Search Results (7,802)

UNS S41003 is a low-cost, low-carbon ferritic stainless steel that exhibits moderate corrosion resistance but limited mechanical performance. This study evaluates the electrochemical behavior of untreated and thermomechanically treated UNS S41003 samples. Corrosion tests were conducted in acidic electrolytes with varying pH to simulate aggressive environments relevant to industrial and structural applications where exposure to acidic media and corrosive pollutants occurs. Potentiodynamic polarization curves for all samples displayed passive regions typically associated with protective oxide film formation; however, localized pitting corrosion was detected post-test. Electrochemical impedance spectroscopy indicated a marked decrease in corrosion resistance as pH decreased. The corrosion resistance of the treated alloy remained comparable to that of the untreated condition, indicating that thermomechanical processing did not detrimentally affect passivity or corrosion performance under the tested conditions. The literature suggests that the applied treatment enhances mechanical properties, supporting the potential use of this alloy in structural components subjected to acidic environments requiring a balance of mechanical strength and corrosion resistance. Full article

Under the current context of the large-scale integration of wind and solar power, the coupling of hydropower with wind and solar energy brings significant impacts on grid stability. To fully leverage the regulatory capacity of hydropower, this paper develops a multi-objective optimization scheduling model for hydropower, wind, and solar that balances generation-side power generation benefit and grid-side peak-regulation requirements, with the latter quantified by the mean square error of the residual load. To efficiently solve this model, Latin hypercube initialization, hybrid distance framework, and adaptive mutation mechanism are introduced into the Strength Pareto Evolutionary Algorithm II (SPEAII), yielding an improved algorithm named LHS-Mutate Strength Pareto Evolutionary Algorithm II (LMSPEAII). Its efficiency is validated on benchmark test functions and a reservoir model. Typical extreme scenarios—months with strong wind and solar in the dry season and months with weak wind and solar in the flood season—are selected to derive scheduling strategies and to further verify the effectiveness of the proposed model and algorithm. Finally, K-medoids clustering is applied to the Pareto front solutions; from the perspective of representative solutions, this reveals the evolutionary trends of different objective trade-off schemes and overall distribution characteristics, providing deeper insight into the solution set’s distribution features. Full article

The growing disposal of used tyres and plastic waste in landfills poses a significant environmental challenge. This study investigates the potential of utilizing used tyre rubber and macro-synthetic fibres (MSFs) made from recycled plastics in fibre-reinforced rubberized concrete (RuFRC). Various percentages of tyre rubber shreds were used to replace coarse aggregates, calculated as 10%, 20%, and 30% of the volume of fine aggregates; fibre dosages (0%, 0.25%, 0.5%, 0.75%, and 1% by volume) were incorporated into the mix, and a series of physical, mechanical, and durability properties were evaluated. The results show that, as the fibre and rubber content increased, the slump of RuFRC decreased, with the lowest value obtained for concrete with 1% fibre and 30% rubber. The density of RuFRC decreases as the rubber percentage increases due to air voids and increased porosity caused by the rubber. The strength properties of RuFRC were found to decline with the increase in the rubber content, with mixes containing 30% rubber exhibiting reductions of about 60% in compressive strength, 27% in tensile strength, and 13% in flexural strength compared to the control specimen. Durability testing revealed that an increased rubber content led to higher water absorption, water penetration, and chloride ion permeability, with 30% rubber showing the highest values. However, lower rubber content (10%) and higher fibre dosages improved the durability characteristics, with water absorption reduced by up to 5% and shrinkage strains lowered by about 7%, indicating better compaction and bonding. These results indicate that RuFRC with moderate rubber and higher fibre content offers a promising balance between sustainability and performance. Full article

Objectives: The primary objective was to investigate and compare the effects of three paired-pulse repetitive trans-spinal magnetic stimulation (PP-rTSMS) protocols on balance control and corticospinal network function. Methods: PP-rTSMS (800 pulses, frequency 100 Hz, intensity 70% of the resting motor threshold) was applied over the eighth thoracic vertebra (Th8) in twenty-seven young healthy individuals. Each proband received three verum sessions (using a verum coil with handle oriented (i) cranially, (ii) caudally, and (iii) laterally) and (iv) one sham session (using a sham coil) in a randomised order. Balance ability (Y Balance Test) and corticospinal network functions (motor evoked potentials (MEPs), cortical silent periods (SCPs)) were tested immediately (i) prior to and (ii) after each interventional session. Results: Each verum session induced a significant improvement in balance ability (cranially (F_1,26 = 8.009; p = 0.009; η² = 0.236), caudally (F_1,26 = 4.846; p = 0.037; η² = 0.157), and laterally (F_1,26 = 23,804; p ≤ 0.001; η² = 0.478) oriented grip) as compared to the sham session. In addition, the laterally oriented coil grip was associated with significantly greater balance benefits than both the cranial (F_1,26 = 10.173; p = 0.004; η² = 0.281) and caudal (F_1,26 = 14.058; p ≤ 0.001; η² = 0.351) grip orientations. No significant intervention-induced effects were detected on corticospinal network functions. Conclusions: Our data show that PP-rTSMS effectively supports balance control and that coil orientation significantly influences these effects. Further studies should test variations of this promising approach on healthy and disabled cohorts. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

This study proposes an improved YOLO11n-seg instance segmentation model to address the limitations of existing models in accurately identifying mature blueberries in complex greenhouse environments. Current methods often lack sufficient accuracy when dealing with complex scenarios, such as fruit occlusion, lighting variations, and target overlap. To overcome these challenges, we developed a novel approach that integrates a Spatial–Channel Adaptive (SCA) attention mechanism and a Dual Attention Balancing (DAB) module. The SCA mechanism dynamically adjusts the receptive field through deformable convolutions and fuses multi-scale color features. This enhances the model’s ability to recognize occluded targets and improves its adaptability to variations in lighting. The DAB module combines channel–spatial attention and structural reparameterization techniques. This optimizes the YOLO11n structure and effectively suppresses background interference. Consequently, the model’s accuracy in recognizing fruit contours improves. Additionally, we introduce Normalized Wasserstein Distance (NWD) to replace the traditional intersection over union (IoU) metric and address bias issues that arise in dense small object matching. Experimental results demonstrate that the improved model significantly improves target detection accuracy, recall rate, and mAP@0.5, achieving increases of 1.8%, 1.5%, and 0.5%, respectively, over the baseline model. On our self-built greenhouse blueberry dataset, the mask segmentation accuracy, recall rate, and mAP@0.5 increased by 0.8%, 1.2%, and 0.1%, respectively. In tests across six complex scenarios, the improved model demonstrated greater robustness than mainstream models such as YOLOv8n-seg, YOLOv8n-seg-p6, and YOLOv9c-seg, especially in scenes with dense occlusions. The improvement in mAP@0.5 and F1 scores validates the effectiveness of combining attention mechanisms and multiple metric optimizations, for instance, segmentation tasks in complex agricultural scenes. Full article

Straw removal is a method used to reduce cadmium (Cd) concentration in contaminated farmland. Experiments in Hunan Province tested different stubble heights (0, 15, 30, 45 cm) in three Cd-polluted paddy fields with different contamination levels. The results showed that lower stubble heights resulted in larger straw biomass and more Cd removed from the field, while the residual biomass and Cd returned to the field decreased accordingly. At stubble heights of 0, 15, 30, and 45 cm, the removed straw biomass accounted for 100%, 69.19%, 48.84%, and 28.17% of the total straw biomass, respectively. The corresponding Cd removal amounts were 12.89, 7.18, 4.18, and 1.83 g ha⁻¹, which constituted 100%, 54.06%, 29.85%, and 12.54% of the total Cd accumulation in straw for the season, respectively. According to the fitted curve, the biomass of returned and removed straw was equal at a stubble height of 31 cm, while at 23 cm, the Cd return and removal amounts were balanced. Rice varieties Huanghuazhan and Nongxiang 42 had better Cd removal but risked grain Cd exceeding limits. Since Cd concentration in straw determines removal efficiency, varieties with high straw Cd accumulation and low grain Cd are more suitable for remediation, rather than high-Cd-accumulating types. Full article

The Red River Jig is a traditional Métis dance practiced among Indigenous and non-Indigenous Peoples. While exercise improves physical health and fitness, the impacts of cultural dances on wholistic health are less clear. This study aimed to investigate the psychosocial (cultural and mental), social, physical function, and physical fitness benefits of a Red River Jig intervention. In partnership with Li Toneur Nimiyitoohk Métis Dance Group, Indigenous and non-Indigenous adults (N = 40, 39 ± 15 years, 32 females) completed an 8-week Red River Jig intervention. Social support, cultural identity, memory, and mental wellbeing questionnaires, seated blood pressure and heart rate, weight, pulse-wave velocity, heart rate variability, baroreceptor sensitivity, jump height, sit-and-reach flexibility, one-leg and tandem balance, and six-minute walk test were assessed pre- and post-intervention. Community, family, and friend support scores, six-minute walk distance (553.0 ± 88.7 m vs. 602.2 ± 138.6 m, p = 0.002), jump, leg power, and systolic blood pressure low-to-high-frequency ratio increased after the intervention. Ethnic identity remained the same while affirmation and belonging declined, leading to declines in overall cultural identity, as learning about Métis culture through the Red River Jig may highlight gaps in cultural knowledge. Seated systolic blood pressure (116.5 ± 7.3 mmHg vs. 112.5 ± 10.7 mmHg, p = 0.01) and lower peripheral pulse-wave velocity (10.0 ± 2.0 m·s⁻¹ vs. 9.4 ± 1.9 m·s⁻¹, p = 0.04) decreased after the intervention. Red River Jig dance training can improve social support, physical function, and physical fitness for Indigenous and non-Indigenous adults. Full article

Study Design: Prospective observational study. Background: Lumbar spinal stenosis (LSS) impairs balance and gait function, increasing fall risk and limiting quality of life. Although postoperative recovery of balance is clinically important, longitudinal data using multidimensional balance assessments are limited. Methods: A prospective cohort study was conducted in 101 patients (mean age 74.9 ± 6.9 years) undergoing surgery for LSS. The Brief Balance Evaluation Systems Test (Brief BESTest), Oswestry Disability Index (ODI), Modified Falls Efficacy Scale (MFES), Zurich Claudication Questionnaire (ZCQ), and Visual Analog Scales (VAS) for pain/numbness were evaluated preoperatively and at 6 and 12 months postoperatively. Changes over time and correlations between Brief BESTest and PROMs were analyzed. Results: The total Brief BESTest score significantly improved from 13.3 ± 5.3 preoperatively to 16.1 ± 5.1 at 6 months and 16.0 ± 5.1 at 12 months (p < 0.01). Subdomains including Anticipatory Adjustments, Postural Responses, Sensory Orientation, and Stability in Gait improved significantly, while Stability Limits did not. At 12 months postoperatively, ODI decreased by 19.1%, ZCQ symptom and function scores improved by 0.8 and 0.9 points, respectively, and VAS scores improved by 17.1 mm for low back pain, 26.5 mm for lower limb pain, and 19.5 mm for numbness, all showing marked improvements from baseline. MFES also increased significantly postoperatively. The Brief BESTest score correlated significantly with MFES and ZCQ-PFS at baseline, and with ODI, ZCQ, and VAS scores at 12 months. Conclusions: Balance ability in LSS patients improved after surgery, as measured by the Brief BESTest, with clinically meaningful changes maintained for 12 months. Improvements in balance were significantly associated with reductions in pain, disability, and fear of falling, suggesting the Brief BESTest is a comprehensive indicator of postoperative recovery. Full article

Determining charge and discharge behavior is essential for optimizing charging strategies and evaluating balancing algorithms in battery energy storage systems and electric vehicles. Conventionally, a sequence of circuit simulations or tedious hardware tests is required to evaluate the performance of the balancing algorithm. To mitigate these problems, this paper proposes a variable capacitor model that can be easily built from the incremental capacity curve. This model provides a direct and insightful R-C time constant method for the charge/discharge time calculation. After validating the model accuracy by experimental results based on the cylindrical lithium-ion cell test, a switched-capacitor active balancing and a passive cell balancing circuit are implemented to further verify the effectiveness of the proposed model in calculating the cell balancing time within 2% error. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

Rapid pavement repair demands materials that combine accelerated strength gains, dimensional stability, long-term durability, and sustainability. However, finding materials or formulations that offer these balances remains a critical challenge. This study systematically evaluates two polymer-modified belitic calcium sulfoaluminate (CSA) concretes—CSAP (powdered polymer) and CSA-LLP (liquid polymer admixture)—against a traditional Type III Portland cement (OPC) control under both laboratory and realistic outdoor conditions. Laboratory specimens were tested for fresh properties, early-age and later-age compressive, flexural, and splitting tensile strengths, as well as drying shrinkage according to ASTM standards. Outdoor 5 × 4 × 12-inch slabs mimicking typical jointed plain concrete panels (JPCPs), instrumented with vibrating wire strain gauges and thermocouples, recorded the strain and temperature at 5 min intervals over 16 weeks, with 24 h wet-burlap curing to replicate field practices. Laboratory findings show that CSA mixes exceeded 3200 psi of compressive strength at 4 h, but cold outdoor casting (~48 °F) delayed the early-age strength development. The CSA-LLP exhibited the lowest drying shrinkage (0.036% at 16 weeks), and outdoor CSA slabs captured the initial ettringite-driven expansion, resulting in a net expansion (+200 µε) rather than contraction. Approximately 80% of the total strain evolved within the first 48 h, driven by autogenous and plastic effects. CSA mixes generated lower peak internal temperatures and reduced thermal strain amplitudes compared to the OPC, improving dimensional stability and mitigating restraint-induced cracking. These results underscore the necessity of field validation for shrinkage compensation mechanisms and highlight the critical roles of the polymer type and curing protocol in optimizing CSA-based repairs for durable, low-carbon pavement rehabilitation. Full article

A hybrid wave energy conversion (WEC) system, integrating a backward bent duct buoy (BBDB) with an oscillating buoy (OB) via a flexible mooring chain, is introduced in this study. Unlike existing hybrid WECs, the proposed system dispenses with rigid mechanical linkages and enables flexible offshore deployment. Flared BBDB and buoy models with spherical, cylindrical, and semi-capsule shapes are designed and tested experimentally in a wave flume using both regular and irregular wave conditions. The effects of nozzle ratio (NR), coupling distance, buoy draft, and buoy geometry are systematically examined to investigate the hydrodynamic performance and energy conversion characteristics. It is found that NR at 110 under unidirectional airflow produces an optimal balance between pressure response, free surface displacement, and energy conversion efficiency. Energy extraction is significantly influenced by the coupling distance, with the hybrid system achieving maximum performance at a specific normalized spacing. The semi-capsule buoy improves power extraction ability and expands effective bandwidth due to asymmetric shape and coupled motion. These findings provide valuable insights into the coupling mechanism and geometric optimization for hybrid WECs. Full article

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties. Full article