Search Results (82,214)

B is considered a valuable additive for TiAl alloys, because it is believed to improve their properties by refining their microstructures. However, the effects of B on the practical properties of TiAl alloys for jet engine blades and the optimal addition amount for achieving balanced properties remain unclear. Specifically, there have been very few studies to date in which the practical properties of alloys have been evaluated across a wide range of B addition levels. Therefore, we evaluated various reliability, cost, and performance properties of jet engine blade materials using cast Ti-45,47Al-2Nb-2Mn (the same as XD alloys), with varying B addition levels. The results showed that, in some cases, low B addition levels (0.1–0.2 at.%) could enhance the impact resistance and high-cycle fatigue performance. However, even low B addition levels negatively impacted the machinability, castability, and creep strength. Further, adding 0.4 B or more significantly reduced most practical properties. Compared to XD alloys, TiAl4822 exhibited a superior balance, which is attributed to the higher B content (1 at.%) in XD alloys and the greater effectiveness of Cr relative to Mn in improving the alloy’s high-temperature impact resistance. Full article

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

To address challenges such as sparse feature representation difficulties and poor robustness in detecting weak targets against sea clutter backgrounds, this study investigates the adaptability of channel modeling and sparse reconstruction techniques for target recognition. It proposes a method for detecting small sea targets that integrates OTFS with deep unfolding. Using OTFS modulation to map signals from the time domain to the Delay-Doppler domain, a sparse recovery model is constructed. Deep unfolding is employed to transform the FISTA iterative process into a trainable network architecture. A GAN model is employed for adaptive parameter optimization across layers, while the CBAM mechanism enhances response to critical regions. A multi-stage loss function design and false alarm rate control mechanism improve detection accuracy and interference resistance. Validation using the IPIX dataset yields average detection rates of 88.2%, 91.5%, 90.0%, and 83.3% across four polarization modes, demonstrating the proposed method’s robust performance. Full article

This study presents the results of research on the pelleting process of pine sawdust with the addition of cherry stone waste, which was carried out using a flat-die pellet press in the context of fuel pellet production. The findings indicate that increasing the proportion of crushed cherry stones in the sawdust mixture from 10% to 20% reduced the pelletizer’s power demand by approximately 14% (from 3.35 to 2.86 kW) and by around 24% (from 3.79 to 2.86 kW), compared with the compaction of sawdust alone. The incorporation of 10% crushed cherry stone waste into pine sawdust slightly improved the kinetic strength of the pellets, increasing it by about 2% (from 94.6 to 96.60%). However, raising the cherry stone content further to 20% resulted in a moderate decrease in kinetic strength, by approximately 5% (from 96.60 to 91.37%). A similar trend was observed for pellet density: the addition of cherry stones (10–20%) slightly reduced the density by about 5.5% (from 1312.02 to 1241.65 kg·m⁻³), accompanied by a small decrease in bulk density. This study also confirmed the high calorific potential of crushed cherry stones, with a heat of combustion of 24.418 MJ·kg⁻¹ (dry basis) and a net calorific value of 22.326 MJ·kg⁻¹. Their incorporation at levels of 10–20% into sawdust mixtures increased the heat of combustion of the pellets by 0.42–0.84% (from 19.959 MJ·kg⁻¹ for sawdust alone at 15% moisture content to 20.042 MJ·kg⁻¹ with a 10% addition and 20.126 MJ·kg⁻¹ with a 20% addition). Moreover, the inclusion of cherry stone waste in the mixture had a beneficial effect on combustion performance, lowering emissions of harmful compounds such as CO, NO, and SO₂, due to the higher combustion temperature achieved. Consequently, the use of cherry stone waste as an additive to sawdust not only enhances the energetic and environmental performance of pellets but also provides an effective pathway for the management of large quantities of fruit industry residues. Full article

In the context of a medical imaging application for preclinical research, specifically cerebrovascular imaging in small animals, this work addresses the challenges associated with using a large-scale 2D ultrasonic array comprising $32\times 32$ elements ($96\lambda \times 96\lambda$ ). The application imposes stringent requirements: operation in the extreme near field, high spatial resolution, high frequency, high frame rate, and imaging within a highly attenuating medium. These demands, combined with current technological limitations, such as element size and constraints on the number of channels that can be driven in parallel, present significant challenges for system design and implementation. To assess system performance, plane wave imaging is employed as a reference modality due to its ability to meet high acquisition speed requirements. Our analysis reveals limitations in spatial coverage and image quality when operating the full aperture under plane wave transmission constraints. To address these limitations, we propose a sparse aperture strategy. When combined with advanced signal processing techniques, this approach enhances both contrast and resolution while preserving acquisition speed, making it a promising solution for high-performance ultrasonic imaging under the demanding conditions of preclinical research. Full article

Enhancing indoor environmental quality while reducing building energy consumption represents a critical challenge for sustainable building design, particularly in hot arid climates where cooling loads dominate energy use. Despite extensive research on green wall systems (GWSs), robust quantitative data on their combined impact on air quality and thermal performance in real-world office environments remains limited. This research quantified the synergistic effects of an active indoor green wall system on key indoor air quality indicators and cooling energy consumption in a contemporary office environment. A comparative field study was conducted over 12 months in two identical office rooms in Dhahran, Saudi Arabia, with one room serving as a control while the other was retrofitted with a modular hydroponic green wall system. High-resolution sensors continuously monitored indoor CO₂, volatile organic compounds via photoionization detection (VOC_PID; isobutylene-equivalent), and PM2.5 concentrations, alongside dedicated sub-metering of cooling energy consumption. The green wall system achieved statistically significant improvements across all parameters: 14.1% reduction in CO₂ concentrations during occupied hours, 28.1% reduction in volatile organic compounds, 20.9% reduction in PM2.5, and 13.5% reduction in cooling energy consumption (574.5 kWh annually). Economic analysis indicated financial viability (2.0-year payback; benefit–cost ratio 3.0; 15-year net present value SAR 31,865). Productivity-related benefits were valued from published relationships rather than measured in this study; base-case viability remained strictly positive in energy-only and conservative sensitivity scenarios. Strong correlations were established between evapotranspiration rates and cooling benefits (r = 0.734), with peak performance during summer months reaching 17.1% energy savings. Active indoor GWSs effectively function as multifunctional strategies, delivering simultaneous air quality improvements and measurable cooling energy reductions through evapotranspiration-mediated mechanisms, supporting their integration into sustainable building design practices. Full article

Introduction: Paralympic powerlifting (PP) is a sport in which the bench press is the sole exercise. Warm-up routines are considered essential for optimal performance. Objectives: This study aims to analyze different types of warm-up protocols—traditional warm-up (TW), post-activation performance enhancement (PAPE), and without warm-up (WW)—and their effects on dynamic strength indicators, core temperature, and skin temperature in athletes with disabilities. Methods: Fourteen nationally ranked PP athletes participated in the study. Their performance was evaluated following different warm-up protocols. Dynamic variables analyzed included Maximum Velocity (VMax), Mean Propulsive Velocity (MPV), and Power output. Additionally, tympanic and skin temperatures were measured. Results: No significant differences were observed in dynamic strength indicators across the different warm-up protocols. Thermographic analysis revealed differences only in the triceps muscle between PAPE and TW (p < 0.001), TW and WW (p = 0.004), and PAPE and WW (p = 0.015). Differences were also observed between TW and WW (p = 0.026). Ten minutes post-warm-up, differences were noted between PAPE and WW (p < 0.001) and between TW and WW (p = 0.001). In the WW condition, significant differences were found between pre-warm-up and 10 min post-warm-up (p = 0.031), as well as between post-warm-up and 10 min later (p = 0.003). Conclusions: The study evaluated the potential impact of warm-ups on dynamic indicators of strength, core temperature, and skin temperature. No differences were found between the warm-up methods for strength indicators. Regarding skin temperature, only the triceps showed differences between the PAPE and Traditional methods. Regarding core temperature, after warm-up and 10 min later, the methods without warm-up showed higher temperatures than the PAPE and Traditional methods. Therefore, in practical applications, warm-up methods do not appear to interfere with strength indicators, with lower skin temperatures for the triceps in the PAPE methods. Full article

The design and production of goods have been completely transformed by additive manufacturing (AM), which makes it possible to create components with intricate and complex geometries that were previously impossible or impractical to produce. However, current technologies continue to produce coarse-surfaced metal components that typically exhibit fatigue properties, resulting in component failure and unfavorable friction coefficients on the printed part. Therefore, to improve the surface quality of the fabricated parts, post-processing of AM-created components is required. With emphasis on electroless nickel plating, ChemPolishing (CP), and ElectroPolishing (EP), this study investigates post-processing methods for stainless steel that is additively manufactured (AM). The rough surfaces created by additive manufacturing (AM) restrict direct use. While ElectroPolishing (EP) achieves high material removal rates but may not be consistent, ChemPolishing (CP) offers uniform smoothening. Nickel plating enhances additive manufacturing (AM) products’ resistance to wear and scratches and corrosion protection. To optimize nickel deposition, medium (6%–9%) and high (10%–13%) phosphorus nickel was tested using the L9 Taguchi design of experiments (DOE). Mechanical properties, including scratch resistance and adhesion, were evaluated using the TABER 5900 reciprocating (Taber Industries, North Tonawanda, NY, USA) abraser apparatus, a 5 N scratch test, and ASTM B-733 thermal shock method. Surface analysis was performed with the KEYENCE VHX-7000 microscope (Keyence Corporation, Itasca, IL, USA), and chemical composition before and after nickel deposition was assessed via the ThermoFisher Phenom XL scanning electron microscope (SEM, Thermo Fisher Scientific, Waltham, MA, USA) Optimal processing conditions, determined using Qualitek-4 software, Version 20.1.0 revealed improvements in both surface finish and mechanical robustness. This comprehensive analysis underscores the potential of nickel-coated additive manufacturing (AM) parts for enhanced performance, offering a pathway to more durable and efficient additive manufacturing (AM) applications. Full article

In response to the issues of insufficient shredding efficiency, severe straw entanglement with equipment, and prone blade damage in existing banana straw crushing and returning machines, this paper innovatively proposes a liquid nitrogen (LN₂) cryo-pretreatment combined with a mechanical incorporation method by, firstly, conducting shear, tensile, and cooling timeliness mechanical experiments on banana straw sheaths using LN₂ low-temperature pretreatment, and then designing a corresponding spray device. Subsequently, an improved BAO-Fuzzy-PID control algorithm is presented, which significantly enhances the control performance of the fuzzy PID controller, with the steady-state error, overshoot, rise time, and settling time being 0, 0, 0.31 s, and 0.25 s, respectively. Finally, field experiments are executed, and the flow control accuracy test results indicated a maximum error of 3.32%, meeting the test requirements. Using spray height and spray angle as experimental factors and banana straw crushing qualification rate as the experimental indicator, a two-factor and five-level banana straw crushing experiment is presented. The optimal spray parameters are determined to be a spray height of 250 mm and a spray angle of 90°. At this point, the banana straw crushing qualification rate is 96.98%, meeting the quality requirements for banana straw crushing and significantly reducing straw entanglement. Full article

Rail transport is widely regarded as a sustainable and environmentally friendly option for long-distance freight and passenger movement during its operation phase. However, its construction and maintenance phases often result in substantial environmental impacts, which must be addressed to improve the overall sustainability of railways. This study aims to identify solutions that improve the performance of railway tracks, reduce maintenance requirements, and minimize environmental impact. With this objective, the potential of artificial inclusions and innovative composite materials in enhancing the sustainability of railway tracks is investigated through a comprehensive methodology, combining experimental, analytical and numerical approaches. A novel composite material, comprising soil, scrap tire aggregates and an adhesive, demonstrated strong potential as a sustainable base layer for ballastless railway tracks, exhibiting minimal strain accumulation (0.29–0.98%) under 50,000 load cycles and adequate damping. Incorporation of cellular artificial inclusions in the substructure layers of ballasted tracks reduced cumulative settlement by up to 33% and slowed track geometry deterioration. Use of planar artificial inclusions beneath a pile-supported railway embankment enhanced the load transfer efficiency and curtailed settlement, while also lowering environmental impact by reducing concrete usage. The findings of this study highlight strong potential of these approaches in improving track performance and the overall sustainability of railways. Full article

Time-domain signal models have been widely applied to single-channel music source separation tasks due to their ability to overcome the limitations of fixed spectral representations and phase information loss. However, the high acoustic similarity and synchronous temporal evolution between vocals and accompaniment make accurate separation challenging for existing time-domain models. These challenges are mainly reflected in two aspects: (1) the lack of a dynamic mechanism to evaluate the contribution of each source during feature fusion, and (2) difficulty in capturing fine-grained temporal details, often resulting in local artifacts in the output. To address these issues, we propose an attention-driven time-domain convolutional network for vocal and accompaniment source separation. Specifically, we design an embedding attention module to perform adaptive source weighting, enabling the network to emphasize components more relevant to the target mask during training. In addition, an efficient convolutional block attention module is developed to enhance local feature extraction. This module integrates an efficient channel attention mechanism based on one-dimensional convolution while preserving spatial attention, thereby improving the ability to learn discriminative features from the target audio. Comprehensive evaluations on public music datasets demonstrate the effectiveness of the proposed model and its significant improvements over existing approaches. Full article

We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often struggle to remain robust under challenging visual conditions, such as low texture, motion blur, or lighting variation. These methods tend to exhibit large performance variance across different environments, primarily due to the limited repeatability and adaptability of hand-crafted keypoints. In contrast, learning-based features offer richer representations and can generalize across diverse domains thanks to data-driven training. However, they often suffer from uneven spatial distribution and temporal instability, which can degrade tracking performance. To address these issues, we propose a hybrid front-end that combines a lightweight deep feature extractor with an image pyramid and grid-based keypoint sampling to enhance spatial diversity. Additionally, a forward–backward optical-flow-consistency check is applied to filter unstable keypoints. The system improves feature tracking stability by enforcing spatial and temporal consistency while maintaining real-time efficiency. Finally, the effectiveness of the proposed VIO system is validated on the EuRoC MAV benchmark, showing a 19.35% reduction in trajectory RMSE and improved consistency across multiple sequences compared with previous methods. Full article

Intent detection and slot filling are fundamental components for constructing intelligent question-answering systems in agricultural domains. Existing approaches show notable limitations in semantic feature extraction and achieve relatively low accuracy when processing domain-specific agricultural queries with complex terminology and contextual dependencies. To address these challenges, this paper proposes an agricultural knowledge-enhanced deep learning approach that integrates agricultural domain knowledge and terminology with advanced neural architectures. The method integrates HanLP-based agricultural terminology processing with BERT contextual encoding, TextCNN feature extraction, and attention-based fusion. Experimental validation on a curated domain-specific agricultural dataset of 8041 melon cultivation queries demonstrates that the proposed model achieves an accuracy of 79.6%, recall of 80.1%, and F1-score of 79.8%, demonstrating significant improvements (7–22% performance gains) over baseline methods including TextRNN, TextRCNN, TextCNN, and BERT-TextCNN models. The results demonstrate significant potential for advancing intelligent agricultural advisory systems and domain-specific natural language understanding applications, particularly for precision agriculture applications. Full article

Drawing from practices at Walmart, we model a supply chain where the manufacturer conducts product R&D while the retailer distributes products to two distinct consumer segments: green-conscious consumers who translate environmental principles into purchasing decisions, and non-green-conscious consumers who are deterred by perceived high costs and information deficits. The retailer engages in green education targeted at non-green-conscious consumers, providing clear product explanations to improve their willingness to pay for sustainable products, though this education is motivated by profit maximization rather than altruistic environmental responsibility. Our analysis reveals that while retailer green education can boost product R&D and adoption under certain conditions, this creates a ‘consumer education paradox’—a situation where green education could further enhance product R&D and adoption, but the retailer forgoes it because doing so does not contribute to profit. This occurs because profit-driven retailers limit education to self-beneficial ranges, creating tension between individual profit maximization and overall environmental performance. We then propose two government subsidy solutions—green product quantity subsidies and product R&D subsidies—to resolve this paradox. Both effectively alleviate the tension, but green innovation subsidies, despite requiring greater government investment, consistently outperform in fostering innovation and adoption, offering superior environmental outcomes. Full article

This paper presents a modified Orthogonal Frequency Division Multiplexing (OFDM) system that combines Discrete Wavelet Transform (DWT) with Discrete Sine Transform (DST) to enhance data rate capacity over traditional Discrete Fourier Transform (DFT)-based OFDM systems. By applying Inverse Discrete Wavelet Transform (IDWT) to the modulated Binary Phase Shift Keying (BPSK) bits, the constellation diagram reveals that half of the time-domain samples after single-level Haar IDWT are zeros, while the other half are real. The proposed system utilizes these $0.5N$ zero values, modulating them with the DST (IDST) and assigning them as the imaginary part of the signal. Performance comparisons demonstrate that the Bit-Error-Rate (BER) of this hybrid DWT-DST configuration lies between that of BPSK and Quadrature Phase Shift Keying (QPSK) in a DWT-based system, while also achieving data rate improvement of $0.5N$. Additionally, simulation results indicate that the proposed approach demonstrates stable performance even in the presence of estimation errors, with less than 3.4% BER degradation for moderate errors, and consistently better robustness than QPSK-based systems while offering improved data rate efficiency over BPSK. This novel configuration highlights the potential for more efficient and reliable data transmission in OFDM systems, making it a promising alternative to conventional DWT or DFT-based methods. Full article