Search Results (21,626)

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging—Sensors and Predictive QC; Supply Chain—Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005–2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost–benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry. Full article

Background/Objective: Sleep is a vital determinant of human health, where both its quantity and quality directly impact physical and mental well-being. Thermoregulation plays a pivotal role in sleep quality, as the body’s ability to regulate temperature varies across different sleep stages. This study examines the effects of a novel real-time temperature adjustment (RTA) mattress, which dynamically modulates temperature to align with sleep stage transitions, compared to constant temperature control (CTC). Through polysomnographic (PSG) assessments, this study evaluates how adaptive thermal regulation influences sleep architecture, aiming to identify its potential for optimizing restorative sleep. Methods: A prospective longitudinal cohort study involving 25 participants (13 males and 12 females; mean age: 39.7 years) evaluated sleep quality across three conditions: natural sleep (Control), CTC (33 °C constant mattress temperature), and RTA (temperature dynamically adjusted: 30 °C during REM sleep; 33 °C during non-REM sleep). Each participant completed three polysomnography (PSG) sessions. Sleep metrics, including total sleep time (TST), sleep efficiency, wake after sleep onset (WASO), and sleep stage percentages, were assessed. Repeated-measures ANOVA and post hoc analyses were performed. Results: RTA significantly improved sleep quality metrics compared to Control and CTC. TST increased from 356.2 min (Control) to 383.2 min (RTA, p = 0.030), with sleep efficiency rising from 82.8% to 87.3% (p = 0.030). WASO decreased from 58.2 min (Control) and 64.6 min (CTC) to 49.0 min (RTA, p = 0.067). REM latency was notably reduced under RTA (110.4 min) compared to Control (141.8 min, p = 0.002). The REM sleep percentage increased under RTA (20.8%, p = 0.006), with significant subgroup-specific enhancements in males (p = 0.010). Females showed significant increases in deep sleep percentage under RTA (12.3%, p = 0.011). Conclusions: Adaptive thermal regulation enhances sleep quality by aligning mattress temperature with physiological needs during different sleep stages. These findings highlight the potential of RTA as a non-invasive intervention to optimize restorative sleep and promote overall well-being. Further research could explore long-term health benefits and broader applications. Full article

This study explores the drying kinetics and film formation behavior of polyvinyl alcohol (PVA)-based and PVA–bentonite composite coatings with initial thicknesses of approximately 2500 µm and 2000 µm. Four coating formulations were investigated, varying in PVA concentration and presence of bentonite as an inorganic filler. The drying process was monitored through changes in solid concentration, residual solvent content, and film thickness over time. Results revealed that coatings with higher PVA content exhibit slower drying rates, due to the transition from evaporation-controlled to diffusion-limited mechanisms, attributed to polymer densification and reduced solvent diffusivity. In contrast, coatings incorporating bentonite dried more rapidly despite their similar or higher total solids content, indicating a beneficial role of bentonite in facilitating moisture transport. Thinner coatings demonstrated faster drying but retained the characteristic mechanistic transitions observed in thicker films. A simple realistic model to simulate the drying rate was also proposed. Overall, the study highlights the significant influence of formulation variables on drying behavior and final film properties, offering valuable guidance for the design and optimization of waterborne coatings in industrial applications. Full article

Red mud, a by-product of aluminum production, leads to significant environmental challenges due to its alkalinity and presence of soluble compounds. This study explores its valorization through agglomeration with blast furnace sludge as a reducing agent to form self-reducing briquettes. Five C/Fe₂O₃ ratios (0.131, 0.262, 0.523, 0.840 and 1.000) were tested to determine the most effective reducing condition, with 0.840 emerging as optimal based on thermal analysis (mass loss of 27.44 wt.% at 1200 °C and iron formation specific energy of 450 J g⁻¹). Briquettes prepared with three agglomeration methods varying in water content (water/starch ratios of 6:1, 12:1 and 18:1) were evaluated through drop, compression and abrasion tests. The agglomeration method with a 12:1 water/solid ratio, involving both starch gelatinization and red mud water absorption, produced the most mechanically resistant briquettes (19.210 MPa). The mechanical and metallurgical properties of the 0.840-2W briquettes after reduction at 700, 950, 1200 and 1450 °C (temperature maintenance for 15 min) were assessed to define the best compromise between the reduction degree and mechanical strength. While reduction at 950 °C led to the weakest structure (0.449 MPa) but poor metallization, 1450 °C ensured the highest degree of reduction (94%) with adequate brittleness to facilitate a possible subsequent magnetic separation. Full article

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport. Full article

This study investigates the uniaxial compression failure of magnesium-based wood-like material (MWM) prisms (100 × 100 × 300 mm³) using digital image correlation (DIC). The results revealed an average compressive strength of 8.76 MPa and a dominant failure mode with Y-shaped or inclined penetrating cracks. A novel piecewise constitutive model was established, combining a quartic polynomial and a rational fraction, demonstrating high fitting accuracy. Critically, the proportional limit was identified to be very low (20–35% of peak stress), attributed to early-stage damage from fiber–matrix interfacial defects. DIC analysis quantitatively distinguished dual crack initiation modes, pure mode I (occurring at ≈100% peak load) and mixed mode I/II (initiating earlier at 90.02% peak load), demonstrating that tensile shear coupling accelerates failure. These findings provide critical mechanistic insights and a reliable model for optimizing MWM in sustainable construction. Future work will explore the material’s behavior under multiaxial loading. Full article

This study investigates hotel employees’ perceptions of AI-driven human resource (HR) management systems within the Accor Group’s properties across three major European cities: Paris, Berlin, and Amsterdam. These diverse urban contexts, spanning a broad portfolio of hotel brands from luxury to economy, provide a rich setting for exploring how AI integration affects employee attitudes and work–life balance. A total of 437 employees participated in the survey, offering a robust dataset for structural equation modeling (SEM) analysis. Exploratory factor analysis identified two primary factors shaping perceptions: AI Perceptions, which encompasses employee views on AI’s impact on job performance, communication, recognition, and retention, and balanced management, reflecting attitudes toward fairness, personal consideration, productivity, and skill development in AI-managed environments. The results reveal a complex but optimistic view, where employees acknowledge AI’s potential to enhance operational efficiency and career optimism but also express concerns about flexibility loss and the need for human oversight. The findings underscore the importance of transparent communication, contextual sensitivity, and continuous training in implementing AI systems that support both organizational goals and employee well-being. This study contributes valuable insights to hospitality management by highlighting the relational and ethical dimensions of algorithmic HR systems across varied organizational and cultural settings. Full article

As global industry enters the digital era, automation is becoming increasingly pervasive. Due to their superior efficiency and reliability, Permanent Magnet Synchronous Motors (PMSMs) are playing an increasingly prominent role in industrial applications. Sliding Mode Control (SMC) has emerged as a modern control strategy that is widely employed not only in PMSM drive systems, but also across broader power and industrial control domains. This technique effectively mitigates key challenges associated with PMSMs, such as nonlinear behavior and susceptibility to external disturbances, thereby enhancing the precision of speed and torque regulation. This paper provides a thorough review and evaluation of recent advancements in SMC as applied to PMSM control. It outlines the fundamentals of SMC, explores various SMC-based strategies, and introduces integrated approaches that combine SMC with optimization algorithms. Furthermore, it compares these methods, identifying their respective strengths and limitations. This paper concludes by discussing current trends and potential future developments in the application of SMC for PMSM systems. Full article

With the rapid development of urbanization and tourism in China, increasing attention has been paid to the protection and utilization of historical and cultural heritage, while tourists’ demands for travel experiences have gradually shifted towards in-depth cultural perception. This paper selects Beijing Fayuan Temple Historic and Cultural District as the research case, and adopts methods such as the LDA (Latent Dirichlet Allocation) topic model, collection and analysis of online text data, and field research to explore the current situation of pedestrian space in Fayuan Temple District and its optimization strategies from the perspective of tourists’ perception. The study found that the dimensions of tourists’ perception of the pedestrian space in Fayuan Temple District mainly include six aspects: historical buildings and relics, tour modes and transportation, natural landscapes and environment, historical figures and culture, residents’ life and activities, and tourists’ experiences and visits. By integrating online text data, questionnaire surveys, and on-site behavioral observations, the study constructed a “physical environment-cultural experience-behavioral network” three-dimensional IPA (Importance–Possession Analysis) evaluation model, and analyzed and evaluated the high-frequency perception elements in tourists’ spontaneous evaluations. Based on the current situation evaluation of the pedestrian space in Fayuan Temple District, this paper puts forward optimization strategies for the perception of pedestrian space from the aspects of block space, transportation usage, landscape ecology, digital technology, and cultural symbol translation. It aims to promote the high-quality development of historical blocks by improving and optimizing the pedestrian space, and achieve the dual goals of cultural inheritance and utilization of tourism resources. Full article

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article

The article presents a new method for multi-agent traffic flow balancing. It is based on the MAROH multi-agent optimization method. However, unlike MAROH, the agent’s control plane is built on the principles of human decision-making and consists of two layers. The first layer ensures autonomous decision-making by the agent based on accumulated experience—representatives of states the agent has encountered and knows which actions to take in them. The second layer enables the agent to make decisions for unfamiliar states. A state is considered familiar to the agent if it is close, in terms of a specific metric, to a state the agent has already encountered. The article explores variants of state proximity metrics and various ways to organize the agent’s memory. It has been experimentally shown that an agent with the proposed two-layer control plane SAMAROH-2L outperforms the efficiency of an agent with a single-layer control plane, e.g., makes decisions faster, and inter-agent communication reduction varies from 1% to 80% depending on the selected similarity threshold comparing the method with simultaneous actions SAMAROH and from 80% to 96% comparing to MAROH. Full article

By employing thin section analysis, scanning electron microscopy (SEM), homogenization temperatures of fluid inclusions, and carbon–oxygen isotope analysis of carbonate cements, this study conducted a temporal-quantitative investigation into the porosity evolution of relatively high-quality reservoirs in the Second Member of the Xujiahe Formation (Xu-2 Member) in the Xinchang area of western Sichuan. The analysis focused on quantifying porosity loss due to compaction, cementation, and porosity enhancement from dissolution. Results indicate that compaction exerted the most significant impact on reservoir quality in the Xu-2 Member, causing over 70% of total porosity loss. Cementation processes, including carbonate cements, silica cements, and authigenic chlorite, further degraded reservoir properties. Authigenic chlorite precipitated earliest at burial depths of 600–800 m, while authigenic quartz and carbonate cements persistently affected the reservoir at depths of 2000–5000 m, reducing porosity by at least 10% (up to 21%). Dissolution processes initiated at approximately 3500 m burial depth, generating secondary porosity of ≥2%, with a maximum increase of 16%. Integrating these findings with the natural gas accumulation history, the coupling relationship between pore evolution and gas accumulation was elucidated. The study reveals that reservoir tightness in the Xu-2 Member developed at burial depths of 4050–5300 m, with large-scale gas accumulation predominantly occurring prior to reservoir densification. The findings provide critical guidance for identifying high-quality tight sandstone reservoirs and optimizing exploration targets in the Xu-2 Member of the Xinchang area, Western Sichuan Basin, thereby supporting efficient development of regional tight gas resources. Full article

Background/Objectives: Pharmaceutical preparation technologies can enhance the bioavailability of poorly water-soluble drugs. Ursolic acid (UA) has been found to possess anti-cancer and hepatoprotective properties, demonstrating its potential as a therapeutic agent; however, its hydrophobicity and low solubility present challenges in the development of drug formulations. This study investigates the preparation of a nano-UA suspension by wet grinding, researches the influence of process parameters on particle size, and explores the rules of particle breakage and agglomeration by combining model fitting. Methods: Wet grinding experiments were conducted using a laboratory-scale grinding machine. The particle size distributions (PSDs) of UA suspensions under different grinding conditions were measured using a laser particle size analyzer. A single-factor experimental design was employed to optimize operational conditions. Model parameters for a population balance model considering both breakage and agglomeration were determined by an evolutionary algorithm optimization method. By measuring the degree to which UA inhibits the colorimetric reaction between salicylic acid and hydroxyl radicals, its antioxidant capacity in scavenging hydroxyl radicals was indirectly evaluated. Results: Wet grinding process conditions for nano-UA particles were established, yielding a UA suspension with a D50 particle size of 122 nm. The scavenging rate of the final grinding product was improved to three times higher than that of the UA raw material (D50 = 14.2 μm). Conclusions: Preparing nano-UA suspensions via wet grinding technology can significantly enhance their antioxidant properties. Model regression analysis of PSD data reveals that increasing the grinding mill’s stirring speed leads to more uniform particle size distribution, indicating that grinding speed (power) is a critical factor in producing nanosuspensions. Full article

This study aims to explore whether the dual-scale management model, formed by integrating service-scale management with land-scale management, can further break through the benefit limits of single land-scale management and unlock additional profit potential in agricultural scale operations. This study used data from a 2024 questionnaire survey of 2166 farming households in Anhui Province and employed a coupling coordination degree model to measure the level of dual-scale management. Subsequently, we utilized OLS regression and mediation effect models to empirically examine the impact of dual-scale management on agricultural operational benefit and their underlying mechanisms. We find that dual-scale management significantly improves agricultural operational benefit. Our measurements show that dual-scale management not only breaks through the upper limit of the optimal operating area inherent in single land-scale management but also yields a greater improvement in agricultural operational benefit than single land-scale management. Heterogeneity analysis reveals that dual-scale management significantly enhances the agricultural operational benefit of farmers in plain areas and farmers with fully developed high-standard farmland. Mechanism analysis indicates that dual-scale management enhances agricultural operational benefit through an endogenous efficiency improvement mechanism and an exogenous risk-burden-sharing mechanism. These findings suggest that fostering a synergistic development system for land-scale management and service-scale management is conducive to improving the economic returns for land scale operators and unlocking new dividend spaces for agricultural scale operation in China’s post-land transfer era. Full article

This paper provides a comprehensive review of Type IV hydrogen tanks, with a focus on materials, manufacturing technologies and structural issues related to high-pressure hydrogen storage. Recent advances in the use of advanced composite materials, such as carbon fibers and polyamide liners, useful for improving mechanical strength and permeability, have been reviewed. The present review also discusses solutions to reduce hydrogen blistering and embrittlement, as well as exploring geometric optimization methodologies and manufacturing techniques, such as helical winding. Additionally, emerging technologies, such as integrated smart sensors for real-time monitoring of tank performance, are explored. The review concludes with an assessment of future trends and potential solutions to overcome current technical limitations, with the aim of fostering a wider adoption of Type IV tanks in mobility and stationary applications. Full article