Search Results (977)

With the exponential growth of user-generated content on video-sharing platforms, the challenge of facilitating efficient searching and browsing of videos has garnered significant attention. To enhance users’ ability to swiftly locate and review pertinent videos, the creation of concise and informative video summaries has become increasingly important. Video-llama is an effective tool for generating video summarization, but it cannot effectively unify and optimize the modeling of temporal and spatial features and requires a lot of computational resources and time. Therefore, we propose MiLoRA-ViSum to more efficiently capture complex temporal dynamics and spatial relationships inherent in video data and to control the number of parameters for training. By extending traditional Low-Rank Adaptation (LoRA) into a sophisticated mixture-of-experts paradigm, MiLoRA-ViSum incorporates a dual temporal–spatial adaptation mechanism tailored specifically for video summarization tasks. This approach dynamically integrates specialized LoRA experts, each fine-tuned to address distinct temporal or spatial dimensions. Extensive evaluations of the VideoXum and ActivityNet datasets demonstrate that MiLoRA-ViSum achieves the best summarization performance compared to state-of-the-art models, while maintaining significantly lower computational costs. The proposed mixture-of-experts strategy, combined with the dual adaptation mechanism, highlights the model’s potential to enhance video summarization capabilities, particularly in large-scale applications requiring both efficiency and precision. Full article

Organic Rankine Cycle (ORC) power plants represent one of the most suitable technologies for the recovery and conversion of low-grade thermal energy. Coupling a micro-scale ORC system with parabolic trough collectors (PTCs) as a thermal energy source can effectively meet the electrical and thermal demands of a domestic user. This study presents the development process of the micro-ORC system, detailing both the results of the numerical model and the implementation of the test prototype. Particular attention is given to the instrumentation and sensors installed on the test bench, the monitoring and data acquisition software, and the error propagation analysis applied to the experimental data. In order to develop a micro-scale ORC plant, a commercial hermetic scroll compressor was tested as an expander with HFC-245fa working fluid. The test campaign required the construction of a dedicated experimental setup, equipped with comprehensive monitoring and control systems. While the first part of this research focused on evaluating the use of a scroll compressor as an expander, the second part aims to thoroughly describe the design of the test bench and the numerical model employed, the boundary conditions adopted, and the optimization strategies implemented to enhance system performance. This paper also describes in detail the measurement methodology and the associated error analysis to ensure comparability between experimental and numerical data. The numerical model was experimentally validated by incorporating the actual measured efficiency of the pump system, estimated at 12%. The comparison revealed a deviation between the experimental and simulated absorbed power of the pump—expressed as a function of the evaporation pressure—of less than 10% in the majority of the tested operating conditions. This confirms the reliability of the model and supports its use in future optimization studies. Full article

The increasing consumer demand for minimally processed foods (MPFs) has highlighted the need for innovative preservation methods that ensure both safety and quality. Among promising biocontrol tools, bacteriophages—viruses that selectively destroy bacteria—have gained significant attention. This review explores the dual role of bacteriophages in the food industry. On one hand, they offer a natural, highly specific, and environmentally friendly means of controlling both pathogenic and spoilage bacteria in MPFs, contributing to improved food safety, extended shelf life, and reduced reliance on antibiotics and chemical preservatives. Their use spans primary production, bio-sanitization, and biopreservation. On the other hand, phages pose significant risks in fermentation-based industries such as dairy, where they can disrupt starter cultures and impair production. This review also examines the regulatory, technological, and safety challenges involved in phage application, including concerns about antibiotic resistance gene transfer, the presence of endotoxins, and scale-up limitations. Ultimately, this paper argues that with proper strain selection and regulation, bacteriophages can become valuable allies in sustainable food systems, despite their potential drawbacks. The application of strictly virulent bacteriophages as part of “green biotechnology” could enhance food quality and improve consumer health safety. By implementing the “farm to fork” strategy, bacteriophages may contribute to the production of health-promoting and sustainable food. Full article

Under increasingly stringent global policies aimed at reducing emissions from shipping, the impact of maritime activities on air quality has garnered significant attention. However, the absence of comprehensive macro-evaluation methods and a limited understanding of regional-scale pollutant emissions introduce substantial uncertainties in assessing emission reduction effectiveness and identifying pollution sources. In this study, we utilized Sentinel-5P satellite data from 2019 to 2024 to examine the spatiotemporal characteristics of six air pollutants (SO₂, NO₂, HCHO, O₃, CO, and CH₄) in China’s coastal areas. We further investigated the correlation between ship density and pollutant concentrations and analyzed the distribution of pollutant concentrations in major coastal ports across China. The results indicate the following: (1) The concentrations of SO₂, HCHO, and CH₄ exhibited a continuous increasing trend, whereas NO₂, CO, and O₃ remained relatively stable or showed a slight decline. All six pollutants demonstrated obvious seasonal variations, with NO₂ and HCHO following a double-peak pattern and O₃, SO₂, CH₄, and CO exhibiting a single-peak pattern. (2) Pollutant concentrations were higher along the northern coast (Yellow Sea and Bohai Sea) and relatively lower in the South China Sea region. Specifically, NO₂, SO₂, and O₃ were higher in the Bohai Sea region; HCHO and CO were more concentrated in the northern coastal area; and CH₄ was elevated in the north and certain ports of the Yangtze River Delta. (3) Ship density displayed a significant positive correlation with NO₂, SO₂, HCHO, CO, and CH₄, indicating that ship emissions are an important source of these pollutants. Although O₃ is not directly emitted by ships, a positive correlation was observed in certain ship-dense areas, primarily due to photochemical reactions involving NO₂ and volatile organic compounds (VOCs). (4) Higher concentrations of NO₂, SO₂, HCHO, CO, and CH₄ were observed in northern ports (e.g., Tianjin Xingang, Qinhuangdao, Tangshan, and Dalian), whereas southern Chinese ports (e.g., Shenzhen, Xiamen, and Haikou) exhibited lower pollution levels. These findings provide a scientific foundation for coastal air pollution control and highlight the necessity of ship emission regulation and integrated multi-pollutant management. Full article

Aiming at the key technology of defect detection in precision additive manufacturing of highly reflective metal materials, this study proposes an enhanced SCK-YOLOV5 framework, which combines polarization imaging and deep learning methods to significantly improve the intelligent identification ability of small metal micro and nano defects. This framework introduces the SNWD (Selective Network with attention for Defect and Weathering Degradation) Loss function, which combines the SIOU Angle Loss with the NWD distribution sensing characteristics. It is specially designed for automatic positioning and identification of micrometer hole defects. At the same time, we employ global space construction with a dual-attention mechanism and multi-scale feature refining technique with selection kernel convolution to extract multi-scale defect information from highly reflective surfaces stably. Combined with the polarization imaging preprocessing and the comparison of enhancement defects under high reflectivity, the experimental results show that the proposed method significantly improves the precision, recall rate, and mAP50 index compared with the YOLOv5 baseline (increased by 0.5%, 1.2%, and 1.8%, respectively). It is the first time that this improvement has been achieved among the existing methods based on the YOLO framework. It creates a new paradigm for intelligent defect detection in additive manufacturing of high-precision metal materials and provides more reliable technical support for quality control in industrial manufacturing. Full article

Background/Objectives: The preoperative improvement of patients’ functional capacity (prehabilitation) has gained attention in the surgical field, especially for colorectal cancer (CRC) patients. Despite the recognized benefits of prehabilitation programs, patients’ motivation to participate in and adhere to them remains a significant challenge. Several studies reported difficulties in recruiting participants and low adherence rates. This systematic review explored patients’ motivation for participation and adherence to prehabilitation programs for colorectal cancer surgery. Methods: A systematic search was conducted in the PubMed (MEDLINE), Embase, Cochrane, CINAHL, and Web of Science databases. Eligible studies included clinical trials published from inception until December 2024, written in English or Dutch, describing barriers and/or motivators affecting patient participation and adherence in prehabilitation programs. Results: A total of 89 studies, including 34 randomized controlled trials, were included. In total, 13,383 patients were included, with 7162 in the prehabilitation cohort. Participation rates ranged from 0 to 99.4%, and adherence rates ranged from 15% to 100%. Factors limiting participation included logistical issues and a busy schedule. Professional guidance, peer support, and regaining a sense of control improved adherence. Medical reasons, conflicting obligations, and intensive exercise limited adherence. Conclusions: This systematic review analyzed the current literature on participation and adherence in prehabilitation programs for colorectal cancer surgery patients. Overcoming logistical barriers and patient concerns through flexible, patient-centered approaches may improve participation and adherence. Future research should focus on large-scale randomized controlled trials, diverse healthcare settings, and strategies to enhance engagement with prehabilitation programs. Full article

Background/Objectives: Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) is a widely used intervention for Parkinson’s disease (PD) and obsessive-compulsive disorder (OCD). While motor and OCD symptom benefits are established, increasing evidence highlights psychiatric side effects. The underlying mechanisms involve stimulation parameters, electrode positioning, and medication adjustments. This systematic review aims to evaluate the short-term and long-term psychiatric effects of STN-DBS and identify influencing factors. Methods: A systematic literature search (PubMed, Scopus, Web of Science, Embase; 2015–2024) was conducted following PRISMA guidelines. Studies examining psychiatric effects of STN-DBS in PD or OCD, reporting quantitative/qualitative psychiatric measures, and specifying stimulation parameters were included. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for observational studies and the Cochrane Risk of Bias Tool for randomized controlled trials (RCTs). Results: A total of 16 studies met the inclusion criteria, with sample sizes from 10 to 149 patients and short- to long-term follow-ups (up to 17 years). Short-term effects included transient hypomania, euphoria, increased impulsivity (especially with medial STN stimulation), and sometimes anxiety reduction. Long-term effects showed a tendency towards apathy and depression (apathy increased significantly in one large cohort), particularly linked to ventromedial STN stimulation or dopaminergic medication reduction. Impulse control disorders (ICDs) improved long-term in one study following medication reduction, while impulsivity slightly worsened in another. Verbal fluency decline was commonly reported, though global cognition often remained stable. Psychiatric outcomes (mood/apathy, attention/memory) depended on stimulation location within STN subregions. Higher total electrical energy delivered (TEED) correlated with depressive trait shifts in one study. Conclusions: STN-DBS has complex psychiatric consequences. Electrode positioning, stimulation parameters (including location within STN subregions and possibly TEED), and medication adjustments significantly influence outcomes. Careful patient selection, preoperative psychiatric screening, optimized programming targeting specific STN subregions, and cautious medication management are essential to minimize psychiatric risks while maximizing therapeutic benefits for motor and OCD symptoms. Full article

Acoustic metamaterials have been widely applied in fields such as sound insulation and noise reduction due to their controllable band structures and unique abilities to manipulate low-frequency sound waves. However, there exists a highly nonlinear mapping relationship between their structural parameters and performance responses, which causes traditional design methods to face the problems of inefficiency and poor generalization. Therefore, this paper proposes a bidirectional modeling framework based on deep learning. We constructed a forward prediction network that integrates an attention mechanism, a multi-scale feature fusion, and a reverse design model that combines an improved autoencoder and cascaded neural network to efficiently model the dispersion performance of acoustic metamaterials. In the feedforward network, the improved forward prediction model shows superior performance compared to the traditional Convolutional Neural Network model and the model based only on the Convolutional Block Attention Module attention mechanism, with a prediction accuracy of 99.65%. It has better fitting ability and stability in the high-frequency part of the dispersion curve. In the inverse network part, compression of the high-dimensional dispersion curves by an improved autoencoder reduces the training time by about 13.5% without significant degradation of the inverse prediction accuracy. The proposed network model provides a more efficient method for the design of metamaterials. Full article

MXenes, a class of two-dimensional materials with appealing properties such as electrical conductivity, mechanical strength, and chemical stability, is rapidly gaining attention for potential applications in various fields, including energy storage, water treatment, biomedicine, and electromagnetic shielding. One of the most exciting developments is their integration with 3D printing technologies, which allows for precise control over material structure and composition. This combination has significantly expanded the scope of MXenes, particularly in electrochemical storage systems like supercapacitors and batteries, where 3D-printed MXene-based materials have demonstrated superior performance. This review article provides a detailed analysis of the synthesis, properties, and applications of MXenes, with a particular focus on their role in additive manufacturing. While the synergy between MXenes and 3D printing offers numerous advantages, challenges such as large-scale production, material stability, and refining processing techniques remain significant hurdles; all these issues are discussed in the present work. Future research directions are also highlighted that aim to enhance scalability, reduce costs, and explore new composite formulations to optimize the performance of MXenes across various applications. Full article

In recent years, with the deepening research on exoskeletons for children with cerebral palsy, increasing evidence has highlighted their unique characteristics. Unlike adult exoskeletons, pediatric exoskeletons cannot be simply realized by scaling down adult designs; instead, special attention must be given to their unique training requirements. Although current studies have incorporated specific design adaptations and summarized the distinct features of these devices, a comprehensive review of control strategies remains lacking. This study adopts a structured narrative review approach, referencing the PRISMA framework to enhance transparency in the literature selection. Relevant publications were identified based on clearly defined inclusion and exclusion criteria, but no formal systematic review or meta-analysis was conducted. The exoskeleton control strategies from the 106 selected articles are classified using a hierarchical framework, dividing them into the supervision layer, action layer, and execution layer, with a further categorization into 12 specific control methods. Findings indicate that the supervision level primarily employs finite state machines and linear phase estimation, while the action level predominantly utilizes position trajectory control, torque trajectory control, and impedance control. At the execution level, closed-loop torque control and position control are commonly adopted. Overall, existing studies still face challenges in personalized adaptation, real-time control, and application scenarios. With advancements in controller hardware and the introduction of novel actuators, emerging technologies such as machine learning, virtual constraints, and sliding mode control may offer promising directions for future pediatric exoskeleton control design. Full article

Objectives: With the advancement of modern society, people in cognitively demanding jobs are increasingly exposed to occupational stress. Prolonged and high-intensity cognitive activities are prone to inducing mental fatigue (MF), which adversely affects both psychological and physiological well-being, as well as task performance. Existing methods for inducing MF often demonstrate limited effectiveness due to insufficient cognitive load from overly simplistic tasks and the potential emotional disturbance caused by prolonged task duration. This study aims to explore a comprehensive cognitive task paradigm that integrates task complexity and time pressure, thereby developing a novel and effective method for inducing MF based on high mental workload (HMW) and the effects of time on task (ToT). Methods: Using convenience sampling, university students from a medical college were recruited as participants. The study was conducted in three steps. In the first step, we constructed a 1-back Stroop (BS) task paradigm by designing tasks with varying levels of complexity and incorporating time pressure through experimental manipulation. In the second step, the efficacy of the BS task paradigm was validated by comparing it with the traditional 2-back cognitive task in inducing HMW. In the third step, an MF induction protocol was established by combining the BS task paradigm with the ToT effect (i.e., a continuous 30 min task). Effectiveness was assessed using validated subjective measures (NASA Task Load Index [NASA-TLX] and Visual Analog Scale [VAS]) and objective behavioral metrics (reaction time and accuracy). Statistical analyses were performed using analysis of variance (ANOVA) and t-tests. Results: The BS task paradigm, which integrates complex cognitive abilities such as attention, working memory, inhibitory control, cognitive flexibility, and time pressure, demonstrated significantly higher NASA-TLX total scores, as well as elevated scores in mental demand, temporal demand, performance, and frustration scales, compared to the 2-back task. Additionally, the BS task paradigm resulted in longer reaction times and lower accuracy. As the BS task progressed, participants exhibited significant increases in mental fatigue (MF), mental effort (ME), mental stress (MS), and subjective feelings of fatigue, while the overall number of correct trials and accuracy showed a significant decline. Furthermore, reaction times in the psychomotor vigilance test (PVT) were significantly prolonged, and the number of lapses significantly increased between pre- and post-task assessments. Conclusions: The BS task paradigm based on complex cognitive abilities and time pressure could effectively induce an HMW state. Combined with the ToT effect, the BS paradigm demonstrated effective MF induction capabilities. This study provides a novel and reliable method for inducing HMW and MF, offering a valuable tool for future research in related fields. Full article

To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large numbers due to their limited availability. However, existing text-to-image methods are computationally intensive and struggle to capture fine details and complex semantic relationships in text and images. To address these challenges, this paper proposes the Diffusion Adapter Network (DANet). It employs a lightweight adapter module to extract visual structural information, enabling the diffusion model to generate Dunhuang patterns with high accuracy, while eliminating the need for expensive fine-tuning of the original model. The attention adapter incorporates a multihead attention module (MHAM) to enhance image modality cues, allowing the model to focus more effectively on key information. A multiscale attention module (MSAM) is employed to capture features at different scales, thereby providing more precise generative guidance. In addition, an adaptive control mechanism (ACM) dynamically adjusts the guidance coefficients across feature layers to further enhance generation quality. In addition, incorporating a cross-entropy loss function enhances the model’s capability in semantic understanding and the classification of Dunhuang patterns. The DANet achieves state-of-the-art (SOTA) performance on the proposed Diversified Dunhuang Patterns Dataset (DDHP). Specifically, it attains a perceptual similarity score (LPIPS) of 0.498, a graph matching score (CLIP score) of 0.533, and a feature similarity score (CLIP-I) of 0.772. Full article

Objective: to assess the association between attention-deficit/hyperactivity disorder (ADHD), type 1 diabetes mellitus (T1D), and cardiovascular comorbidities in adults. Methods: The Adult Self-Report Scale V1.1 (ASRS) for ADHD symptoms was electronically sent to 2069 adults with T1D. Cardiometabolic conditions, laboratory measurements, and PHQ-2/PHQ-9 depression scores were obtained from the electronic medical record. Results: In total, 292 (14.1%) individuals responded and 279 consented to medical records extraction. The average age was 47.4 years (SD: ±18.9), 64.2% were women, 95.7% were non-Hispanic white, and the mean HbA1c level was 7.7% (±1.5%). Of 273 completing ASRS, 87 med ADHD criteria (ASRS positive, 31.9%), and 42 (15.4%) had an ADHD diagnosis or medication. Women had higher scores than men. ADHD symptoms decreased with age, but remained significantly higher than the general population levels. HbA1c levels were positively associated with the ASRS scores (Spearman’s r = 0.28, p < 0.0001). ASRS positive individuals had worse glycemic control (HbA1c ≥ 8.0%, adjusted OR 2.3, 95%CI: 1.3–4.1, p < 0.0001) and higher PHQ-9 scores (10 ± 7.3 vs. 6.1 ± 6, χ²₍₁₎ = 9.2, p = 0.002) than the ASRS negative group. No associations were found between ASRS scores and cardiometabolic diseases, or other laboratory or clinical measurements. Conclusions: Many adults with T1D exhibit undiagnosed ADHD symptoms, which correlate with poorer glycemic control and depression. Further research with larger samples is needed to investigate ADHD prevalence and impacts in this group. Full article

Carbon dioxide (CO₂) clathrate hydrate is gaining attention as a promising material for cold thermal energy storage (CTES) due to its high energy storage capacity and low environmental footprint. It shows strong potential in building applications, where space cooling accounts for nearly 40% of total energy use and over 85% of electricity demand in developed countries. CO₂ hydrates are also being explored for use in refrigeration, cold chain logistics, supercomputing, biomedical cooling, and defense systems. With the growing number of applications in mind, this review focuses on the thermal behavior of CO₂ hydrates and their environmental impact. It highlights recent efforts to reduce formation pressure and temperature using chemical promoters and surfactants. This paper also reviews key experimental techniques used to study hydrate properties, including Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), high-pressure differential scanning calorimetry (HP-DSC), and the T-history method. In lifecycle comparisons, CO₂ hydrate systems show better energy efficiency and lower carbon emissions than traditional ice or other phase-change materials (PCMs). This review also discusses current commercialization challenges such as high energy input during formation and promoter toxicity. Finally, practical strategies to move CO₂ hydrate-based CTES from lab-scale studies to real-world cooling and temperature control applications are discussed. Full article

Background and Objectives: Psychological disorders are prevalent and distressing. Early treatment initiation can prevent adverse outcomes and reduce healthcare system impacts. Improving mental health literacy (MHL)—one’s knowledge regarding psychological disorders—and treatment-seeking attitudes (TSAs) is key in early treatment initiation. Examining the facets of dispositional mindfulness—the capacity to pay attention to present-moment experiences with acceptance—may offer more granular insights into understanding MHL and TSAs. This study examined (a) associations between mindfulness facets and MHL and TSAs, (b) facets’ prediction of MHL and TSAs beyond demographics, (c) moderation of the MHL–TSA relationship by mindfulness facets, and (d) mediation of mindfulness–TSA relationships via general self-efficacy (GSE). Methods: A community sample of 299 adults was recruited online (TurkPrime) and completed demographic questions and self-report measures: Five-Facet Mindfulness Questionnaire-15, Mental Health Literacy Scale, Mental Help-Seeking Attitudes Scale, and General Self-Efficacy Scale. Results: Describe, Non-Judgment, and Act with Awareness were modestly associated with MHL; all five facets correlated with TSAs. Hierarchical regressions controlling for demographics showed that Describe and Non-Reactivity predicted MHL, while Act with Awareness uniquely predicted TSAs. Non-Reactivity moderated the MHL–TSA relationship, with higher Non-Reactivity amplifying the relationship. GSE fully mediated relationships between Observe and Non-Judgment with TSAs, suggesting self-efficacy is a key mechanism of these facets. Conclusions: Interventions cultivating Non-Reactivity, Describe, and Act with Awareness may improve the translation of mental health knowledge into treatment-seeking behaviors. Future research should explore how mindfulness facets independently and interactively foster early intervention and treatment engagement. Full article