Search Results (2,554)

Objectives: This study investigated the relationship between interstitial and blood glucose concentrations following ingestion of carbohydrate-containing drinks differing in carbohydrate amount, osmolarity, and glycaemic index. Methods: Ten healthy adults (nine male; age: 22 ± 1 y; height: 177 ± 12 cm; weight: 75 ± 14 kg) completed a double-blind, randomised cross-over study with seven beverage trials varying in carbohydrate (CHO) characteristics. Blood samples were collected at rest and over two hours, while interstitial glucose ([iG]) was recorded using a continuous glucose monitor (Supersapiens, Atlanta, USA). Glycaemic metrics and mean absolute relative difference (MARD) were calculated for hypoglycaemic, euglycaemic, and hyperglycaemic ranges. Data were analysed using repeated-measures ANOVA with Bonferroni correction and paired t-tests (p ≤ 0.05). Results: Interstitial and blood glucose concentrations were similar at baseline but diverged post-ingestion. MARD varied by glucose rate and direction, exceeding 20% during rapid declines (>2 mg/dL/min), where [iG] underestimated [BG] by −7.3 ± 27.1 mg/dL. Accuracy was highest during stable glucose (MARD = 10.5 ± 8.6%). Carbohydrate amount and glycaemic index influenced peak glucose, whereas beverage concentration (5–20%) had minimal effect when CHO amount was fixed, though variation in CGM agreement appeared during post-peak declines. Conclusions: CGM tracked blood glucose overall but showed reduced accuracy during rapid falls or hypoglycaemia. Carbohydrate properties influenced glycaemic response but not sensor agreement when CHO load was constant. Glucose rate and direction of change are key considerations for interpreting CGM data in research and applied settings. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

Fly ash substitution for cement in Portland cement concrete (PCC) has been regarded as a sustainable solution, but its widespread application remains constrained by concerns over mechanical performance and durability of PCC, especially at higher replacement rates. This study evaluates PCC mixes incorporating fly ash Type C (FA-C) or Type F (FA-F) across cement replacement rates from 10% to 90%, tracking fresh-state workability, compressive strength, and surface electrical resistivity at 7, 14, and 28 curing days. A process-based life cycle assessment (LCA) with the TRACI 2.1 method quantified global warming potential (GWP, kg CO₂/m³) under a raw-material-plus-batching-electricity boundary for each mix. A Performance Index (PI) normalizes GWP against both compressive strength and electrical resistivity, producing a performance-weighted environmental efficiency metric (GWP/PI). A sensitivity analysis across five weighting scenarios tested the robustness of mix rankings under varying priorities for structural versus ironic transport resistance performance, and a structural threshold analysis identified mixes meeting strength requirements. FA-C at 50% cement replacement exceeded the OPC control in 28-day compressive strength (42.9 vs. 36.2 MPa) and electrical resistivity (9.88 vs. 8.50 kΩ·cm), while reducing GWP by 48.3% relative to the OPC control (40.24 kg CO₂/m³). FA-F at 30–50% replacement exhibited a distinct strength–resistivity decoupling, demonstrating that strength only evaluation underrepresents the environmental efficiency of durability-critical applications. The GWP/PI metric revealed that raw GWP reduction alone misrepresents environmental efficiency. FA-C at 50% achieved a GWP/PI of 17.73, which is a 56% improvement over the OPC control. These findings question the conventional <30% substitution ceiling at 28 days under standard moisture curing and demonstrate that performance-weighted LCA metrics provide a more informed basis for sustainable concrete mix design. Full article

The mechanical response of cellular structures is governed not only by relative density and average cell geometry but also by the spatial arrangement of cells. However, the manner in which arrangement-dependent effects evolve with increasing cell number has not been systematically clarified. In this study, the compressive behavior of closed-cell structures with varying cell numbers was investigated using finite element analysis under dynamically equilibrated compression conditions while maintaining constant relative density and identical material parameters. Cellular models were generated using hierarchical Poisson disk sampling combined with Voronoi tessellation. The number of cells was increased through three distinct approaches: mirror replication of a reference structure, enlargement of the overall specimen size, and refinement of cell size under fixed external dimensions. To characterize arrangement-dependent effects, two distinct features of the compressive response were introduced: averaging, defined as a reduction in variability across responses from different initial cell arrangements, and smoothing, defined as the suppression of abrupt stress fluctuations within an individual response. Quantitative metrics were employed to evaluate both effects. Averaging was observed in plate-type models compressed in the z-direction and in fixed-size models, whereas mirror-connected models retained strong arrangement dependence despite large cell numbers. Smoothing occurred predominantly in plate-type models compressed in the z-direction and was strongly correlated with the number of cell layers aligned along the compression direction rather than with total cell number alone. The simulations were conducted in a dynamically equilibrated regime in which internal stress equilibrium was achieved during deformation. These results demonstrate that compressive behavior is governed not only by cell number but also by structural arrangement and directional cell-layer alignment, providing mechanistic insight into the transition from arrangement-dependent variability to stable macroscopic response under dynamic compression. Full article

This study aims to address the need to operationalize Industry 5.0 (I5.0) by developing a comprehensive Assessment Framework for the adoption of the Human Centricity, Environmental Sustainability, and Industrial Resilience pillars. While existing models largely focus on technological maturity, they fail to provide measurable tools for evaluating I5.0 adoption. To bridge this gap, the paper proposes an Assessment Framework based on a structured set of Key Performance Indicators (KPIs) developed within the EU-funded PROSPECTS 5.0 project. The methodology combines an extensive literature review, a workshop with relevant stakeholders, a Delphi survey with experts, and empirical refinement conducted through workshops involving 14 companies across multiple sectors and of varying sizes. The results highlight that organizations predominantly measure traditional indicators such as health and safety, energy consumption, and supply chain robustness, while underestimating emerging dimensions such as human empowerment, social inclusion, circularity, and advanced human–machine collaboration. The framework introduces a set of KPIs for each of the I5.0 pillars, supporting structured assessment across different industrial contexts while allowing sector-specific adaptation. The findings reveal a gap between the perceived importance of several sustainability and human-centric metrics and their actual implementation. This framework allows organizations to self-assess their practices, guide strategic decisions, and align technological growth with societal and environmental goals. Full article

The development of Software-Defined Networks (SDNs) introduces new challenges in network security, particularly in detecting Distributed Denial of Service (DDoS) attacks and network anomalies. Due to the centralized architecture of SDN, traditional detection methods are often insufficient in dynamic environments. Therefore, machine learning techniques are increasingly applied to improve detection effectiveness. This paper analyzes the impact of network topology on the performance of machine learning-based detection methods in SDN environments. A controlled experimental setup based on the RYU controller and OpenFlow 1.3 was implemented using Mininet. Two network topologies (linear and hierarchical) were evaluated under multiple attack scenarios, including TCP SYN flood and TCP/UDP port scanning. Two supervised learning models, Random Forest (RF) and K-Nearest Neighbors (KNN), were implemented and compared using standard evaluation metrics: accuracy, precision, recall, F1-score, and detection time. The results show that Random Forest significantly outperforms KNN, achieving up to 100% accuracy and detection times as low as 4.24 s, while KNN exhibits lower stability and reduced recall in anomaly detection scenarios. The study demonstrates that network topology has a measurable impact on both detection performance and latency. The observed effects varied across attack scenarios and machine learning models. Hierarchical topology generally improved detection sensitivity in DDoS scenarios, while linear topology often enabled lower detection latency during selected anomaly detection experiments. The results indicate that both machine learning model selection and network topology should be jointly considered when designing intrusion detection systems for SDN environments. These findings contribute to improving the effectiveness and responsiveness of security mechanisms in modern programmable networks. Full article

Optimal sizing of hybrid renewable energy systems for desert camps is a multi-objective problem that must account for cost, reliability, component degradation, and uncertainty. This paper introduces an improved multi-objective neural architecture search (IMONAS) framework for hybrid nanogrid sizing in the desert environment of Hafr Al-Batin, Saudi Arabia. The framework combines neural optimization, stochastic uncertainty modeling, and explicit battery degradation modeling, a combination not addressed in the reviewed studies for this application. Six test cases are examined by varying uncertainty assumptions, battery degradation, and the annual duration of uncertain operation. For each case, IMONAS provides Pareto-front solutions that specify the photovoltaic, diesel generator, battery autonomy, and inverter choices while minimizing the cost of energy (COE) and the loss of power supply probability (LPSP). IMONAS is compared with the original MONAS and five other multi-objective optimization methods. In addition to visual Pareto-front comparisons, the assessment uses Pareto-dominance indicators, namely the C-metric and an aggregated score derived from pairwise C-metric comparisons across the algorithms and cases. The results provide a validated sizing framework for remote arid-region nanogrids under uncertainty and battery degradation. Full article

Planning a low-detectability route for a fixed-wing UAV in mountainous environments with radar-based sensing constraints remains highly challenging. Conventional approaches struggle to simultaneously ensure both path quality and operational safety. To address this problem, this paper proposes a two-layer planning framework in which a Risk-A* algorithm provides a global reference route, while a model predictive control (MPC) scheme performs online receding-horizon trajectory optimization. The proposed method combines prior radar-platform information with time-varying detection-risk cues to generate terrain-masked and detection-feasible trajectories. In this study, the framework is instantiated and evaluated on a representative fixed-wing medium-altitude long-endurance (MALE) UAV, where “medium-altitude” denotes the platform class rather than the flight altitude maintained during the low-altitude flight segment. As a result, the UAV can complete the entire flight while reducing the detection-risk metric and overall planning cost. Simulation results on two DEM-based mountainous terrain zones, with one nominal start-goal pair specified in each terrain zone and 50 repeated executions conducted for each scenario, demonstrate that the Risk-A*-MPC framework may yield slightly longer paths and flight times; however, it consistently satisfies the no detection-threshold-exceedance requirement under the tested conditions. In the two main terrain-zone scenarios, the recorded maximum MPC solve time was 0.812 s, which remained below the 3 s control update period and supports the real-time executability of the online MPC layer on the tested computational platform. Full article

Accurate spot-centroid localization is fundamental for determining optical metrics such as modulation transfer function (MTF) and effective focal length (EFL). Conventional methods struggle under non-ideal conditions—asymmetric spots, high noise, and vibration—and mid-wave infrared (MWIR) vibration has received little attention. To address these gaps, we propose multi-scale Gaussian fitting with subpixel edge reconstruction (MSGF-SER), combining image pyramid fitting, Zernike-moment edge extraction, and adaptive eccentricity-weighted fusion. Validated on simulated spots with varying SNRs and experimental sequences (visible off-axis aberration, long-wave infrared (LWIR) high-noise, MWIR micro-vibration), MSGF-SER achieved a noise-free RMSE of 0.03 pixel and 0.84 pixel at 5 dB SNR. On real MWIR vibration sequences, the Y-direction standard deviation (STD) dropped to 0.098 pixel, and the trajectory displacement variance was more than an order of magnitude lower than that of conventional methods. MTF deviations remained within 0.01, and the deviation of the measured mean EFL from the nominal focal length was better than 0.05 mm, and the STD was below 0.02 mm. These results demonstrate that MSGF-SER substantially improves centroid localization accuracy, repeatability, and smoothness under challenging conditions, providing reliable support for high-precision optical system parameter measurement. Full article

This retrospective quantitative data analysis study aimed to investigate sex- and age-related differences in the physiological distribution of [¹⁸F]FDOPA uptake in long axial field-of-view (LAFOV) PET images across a range of organs and tissues. A retrospective quantitative data analysis study of 106 anonymized PET/CT images acquired from vertex to mid-thigh with minimal abnormalities, divided in two gender groups and two age groups was used for this study. The mean and max lean body mass weighted standardized uptake values ($SU{L}_{mean}$, $SU{L}_{max}$), target-to-background ratios ($TBR$), and coefficients of variation ($CoV$) were used to quantify tracer uptake. Sex- and age-related differences in uptake were organ- and metric-specific. Most organs showed comparable uptake between males and females. However, males exhibited higher absolute uptake in metabolically active organs and females showed greater intra-organ heterogeneity. Aging was generally associated with increased tracer uptake and variability, especially in women, with the hip showing higher uptake in younger individuals. Statistically significant differences were most prominent in women and varied by organ and metric. In conclusion, both sex and age significantly influence [¹⁸F]FDOPA PET tracer uptake and variability in an organ- and metric-specific manner. Incorporating sex- and age-adjusted reference values may improve the accuracy and personalization of PET imaging in clinical and research settings. Full article

High-fidelity motion cueing in driving simulators is essential for delivering a realistic and immersive user experience. However, the trade-off between motion accuracy and computational efficiency often hinders achieving this. Fixed-horizon Model Predictive Control (MPC)-based Motion Cueing Algorithm (MCA) frameworks frequently struggle to adapt to rapid dynamic changes in vehicle behaviour, resulting in suboptimal simulator responses. Their reliance on worst-case horizon tuning can result in inefficient platform usage and increased computational load, limiting computational efficiency and practical deployment. This study presents an adaptive MPC-based MCA designed to enhance the fidelity of motion platforms used in vehicle dynamic simulations. The proposed method dynamically adjusts the MPC prediction horizon to improve overall simulation performance while minimising motion sensation error. Within the simulation environment, the prediction horizon is adaptively updated at each simulated control step according to recent tracking-performance metrics, enabling responsiveness to varying vehicle dynamic models and driving scenarios. The system was developed and implemented using Python and MATLAB environments, with Long Short-Term Memory (LSTM) networks employed to enhance the adaptability and precision of prediction horizon adjustments. Due to safety constraints, the proposed framework was evaluated exclusively within a simulation environment and compared against both classical MPC-based MCA and RL MPC-based MCA. Experimental results demonstrate that the proposed adaptive framework improves workspace utilisation and substantially reduces computational load compared with the classical and RL-based MPC-based MCA approaches, while maintaining competitive motion cueing tracking performance. The adaptive system effectively enhances linear displacement (LD), ensuring better alignment of motion cues with platform constraints. While minor trade-offs were observed in root mean square error (RMSE) and correlation coefficients (CCs) for sensed angular velocity (SAV) and sensed specific force (SSF), the framework improves workspace utilisation and computational efficiency while maintaining competitive motion cueing performance. Furthermore, the adaptive LSTM-MPC framework substantially reduces computational load, achieving approximately 44.26 times faster execution compared with the classical MPC-based MCA and approximately 30.03 times faster execution compared with the RL MPC-based MCA. These findings highlight the potential of integrating deep learning (DL) with MPC to optimise the trade-off between motion cueing performance, platform utilisation, and computational efficiency in driving simulators. Full article

Injuries of the metacarpophalangeal joint are a major cause of morbidity and catastrophic fracture in racing horses, yet early osseous pathology is often difficult to detect using conventional imaging. This pilot study aimed to correlate sodium fluoride Positron Emission Tomography (¹⁸F-NaF PET) radiopharmaceutical uptake with gross and histopathologic changes in the distal third metacarpal bone (MC3) and proximal first phalanx (P1). Five horses (three racing Thoroughbreds with fetlock injury and two non-racing controls) underwent ante-mortem ¹⁸F-NaF PET and cone-beam CT imaging (CBCT), followed by post-mortem gross and histologic examination of predefined anatomic sites. Quantitative PET measures, including maximum standardized uptake value (SUV_max), SUV_ratio, and PET grade, were compared with gross pathology and histopathologic scores for cartilage and subchondral bone. While there were significant regional correlations between PET metrics and gross or histologic scores at select sites, our results need to be considered in light of the small number of horses evaluated. Correlations between PET metrics and gross pathology score were identified on the distal metacarpus on the lateral dorsal condyle and on proximal P1 for lateral dorsal and mid-P1. Correlation of PET metrics and hyaline cartilage histopathology scores were found for dorsal medial and lateral P1, parasagittal dorsolateral P1 and the medial parasagittal groove of MC3. Correlation of PET metrics and histologic subchondral bone scores were significant for medial palmar condyle, medial parasagittal groove, and parasagittal palmar lateral of MC3. For P1, PET metrics and histologic subchondral bone scores were significantly correlated for parasagittal mid-lateral and medial dorsal regions. Overall, these findings demonstrate that ¹⁸F-NaF PET identifies localized bone remodeling that corresponds to histologic and gross pathology at specific fetlock regions, supporting its utility for detecting osseous injury, although relationships varied by anatomic location. Further work with larger numbers of horses is needed. Full article

Patients with diabetes and/or peripheral artery disease (PAD) are at risk for lower limb amputation and a subsequently higher mortality risk. Peer support interventions have been shown to increase diabetes self-management and glycemic control. This scoping review aims to synthesize the current literature on peer support interventions in reducing lower limb amputations. A PubMed search was conducted in June of 2023, excluding publications prior to 2000, focusing on two themes: (1) peer support and (2) the patient population of interest (i.e., individuals with diabetic foot disease and/or PAD). Studies were included if they addressed the population of interest, involved a peer support intervention to improve lower extremity health, and had outcomes pertaining to the health of the lower extremities or programmatic metrics such as participant satisfaction or program adherence. Out of 1730 publications initially identified, six met the inclusion criteria. These six studies were categorized as group foot care education studies (n = 4) or group cognitive behavioral studies (n = 2). The group foot care education studies showed mixed results, which varied from no effect to significant improvements in foot care, self-management, and complications. There was a trend of improvement in self-management behaviors and physical activity in cognitive behavioral interventions. Despite showing promise in other settings, there have been limited investigations of peer support interventions to improve lower extremity outcomes and avert amputations in persons with diabetes and/or PAD. Further studies are required to conclusively determine the efficacy of peer support interventions to reduce lower extremity amputation rates. Full article

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization. Full article

The introduction of noise during image acquisition and transmission is inevitable, leading to a significant reduction in the accuracy of image processing tasks, such as target classification, localization, and recognition. To address this issue, this paper proposes a novel robustness-oriented pooling module called Gaussian adaptive pooling. Drawing on the principles of Gaussian filters, the method introduces a Gaussian weight for feature values in the pooling operation, thus integrating filtering and pooling in a novel manner. This approach is both lightweight and versatile, requiring no additional learnable parameters, and enables seamless integration into neural network architectures with pooling layers. Rigorous mathematical derivations and simulation experiments show that our proposed Gaussian adaptive pooling method surpasses conventional methods (average-pooling and max-pooling) in noise handling. Furthermore, its robustness is comparable to traditional pooling methods in addressing challenges such as rotations, scalings, and translations. Extensive evaluations across multiple computer vision tasks—including image classification (CIFAR-10/100), object detection (MS COCO and RTTS), and semantic segmentation (CamVid)—confirm its effectiveness. Specifically, under varying levels of noise and degraded conditions, Gaussian adaptive pooling achieves significant improvements in standard performance metrics compared to conventional pooling methods. For instance, it delivers notable quantitative gains across different tasks including up to a 12.67% increase in mean intersection over union on the CamVid dataset for semantic segmentation and a 1.1% mAP50 enhancement on the real-world RTTS dataset for object detection. Full article