Search Results (21,477)

The rising demand for sustainable agriculture and circular resource management has intensified interest in converting wastewater sludge into value-added products. This review explores the transformation of sewage sludge into slow- and controlled-release fertilizers (CRFs), with a focus on biochar production and encapsulation technologies. Sewage sludge is rich in essential macronutrients (N, P, K), micronutrients, and organic matter, making it a promising feedstock for agricultural applications. However, its use is constrained by challenges including compositional variability, presence of heavy metals, pathogens, and emerging contaminants such as microplastics and PFAS (Per- and Polyfluoroalkyl Substances). The manuscript discusses a range of stabilization and conversion techniques, such as composting, anaerobic digestion, pyrolysis, hydrothermal carbonization, and nutrient recovery from incinerated sludge ash. Special emphasis is placed on coating and encapsulation technologies that regulate nutrient release, improve fertilizer efficiency, and reduce environmental losses. The role of natural, synthetic, and biodegradable polymers in enhancing release mechanisms is analyzed in the context of agricultural performance and soil health. While these technologies offer environmental and agronomic benefits, large-scale adoption is hindered by technical, economic, and regulatory barriers. The review highlights key challenges and outlines future perspectives, including the need for advanced coating materials, improved contaminant mitigation strategies, harmonized regulations, and field-scale validation of CRFs. Overall, the valorisation of sewage sludge into CRFs presents a viable strategy for nutrient recovery, waste minimization, and sustainable food production. With continued innovation and policy support, sludge-based fertilizers can become a critical component of the green transition in agriculture. Full article

Artificial intelligence (AI) often suffers from high energy consumption and complex deployment in resource-constrained environments, leading to a structural mismatch between capability and deployability. This review takes two representative scenarios—energy-first and performance-first—as the main thread, systematically comparing cloud, edge, and fog/cloudlet/mobile edge computing (MEC)/micro data center (MDC) architectures. Based on a standardized literature search and screening process, three categories of miniaturization strategies are distilled: redundancy compression (e.g., pruning, quantization, and distillation), knowledge transfer (e.g., distillation and parameter-efficient fine-tuning), and hardware–software co-design (e.g., neural architecture search (NAS), compiler-level, and operator-level optimization). The purposes of this review are threefold: (1) to unify the “architecture–strategy–implementation pathway” from a system-level perspective; (2) to establish technology–budget mapping with verifiable quantitative indicators; and (3) to summarize representative pathways for energy- and performance-prioritized scenarios, while highlighting current deficiencies in data disclosure and device-side validation. The findings indicate that, compared with single techniques, cross-layer combined optimization better balances accuracy, latency, and power consumption. Therefore, AI miniaturization should be regarded as a proactive method of structural reconfiguration for large-scale deployment. Future efforts should advance cross-scenario empirical validation and standardized benchmarking, while reinforcing hardware–software co-design. Compared with existing reviews that mostly focus on a single dimension, this review proposes a cross-level framework and design checklist, systematizing scattered optimization methods into reusable engineering pathways. Full article

N-Acetylneuraminic acid (Neu5Ac), the predominant form of sialic acids (Sias), is extensively utilized in the food, pharmaceutical, and cosmetic industries. Microbial fermentation serves as a critical production method for its economical, eco-friendly, and scalable production. Escherichia coli and Bacillus subtilis, as primary industrial workhorses for Neu5Ac production, have been extensively investigated owing to their well-characterized genetic frameworks and mature molecular toolkits. Nevertheless, the intricate regulatory networks inherent to microbial systems present formidable obstacles to the high-efficiency biosynthesis of Neu5Ac. This review delineates the genetic and molecular mechanisms underlying Neu5Ac biosynthesis in both E. coli and B. subtilis. Furthermore, the rational and irrational strategies for constructing Neu5Ac microbial cell factories are systematically summarized, including the application of rational metabolic engineering to relieve feedback regulation, reconfigure metabolic networks, implement dynamic regulation, and optimize carbon sources; as well as the use of irrational strategies including directed evolution of key enzymes and high-throughput screening based on biosensors. Finally, this review addresses current challenges in Neu5Ac bioproduction and proposes integrative solutions combining machine learning with systems metabolic engineering to advance the construction of high-titer Neu5Ac microbial cell factory and the refinement of advanced fermentation technologies. Full article

The use of reclaimed water is crucial to prevent pollution from wastewater discharges and mitigate the water deficit faced by irrigation districts or other non-potable water users. Therefore, the zero-discharge strategy represents a significant challenge for coastal cities affected by marine pollution from effluents. In regions such as the Mediterranean arc, agricultural areas located near these cities are increasingly exposed to reduced water allocations or rising irrigation demands due to the impacts of climate change. To address this dual challenge, a circular system is proposed through the implementation of hybrid treatment technologies that enable zero wastewater discharge into the sea. This approach would contribute up to 30 hm³ of reclaimed water annually for irrigation, covering approximately 27,000 hectares of cropland in the province of Alicante. The proposed system integrates advanced techniques, such as reverse osmosis, to ensure irrigation water quality, while also considering partial blending strategies to optimize resource use. Additionally, constructed wetlands are incorporated to regulate and treat the reject streams produced by these processes, minimizing their environmental impact. This combined strategy enhances water reuse efficiency, strengthens agricultural resilience, and provides a sustainable model for managing water resources in coastal Mediterranean regions. Full article

Biomass-derived hard carbon (BHC) has attracted considerable attention as a sustainable and cost-effective anode material for sodium-ion batteries (SIBs), owing to its natural abundance, environmental friendliness, and promising electrochemical performance. This review provides a detailed overview of recent progress in the synthesis, structural design, and performance optimization of BHC materials. It encompasses key fabrication routes, such as high-temperature pyrolysis, hydrothermal pretreatment, chemical and physical activation, heteroatom doping, and templating techniques, that have been employed to control pore architecture, defect density, and interlayer spacing. Among these strategies, activation-assisted pyrolysis and heteroatom doping have shown the most significant improvements in sodium (Na) storage capacity and long-term cycling stability. The review further explores the correlations between microstructure and electrochemical behavior, outlines the main challenges limiting large-scale application, and proposes future research directions toward scalable production and integration of BHC anodes in practical SIB systems. Overall, these advancements highlight the strong potential of BHC as a next-generation anode for grid-level and renewable energy storage technologies. Full article

The lion’s paw scallop (Nodipecten subnodosus) is a commercially valuable pectinid whose postharvest quality strongly depends on storage and handling conditions. This study investigated the combined effects of seasonality, postmortem time, and storage temperature on energy metabolism in the adductor muscle, focusing on metabolites associated with rigor mortis and freshness. Adult scallops (~10 cm shell height) were harvested in four seasons (spring, summer, autumn, winter), transported under commercial conditions for approximately 2 h, and stored at 0, 5, and 10 °C for 48 h. Muscle samples were collected every 8 h and analyzed for ATP, ADP, AMP, glycogen, arginine phosphate (Arg-P), and free arginine using HPLC and enzymatic assays. In addition, the adenylate energy charge (AEC) was determined in freshly harvested and post-transport specimens. Initial ATP concentrations ranged from 4.2 to 6.5 µmol/g, with higher levels in winter, while Arg-P varied from 3.1 to 4.8 µmol/g. Seasonality significantly influenced all metabolites except arginine, and transport markedly reduced ATP and AEC, particularly in spring and autumn. Storage at 0 °C resulted in rapid ATP depletion (<1.0 µmol/g within 12 h) and AMP accumulation (>3.0 µmol/g), indicating accelerated energy collapse. In contrast, scallops stored at 5 and 10 °C maintained ATP levels above 2.5 µmol/g for up to 24 h, delaying rigor mortis, reducing postmortem contraction, and preserving muscle texture and appearance. Overall, these findings demonstrate that moderate refrigeration represents a physiologically suitable and technologically advantageous strategy to optimize scallop postharvest handling, extend shelf life, and enhance product quality for the fresh seafood market. Full article

Research on unmanned aerial vehicle (UAV) path planning technology in urban operation scenarios faces the challenge of multi-objective collaborative optimization. Currently, mainstream path planning algorithms, including the multi-objective particle swarm optimization (MOPSO) algorithm, generally suffer from premature convergence to local optima and insufficient stability. This paper proposes a Zaslavskii chaotic multi-objective particle swarm optimization (ZAMOPSO) algorithm to address these issues. First, three-dimensional urban environment models with asymmetric layouts, symmetric layouts, and no-fly zones were constructed, and a multi-objective model was established with path length, flight altitude variation, and safety margin as optimization objectives. Second, the Zaslavskii chaotic sequence perturbation mechanism is introduced to improve the algorithm’s global search capability, convergence speed, and solution diversity. Third, nonlinear decreasing inertia weights and asymmetric learning factors are employed to balance global and local search abilities, preventing the algorithm from being trapped in local optima. Additionally, a guidance particle selection strategy based on congestion distance is introduced to enhance the diversity of the solution set. Experimental results demonstrate that ZAMOPSO significantly outperforms other multi-objective optimization algorithms in terms of convergence, diversity, and stability, generating Pareto solution sets with broader coverage and more uniform distribution. Finally, ablation experiments verified the effectiveness of the proposed algorithmic mechanisms. This study provides a promising solution for urban UAV path planning problems, while also providing theoretical support for the application of swarm intelligence algorithms 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

Parasites of the Leishmania genus are globally distributed and cause various clinical presentations in animals and humans, collectively known as leishmaniasis. The genomes of Leishmania and other trypanosomatids exhibit remarkable plasticity, shaped by several distinctive genetic features. Although these features can hinder the application of next-generation DNA sequencing (NGS) technologies, NGS data have been successfully used to characterize the whole-genome diversity of circulating Leishmania strains. The results complement and are broadly aligned with previous findings obtained with more traditional methods, offering greater resolution when working with geographically closer strains. In this review, we summarize advances over the past two decades in characterizing the genome diversity of Leishmania parasites using NGS strategies. We also discuss the application of these strategies to elucidate other aspects relevant to the epidemiology of these parasites, including their population structure and mode of reproduction. The vast majority of the studies to date have focused on species within the L. donovani/infantum complex or the L. (Viannia) subgenus, highlighting the need to incorporate other relevant underrepresented species and regions from both the Old and New World. Full article

Background/Objectives: Diabetes mellitus is a major global health concern requiring both preventive strategies and patient-centered clinical management. This study evaluated knowledge, lifestyle behaviors, treatment adherence and the use of digital tools among Romanian patients with diabetes in a real-world setting. Methods: A cross-sectional observational study was conducted on 100 patients recruited from community pharmacies in Iași, Romania. Data were collected using a structured 27-item questionnaire addressing demographics, disease management, adherence, lifestyle factors, and complications. Results: Most participants had type 2 diabetes. Engagement in healthy behaviors was suboptimal, with low levels of daily physical activity and limited routine glucose monitoring. Complications and treatment-related side effects were frequent, reflecting a high disease burden. Treatment adherence was significantly higher among younger patients, those with type 1 diabetes and individuals who had set long-term health goals. The use of digital technologies was low overall but more common in younger participants. Conclusions: This study identifies critical gaps in lifestyle practices, adherence and technology uptake among Romanian patients with diabetes. These findings carry important clinical implications, as poor adherence and limited self-management are closely linked to complications and higher healthcare spending. The findings suggest that patient education, integrating validated digital tools and enhancing the role of community healthcare providers could support better adherence and reduce long-term complications. Full article

The exponential growth of space objects in near-Earth and geostationary orbits has posed severe threats to space environment safety, with debris clouds from spacecraft breakup events being a critical concern. Debris cloud tracing, as a key technology for locating breakup points, faces dual challenges of insufficient precision in analytical methods and excessive computational load in numerical methods. To balance traceability accuracy with computational efficiency, this paper proposes a breakup time determination method integrating a clustering algorithm and the minimization of average relative distance. The method first calculates the average relative distance between fragment pairs and preliminarily estimates the breakup epoch using a golden section step-size optimization strategy. Subsequently, the density peak clustering (DPC) algorithm is introduced to eliminate abnormal fragments. The breakup epoch is then refined based on the cleansed fragment dataset, achieving high-precision localization. Validation through simulations of real breakup events demonstrates that this method significantly improves localization accuracy. It establishes a highly reliable temporal benchmark for space collision tracing, debris diffusion prediction, and orbital safety management. Full article

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For intra-dataset evaluation, we conduct K-fold cross-validation on two benchmark datasets, DFDC and Celeb-DF (v2), including downsampled versions to simulate real-world degradation. The results show that our method maintains high accuracy even under significant quality loss, achieving up to 98% accuracy on the Celeb-DF (v2) dataset and outperforming several state-of-the-art methods. To improve the transferability of the detection models, we introduce an integrated filtering strategy based on NR-IQA thresholding, which enhances performance in cross-dataset transfer scenarios. This approach yields up to 7% improvement in detection accuracy under challenging cross-domain conditions. 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

AI-based assistive technologies (AIATs) are increasingly recognised as essential tools to enhance accessibility, independence, and inclusion for visually impaired students in higher education. However, limited evidence exists regarding the determinants of their acceptance and use, particularly in terms of psychosocial factors. This study aimed to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating physical self-esteem (PSE) as a moderator and behavioural intention (BI) as a mediator within a single model. Data were collected through a validated questionnaire administered to 395 visually impaired undergraduates across five Saudi universities. Constructs included effort expectancy (EE), performance expectancy (PE), facilitating conditions (FCs), social influence (SI), BI, and PSE. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used for analysis. Results showed that PE and SI significantly predicted both BI and adoption, while EE strongly predicted BI but not AIAT adoption; FC had no significant influence on either outcome. BI positively affected AIAT adoption and mediated the effects of PE, EE, and SI, but not FC. Moderation analysis indicated that PSE strengthened the influence of PE, EE, and SI on BI and adoption. These findings underscore the significance of psychological factors, particularly self-esteem, in promoting the adoption of AIAT and offer guidance for developing inclusive educational strategies. Full article

In response to escalating global concerns about waste generation throughout the product life cycle, the Circular Economy (CE) has emerged as a central alternative to the dominant linear economic model. The integration of CE principles into supply chain management is manifested in Circular Supply Chain Management (CSCM), offering a novel perspective on supply chain sustainability. Despite the growing research interest in developing CSCM to enhance supply chain sustainability, assessment approaches of this concept are notably absent in the literature. This study addresses this gap by focusing on the assessment and performance measurement of circular practices in the context of supply chains. At first, the research presents a bibliometric analysis to delve into the performance and science mapping of CSCM assessment, providing a comprehensive view of the scientific landscape. Subsequently, a content analysis is then used to identify current assessment approaches, focusing on frameworks, methodologies, barriers, enablers, and CE strategies. The study proposes a conceptual model based on the SCOR framework, including core categories such as enablers (business model, technology, collaboration, design) and results (material, water, energy flows) represented by the Rs strategies. This model contributes to bridging theoretical gaps and guiding practitioners and policymakers in the design, evaluation, and implementation of circular supply chains. Full article