choice categorization task with 10 stimuli (female/male poser × emotion). A remote eye tracker (60 Hz) recorded fixations mapped to eyes, nose, and mouth areas of interest (AOIs). The analyses combined accuracy and decision-time statistics with heatmap comparisons of misclassified versus accurate trials within the same image. Overall accuracy was 87.8% (439/500). Misclassification patterns depended on the target emotion, but not on participant gender. Fear male was most often misclassified (typically as disgust), and sadness female was frequently labeled as fear or disgust; disgust was the most incorrectly attributed response. For accurate trials, decision time showed main effects of emotion (p < 0.001) and participant gender (p = 0.033): happiness was categorized fastest and anger slowest, and women responded faster overall, with particularly fast response times for sadness. The AOI results revealed strong main effects and an AOI × emotion interaction (p < 0.001): eyes received the most fixations, but fear drew relatively more mouth sampling and sadness more nose sampling. Crucially, heatmaps showed an upper-face bias (eye AOI) in inaccurate trials, whereas accurate trials retained eye sampling and added nose and mouth AOI coverage, which aligned with diagnostic cues. These findings indicate that the scanpath strategy, in addition to information availability, underpins success and failure in basic-emotion recognition, with implications for theory, targeted training, and affective technologies. Full article

Battery electric vehicles (BEVs) play a key role in reducing CO₂ emissions and enabling a climate-neutral economy. However, they suffer from reduced range in cold conditions due to electric cabin heating. Electrically heated thermal energy storage (TES) systems can decouple heat generation from demand, thereby preventing a loss of range. For this purpose, a novel concept based on a directly electrically heated ceramic solid media TES is investigated, aiming to achieve high storage density while enabling both high charging and discharging powers. To assess the feasibility of the proposed TES concept in BEVs, a holistic evaluation of central aspects is conducted, including experimental characterization for material selection, experimental investigations on electrical contacting, and simulations of the electrothermal charging and thermal discharging processes under vehicle-relevant conditions. As a result of the material characterization, a promising material—a silicon carbide-based composite—was identified, which meets the electrothermal requirements under typical household charging conditions and allows reliable operation with silver-metallized electrodes. Design studies with this material show gravimetric energy densities—including thermal insulation demand—exceeding 100 Wh/kg, storage utilization of up to 90%, and fast charging within 25 min, while offering 5 kW at flexible temperature levels for cabin heating during thermal discharging. These results show that the basic prerequisites for such storage systems are met, while further development—particularly in terms of material improvements—remains necessary. Full article

Greater Casablanca, one of Africa’s largest metropolitan regions, is undergoing significant spatial and demographic transformation. Yet, the underlying patterns of these dynamics remain poorly understood. This study investigates population dynamics and spatial inequalities in Greater Casablanca between 2014 and 2024. The analysis combines geospatial data, regression modeling, and clustering techniques to explore the interplay between demographic change, housing affordability, public-transport accessibility, and economic activity, providing a data-driven perspective on how these factors shape spatial inequalities and the region’s urban development trajectory. The results reveal a clear core–periphery divide. The central prefecture has lost population despite continued land consumption, while peripheral communes have experienced rapid demographic and economic expansion. This growth is strongly associated with affordable housing and high rates of new-firm formation, but it occurs where transport access remains weakest. Cluster analysis identifies four socio-spatial types, ranging from a shrinking but well-served core to fast-growing, poorly connected peripheries. The study underscores the need for integrated policy interventions to improve transport connectivity, implement inclusive housing strategies, and manage economic decentralization in ways that foster balanced and sustainable metropolitan development. By situating Greater Casablanca’s trajectory within global urbanization debates, this research extends core–periphery and shrinking-city frameworks to a North African context and provides evidence-based insights to support progress towards Sustainable Development Goal 11. Full article

This article examines the global efforts to govern and regulate Artificial Intelligence (AI) in response to its rapid development and growing influence across many parts of society. It explores how governance takes place at multiple levels, including international bodies, national governments, industries, companies, and communities. The study draws on a wide range of official documents, policy reports, and international agreements to build a timeline of key regulatory and standardization milestones. It also analyzes the challenges of coordinating across different legal systems, economic priorities, and cultural views. The findings show that while some progress has been made through soft-law frameworks and regional partnerships, deep divisions remain. These include unclear responsibilities, uneven enforcement, and risks of regulatory gaps. The article argues that effective AI governance requires stronger international cooperation, fair and inclusive participation, and awareness of power imbalances that shape policy decisions. Competing global and commercial interests can create obstacles to building systems that prioritize the public good. The conclusion highlights that future governance models must be flexible enough to adapt to fast-changing technologies, yet consistent enough to protect rights and promote trust. Addressing these tensions is critical for building a more just and accountable future of AI. Full article

Automotive manufacturing quality control faces persistent challenges such as limited defect samples, cross-domain variability, and the demand for interpretable decision-making. This work presents an explainable few-shot anomaly detection framework that integrates EfficientNet-based feature extraction, adaptive prototype learning, and component-specific attention mechanisms to address these requirements. The system is designed for rapid adaptation to novel defect types while maintaining interpretability through a multi-modal explainable AI module that combines visual, quantitative, and textual outputs. Evaluation on automotive datasets demonstrates promising performance on evaluated automotive components, achieving 99.4% accuracy for engine wiring inspection and 98.8% for gear inspection, with improvements of 5.2–7.6% over state-of-the-art baselines, including traditional unsupervised methods (PaDiM, PatchCore), advanced approaches (FastFlow, CFA, DRAEM), and few-shot supervised methods (ProtoNet, MatchingNet, RelationNet, FEAT), and with only 0.63% cross-domain degradation between wiring and gear inspection tasks. The architecture operates under real-time industrial constraints, with an average inference time of 18.2 ms, throughput of 60 components per minute, and memory usage below 2 GB on RTX 3080 hardware. Ablation studies confirm the importance of prototype learning (−4.52%), component analyzers (−2.79%), and attention mechanisms (−2.21%), with K = 5 few-shot configuration providing the best trade-off between accuracy and adaptability. Beyond performance, the framework produces interpretable defect localization, root-cause analysis, and severity-based recommendations designed for manufacturing integration with execution systems via standardized industrial protocols. These results demonstrate a practical and scalable approach for intelligent quality control, enabling robust, interpretable, and adaptive inspection within the evaluated automotive components. Full article

Background/Objectives: To delineate integrated lifestyle profiles combining physical activity (PA) and dietary intake (DI) and test their links with lower limb injury in physically active young adults. Methods: We analyzed a cross-sectional convenience sample of university students (men: n = 91, 20.5 ± 1.0 years; women: n = 118, 20.3 ± 0.8 years). PA (IPAQ) and DI (QEB) were assessed alongside self-reported injuries. Latent class modeling derived PA–DI profiles. Injury prevalence across profiles was compared (χ²), and logistic regression examined injury odds adjusting for sex, age, and BMI. Results: Four profiles emerged. Two reflected less healthy patterns (Profiles 2–3) and two healthier ones (Profiles 1, 4). Profile 4 showed higher vegetables/legumes/fermented milk and lower fast food/sugary drinks; Profile 3 combined greater sitting and fried/sweetened items with lower walking/milk intake. Overall injury prevalence was 56.9%, ranging from 44.1% (Profile 2) to 66.7% (Profile 4 exceeded Profile 2 in pairwise comparison (χ² (1) = 5.08, p = 0.024)). In adjusted models, men had higher injury odds (OR = 1.94, 95% CI: 1.09–3.48, p = 0.025); profile membership was not independently predictive, and profile × sex interactions were null. Conclusions: Young adults cluster into distinct PA–DI patterns that differ behaviorally, but sex—rather than profile—was the most consistent correlate of injury. Prevention should integrate lifestyle screening with sex-specific strategies. Full article