Search Results (30,189)

Mobile augmented reality games (MARGs) generate rich digital traces of how students engage with complex, place-based learning tasks. This study analyses gameplay logs from the Art Nouveau Path, a location-based MARG within the EduCITY Digital Teaching and Learning Ecosystem (DTLE), to develop a learning analytics workflow that uses detailed gameplay logs to inform sustainability-focused educational design. During the post-game segment of a repeated cross-sectional intervention, 439 students in 118 collaborative groups completed 36 quiz tasks at 8 Art Nouveau heritage Points of Interest (POI). Group-level logs (4248 group-item responses) capturing correctness, AR-specific scores, session duration and pacing were transformed into interpretable indicators, combined with error mapping and cluster analysis, and triangulated with post-game open-ended reflections. Results show high overall feasibility (mean accuracy 85.33%) and a small subset of six conceptually demanding items with lower accuracy (mean 68.36%, range 58.47% to 72.88%) concentrated in specific path segments and media types. Cluster analysis yields three collaborative gameplay profiles, labeled ‘fast but fragile’, ‘slow but moderate’ and ‘thorough and successful’, which differ systematically in accuracy, pacing and engagement with AR-mediated tasks. The study proposes a replicable event-based workflow that links mobile AR gameplay logs to design decisions for heritage-based education for sustainability. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

(1) Background: In hospitals, mattresses are often relocated for cleaning or patient transfer, leading to mismatches between actual and recorded bed locations. Manual updates are time-consuming and error-prone, requiring an automatic localization system that is cost-effective and easy to deploy to ensure traceability and reduce nursing workload. (2) Purpose: This study presents a pragmatic, large-scale implementation and validation of a BLE-based localization system using RSSI measurements. The goal was to achieve reliable room-level identification of smart mattresses by leveraging existing hospital infrastructure. (3) Results: The system showed stable signals in the complex hospital environment, with a 12.04 dBm mean gap between primary and secondary rooms, accurately detecting mattress movements and restoring location confidence. Nurses reported easier operation, reduced manual checks, and improved accuracy, though occasional mismatches occurred when receivers were offline. (4) Conclusions: The RSSI-based system demonstrates a feasible and scalable model for real-world asset tracking. Future upgrades include receiver health monitoring, watchdog restarts, and enhanced user training to improve reliability and usability. (5) Method: RSSI–distance relationships were characterized under different partition conditions to determine parameters for room differentiation. To evaluate real-world scalability, a field validation involving 266 mattresses in 101 rooms over 42 h tested performance, along with relocation tests and nurse feedback. Full article

This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

Chronic obstructive pulmonary disease (COPD) and tuberculosis (TB) increasingly co-occur in low- and middle-income countries and aging populations. Prior pulmonary TB is a robust, smoking-independent determinant of COPD and is linked to persistent systemic inflammation, endothelial dysfunction, dyslipidemia, and hypercoagulability axes that also amplify cardiovascular disease (CVD) risk. We conducted a targeted narrative non-systematic review (2005–2025) of PubMed/MEDLINE, Embase, Scopus, and Web of Science, selecting studies for clinical relevance across epidemiology, clinical phenotypes, pathobiology, biomarkers, risk scores, sleep-disordered breathing, and management. No quantitative synthesis or formal risk-of-bias assessment was performed. Accordingly, findings should be interpreted as a qualitative synthesis rather than pooled estimates. Prior TB is associated with a distinctive COPD phenotype characterized by mixed obstructive–restrictive defects, reduced diffusing capacity (DLCO), radiographic sequelae, and higher exacerbation/hospitalization burden. Mechanistic insights: Convergent mechanisms chronic immune activation, endothelial injury, prothrombotic remodeling, molecular mimicry, and epigenetic reprogramming provide biologic plausibility for excess CVD, venous thromboembolism, and pulmonary hypertension. Multimarker panels spanning inflammation, endothelial injury, myocardial strain/fibrosis, and coagulation offer incremental prognostic value beyond clinical variables. While QRISK4 now includes COPD, it does not explicitly model prior TB or COPD-TB outcomes, but data specific to post-TB cohorts remain limited. Clinical implications: In resource-constrained settings, pragmatic screening, prioritized PAP access, guideline-concordant pharmacotherapy, and task-shifting are feasible adaptations. A history of TB is a clinically meaningful modifier of cardiopulmonary risk in COPD. An integrated, multimodal assessment history, targeted biomarkers, spirometry/lung volumes, DLCO, 6 min walk test, and focused imaging should guide individualized care while TB-aware prediction models and implementation studies are developed and validated in high-burden settings. Full article

Objective: To test whether inpatient electrical muscle stimulation (EMS) using Russian current (5 kHz carrier, 50 Hz modulation; 4 s ON/6 s OFF) improves mobility and balance in elderly people with chronic cerebral ischaemia. Design: Prospective single-centre controlled observational pilot, embedded in routine inpatient rehabilitation; no concealed randomisation (EMS + standard care; sham EMS + standard care; standard care only (control)). Methods: A single-centre controlled observational study with three groups was conducted (EMS n = 27, control n = 10, sham n = 7) with 3–9 sessions over 2 weeks (20 min; quadriceps and calves). Pre/Post Outcomes: Tinetti (balance/gait), Rivermead Mobility Index, Timed Up and Go (TUG), ankle extensor maximal voluntary force (MVF), stabilography (statokinesiogram path length (L), mean velocity of COP (V), sway area (S), and myotonometry; ANOVA, α = 0.05). Ethics approval and informed consent were obtained. Between-group differences in change scores were evaluated descriptively, and no formal hypothesis-testing was planned. Results: EMS showed significant gains versus control/sham—higher Tinetti total and Rivermead scores, faster TUG, higher MVF, and improved stabilography in the eyes-closed condition (reduced L, V, and S), with good tolerability and no serious adverse events (SAEs). Conclusions: Short-course Russian-current EMS is feasible and associated with clinically meaningful improvements in balance, gait, and strength in elderly patients with chronic cerebral ischaemia; however, larger randomised trials are warranted. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

To address the deficiencies in global search capability and population diversity decline of the black-winged kite algorithm (BKA), this paper proposes an enhanced black-winged kite algorithm integrating opposition-based learning and quasi-Newton strategy (OQBKA). The algorithm introduces a mirror imaging strategy based on convex lens imaging (MOBL) during the migration phase to enhance the population’s spatial distribution and assist individuals in escaping local optima. In later iterations, it incorporates the quasi-Newton method to enhance local optimization precision and convergence performance. Ablation studies on the CEC2017 benchmark set confirm the strong complementarity between the two integrated strategies, with OQBKA achieving an average ranking of 1.34 across all 29 test functions. Comparative experiments on the CEC2022 benchmark suite further verify its superior exploration–exploitation balance and optimization accuracy: under 10- and 20-dimensional settings, OQBKA attains the best average rankings of 2.5 and 2.17 across all 12 test functions, outperforming ten state-of-the-art metaheuristic algorithms. Moreover, evaluations on three constrained engineering design problems, including step-cone pulley optimization, corrugated bulkhead design, and reactor network design, demonstrate the practicality and robustness of the proposed approach in generating feasible solutions under complex constraints. Full article

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy. Full article

Background/Objectives: Artificial intelligence (AI) is an emerging technology for diagnosing liver fibrosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD), but a comprehensive synthesis of its performance is lacking. This systematic review (SR) aimed to evaluate the current evidence of AI models for diagnosing or staging liver fibrosis in patients with MASLD compared to conventional diagnostic tools. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, Embase, LILACS, IEEE Series, and Association for Computing Machinery (ACM). Primary studies applying AI to diagnose fibrosis in adults with MASLD were included. Risk of bias was assessed using the QUADAS-2 tool, and methodological reporting was evaluated according to the MINimum Information for Medical AI Reporting (MINIMAR) guideline. A narrative synthesis was performed, grouping studies by data type (clinical/laboratory vs. imaging) and summarizing diagnostic performance and clinical application. A frequency-based analysis was applied to identify the most recurrent predictive features, and an analysis of the AI architecture and application was reported. The review was registered in PROSPERO (CRD420251035919). Results: Twenty-one studies were included, encompassing 19,221 patients and 5237 images. Across studies, AI models consistently outperformed non-invasive scores such as Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS). The most frequent predictive variables were identified. Despite an overall low risk of bias, methodological transparency and external validation were limited. Conclusions: AI is feasible for the non-invasive diagnosis of liver fibrosis in MASLD, demonstrating superior accuracy to standard clinical scores. Broader clinical application is limited by the lack of external validation and high heterogeneity among the studies. Prospective validation in diverse, multicenter cohorts is essential before AI can be integrated into routine clinical practice. Full article

This study develops a green, high-performance, cement-based grout by replacing manufactured sand with iron tailings sand (ITS) at ratios of 0–50% to address resource depletion. Fluidity, mechanical strength, and expansion rates were experimentally evaluated to determine engineering feasibility. The results indicate that while ITS inclusion reduces fluidity due to particle morphology, it significantly enhances compressive strength through a physical filling effect. Specifically, the 30% replacement group achieved a peak 28-day compressive strength of 100.4 MPa. Comprehensive analysis identifies 40% as the optimal replacement rate, where the grout strictly satisfies relevant industry specifications regarding fluidity, early strength, and volume stability. This research demonstrates the practical significance of utilizing industrial solid waste to produce high-performance sleeve grout for prefabricated construction. Full article

Pain in children and adolescents remains an underestimated and undertreated condition, with long-term physical and psychosocial consequences. Non-invasive neuromodulation has emerged as a promising, low-risk approach for managing acute and chronic pain by modulating central and peripheral neural pathways. This systematic review followed PRISMA 2020 guidelines to evaluate the efficacy, safety, and clinical applicability of non-invasive neuromodulation techniques in pediatric pain. Searches were conducted in PubMed, Embase, Scopus, Web of Science, Cochrane CENTRAL, and ScienceDirect for randomized controlled trials (RCTs) published between 2015 and 2025. Six RCTs met the inclusion criteria, encompassing percutaneous electrical nerve field stimulation (PENFS), transcutaneous auricular vagus nerve stimulation (taVNS), transcutaneous electrical acupoint stimulation (TEAS), and transcutaneous electrical nerve stimulation (TENS). Four trials reported significant reductions in pain intensity alongside improvements in functional outcomes and quality of life, particularly in functional abdominal pain and postoperative contexts. Most studies showed low or moderate risk across domains, with appropriate randomization and blinded assessment. No serious adverse events were reported, confirming an excellent safety profile. These findings support non-invasive neuromodulation as a feasible and well-tolerated adjunct to conventional pediatric pain management. Further high-quality trials are warranted to standardize protocols and explore mechanisms of neuroplasticity in the developing nervous system. PROSPERO (CRD420251170866). Full article

Green hydrogen production and the use of fuel cells (FCs) in microgrid (MG) systems have become viable and feasible solutions due to their continuous cost reduction and advancements in technology. Furthermore, green hydrogen electrolyzers and FC can mitigate fluctuations in renewable energy generation and various demand-related disturbances. Proper incorporation of electrolyzers and FCs can enhance load frequency control (LFC) in MG systems. However, they are subjected to multiple false data injection attacks (FDIAs), which can deteriorate MG stability and availability. Moreover, most existing LFC control schemes—such as conventional PID-based methods, single-degree-of-freedom fractional-order controllers, and various optimization-based structures—lack robustness against coordinated and multi-point FDIAs, leading to significant degradation in frequency regulation performance. This paper presents a new, modified, multi-degree-of-freedom, cascaded fractional-order controller for green hydrogen-based MG systems with high fluctuating renewable and demand sources. The proposed LFC is a cascaded control structure that combines a 1+TID controller with a filtered fractional-order PID controller (FOPIDF), namely the cascaded 1+TID/FOPIDF LFC control. Furthermore, another tilt-integrator derivative electric vehicle (EV) battery frequency regulation controller is proposed to benefit from EVs installed in MG systems. The proposed cascaded 1+TID/FOPIDF LFC control and EV TID LFC methods are designed using the powerful capability of the exponential distribution optimizer (EDO), which determines the optimal set of design parameters, leading to guaranteed optimal performance. The effectiveness of the newly proposed cascaded 1+TID/FOPIDF LFC control and design approach employing multi-generational-based two-area MG systems is studied by taking into account a variety of projected scenarios of FDIAs and renewable/load fluctuation scenarios. In addition, performance comparisons with some featured controllers are provided in the paper. For example, in the case of fluctuation in RESs, the measured indices are as follows: ISE (1.079, 0.5306, 0.3515, 0.0104); IAE (15.011, 10.691, 9.527, 1.363); ITSE (100.613, 64.412, 53.649, 1.323); and ITAE (2120, 1765, 1683, 241.32) for TID, FOPID, FOTID, and proposed, respectively, which confirm superior frequency deviation mitigation using the proposed optimized cascaded 1+TID/FOPIDF and EV TID LFC control method. Full article

Background/Objectives: The present exploratory study evaluates the feasibility and psychological effects of a structured Qigong intervention implemented in an Italian university setting. Qigong is a traditional Chinese mind–body practice combining gentle movements, breathwork, and mindful attention, aimed at enhancing mind–body integration and interoceptive awareness. Methods: A total of 332 undergraduate students voluntarily enrolled in a 12-week Qigong program. The intervention was based on Neidan Qigong and integrated both static and dynamic exercises. Psychological functioning was assessed through several self-report measures evaluating a range of constructs, including mindfulness (FFMQ), interoceptive ability (MAIA), perceived stress (PSS), depression, anxiety, and stress (BDI; DASS-21; STAI Y), emotion regulation (DERS), alexithymia (TAS), and sleep quality (PSQI). Results: A total of 114 students completed the intervention. The protocol was well received by participants and demonstrated high feasibility in the academic context, with good attendance rates and overall engagement. Preliminary findings indicate consistent improvements across several psychological domains. Conclusions: The results suggest that Qigong may be associated with improvements in mental health and well-being in young adults and may represent a promising, low-cost intervention. The findings should be interpreted as preliminary. Further research using controlled and methodologically rigorous designs is needed to assess the stability of these effects over time, incorporate physiological measures, and clarify the specific therapeutic contribution of spontaneous movement within Qigong practice. Full article