Search Results (1,440)

Feedrate planning is a critical process in computer numerical control (CNC) machining, playing a key role in ensuring machining quality and improving efficiency. This paper proposes a feedrate planning method based on feedrate profile shaping to satisfy machine axis constraints, including axis velocity, acceleration, and jerk limits. First, the five-axis machining path is represented using parametric curves. By combining the geometric characteristics of the path with machine axis velocity constraints, the upper bound of the feedrate under static constraints is derived. On this basis, machine axis acceleration and jerk constraints are further incorporated to establish feedrate planning criteria, thereby obtaining a distribution of feasible points that satisfies dynamic constraints. Then, a feedrate curve is generated using a profile shaping strategy based on the feasible point distribution, and further optimized through a corner shaping method. As a result, the planned feedrate strictly satisfies machine axis constraints along the entire tool path while ensuring continuity and smoothness of the feedrate profile. Finally, the effectiveness and reliability of the proposed method are validated through simulations of the parametric curve and experimental machining of an impeller blade. Full article

Laser welding is widely used in complex manufacturing processes and valued for its reliability, flexibility, and high energy density. However, achieving the desired weld quality requires the detection and, ideally, the prevention of defects. Besides other methods, machine learning (ML) has been integrated into laser welding with the primary goal of process optimization and quality improvement, for example, by enabling process adaptation before or during welding to reduce defects. This survey systematically reviews publications from 2015 to 2025 that integrate machine learning and deep learning methods into laser welding optimization or adaptation processes. An extensive analysis identifies which parts of the process and for what purposes ML methods are researched and implemented and how they are evaluated, as well as the sensors, lasers, and materials involved. Furthermore, the findings are analyzed and organized into taxonomies that define overarching meta-categories into which existing approaches can be classified and contextualized. The results reveal that various ML approaches are applied for tasks, such as surrogate modeling, process planning, direct control, and virtual sensing and monitoring. Although many different control parameters and optimization targets are considered, laser power and welding speed dominate as the most frequently adjusted parameters, while penetration depth and weld geometry-related properties are the most common optimization targets. Finally, the survey identifies major challenges, including the lack of benchmarking datasets, standardized evaluation protocols, and interpretable models. Full article

This study presents an integrated framework that couples three-dimensional geotechnical ground modeling with a HAZUS-based urban seismic vulnerability assessment for Seoul, Korea. Over 63,000 boreholes, in situ seismic tests, and building inventory records were compiled into a unified relational database following rigorous multi-stage quality control. A multi-parameter N–Vs regression model was calibrated to supplement missing shear-wave velocity (Vs) data, reducing prediction errors by more than 20% relative to conventional empirical equations. Based on the quality-controlled Vs dataset, a high-resolution three-dimensional Vs–ground model was constructed to represent subsurface heterogeneity and associated uncertainty across the metropolitan area. The building inventory, comprising approximately 700,000 structures, was standardized according to the HAZUS structural taxonomy and mapped to Korean seismic design eras, enabling a Seoul-adapted vulnerability assessment in which exposure characterization and seismic demand are localized. Site-specific ground-motion amplification and response spectra derived from the 3D ground model were used to modify the spectral acceleration input to the HAZUS fragility functions. Results reveal pronounced spatial variability in site conditions, with northern mountainous zones corresponding primarily to NEHRP Site Class B, central districts to Class C, and southern alluvial basins to Classes D–E, producing amplification differences of up to 1.7 under identical input spectral accelerations. High-risk zones such as Gangnam, Songpa, and Yeouido exhibit concentrated expected damage where thick alluvial deposits coincide with dense stocks of mid-rise reinforced-concrete buildings. Overall, the study demonstrates that integrating high-resolution 3D geotechnical ground models with HAZUS-based fragility analysis provides a physically consistent and data-driven basis for urban-scale seismic risk assessment and resilience planning. Full article

The rhizosphere, a confined area of soil plant roots, is an intersection of microbial activity and root exudates. Known as the rhizosphere effect, it enhances crop yield and sustainability by improving nutrient availability, beneficial compounds, and pathogen control. This study combines a field-based rhizosphere–bulk soil comparison for peanut with a geostatistical approach to quantify the spatial variability of rhizosphere-driven changes in soil quality indicators in the Ghardaïa region (southern Algeria), which is known for its sandy–clay and sandy–loam soils. Samples of rhizosphere and bulk soils were prospected using a systematic plan. Subsequently, the pH, electrical conductivity, calcium carbonate, organic matter, total nitrogen, available phosphorus, total potassium, and soluble sodium were determined for each soil (rhizosphere and bulk soil). To assess the spatial variability of rhizosphere soil parameters, semi-variograms of the fitted models were generated using auto-kriging. The results showed that both types of soils were moderately alkaline, with a reduction of 5.52% in the pH of the rhizosphere compared to the bulk soils. Soils were relatively low in organic matter, with only 3.3% of soils having organic matter levels above 20 g kg⁻¹. However, organic matter contents were consistently higher in the rhizosphere (8.51 ± 4.59 g kg⁻¹) than in the bulk soil (6.78 ± 3.52 g kg⁻¹). In the rhizosphere, an increase of 10% in labile phosphorus was noted. Total nitrogen was increased by 52.57%. T-tests suggested no significant difference in potassium and sodium levels, and they were moderately present in both soils. Significantly positive relationships were noted between available phosphorus and total nitrogen (R = 0.59, p < 0.001). However, negative correlations were revealed between pH and organic matter available phosphorus (R = −0.77, p < 0.001) and pH and total nitrogen (R = −0.56, p < 0.01). These results indicate the effects of rhizosphere interactions on soil property improvements and their implications for sustainable agricultural practices, including crop rotation, intercropping, and green manure applications. Full article

Background: Attention Deficit Hyperactivity Disorder (ADHD) is associated with executive function deficits—such as planning, organization, and prospective memory—that impair autonomy and daily functioning, increase family stress, and create challenges in educational contexts. These consequences underscore the need for accessible and ecologically valid interventions addressing the cognitive, familial, and educational dimensions. Traditional approaches often lack ecological validity, and pharmacological treatment shows a limited impact on functional cognition. Objectives: This protocol outlines a feasibility study of DiverAcción, a web-based telerehabilitation system designed to enhance functional cognition through interactive and gamified tasks integrated into a comprehensive healthcare programme. Methods: A quasi-experimental feasibility study before and after the study will recruit 30 participants aged 9 to 17 years with ADHD. The study comprises an initial face-to-face session for instructions and baseline assessment (T0), followed by twelve supervised online sessions over six weeks. Therapeutic support is provided via integrated chat, email, and two scheduled videoconference check-ins. Feasibility Outcomes: include recruitment, adherence, retention, usability (SUS), acceptability (TAM), satisfaction, user-friendly design, therapeutic alliance (WAI-I), and professionals’ attitudes toward technology (e-TAP-T). Exploratory Measures: include parental self-efficacy (BPSES), parenting stress (PSI-4-SF), ADHD symptomatology (SNAP-IV), executive functioning (BRIEF-2), time management (Time-S), emotional regulation (ERQ-CA), prospective memory (PRMQ-C), and health-related quality of life (KIDSCREEN-52). Analyses emphasize descriptive statistics for feasibility metrics (recruitment, adherence, retention, dropout and fidelity). Assessments are conducted post-intervention (T1) and at three-month follow-up (T2) and analyzed relative to baseline using repeated-measures ANOVA or Friedman tests, depending on data distribution. Conclusions: This feasibility protocol will provide preliminary evidence on the usability, acceptability, and implementation of DiverAcción. Findings will guide refinements and inform the design of a subsequent randomized controlled trial. Full article

Chronic pain is inherently multifactorial, with biological, psychological, and social factors contributing to neuropathic pain (NP) and central sensitization (CS) syndromes. Comorbidity between functional disorders and the lack of clinical biomarkers adds to the challenge of diagnosis and treatment, leading to frustration for healthcare professionals and patients. Available treatments are limited, increasing patient suffering with personal and financial costs. This systematic review examined multisensory processing alterations in chronic pain and reviewed current pharmacological and non-pharmacological interventions. A structured search was conducted on the PubMed database using the keywords Central Sensitization, Fibromyalgia, Complex Regional Pain Syndrome, and Neuropathic Pain, combined with the keywords Vision, Audition, Olfaction, Touch, Taste, and Proprioception. Papers were then filtered to discuss current treatment approaches. Articles within the last five years, from 2018 to 2023, have been included. Papers were excluded if they were animal studies; investigated tissue damage, disease processes, or addiction; or were conference proceedings or non-English. Results were summarized in table form to allow synthesis of evidence. As this study is a systematic review of previously published research rather than a clinical trial or experimental investigation, the risk of bias was assessed independently by at least two reviewers. 138 studies were identified and analyzed. Of these, 96 focused primarily on treatment options for chronic pain and were analyzed for this systematic review. There were a few emerging themes. No one therapy is effective, so a multidisciplinary approach to diagnosis, including pharmacological, somatic, and psychological treatment, is generally predicted to achieve the best outcomes. Cranial neurovascular compromise, especially of the trigeminal, glossopharyngeal, and potentially the vestibulocochlear nerve, is being increasingly revealed with the advancement of neuroimaging. Cortical and deep brain stimulation to evoke neuroplasticity is an emerging and promising therapy and warrants further investigation. Finally, including patients in their treatment plan allows them control and offers the ability to self-manage their pain. Risk of bias limits the ability to judge the quality of evidence. Full article

Despite decades of research, autonomous forklifts remain deployed at a small scale (2–50 vehicles), while industrial warehouses require coordinating hundreds of vehicles in environments shared with human workers. This systematic review analyzes forklift-specific autonomous technologies published between 2010 and 2025 across major robotics databases (including IEEE Xplore, ACM, Elsevier, and related venues) to identify deployment barriers. Following the PRISMA guidelines, we systematically selected 122 peer-reviewed papers addressing forklift-specific challenges across eight subsystems: vehicle modeling, localization, planning, control, vision-based manipulation, multi-vehicle coordination, and safety. We synthesized 80 methods through 8 standardized comparison tables with quality assessment based on validation rigor. State-of-the-art approaches demonstrate strong laboratory performance: localization achieving ±1.4 mm accuracy, control enabling sub-centimeter manipulation, planning reducing mission times by 2–55%, vision reaching 98%+ recognition, and safety frameworks cutting rollover risk by 53–59%. However, validation predominantly occurs at laboratory scale, revealing a critical deployment gap. These achievements do not scale to industrial environments due to fleet coordination complexity, payload variability, and unpredictable human behavior. Our contributions include the following: (1) performance rankings with technology selection guidance, (2) systematic gap characterization, and (3) research priorities addressing mixed-fleet coordination, learning-enhanced control, and human-aware safety. This review was not prospectively registered. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Multimodal electric transport systems demand substantial active and reactive energy, making power-quality management essential for ensuring efficient and reliable operation. This paper analyses reactive-energy transport in mass-transit networks and introduces a unified current-based framework that enables a consistent interpretation of the conventional power factor under harmonic distortion, fundamental unbalance, and short-term load fluctuation, without modifying its original definition. The framework enables a consistent assessment of compensation needs, independent of billing schemes, and is aligned with the way modern compensation equipment is specified and controlled. Applied to the Metro de Medellín system, field measurements and digital simulations show that traditional reactive-energy limits fail to distinguish between harmful and beneficial operating conditions, leading to disproportionate charges under the former Colombian regulation. Beyond this case, the proposed framework is directly applicable to other electric-mobility systems—including railways, trams, trolleybuses, and electric-bus networks—providing clearer technical signals for compensation planning and offering a comprehensive basis for future regulatory approaches that integrate multiple power-quality phenomena. Full article

Spine stereotactic radiotherapy and radiosurgery (SSRS) techniques, encompassing both fractionated stereotactic treatments and single-fraction radiosurgery, are widely used for the management of spinal metastases due to their ability to deliver highly conformal radiation while limiting dose to adjacent critical structures. Clinical outcomes following SSRS, including durable local control and acceptable toxicity, have been reported previously in multiple institutional series. In this manuscript, we describe the clinical workflow used to deliver SSRS at a high-volume academic center, with emphasis on the medical physics processes that support routine clinical practice. Key elements of the workflow include patient selection, treatment region-specific immobilization, CT and MRI-based simulation, treatment planning, patient-specific quality assurance, and image-guided treatment delivery. Rather than presenting new outcome data, this work provides a descriptive overview of how established SSRS techniques are integrated into day-to-day clinical care. Full article

Background/Objectives: Isolated cleft lips and/or palates often require orthognathic treatment. Traditional planning based on 2D images and plaster models limits precision; therefore, virtual surgical planning (VSP) and Computer-Aided Design and Computer-Aided Manufacturing (CAD/CAM) technologies are increasingly being used. The aim of this scoping review was to analyze the techniques, outcomes, and gaps in research on VSP in orthognathics for patients with isolated (non-syndromic) clefts. Methods: Searches were conducted in July 2025 in seven databases (including PubMed, Scopus, and Cochrane) without language restrictions, in accordance with the PRISMA guidelines for scoping reviews. Of the 2836 records, 36 publications were eligible after deduplication and full-text screening, and their Level of Evidence (LoE) was assessed using the Oxford CEBM scale. A risk of bias assessment was also conducted according to JBI tools. Results: The identified studies primarily comprised LoE III and IV; there were no systematic reviews or randomized controlled trials (LoE I). Descriptions of bimaxillary procedures and LeFort I osteotomies dominated. The most commonly used software was ProPlan CMF, Dolphin 3D, and Rhinoceros, although other tools have emerged in recent years. The available studies suggest that VSP increases translational and rotational accuracy and facilitates individualized treatment, and bimaxillary procedures bring better functional and aesthetic outcomes in patients with severe maxillary hypoplasia. Conclusions: Despite the growing interest in VSP in orthognathics, the scientific evidence is limited and mostly of lower quality. Well-designed prospective studies are needed to assess the long-term stability, quality of life, and cost-effectiveness of modern technologies. Full article

Controlling pre-analytical conditions for medical biology tests, particularly during transport, is crucial for complying with the ISO 15189 standard and ensuring high-quality medical services. The use of drones, also known as unmanned aerial vehicles, to transport clinical samples is growing in scale, but requires prior validation to verify that there is no negative impact on the test results provided to doctors. This study aimed to establish a secure, high-quality solution for transporting biological samples by drone in a coastal region of France. The 80 km routes passed over several densely populated urban areas, with take-off and landing points within hospital grounds. The analytical and clinical impact of this mode of transport was compared according to two protocols: an interventional clinical trial on 30 volunteers compared to the reference transport by car, and an observational study on samples from 126 hospitalized patients compared to no transport. The system enabled samples to be transported without damage by maintaining freezing, refrigerated, and room temperatures throughout the flight, without any significant gain in travel time. Analytical variations were observed for sodium, folate, GGT, and platelet levels, with no clinical impact on the interpretation of the results. There is a risk of time-dependent alterations of blood glucose measurements in heparin tubes, which can be corrected by using fluoride tubes. This demonstrated the feasibility and security of transporting biological samples over long distances in line with the ISO 15189 standard. Controlling transport times remains crucial to assessing the quality of analyses. It is imperative to devise contingency plans for backup solutions to ensure the continuity of transportation in the event of inclement weather. Full article

This study contributes to the advancement of circular economy practices in polymer manufacturing by applying machine learning algorithms (MLA) to predict the tensile strength of recycled low-density polyethylene (LDPE) building films. As the construction and packaging industries increasingly seek eco-efficient and low-carbon materials, recycled LDPE offers a valuable route toward sustainable resource management. However, ensuring consistent mechanical performance remains a challenge when reusing polymer waste streams. To address this, tensile tests were conducted on LDPE films produced from recycled granules, measuring tensile strength, strain, mass per unit area, thickness, and surface roughness. Three established machine learning algorithms—feed-forward Neural Network (NN), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were implemented, trained, and optimized using the experimental dataset using R statistical software (version 4.4.3). The models achieved high predictive accuracy, with XGBoost providing the most robust performance and the highest level of explainability. Feature importance analysis revealed that mass per unit area and surface roughness have a significant influence on film durability and performance. These insights enable more efficient production planning, reduced raw material usage, and improved quality control, key pillars of sustainable technological innovation. The integration of data-driven methods into polymer recycling workflows demonstrates the potential of artificial intelligence to accelerate circular economy objectives by enhancing process optimization, material performance, and resource efficiency in the plastics sector. Full article

Background/Objectives: Lower limb amputation (LLA) negatively affects the physical and psychological health of individuals, leading to a lower quality of life and sedentary lifestyle. The objective of this scoping review is to search for evidence regarding physical activity interventions in individuals with LLA, investigating improvements in specific outcomes related to quality of life and performance. Methods: PRISMA guidelines—extension for scoping reviews—were used to structure the study. The research was conducted between 26 July 2023 and 30 September 2023; it was structured by defining two PICO questions (P = amputation, I = physical exercise, O1 = quality of life, and O2 = performance) through Pubmed, Cochrane, and Pedro databases. The study included subjects with LLA of any etiology, in prosthetic or pre-prosthetic phase, practicing non-competitive physical activity. The results were then subjected to both qualitative and quantitative analysis. Results: Of the 615 studies identified, 18 were included in the review. They consisted of 6 systematic reviews (SR), 5 RCTs, 4 case–control studies, 1 case report (CR), and 2 cross-sectional (CS). Physical activity (PA) interventions were extremely heterogeneous and were, therefore, categorized into 6 modalities: surveys were the most reported strategies (57%), followed by personalized training (23%), strength training (13%), endurance training (13%), combined training (2%), and gait training (5%). Due to the heterogeneity of the studies, the variety of interventions proposed and the different outcomes registered, there is no evidence that one approach is more effective than another, while each group showed benefits on different specific outcomes. In total, five outcome categories were identified: quality of life was the most frequently analysed (42%), followed by cardiovascular fitness (20%), muscular fitness (14%), gait parameters (13%), functionality and disability (11%). Conclusions: PA represents a valuable strategy for improving performance and quality of life in individuals with LLA, offering a variety of interventions. Although there is no evidence that one strategy is better than the others, each activity has proven to be effective on specific outcomes, therefore, the choice must depend on the patient’s necessities. The preferred option should be the personalization of the training according to individual needs, coupled with long-term planning and remote monitoring. Creating meeting places and supporting occasions for sports activities could be a valid option. Further research could help to clarify the benefits of such interventions and enhance the understanding of how to optimize the management of LLA patients. Full article

Cropland fires are an important source of air pollution emissions and have a significant impact on regional air quality and human health. Although straw-burning ban policies have been implemented to mitigate emissions, the dynamics of PM_2.5 emissions from cropland fires under such stringent regulations are still not fully understood. This study utilizes PM_2.5 emission data from the Global Fire Assimilation System (GFAS), land-cover data from CLCD, and PM_2.5 concentration data from ChinaHighAirPollutants (CHAP) to examine the dynamic evolution of PM_2.5 emissions from cropland fires under straw-burning ban policies across China and to assess their environmental impacts. The results show that the 2013 Air Pollution Prevention and Control Action Plan initiated the development of provincial straw-burning ban policies. These policies resulted in a drastic reduction in PM_2.5 emissions from cropland fires in North China (NC), with a 65% decrease in 2022 compared to the 2012 peak. In contrast, a notable lagged effect was observed in Northeast China (NEC), where the increasing trend of PM_2.5 emissions was not reversed until 2017. By 2022, emissions in this region had declined by 53% and 45% compared to the 2015 peak and 2017 sub-peak, respectively. Moreover, significant regional differences were found in the environmental impacts of PM_2.5 emissions from cropland fires, with strong effects during summer in NC and during spring and autumn in NEC. This study provides empirical support for understanding the environmental impacts of cropland fires in key regions of China and offers critical insights to inform and refine related pollution control policies. Full article