Search Results (19,697)

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

Polycaprolactone (PCL) is one of the most widely used polymers in tissue engineering owing to its excellent biocompatibility, biodegradability, and processability. Nevertheless, most previous studies have primarily employed research-grade PCL, thereby limiting its clinical translation. In this study, four types of medical-grade PCL (RESOMER^® C203, C209, C212, and C217) were systematically evaluated for their applicability in three-dimensional (3D) printing, with respect to printability, mechanical characteristics, chemical stability, and biodegradation behavior. Among these, C209 and C212 exhibited superior printability and mechanical strength. FT-IR analysis showed that the chemical structure of PCL remained unchanged after both 3D printing and E-beam sterilization, while compressive testing demonstrated no significant differences in mechanical characteristics. In vitro degradation assessment revealed a time-dependent decrease in molecular weight. For kinetic analysis, both C209 and C212 were fitted using pseudo-first-order and pseudo-second-order models, which yielded comparable coefficients of determination (R²), suggesting that degradation may be governed by multiple factors rather than a single kinetic pathway. Taken together, these findings indicate that medical-grade PCL, particularly C209 and C212, is highly suitable for 3D printing. Furthermore, this study provides fundamental insights that may facilitate the clinical translation of PCL-based scaffolds for tissue engineering and biomedical implantation. Full article

Background/Objective: This study examined whether a brief measure combining subjective cognitive concerns and financial interference, termed Subjective Cognitive Decline-Financial (SCD-F), is associated with excess spending behavior in older adults. Methods: Community-dwelling older adults, some with early memory loss and some with no cognitive complaints (N = 150, M age = 72.6), provided 12 months of checking account statements and participated in interviews to clarify aspects of their personal financial behaviors. SCD-F was defined by asking if memory decline was interfering with financial decision-making or transactions. A 3-point SCD-F measure was created. Excess spending was determined by checking whether account expenditures exceeded all sources of income. Nonparametric tests (Kruskal–Wallis and Mann–Whitney U) and multiple regression models assessed group differences and predictors. Results: Group differences in excess spending were pronounced (H(2) = 15.75, p < 0.001). Those in the high SCD-F group had a significantly greater likelihood of excess spending (Z = −4.11; r = 0.43) and higher excess spending percentages (Z = −4.11; r = 0.43) compared to those with no memory loss. Regression analyses indicated that SCD-F was the strongest predictor of excessive spending (β = 0.40, t = 5.43, p < 0.001), even after controlling for age, gender, race, and education (R² = 0.235, F(5,144) = 8.86, p < 0.001). Conclusions: A brief self-report measure, SCD-F, effectively identifies older adults at risk of financial mismanagement, even absent formal cognitive impairment. Monitoring subjective cognitive concerns together with financial interference could enable early intervention. This brief measure may be useful in clinical settings as a screening tool, and in large national surveys. Full article

High-resolution CO₂ fossil fuel emission data are critical for developing targeted mitigation policies. As a key approach for estimating spatial distributions of CO₂ emissions, top–down methods typically rely upon spatial proxies to disaggregate administrative-level emission to finer spatial scales. However, conventional linear regression models may fail to capture complex non-linear relationships between proxies and emissions. Furthermore, methods relying on nighttime light data are mostly inadequate in representing emissions for both industrial and rural zones. To address these limitations, this study developed a multiple proxy framework integrating nighttime light, points of interest (POIs), population, road networks, and impervious surface area data. Seven machine learning algorithms—Extra-Trees, Random Forest, XGBoost, CatBoost, Gradient Boosting Decision Trees, LightGBM, and Support Vector Regression—were comprehensively incorporated to estimate high-resolution CO₂ fossil fuel emissions. Comprehensive evaluation revealed that the multiple proxy Extra-Trees model significantly outperformed the single-proxy nighttime light linear regression model at the county scale, achieving R² = 0.96 (RMSE = 0.52 MtCO₂) in cross-validation and R² = 0.92 (RMSE = 0.54 MtCO₂) on the independent test set. Feature importance analysis identified brightness of nighttime light (40.70%) and heavy industrial density (21.11%) as the most critical spatial proxies. The proposed approach also showed strong spatial consistency with the Multi-resolution Emission Inventory for China, exhibiting correlation coefficients of 0.82–0.84. This study demonstrates that integrating local multiple proxy data with machine learning corrects spatial biases inherent in traditional top–down approaches, establishing a transferable framework for high-resolution emissions mapping. Full article

This research is focused on driving stability issues, which can be caused by specifics of electric vehicle (EV) powertrains. Specific driving conditions, such as intensive road curvature and low grip, require precise control from the driver and very accurate and not delayed vehicle stabilization from its active safety systems. These systems, typically anti-lock braking systems (ABS) and electronic stability programs (ESP), perform their tasks sufficiently well, but new vehicle architectures are forcing a reassessment of their reliability, sometimes requiring additional safety subsystems. In the context of EV architecture and its propulsion systems, a possible lack of stability is anticipated when operating intensive regenerative braking in EVs with a rear–wheel–drive transmission. Experimental research conducted on two popular electric vehicles confirmed this hypothesis, as additional oversteering occurs even when ESP systems have intervened. Based on the experiment, a theoretical simulation model of an EV with regenerative braking on the rear axle was created and validated in MATLAB/Simulink (R2024a). The simulations showed how relevant this issue is and how limited stability systems are; therefore, new strategies were proposed and theoretically tested to ensure car safety. These dedicated regenerative braking control subsystems enable optimal use of regenerative braking and ensure more reliable stability in slippery corners. Full article

African trypanosomiasis is a protozoan disease that affects both humans and animals. Human African Trypanosomiasis (HAT) is a Neglected Tropical Disease targeted for elimination in 2030. Although WHO has not reported HAT from Nigeria in the last decade, there are published studies reporting seroprevalence, parasite detection/isolation, and animal reservoirs potentially involved in HAT transmission in Nigeria. Interestingly, the burden of Animal African Trypanosomiasis (AAT) continues to increase. In this study, we synthesized published reports on the prevalence of HAT and AAT in Nigeria from 1993–2021, the trypanosome species involved, the spread of animal reservoirs, and the variability in diagnostic methodologies employed. A scoping review was performed following the methodological framework outlined in PRISMA-ScR checklist. Sixteen eligible studies published between 1993 and 2021 were reviewed: 13 for AAT and 3 for HAT. Varying prevalence rates were recorded depending on the diagnostic methods employed. The average prevalence reported from these studies was 3.3% (HAT), and 27.3% (AAT). Diagnostic methods employed include microscopy, PCR and Card Agglutination Test for Trypanosomiasis (CATT). Cattle, pigs, and dogs were identified as carriers of human-infective trypanosomes. This study highlights the scarcity of HAT epidemiological studies/data from Nigeria, the high prevalence, complex epidemiology, limited attention and surveillance of African Trypanosomiasis in Nigeria. Remarkably, WHO records do not reflect the published data showing evidence of HAT prevalence/cases in Nigeria. Unfortunately, diagnostics challenges and unrealistic disease reporting protocols seem to limit HAT reporting from Nigeria. Therefore, adequately coordinated epidemiological surveys and targeted intervention policies are imperative to ascertain the true epidemiological status of HAT in Nigeria and prevent disease re-emergence towards achieving WHO’s elimination targets. The presence of animal carriers of human-infective trypanosomes underscores the importance of a one-health approach to combat African trypanosomiasis effectively. Full article

In conventional logistics optimization problems, an objective function describes the relationship between parameters. However, in many industrial practices, such a relationship is unknown, and only observational data is available. The objective of the research is to use machine learning-based regression models to uncover patterns in the warehousing dataset and use them to generate an accurate objective function. The models are not only suitable for prediction, but also for interpreting the effect of input variables. This data-driven approach is consistent with the automated, intelligent systems of Industry 4.0, while Industry 5.0 provides opportunities for sustainable, flexible, and collaborative development. In this research, machine learning (ML) models were tested on a fictional dataset using Automated Machine Learning (AutoML), through which Light Gradient Boosting Machine (LightGBM) was selected as the best method (R² = 0.994). Feature Importance and Partial Dependence Plots revealed the key factors influencing storage performance and their functional relationships. Defining performance as a cost indicator allowed us to interpret optimization as cost minimization, demonstrating that ML-based methods can uncover hidden patterns and support efficiency improvements in warehousing. The proposed approach not only achieves outstanding predictive accuracy, but also transforms model outputs into actionable, interpretable insights for warehouse optimization. By combining automation, interpretability, and optimization, this research advances the practical realization of intelligent warehouse systems in the era of Industry 4.0. Full article

Reliable analysis of remote photoplethysmography (rPPG) signals depends on identifying physiologically plausible pulses. Traditional approaches rely on clustering self-similar pulses, which can discard valid variability. Automating pulse quality assessment could capture the true underlying morphology while preserving physiological variability. In this manuscript, individual rPPG pulses were manually labelled as plausible, borderline and implausible and used to train multilayer perceptron classifiers. Two independent datasets were used to ensure strict separation between training and test data: the Vision-MD dataset (4036 facial videos from 1270 participants) and a clinical laboratory dataset (235 videos from 58 participants). Vision-MD data were used for model development with an 80/20 training–validation split and 5-fold cross-validation, while the clinical dataset served exclusively as an independent test set. A three-class model was evaluated achieving $F_1$-scores of 0.92, 0.24 and 0.79 respectively. Recall was highest for plausible and implausible pulses but lower for borderline pulses. To test separability, three pairwise binary classifiers were trained, with ROC-AUC > 0.89 for all three category pairs. When combining borderline and implausible pulses into a single class, the binary classifier achieved an $F_1$-score of 0.93 for the plausible category. Finally, usability analysis showed that automated labelling identified more usable pulses per signal than the previously used agglomerative clustering method, while preserving physiological variability. Full article

Logistics and warehouse operations experience an increasing pressure to adopt sustainable practices. The logistics industry generates substantial material waste, with cardboard being the primary packaging material. Adopting Circular Economy (CE) principles to control this waste is important for enhancing sustainability. However, there is a lack of studies on transforming warehouses into more sustainable operations. This paper studies the ability to transform the linear supply chain of a distribution warehouse into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A structured framework is presented to outline the project’s methodology and illustrate the steps taken to apply the concept of CE. The paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate various scenarios. This study contributes by showing how discrete-event simulation combined with VSM and 6R principles can provide operational insights under data-constrained conditions. Bridging the gap between theory and practice. Multiple operational scenarios were modelled and run, including peak and off-peak demand periods, as well as a sensitivity analysis for recycling durations. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the most feasible solution. Results indicate that introducing recycling activities created some bottlenecks in the system and reduced its efficiency. Furthermore, suggestions for future improvements are presented, ensuring that on-site actions are grounded in a simulation that reflects reality. Full article

Soil organic matter (SOM) plays a fundamental role in soil fertility and ecosystem functioning. In calcareous soils, SOM quantification is often challenging due to carbonate interference. This study aimed to compare three common analytical methods for SOM determination: the Anne method, the modified Walkley–Black method, and the Loss on Ignition (LOI) method, with and without decarbonation. Twenty-five soil samples were collected from a calcareous parcel in the Bordj Bou Arreridj region (Algeria), and SOM content was analysed using all methods. The results revealed substantial variability in SOM content across methods, reflecting differences in sensitivity to carbonates and efficiency of organic carbon oxidation. The Anne method, considered the reference technique, yielded the highest mean SOM content (3.61%), followed by LOI without decarbonation (3.41%), the modified Walkley–Black method (2.96%), and LOI with decarbonation (2.55%). Strong correlations were observed between methods, particularly between the Anne method and LOI with decarbonation (R² = 0.91), confirming the latter as a reliable alternative. Decarbonation significantly reduced SOM overestimation, as demonstrated by paired statistical tests and a large effect size (Cohen’s d = 2.95). Linear regression models were established to estimate SOM from LOI results, providing a cost-effective approach for rapid assessment. These findings highlight the importance of method selection according to soil type, the need for standardised protocols, and the value of LOI with decarbonation as a robust, practical, and economical method for SOM determination in calcareous soils. Full article

Background: Leishmaniasis is a zoonotic vector-borne disease and a significant global public health concern worldwide and in Algeria. In this study, we investigated the potential role of ticks and fleas as carriers of Leishmania in endemic regions of Algeria. Methods: Adult ectoparasites were collected from reservoir dogs and cohabiting animals across three provinces: Tizi-Ouzou (northeast), M’Sila (southeast), and Tébessa (extreme east). A subset of 247 ectoparasites was randomly selected for Leishmania DNA screening using ITS1-PCR. Results: Morphological identification revealed two tick species, Rhipicephalus turanicus (378 specimens) and Rhipicephalus sanguineus s.l (127 specimens), and one flea species, Ctenocephalides felis (94 specimens). Dogs were the most heavily infested hosts (74.12%), followed by sheep (9.51%) and cats (9.34%). Leishmania DNA was detected in 36.43% (90/247) of the tested specimens, with higher positivity in ticks (41.32%) compared to fleas (17.64%). Infection rates varied by host species, with dogs harboring the majority of positive ectoparasites (62/90), primarily R. sanguineus s.l (19/30) and R. turanicus (40/115). Leishmania DNA was also detected in ectoparasites collected from cats and sheep, whereas goats and rabbits were free from Leishmania DNA. Conclusions: This investigation highlights the high detection rate of Leishmania DNA in ticks and fleas from animals in Algerian endemic regions, indicating exposure to infected hosts. Together with previous reports, these findings support the view that ticks and fleas may act as incidental hosts or mechanical carriers of the parasite. However, their role in parasite transmission remains unconfirmed and warrant further investigation, particularly through studies assessing vector competence. These results emphasize the need for additional research to clarify the contribution of these ectoparasites to Leishmania transmission and multi-host dynamics. Full article

Tires are composed of various rubber polymers and reinforcing carcasses, and their wet skid resistance is influenced by the coupled effects of multiple factors. The braking force coefficient (BFC) is the primary performance indicator for evaluating tire wet skid resistance. This study proposes a novel method for evaluating the BFC of tires by integrating laboratory-simulated wet road tests with finite element simulations. A 295/60R22.5 all-steel radial tire was selected as the test object, and the simulation results showed good agreement with the experimental data, with a BFC error of 7.14%. This consistency confirms the reliability and accuracy of the proposed model in predicting tire wet grip performance. This study also investigated the effects of different working conditions of the tested tire on the BFC. The results showed that the wet grip performance of the tire on wet concrete surfaces was significantly lower than that on wet asphalt surfaces. Specifically, the BFC increased with the increase in braking slip ratio, decreased slightly with the rise in tire inflation pressure, and exhibited relatively low sensitivity to vertical load variations. All these results demonstrate that this integrated evaluation method provides targeted guidance for the mechanical performance optimization of tire tread rubber composites. Full article

Temperature-induced instability in early-age rheology poses a major challenge to the pumpability and application of robotic plastering mortars. This study systematically investigates the temperature-dependent effects of a high-viscosity (75,000 mPa·s) hydroxyethyl methyl cellulose (HEMC) on the rheological properties and early microstructural evolution of mortars at 5 °C, 20 °C, and 40 °C. Mortars with HEMC dosages from 0 to 0.25 wt% were tested using rheological measurements, ultrasonic pulse velocity (UPV), and complementary microstructural analyses (XRD, FTIR, and SEM–EDS). Results show that HEMC reduced the initial static yield stress while monotonically increasing plastic viscosity, with the thickening effect more pronounced at higher temperatures. Notably, at 40 °C, the initial plastic viscosity of a 0.25% HEMC mix reached 14.4 Pa·s, a 133% increase compared to the control group. HEMC also effectively retarded the time-dependent increase in yield stress and stabilized plastic viscosity, thereby mitigating the adverse influence of elevated temperature. UPV confirmed that HEMC delayed microstructural formation, in agreement with the observed retardation of hydration reactions. At 40 °C, a 0.10% HEMC dosage postponed the percolation threshold from 67 min to 150 min, highlighting its strong retardation effect. Microstructural tests further revealed that HEMC delayed CH formation, refined C–S–H gels, and reduced the crystallinity of AFt, supporting the rheological and ultrasonic findings. A statistically significant, moderate-to-strong correlation (r = 0.88, R² = 0.77, p < 0.001) was established between static yield stress and UPV, indicating that macroscopic rheological resistance responds to microstructural evolution. Based on these results, the recommended HEMC dosages to achieve stable rheological performance are 0.05–0.10% at 5 °C, 0.10–0.15% at 20 °C, and 0.15–0.20% at 40 °C. Full article

To solve the problems of large root damage and incomplete seedling blocks (SBs) in rice machine transplanting, this study numerically optimized the root blanket-cutting device for rice blanket seedling cutting and throwing transplanters based on the discrete element method (DEM) and multi-body dynamics (MBD) coupling method. A longitudinal sliding cutter (LSC)–substrate–root interaction model was established. Based on the simulation tests of Center Composite Design and response surface analysis, the sliding angle and cutter shaft speed of the LSCs arranged at the circumferential angles (CAs) of 0°, 30°, and 60° were optimized. The simulation results indicated that the LSC arrangement CA significantly affected the cutting performance, with the optimal configuration achieved at a CA of 60°. Under the optimal parameters (sliding angle of 57°, cutter shaft speed of 65.3 r/min), the average deviation between the simulated and physical tests was less than 11%, and the reliability of the parameters was verified. A seedling needle–substrate–root interaction model was established. The Box–Behnken Design method was applied to conduct simulation tests and response surface optimization, focusing on the picking angle, needle width, and rotary gearbox speed. The simulation results showed that the picking angle was the key influencing factor. Under the optimal parameters (picking angle of 20°, seedling needle width of 15 mm, rotary gearbox speed of 209 r/min), the average deviation between the simulated and physical tests was less than 10%, which met the design requirements. This study provides a new solution for reducing root injury, improving SB integrity, and reducing energy consumption in rice transplanting, and provides theoretical and technical references for optimizing transplanting machinery structure and selecting working parameters. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article