Search Results (1,372)

Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which creates significant barriers for custom-built UAVs. Existing trajectory prediction methods are primarily designed for motion forecasting from dense historical observations, which are unsuitable for scenarios lacking historical data (e.g., takeoff phases) or requiring trajectory generation from sparse waypoint specifications (4–6 constraint points). This distinction necessitates architectural designs optimized for spatial interpolation rather than temporal extrapolation. To address these limitations, we present a segmented LSTM framework for complete autonomous flight trajectory prediction. Given target waypoints, our architecture decomposes flight operations, predicts different maneuver types, and outputs the complete trajectory, demonstrating new possibilities for mission-level trajectory planning in autonomous UAV systems. The system consists of a global duration predictor (0.124 MB) and five segment-specific trajectory generators (∼1.17 MB each), with a total size of 5.98 MB and can be deployed in various edge devices. Validated on real Crazyflie 2.1 data, our framework demonstrates high accuracy and provides reliable arrival time predictions, with an Average Displacement Error ranging from 0.0252 m to 0.1136 m. This data-driven approach avoids complex parameter configuration requirements, supports lightweight deployment in edge computing environments, and provides an effective solution for multi-UAV coordination and mission planning applications. Full article

Cardiovascular disease (CVD), diabetes, and obesity pose major public health challenges in the United Arab Emirates (UAE), contributing substantially to morbidity, mortality, and healthcare expenditure. Despite progress in expanding access and service delivery, Health Quality Management (HQM) practices remain constrained. This study represents one of the first comprehensive, co-productive efforts to evaluate and strengthen HQM for CVD, diabetes and obesity in the UAE. Using a sequential, multi-phase design, it integrates evidence synthesis with the active engagement of interest groups to bridge gaps between research, policy, and practice. Phase 1 involves a scoping review to establish an evidence base on existing HQM practices and system-level challenges. Phase 2 conducts mapping and interviews with health professionals, policymakers, and patients to capture contextual insights. Phase 3 synthesizes findings to identify critical gaps, opportunities, and emerging research questions that can guide future inquiry. Phase 4 convenes consultative and consensus-building workshops to co-produce actionable recommendations and facilitate knowledge translation and exchange among health authorities, academic institutions, and other interest groups. Guided by the Institute of Medicine’s quality domains, the Donabedian model, and WHO quality indicators, this study situates HQM within the UAE’s ongoing shift toward value-based healthcare. The expected outcomes include the identification of key barriers to and facilitators of effective HQM, the formulation of context-specific recommendations to strengthen performance and coordination, production of knowledge translation outputs and the generation of new research priorities, thus contributing to achieving UAE Vision 2031 and global NCD targets. Full article

With the rapid development of the global urbanization process, the resource utilization of construction waste has become one of the core issues of the development of a circular economy and has been widely concerned by the international community. However, China’s resource utilization efficiency in this field is still in the development stage, and cthere is still a gap with developed countries. It is urgent to systematically solve scientific problems such as low resource utilization efficiency, prominent technical bottlenecks, and imperfect whole process management mechanisms, so as to realize the coordinated high-quality development of the economy, society, and the environment. In order to scientifically predict the generation trend of construction waste and assess the resource potential, this study takes Beijing as the research object. Based on the historical data samples of construction waste in Beijing from 2001 to 2024, the analysis framework of “output estimation—trend prediction—value evaluation” is constructed. The ARIMA model is selected as the core tool of prediction, because it can match the phased change characteristics of construction waste output with the development of the city in time series data processing. Combined with the cost–benefit analysis method, it makes a quantitative analysis of the future production scale of construction waste and the economic benefits of resource utilization in Beijing. The research results show that from 2025 to 2034, the production of construction waste in Beijing will show a trend of first decreasing and then increasing, and it will reach 13.599 million tons by 2034. The resource utilization of construction waste in the next 10 years is expected to bring about USD 2.998 billion of economic benefits. This prediction result may be related to the policy guidance of Beijing’s urban renewal, changes in construction activities, and industrial technology upgrading. Accordingly, this study puts forward countermeasures and suggestions to help the development of industrialization, providing theoretical support and practical references for the sustainable development of the resource utilization of construction waste. Full article

The recognition of trauma-induced coagulopathy (TIC) as an endogenous response to traumatic injuries rather than a consequence of therapeutic interventions has shifted the clinical approach toward an early and physiologically based hemostatic resuscitation. Prompt identification and correction of fibrinolysis and fibrinogen level derangements, dysregulated thrombin generation, and platelet dysfunction represent the cornerstones of the treatment strategies. Currently available viscoelastic hemostatic assays (VHAs) are point-of-care devices able to rapidly assess the phases of clot initiation, propagation, stabilization, and degradation, as well as isolate the contribution of specific elements—e.g., fibrinogen—to the coagulation process in fully automated analyses by multi-channel single-use cartridges. As a result, in the last decade, VHAs have been widely investigated as tools to implement individualized protocols of hemostatic resuscitation. Current guidelines support their use to optimize transfusion load in a goal-directed strategy. Nevertheless, contrasting evidence has emerged regarding the improvement in main clinical outcomes induced by the VHA-based algorithm of hemostatic resuscitation compared with those guided by conventional coagulation tests, and their place in the management of this peculiar population is still a matter of debate. We propose a narrative review ranging from TIC physiopathology as a proper substrate for viscoelastic diagnostic technique, through the strengths and weaknesses of VHAs, to their application in clinical practice. Full article

Inefficient management of dissolved oxygen (DO) in intensive aquaculture systems limits fish welfare and productivity by creating oxygen-deficient zones and promoting hydrodynamic conditions that hinder their dispersion. Because water movement directly influences how oxygen is transported and mixed within the culture unit, inadequate flow management can allow localized hypoxia to persist even when total oxygen input appears sufficient. To address this issue, this study proposes an integrated methodology that combines in situ respirometry measurements with Computational Fluid Dynamics (CFD) simulations to evaluate the spatial distribution of DO and diagnose the operational performance of aquaculture systems. The methodology quantifies oxygen consumption using intermittent-flow respirometry, applies a three-dimensional two-phase CFD model (water–oxygen) incorporating experimental oxygen consumption rates as boundary conditions, and validates the model under real operating conditions, focusing on active metabolism as the most demanding physiological state. The model generates a spatial distribution of DO patterns that are significantly modified by pond geometry, water flow characteristics, the metabolism of the fish and fish positioning. The differences between experimental and simulated values ranged from 7.8% to 10.7%, confirming the accuracy of the proposed method. The integration of in situ metabolic measurements with CFD modeling provides a realistic representation of DO dynamics, enabling system optimization and promoting more efficient and sustainable aquaculture. Full article

Background: Quantifying intraoperative nociceptive responses under general anesthesia remains challenging, particularly during complex procedures such as spinal surgery. The Nociception Level (NoL) index is a multiparametric tool designed to reflect the dynamic balance between nociception and analgesia in anesthetized patients. This study aimed to evaluate NoL fluctuations during predefined phases of spinal surgery and assess their relationship to anesthetic administration. Methods: This prospective observational study enrolled 44 adult patients undergoing lumbar discectomy, laminectomy, or spinal fusion under remifentanil–propofol anesthesia. Continuous NoL monitoring was performed using the PMD100™ system. Sixteen anatomically and procedurally defined surgical phases were analyzed. The primary outcome was the mean NoL value in each phase. The secondary outcome was the association between NoL values and intraoperative infusion rates of remifentanil and propofol. Repeated-measures ANOVA with Bonferroni correction was used for phase comparisons. Results: Mean NoL values remained within the target range (10–25) in most phases. However, significant elevations were observed during pedicle screw insertion (mean 27.9, SD ± 17.7), cage insertion (27.6, SD ± 10.5), and flavectomy (28.0, SD ± 27.0), indicating increased nociceptive burden. The lowest NoL values occurred during skin closure (16.6, SD ± 11.2) and discectomy (18.0, SD ± 2.8). Propofol and remifentanil infusion rates remained within standard clinical ranges but were slightly elevated during high-NoL phases. Conclusions: Despite standardized anesthesia, distinct nociceptive peaks were observed during specific stages of spinal surgery. These findings suggest that NoL monitoring may help identify high-nociception phases and guide tailored analgesic strategies. Future randomized trials are warranted to assess whether protocolized NoL-guided anesthesia improves intraoperative management and postoperative outcomes. Full article

Background/Objectives: Artificial intelligence (AI)-based tools are increasingly being used in tailored nutrition management, and evaluating their compliance with guidelines is significant in clinically sensitive areas, including bariatric surgery. This study aimed to investigate the extent to which diet plans recommended by AI models in the early period following sleeve gastrectomy (SG) align with current clinical nutrition guidelines (ASMBS, AACE/TOS). Methods: A total of 360 menu plans were generated using three AI platforms—ChatGPT-4o, DeepSeek V3, and Grok-3—for 40 simulated patients (20 females, 20 males; BMI 32–45 kg/m²) across three postoperative stages: liquid (day 5), puree (day 16), and solid (day 35). The energy and nutrient contents of the menus were analyzed using BeBiS 8.1; an experienced dietitian assessed compliance with the guidelines using a structured checklist. Nutrient intakes and guideline compliance scores were examined using within-patient Friedman tests followed by Bonferroni-adjusted pairwise comparisons. Results: ChatGPT-4o demonstrated the highest overall compliance scores, particularly in the liquid and puréed phases, while DeepSeek produced higher values for several micronutrients. All models showed substantial gaps in essential postoperative recommendations, most notably thiamine and multivitamin supplementation. Conclusions: Although LLMs can generate partially guideline-concordant postoperative diet plans, they consistently omit several critical elements of bariatric nutrition care. These findings indicate that LLM-generated menus may serve as supportive educational tools, and diet planning must be performed under the guidance of a specialist dietitian. This simulation does not assess clinical safety, efficacy, or patient outcomes and should not be used as a substitute for dietitian-led postoperative nutrition care. Full article

Gallium nitride (GaN) has emerged as one of the most promising wide-bandgap semiconductors for next-generation space photovoltaics. In contrast to conventional III–V compounds such as GaAs and InP, which are highly efficient under terrestrial conditions but suffer from radiation-induced degradation and thermal instability, GaN offers an exceptional combination of intrinsic material properties ideally suited for harsh orbital environments. Its wide bandgap, high thermal conductivity, and strong chemical stability contribute to superior resistance against high-energy protons, electrons, and atomic oxygen, while minimizing thermal fatigue under repeated cycling between extreme temperatures. Recent progress in epitaxial growth—spanning metal–organic chemical vapor deposition, molecular beam epitaxy, hydride vapor phase epitaxy, and atomic layer deposition—has enabled unprecedented control over film quality, defect densities, and heterointerface sharpness. At the device level, InGaN/GaN heterostructures, multiple quantum wells, and tandem architectures demonstrate outstanding potential for spectrum-tailored solar energy conversion, with modeling studies predicting efficiencies exceeding 40% under AM0 illumination. In this review article, the current state of knowledge on GaN materials and device architectures for space photovoltaics has been summarized, with emphasis placed on recent progress and persisting challenges. Particular focus has been given to defect management, doping strategies, and bandgap engineering approaches, which define the roadmap toward scalable and radiation-hardened GaN-based solar cells. With sustained interdisciplinary advances, GaN is anticipated to complement or even supersede traditional III–V photovoltaics in space, enabling lighter, more durable, and radiation-hard power systems for long-duration missions beyond Earth’s magnetosphere. Full article

This paper describes an approach for developing decision support systems (DSS) for strategic and tactical operating room (OR) planning and scheduling problems. These problems involve assigning amounts of time and specific time blocks in the ORs to surgical specialties and/or surgeons. A four-phase iterative software development lifecycle (SDLC) approach is used to develop a DSS that has a graphical user interface, a data management system, and optimization and simulation systems that incorporate mathematical programming models, solution methods, and discrete event simulation models. Results from the computational experience show that the plans generated by the DSS utilize at least 78% of the available OR time on average and use the downstream recovery ward (RW) beds in a balanced way that never exceeds the number of available beds. Full article

The end-of-life phase of a building, which includes demolition and waste disposal, represents a crucial aspect of sustainable construction. In Europe, construction and demolition (C&D) waste accounts for approximately 40% of the total waste generated in the EU, making its management a global challenge. The EU Construction & Demolition Waste Management Protocol (2024) emphasizes the importance of evaluating, before proceeding with the demolition of a building, whether renovation could be a more efficient solution, considering economic, environmental, and technical aspects. From an economic perspective, demolition costs vary depending on several factors, including project size, structural complexity, techniques employed (conventional or non-conventional), materials to be removed, and local regulations. In addition to the direct costs of the intervention, it is essential to consider indirect impacts, such as the management of construction and demolition (C&D) waste, the removal of hazardous substances, and potential environmental damage to be mitigated. This study analyzes a case located in Italy, in the municipality of Caivano (Metropolitan City of Naples, in Campania region), concerning a building that required energy efficiency improvements and seismic upgrades. The decision to demolish and rebuild proved to be economically more advantageous than renovation, while also allowing a 35% increase in volume, enabling the creation of a greater number of housing units. Through the analysis of this real case study, the aim is to highlight how investments in demolition, if properly planned, designed, assessed, and managed, can effectively contribute to building redevelopment, supporting the transition towards a sustainable construction model in line with the principles of the circular economy. Full article

In response to the challenges posed by the complex evolution of risks and the static nature of traditional assessment methods during the construction of long-span prestressed concrete (PC) continuous rigid-frame bridges, this study proposes a risk assessment framework that integrates visual perception with dynamic probabilistic reasoning. By combining an improved YOLOv8 model with the Decision-making Trial and Evaluation Laboratory–InterpretiveStructure Modeling (DEMATEL–ISM) algorithm, the framework achieves intelligent identification of risk elements and causal structure modelling. On this basis, a dynamic Bayesian network (DBN) is constructed, incorporating a sliding window and forgetting factor mechanism to enable adaptive updating of conditional probability tables. Using the Tongshun River Bridge as a case study, at the identification layer, we refine onsite targets into 14 risk elements (F1–F14). For visualization, these are aggregated into four categories—“Bridge, Person, Machine, Environment”—to enhance readability. In the methodology layer, leveraging causal a priori information provided by DEMATEL–ISM, risk elements are mapped to scenario probabilities, enabling scenario-level risk assessment and grading. This establishes a traceable closed-loop process from “elements” to “scenarios.” The results demonstrate that the proposed approach effectively identifies key risk chains within the “human–machine–environment–bridge” system, revealing phase-specific peaks in human-related risks and cumulative increases in structural and environmental risks. The particle filter and Monte Carlo prediction outputs generate short-term risk evolution curves with confidence intervals, facilitating the quantitative classification of risk levels. Overall, this vision-guided dynamic risk assessment method significantly enhances the real-time responsiveness, interpretability, and foresight of bridge construction safety management and provides a promising pathway for proactive risk control in complex engineering environments. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

Background: Transition from paediatric to adult care in congenital heart disease (CHD) represents a pivotal and vulnerable phase that critically influences long-term survival, morbidity, and quality of life. Advances in paediatric cardiology and surgery have generated a rapidly growing population of adults with congenital heart disease who exhibit complex, lifelong, and multidisciplinary needs. However, survival does not equate to cure, and discontinuity of care during adolescence remains a major predictor of adverse outcomes. Despite widespread recognition of their importance, transition programmes are heterogeneous worldwide, and standardised, evidence-based protocols are missing. Objective: This review calls for action acknowledging the urgent need for structured and standardised transition programmes in CHD care, integrating the key elements that should be addressed in any programme to optimise outcomes. Content: Transition should be understood as a multidisciplinary, longitudinal process integrating medical management, patient and family education, psychological preparation, and societal inclusion. Core domains include tailored physical activity, nutritional counselling, cardiovascular risk factor management, infective endocarditis prevention, reproductive health, psychosocial support, and engagement of primary care providers, educators, and employers. Evidence demonstrates that structured transition programmes enhance health literacy, adherence, and self-management, while reducing loss to follow-up. The active involvement of primary care providers, psychologists, educators, and employers is essential to sustain holistic and equitable care. Conclusions: Transition should be reframed as an essential, lifelong component of CHD care. The development and implementation of standardised, multidisciplinary, evidence-based transition protocols are urgently required to ensure continuity, empower patients, and optimise long-term clinical and psychosocial outcomes for adults with CHD. Full article

The shift toward remote and automated admission processes in higher education introduces new challenges, including evaluator subjectivity and risks of applicant fraud. The FAIR-VID project addresses these issues by developing an artificial intelligence system that integrates multimodal data fusion with semi-supervised deep learning to assess applicant video interviews, submitted documents, and form data. This paper presents the project’s data preprocessing pipeline, designed to fuse heterogeneous modalities and to support seamless interaction between AI agents and human decision-makers throughout the admission workflow. The proposed process is intentionally general, making it applicable not only to international university admissions but also to broader human resource management and hiring contexts. Emphasis is placed on the need for robust and transparent AI adoption in admission and recruitment, supported by open-source modules and models at every stage of interaction between applicants and institutions. As a proof of concept, we provide open-source solutions for the analysis of video interviews, images, and documents enriched with semantic descriptions generated by large multimodal and complementary AI models. The paper details the multi-phase implementation of this pipeline to create structured, semantically rich datasets suitable for training advanced deep learning systems for comprehensive applicant assessment and fraud detection. Full article

This study investigates the conformable nonlinear Schrödinger equation (NLSE) with self-phase modulation (SPM) and Kudryashov’s generalized refractive index, crucial for pulse propagation in optical fibers. By applying the modified simplest equation method, we derive several novel soliton solutions and investigate their dynamic behavior within the NLSE framework enhanced with a conformable derivative. The governing conformable NLSE also exhibits symmetry patterns that support the structure and stability of the constructed soliton solutions, linking this work directly with symmetry-based analysis in nonlinear wave models. Furthermore, various graphs are presented through 2D, 3D, and contour plots. These visualizations highlight different soliton profiles, including kink-type, wave, dark, and bell-shaped solitons, showcasing the diverse dynamics achievable under this model, influenced by SPM and Kudryashov’s generalized refractive index. The influence of the conformable parameter and temporal effects on these solitons is also explored. These findings advance the understanding of nonlinear wave propagation and have critical implications for optical fiber communications, where managing pulse distortion and maintaining signal integrity are vital. Full article