Search Results (24,025)

Micro heat exchangers (MHXs) have emerged as a critical technology for advanced thermal management in the food and pharmaceutical industries due to their high surface area-to-volume ratios, compact design, and precise temperature control. This review provides a systematic and integrated analysis of MHX technology, covering their fundamental principles, classification, design methodologies, performance enhancement techniques, and industrial applications. Unlike existing reviews, the present work establishes a unified framework that links microscale heat transfer mechanisms, such as Brownian motion, surface corrugation effects, and non-dimensional parameters, with practical design choices, manufacturing routes, and the process requirements specific to food and pharmaceutical systems. The subsequent sections explore the key performance-influencing factors, including channel geometry, surface enhancement strategies, nanofluid utilization, and governing non-dimensional numbers (e.g., Nusselt, Reynolds, and Knudsen numbers), which are systematically compared across different operating regimes. Recent advances in materials and fabrication techniques, such as laser ablation, lithography, micro-milling, embossing, and additive manufacturing, are analyzed with respect to their scalability, thermal–hydraulic performance, and industrial feasibility. Furthermore, the review highlights the emerging trends in micro heat exchanger (MHX) optimization, including computational fluid dynamics (CFD)-driven design, smart monitoring systems, and energy-efficient integration within processing lines. Finally, the paper also identifies the key challenges and limitations of micro heat exchangers, including pressure drop, fouling, scaling, manufacturing complexity, and cost constraints. These are critically discussed along with future research directions aimed at improving reliability and sustainability. By consolidating the dispersed research outcomes into a coherent, design-oriented perspective, this review offers new insights and practical guidance for researchers, engineers, and industry practitioners seeking to advance the deployment of MHXs in food and pharmaceutical processing. Full article

To the authors’ knowledge, this is the first industrial machine learning (ML) study focused on wet vertical stirred media milling. The study develops and validates machine learning (ML) models to predict the key operating parameters, namely mill discharge product size, mill feed slurry flow rate, mill power draw, and the specific energy consumption of an industrial wet vertical stirred media mill operating at a copper plant. A physics-guided workflow was adapted, combining relief coefficient-based variable screening with fundamental stirred milling principles to define 20 different structured model input scenarios. In the scope, six regression approaches, linear regression (LR), fine tree regression (FTR), support vector regression (SVR), random forest regression (RFR), artificial neural network regression (ANN), and Gaussian process regression (GPR), were trained and validated using plant sensor data and evaluated using R² and RMSE. Overall performance was reasonable, with GPR providing the highest predictive accuracy, followed by RFR/ANN, while LR, SVR, and FTR performed lower. The potential benefit of feed size was also assessed conceptually through an upper-bound sensitivity analysis, representing a best-case scenario where an online feed size measurement would be available. Because the feed size descriptor (F₈₀) was not independently measured but derived from an energy–size relationship, the associated accuracy gains are reported as theoretical upper-bound indications rather than independent predictive capability. Overall, the findings support ML-based decision support in stirred milling operations and motivate future work using independently measured feed size (or reliable proxy sensing). Full article

The strong electromagnetic interference environment on the battlefield has brought new challenges to the networking collaboration of jammers and the estimation of jamming effects. Traditional successful jamming indicators are difficult to meet the needs of continuous, low-power, and flexible jamming, causing difficulties in emergency scheduling of jamming resources. Aiming at the overall degradation of the communication party’s signal reception quality, this paper proposes the restrictive conditions of “overall limited jamming” and the analysis and evaluation index of “multistage jamming-to-signal ratio (J/S)”, which meets the scheduling requirements of distributed jamming resources in harsh environments. Based on the jammer layout that can achieve overall high-intensity jamming, the electromagnetic environment estimation, power scheduling, and collaboration strategies of jammers are designed, a communication countermeasure game algorithm under blocked networking collaboration is established, and the independent dynamic scheduling of jamming resources is realized. The experimental results show that the Concentric Circle Broadcasting Algorithm (CCBA) not only maintains effective communication jamming (the proportion of high-intensity jamming is no less than 50%, and the proportion of normal signal reception of communication nodes is no more than 6%), but also extends the system operation duration by 66.8–269.6% compared with the comparative algorithms for the 600 MHz fixed-frequency and 1 MHz bandwidth communication system. This work is limited to the line-of-sight (LOS) scenario, and future research will extend it to non-line-of-sight (NLOS) scenarios. Full article

Differentiating AI-generated, real, or imitated artworks is becoming a tedious and computationally challenging problem in digital art analysis. AI-generated art has become nearly indistinguishable from human-made works, posing a significant threat to copyrighted content. This content is appearing on online platforms, at exhibitions, and in commercial galleries, thereby escalating the risk of copyright infringement. This sudden increase in generative images raises concerns like authenticity, intellectual property, and the preservation of cultural heritage. Without an automated, comprehensible system to determine whether an artwork has been AI-generated, authentic (real), or imitated, artists are prone to the reduction of their unique works. Institutions also struggle to curate and safeguard authentic pieces. As the variety of generative models continues to grow, it becomes a cultural necessity to build a robust, efficient, and transparent framework for determining whether a piece of art or an artist is involved in potential copyright infringement. To address these challenges, we introduce ArtUnmasked, a practical and interpretable framework capable of (i) efficiently distinguishing AI-generated artworks from real ones using a lightweight Spectral Artifact Identification (SPAI), (ii) a TagMatch-based artist filtering module for stylistic attribution, and (iii) a DINOv3–CLIP similarity module with patch-level correspondence that leverages the one-shot generalization ability of modern vision transformers to determine whether an artwork is authentic or imitated. We also created a custom dataset of ∼24K imitated artworks to complement our evaluation and support future research. The complete implementation is available in our GitHub repository. Full article

The future of innovation and economic growth depends on our ability to nurture the next generation of scientists. The global shortage of qualified STEM (Science, Technology, engineering, Mathematics) teachers has led many countries to expedite the transition of subject-matter experts from industry and academia into teaching roles. These second-career science student teachers typically participate in accelerated training programs designed to address urgent shortages. This study addresses a gap in the literature regarding effective pedagogical interventions for career-changing professionals in STEM fields, focusing on the experience and transformation of second-career science student teachers. This qualitative case study explores how a Challenge-Based Learning (CBL) course fosters the development of pedagogical competences via developing an instructional unit collaboratively, among five second-career science student teachers enrolled in an accelerated teacher education program. Drawing on data collected through instructors’ field notes, iterative work-in-progress lesson drafts, and reflective final papers, the study employs qualitative content analysis to trace changes in participants’ instructional approaches and professional identity. Findings reveal that engagement with the CBL framework promoted a significant shift from teacher-centered to learner-centered instruction, as participants increasingly integrated collaborative learning, inquiry-based activities, and reflective practices into their lesson planning and classroom teaching. The iterative nature of CBL, which emphasizes real-world problem-solving and structured opportunities for reflection and peer feedback, was instrumental in supporting participants’ adaptive expertise and confidence as novice teachers. Moreover, the course experience contributed to the emergence of a professional teaching identity, with participants reporting greater self-efficacy, a stronger sense of belonging to the teaching community, and increased motivation to persist in the profession. The results underscore the potential of integrating CBL and learning sciences principles into accelerated teacher preparation programs to enhance both cognitive and affective dimensions of teacher development. Full article

Background: Chronic pain is a prevalent and disabling condition that affects physical health but also cognitive domains. Executive functions, including inhibitory control, cognitive flexibility, and working memory, essentials for self-regulation, treatment adherence, and coping with symptoms, are particularly compromised. Physiotherapy interventions, traditionally aimed at physical outcomes, may also influence executive functions; however, their impact remains unclear. Objective: This review aimed to synthesize current evidence regarding the effects of physiotherapy-related interventions on executive function in adults with chronic pain. Methods: The review followed the Cochrane Handbook and Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, and the protocol was registered in PROSPERO (CRD42024611800). A comprehensive search was performed. Randomized controlled trials (RCTs) included adults with chronic pain (≥3 months) whose executive function outcomes were evaluated after physiotherapy-based interventions. Results: Out of 12,391 records, 10 randomized controlled trials were included. Populations primarily had fibromyalgia, chronic low back pain, and chronic musculoskeletal pain. Interventions encompassed transcranial direct current stimulation (tDCS), transcranial magnetic stimulation (rTMS), neurofeedback, structured exercise, and multimodal physical-cognitive-mindfulness training. Intervention durations ranged from one session to 16 weeks. Executive function was assessed with diverse neuropsychological tests. tDCS improved attention, inhibitory control, cognitive flexibility, and working memory. Exercise interventions showed benefits in working memory and inhibitory control. Conclusions: Preliminary evidence suggests that physiotherapy interventions, particularly anodal tDCS and structured exercise, may improve executive functions in individuals with chronic pain. Future trials should incorporate long-term follow-up. Integrating cognitive targets into physiotherapy may enhance the multidimensional management of chronic pain. Full article

This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1$/h_{px}$, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behavior in parts of the tests. This work is a feasibility study and does not claim Advanced Driver Assist Systems (ADAS) functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image-level proxy metrics, not as physical sensor characteristics. Full article

To better understand how botanical products affect non-target organisms, the present review focuses on the toxicity of botanical pesticides to organisms other than targeted pests, to trace a panorama on the future of sustainable agricultural models worldwide, considering the importance of ecotoxicological studies in the development of new pesticides, including botanical kinds, which are commonly recognized as essentially harmless. The review summarizes published work gathered from digital databases and highlights modern trends in pest management research and the development of novel bioinputs, including a discussion of the world’s current legislation regarding relevant agricultural innovations and field obstacles. Nanotechnology techniques are discussed as major innovations employed in the pest control field, and their employment in improving botanical pesticides is addressed and explored. In this work, we analyze the factors involved in determining the success of botanical products and their importance in the implementation of a more sustainable approach to managing crops. The results indicate a significant lack of studies focused on the effects of botanical products on non-target organisms and an increase in studies with nanoformulations. Full article

The global population explosion and accelerated industrialization have led to an increasing shortage of fossil fuels and environmental contamination, underscoring the urgent need to develop innovative energy storage technologies to improve energy utilization efficiency. As pivotal components in thermal energy storage (TES) systems, phase change materials (PCMs) enable spatiotemporal matching between thermal energy supply and demand through latent heat absorption and release during phase transitions. Organic PCMs are considered ideal candidates for thermal energy storage due to their high energy storage density, stable phase transition temperature, low supercooling, and negligible phase separation. However, inherent drawbacks such as low thermal conductivity, liquid leakage, limited light absorption, and lack of functionality have hindered their widespread application in advanced thermal management systems. Herein, we systematically summarize cutting-edge functionalization strategies for PCMs, progressing from conventional methods like thermal conductive particle blending and microencapsulation to the emerging design of 3D porous thermally conductive skeletons, including metal foams, boron nitride aerogels, carbon-based aerogels, and MXene aerogels. These frameworks not only enhance thermal transport via continuous conductive pathways and impart shape stability through capillary encapsulation but also, when integrated with photo-thermal, electro-thermal, and magneto-thermal conversion properties, enable broad applications in solar photo-thermal/photo-thermo-electric conversion, thermal management of electronics and batteries, building efficiency, and wearable thermal regulation. The review further addresses current challenges and future directions, highlighting scalable 3D framework fabrication, the shift to active thermal management, and innovative applications beyond conventional domains. By establishing a microstructure–property–application correlation, this work provides valuable insights for developing next-generation high-performance multifunctional phase change composites. Full article

The ankle-brachial index (ABI) is the most non-invasive technique used for diagnosing and assessing peripheral artery disease (PAD), although it is operator-dependent and limited by arterial calcification. Since photoplethysmography (PPG) is a non-invasive, low-cost, and easy-to-use technique that is not limited by arterial compressibility, PPG morphological parameters have been studied for PAD diagnosis, mostly based on mean values. In this work, the relationship between variability indices of PPG morphological parameters and ABI was studied in 52 legs of 32 PAD patients. The morphological PPG parameters, including amplitude, pulse transit time (PTT), and maximum systolic slope, were measured. The mean, standard deviation, and frequency spectral energy for very low, low, and high frequencies were computed as PPG morphological variability indices. The variability indices of PPG morphological parameters have a significant correlation with ABI, indicating that they differ not only between legs with altered and normal ABI but also that they may relate to PAD progression. Fourteen of the 15 variability indices showed significant diagnostic value, with the standard deviation of PTT being the most effective (sensitivity of 96% and specificity of 71%). The differences between normal and non-compressible legs were not significant. The comparison between contralateral legs was also not significant. This suggests that variability indices may provide valuable insights into changes in physiological regulatory mechanisms as PAD progresses, which could aid in the diagnosis, assessment, and prognosis of PAD in future research. Full article

Side-scan sonar is essential to underwater target detection, yet its effectiveness is hindered by scarce annotated data and complex acoustic artifacts. This study systematically evaluates four YOLO variants, YOLOv8n, YOLOv10n, YOLOv11n, and the newly released YOLOv13n, on two public side-scan sonar datasets with limited samples and severe class imbalance. We assess detection accuracy, computational efficiency, inference speed, and transfer learning using COCO pre-trained weights, as well as the impact of optimizer choice between SGD and AdamW. The results reveal distinct strengths: YOLOv8n achieves the fastest inference at 60.98 FPS, with a competitive mAP50 of 0.906, ideal for real-time applications. YOLOv11n offers the best accuracy–efficiency balance, attaining the highest recall of 0.859 and mAP50 of 0.917. YOLOv13n demonstrates exceptional precision of 0.993 and high-IoU localization, with an mAP75 of 0.760. Transfer learning consistently boosts performance, with average mAP50:95 gains exceeding 54% on the more challenging dataset, highlighting its critical role in overcoming data scarcity. SGD generally outperforms AdamW, confirming its suitability as the default optimizer. These findings provide practical guidelines: YOLOv8 for real-time needs, YOLOv11 for balanced performance, and YOLOv13 for precision-critical tasks with ample resources. This work also establishes a benchmark for future underwater autonomous system research. Full article

The increasing demand for efficient last-mile delivery has spurred interest in optimizing vehicle and drone routing. This review presents a novel classification of synchronization levels: (i) non-synchronized scenarios, where vehicles and drones operate independently; (ii) low synchronization level scenarios, where one party is passive in the delivery process; (iii) high synchronization level scenarios, where both parties cooperate using diverse strategies. The primary objective is to identify and classify functional preferences of vehicles and drones across these synchronization scenarios. We offer a unique perspective by analyzing the functional setups of vehicles and drones along with synchronization aspects like drone flight synchronization and vehicle synchronization. To the best of our knowledge, these detailed setups based on the operational functionalities of vehicles and drones in last-mile delivery has not been previously explored in the literature. Through a systematic review of the literature, we identify key challenges and emerging trends in vehicle and drone route planning within these scenarios which enable researchers to systematically understand and design vehicle–drone delivery systems. This paper integrates existing models and solution methods and provides new insights into the interactions between vehicle and drone functionalities in last-mile delivery. By analyzing solutions across different synchronization scenarios, it guides researchers in choosing appropriate methodologies and identifying future research directions. Our work presents a novel classification framework, enabling a comprehensive understanding of how the functional setups of vehicles and drones under different synchronization levels influence route planning, thus offering both theoretical and practical insights for advancing last-mile delivery optimization. Full article

Electric vehicle (EV) adoption is generating a rapidly increasing stream of retired lithium-ion batteries for second-life deployment. However, thermal safety concerns continue to limit their reuse. This paper reviews second-life battery (SLB) thermal safety and management and organizes existing work through a mechanism-to-deployment framework linking four domains: degradation mechanisms, cell screening, pack configuration, and monitoring. Evidence indicates that thermal risk depends on the degradation pathway rather than capacity fade. In fact, cells with comparable capacity can exhibit substantially different trigger temperatures depending on whether lithium plating or solid-electrolyte interphase (SEI) growth dominates. Therefore, capacity-based screening is insufficient because cells that satisfy capacity thresholds may still remain thermally unstable. The four domains are tightly coupled: the degradation pathway determines screening requirements; screening outcomes constrain pack design; pack topology influences fault escalation; and together these factors determine what monitoring can reliably detect. This review highlights three gaps and outlines future research directions in the field of SLB thermal safety and management: limited aged-cell thermal characterization by degradation pathway, insufficient diagnostic validation under industrial-throughput conditions, and the incomplete translation of screening outputs into design rules. Full article

Ammonia (NH₃) has emerged as a promising carbon-free fuel for next-generation green energy systems due to its high hydrogen density, ease of storage and transport, and compatibility with existing infrastructure. These attributes contrast with hydrogen, which presents major challenges related to storage, safety, and high-pressure handling. Thus, ammonia offers a more practical alternative for combustion-based applications. However, its low reactivity and complex vaporization behavior, particularly under flash-boiling conditions, pose challenges for accurate modeling. This study presents a comprehensive numerical investigation of liquid-ammonia spray behavior under a range of ambient pressures, encompassing both flash-boiling and non-flashing conditions. Simulations were conducted using the Lagrangian particle tracking method, coupled with various turbulence models (the renormalization group (RNG) family, k-ω family, ς − f, V2F models) to evaluate their predictive performance. Validation against experimental data for liquid and vapor penetration demonstrated that the V2F model achieved the best overall balance between accuracy and computational efficiency. Under strong flash-boiling conditions (2 bar), rapid droplet breakup and notable cooling were observed, with droplet temperatures decreasing to approximately 235 K within a few millimeters of the nozzle. In contrast, the cooling effect was more moderate under non-flashing conditions at higher ambient pressures (10–15 bar). Although the current findings were based on numerical simulations, experimental studies are ongoing to validate and refine the modeling framework further. This work provided valuable insights into the coupled effects of turbulence, phase change, and thermal transport in superheated ammonia sprays. Future research will build upon these results by extending the model to NH₃/H₂ dual-fuel systems, refining turbulence-phase interaction models, and exploring the potential application of ammonia-based flash-boiling cooling systems for electric vehicle (EV) battery thermal management. Full article

Background/Objectives: The multifaceted concept of body image (BI) refers to an individual’s attitudes and impressions of their body. Negative BI is associated with a number of harmful health consequences, including obesity, eating disorders, and symptoms of sadness. The contemporary digital era, marked by the dominance of platforms, has brought about a considerable transformation in the landscape of BI issues. This study’s goal is to compile and assess the connections between social media (SM) use, body weight, and BI in adult populations. Methods: This is a narrative review that comprehensively searches across multiple academic databases, such as PubMed, Medline, Scopus, Web of Science, and Google Scholar. Studies that used SM (online blogs, microblogs, content communities, or social networking sites) for engagement (e.g., sharing, commenting, liking) or image-related activities (e.g., viewing, posting, or engaging with images) with healthy adults (aged 18–70 years) of any body mass index (BMI kg/m²) met the inclusion criteria. Included were observational and experimental studies that examined habitual SM use. Only peer-reviewed works published in English between 2015 and 2025 met the search criteria. Results: The currently available findings suggest that obese people are more dissatisfied with their bodies than people of normal weight, and obese women are more dissatisfied with their bodies than their peers of normal weight. Furthermore, experimental studies have demonstrated that immediate BI is adversely affected by acute exposure to idealized social media photographs. Conclusions: Policies should support specialized training that emphasizes a holistic approach to health and puts functionality and health above attractiveness. This training is crucial for dispelling weight-related stigmas and enabling healthcare providers to offer compassionate treatment that supports mental and physical health. Future research must concentrate on internalization and social pressure or reinforcement because these subjects have not gotten as much emphasis in prior studies. Such mechanism research could help better contextualize the role of recently introduced SM items. Full article