Search Results (44,252)

This study investigates ultrasonic vibration-assisted (UV) CNC milling of hardened 90CrSi steel cylindrical surfaces, with emphasis on ultrasonic horn design, experimental analysis, and multi-objective optimization of machining parameters, addressing the need for an integrated framework combining system design, experimental validation, and multi-objective optimization. A quarter-wavelength ultrasonic horn was designed and experimentally validated to operate at a frequency of 20 kHz. By adjusting the horn–workpiece system, stable vibration amplitudes were achieved to enable effective ultrasonic-assisted milling of cylindrical surfaces. Milling experiments based on a Box–Behnken design were conducted to examine the effects of vibration amplitude, cutting speed, feed rate, and radial depth of cut on material removal rate (MRR) and surface roughness (Ra). Surrogate models using response surface methodology (RSM) and Gaussian process regression (GPR) were developed to predict machining performance. A GPR-assisted NSGA-II algorithm was then applied to simultaneously maximize MRR and minimize Ra, resulting in a well-defined Pareto front that reveals the trade-off between machining productivity and surface quality. Furthermore, an AHP-based decision-making approach was employed to select preferred machining conditions from the Pareto-optimal solutions. The GPR models demonstrated high predictive accuracy (R² > 0.98), and validation experiments confirmed the reliability of the predicted optimal results, with deviations below 5%. In addition, a comparative analysis between ultrasonic-assisted and conventional milling showed that MRR increased by 10.81–40.17%, Ra decreased by 27.11–44.44%, and cutting force was reduced by 14.2–42.65%, providing direct experimental evidence of improved machinability. The results demonstrate that the proposed integrated framework provides an effective strategy for optimizing ultrasonic vibration-assisted milling processes and improving the machinability of hardened 90CrSi cylindrical surfaces. Overall, the proposed framework provides a practical and cost-effective strategy for enhancing machining performance and offers a robust approach for multi-objective optimization of ultrasonic vibration-assisted milling processes. Full article

Background: Mega-events like the FIFA World Cup (FWC) present unique and substantial challenges for individuals in recovery from substance use disorders (SUDs), primarily by increasing the risk of relapse. We employed a qualitative design using reflexive thematic analysis to explore the behavior of patients with SUDs during the 2022 FWC and to evaluate institutional strategies for mitigating related risks. Methods: We purposively sampled 32 participants who were present at the Naufar Center during the 2022 FWC: (i) thirteen adult patients with SUDs who were receiving treatment, and (ii) nineteen healthcare practitioners. Semi-structured patient interviews were conducted, and focus group discussions were held with a multidisciplinary team, including psychologists, nurses, and physicians. Individuals’ experiences regarding patterns in substance use behavior, environmental triggers, and the effects of institutional interventions were examined. Thematic analysis was employed to identify patterns, risks, and effective strategies. Results: Most patients maintained abstinence and only had cravings for alcohol. Triggers included public celebrations, emotional excitement, and the increased availability of addictive substances. Psychologists and physicians reported signs of behavioral destabilization; nurses observed some behavioral changes and noted logistical challenges. The participants acknowledged the supportive measures provided by Naufar, including the accessibility of clinical services, individualized therapy, social and recreational programming, and protective fan zones, which enabled them to participate in various activities during the event. Conclusions: The 2022 FWC created considerable psychological and environmental triggers for high exposure to alcohol and other substances. The supportive structured activities and tailored interventions were helpful in mitigating the risk of relapse, maintaining treatment engagement and ensuring recovery. Further research is required to explore the implications for recovery-oriented practices during culturally and socially high-risk events. Full article

Soil adhesion and abrasive wear severely degrade the performance and service life of soil-covering rollers in no-tillage seeders, particularly in the heavy clay black soil regions of Northeast China. To address the critical issues of soil adhesion and wear on soil-covering rollers used in no-tillage seeders within black soil regions, this study presents a surface engineering strategy that integrates a bionic micro-texture with a functional composite coating. Inspired by the crescent-shaped pits on the body surface of Procambarus clarkii, a bionic texture was designed and combined with a PTFE/PDMS/TiO₂ composite coating. Key parameters were optimized using response surface methodology, yielding a TiO₂ mass fraction of 6%, coating thickness of 40 μm, remaining texture depth of 50 μm, and texture spacing of 250 μm. A prototype was fabricated and evaluated through orthogonal field experiments in two distinct soil environments. In clay soil (15–25% moisture content), soil moisture and vertical load significantly influenced anti-adhesion performance, with recommended operating parameters of 600 N vertical load and a speed range of 10.8–14.4 km·h⁻¹. In sandy soil (8–18% moisture content), vertical load and operating speed had significant effects on wear resistance, with optimal parameters identified as 600 N vertical load and 10.8 km·h⁻¹. Verification tests confirmed stable low-adhesion and low-wear performance under varying moisture conditions. Compared to conventional and PTFE-coated rollers, the bionic roller reduced soil adhesion by 82.62% and 74.02%, respectively, in high-moisture clay soil, and reduced wear loss by 36.81% and 28.97%, respectively, in dry sandy soil. These results demonstrate that the synergistic “structure–material” design, which leverages stress dispersion and storage from the bionic texture alongside low surface energy and enhanced wear resistance from the composite coating, offers a promising approach for improving the durability and performance of soil-engaging agricultural components. Full article

This study presents research on optimizing the parameters of the passivation process for precipitation-hardening stainless steels (PHSS) to improve the corrosion resistance of AM350 and CUSTOM 450 alloys, which are extensively utilized in the aerospace and aviation sectors, since, as this is a complex process, it requires the implementation of a robust methodological approach that allows for multi-response optimization. Experiments were designed using the Taguchi method, which offered a strong framework for examining the impact of material, type of passivation solution, concentration, temperature, and passivation process time on the corrosion resistance of both PHSS alloys. To confirm the ideal PHSS passivation process parameters and measure the significance of each component, gray relational analysis (GRA) and analysis of variance (ANOVA) were also employed. The combined use of the Taguchi/GRA represents a robust and efficient methodological approach to the multi-response optimization of complex processes, overcoming the limitations inherent in the individual application of each technique. It was determined that the optimized parameters were a PHSS AM 350, a solution composed of a combination of citric acid and oxalic acid, acid concentration of 25% v/v, temperature of 50 °C, and time of 120 min. This combination of parameters resulted in significant improvements of up to 55% in corrosion resistance in the H₂SO₄ and NaCl evaluation solutions, demonstrating the effectiveness of the optimized conditions. This work emphasizes the efficacy of integrating Taguchi, GRA, and ANOVA techniques to significantly reduce the corrosion rate of PHSS undergoing the passivation process using alternatives to nitric acid. The integration of the Taguchi methodology with GRA enables the normalization and combination of responses with different scales and performance criteria into a single gray relational index, facilitating the overall evaluation of the system. Full article

Wearable sensor-based human activity recognition (HAR) has gained growing significance in healthcare monitoring and assisted living systems. Although considerable advances have been made in classifying dynamic movements, stationary activities—such as sleeping, driving, and watching TV—remain difficult to distinguish owing to their weak sensor signatures and limited discriminative cues. This paper presents the multi-scale hybrid attention temporal network (MHAT-Net), a deep learning framework whose key architectural novelty lies in the parallel (non-sequential) dual-pathway temporal modeling: a BiGRU branch and a transformer encoder branch operate simultaneously on the same spatially encoded representation, combined via a learnable attention-based fusion module. This design targets the underexplored problem of distinguishing stationary activities from weak inertial sensor signatures. The architecture is built upon three integrated components: (1) a multi-branch CNN with kernel sizes three, five, and seven combined with channel attention for adaptive spatial feature extraction across multiple temporal scales; (2) parallel bidirectional gated recurrent unit (BiGRU) and transformer encoder pathways for jointly capturing short-range sequential patterns and long-range temporal correlations; and (3) an attention-driven fusion module that adaptively weights the outputs of both temporal branches. The model was assessed on a publicly available benchmark comprising three motionless activity categories collected from 25 participants via smartphone sensors. In 5-fold cross-validation, MHAT-Net attained 97.42% (±4.69%) accuracy with accelerometer data and 92.31% (±0.31%) with gyroscope data, substantially exceeding the accuracies of five baseline architectures: CNN, LSTM, BiLSTM, GRU, and BiGRU. Ablation experiments identified multi-scale spatial feature extraction as the most influential module (2.21–2.47% contribution), followed by the hybrid temporal modeling components. Cross-modality analysis confirmed that accelerometer signals yielded richer discriminative content for stationary activities, while MHAT-Net sustained consistent performance across both sensor types. The proposed integration of multi-scale spatial encoding, hybrid temporal modeling, and multi-level attention gives MHAT-Net the ability to reliably detect subtle activity-specific patterns, establishing a new benchmark in wearable sensor-based recognition for comprehensive daily behavior monitoring. Full article

Understanding human–nature–animal relationships is central to conservation and visitor management, yet these relationships are commonly studied through language-based surveys that may exclude participants across age groups and diverse cultural or educational backgrounds. This limitation highlights the need for more inclusive and experience-sensitive approaches capable of capturing relational meanings beyond verbal expression. This study adopts a visual participatory approach in which volunteer tourists were invited to draw circles representing themselves in relation to images of a giant panda, nature, and a pet. Extending the visual idea of Schultz’s Inclusion of Nature in Self in the Connection to Nature Index, more than 1000 tourists at the Chengdu Research Base of Giant Panda Breeding produced over 3000 drawings. These drawings were systematically coded along three dimensions—circle size, orientation, and spatial relationship—and analyzed using multinomial logistic regression and non-parametric tests. The results reveal consistent yet differentiated patterns of visual representation. Participants most frequently expressed relationships with nature and pets through enclosing circles, suggesting spatial inclusion, whereas relationships with the giant panda were more often represented through separate but proximal positioning, indicating a more mediated or observational mode of connection. Demographic factors, including age, residence, and visitation stage, significantly influenced drawing configurations, supporting the interpretation of connection as a context-sensitive and dynamic process rather than a fixed individual trait. Associations between drawing dimensions and self-reported pro-environmental orientation and momentary well-being were observed, although these relationships should be interpreted cautiously given the use of brief, context-specific indicators. Overall, the findings demonstrate that participatory drawing can function as both a research instrument and an engagement tool, enabling diverse visitor groups—including children—to express relational understandings of nature and wildlife. For conservation practice, such visual methods offer a scalable and low-barrier approach to visitor engagement, with potential applications in environmental education, interpretation design, and the assessment of human–animal relationships in situ. Full article

Household rainwater tanks (HRWTs) have re-emerged globally as a decentralised strategy to address water scarcity, climate variability, and increasing urban water demand. In several jurisdictions, including New South Wales, Australia, rainwater tanks have been chosen to meet the mandatory potable water reduction target in new residential developments for nearly two decades; however, growing evidence indicates persistent underutilisation and variable performance in practice. Despite their recognised benefits in reducing potable water demand, mitigating stormwater runoff, and enhancing urban resilience, the global HRWT research landscape remains fragmented across disciplinary and thematic boundaries. This paper presents a multifaceted review, defined here as an approach that synthesises multiple perspectives on the topic. It integrates systematic mapping of peer-reviewed literature with a critical thematic analysis across four dominant research domains: technological and design innovation, policy and governance frameworks, environmental performance, and social–behavioural dimensions. The findings reveal a strong research focus on technical optimisation, while policy effectiveness, environmental trade-offs, and household-level behavioural factors receive comparatively uneven attention. Regulatory and incentive-based instruments are shown to produce inconsistent outcomes, shaped by local institutional capacity to design, implement, enforce, and sustain programs, as well as by climatic context and household acceptance. Environmental assessments identify both benefits and burdens, including energy use, treatment requirements, and operational complexity. Social and behavioural studies indicate growing acceptance of household rainwater tank (HRWT) systems. However, financial constraints, local conditions, and ongoing maintenance demands continue to influence adoption and performance. A key insight from this review is the limited attention given to households’ lived experiences, particularly how users adopt, adapt, operate, and maintain HRWT systems over time. This gap constrains progress across technical, policy, environmental, and social dimensions and risks cycles of early policy uptake followed by stagnation. The review highlights the need to integrate household perspectives into future research, policy design, and industry practice to improve system performance, user experience, and the long-term contribution of HRWTs to sustainable urban water management. Full article

Indoor environments often contain numerous areas with sparse structural features, such as long corridors, large atriums, and glass curtain walls, and other scenarios. These conditions can lead to difficulties in loop closure detection and accumulated positioning errors, resulting in localization drift or even mapping failure during map construction. This paper proposes an indoor mapping algorithm called IS-LEGO-LOAM that integrates tightly coupled LiDAR-IMU fusion and Scan Context. A tightly coupled LiDAR-IMU odometry is constructed, and an adaptive covariance matrix is designed to solve the problems of abnormal LiDAR echoes and insufficient effective feature extraction caused by sparse indoor feature points. By introducing the Scan Context global descriptor and adopting the strategies of vector nearest neighbor search and similarity score matching, the drift problem in large-scale scenes is alleviated. Finally, validation is performed on the KITTI dataset and in real-world scenarios, respectively. Experiments show that the improved IS-LEGO-LOAM achieves superior mapping performance. Full article

Aiming at the problems of the large prediction error of model-driven algorithms and poor interpretability (even potential violation of physical laws) of pure data-driven algorithms in the prediction of aerospace vehicle plume characteristics, a physics mechanism-guided prediction algorithm for aerospace vehicle plume characteristics was proposed. Taking the long short-term memory (LSTM) network as the backbone, this algorithm constructed a hybrid physics–data model by embedding the prior knowledge of physical laws and empirical rules into the neural network, and designed a loss function combined with physical mechanisms to guide network training. The aerospace vehicle plume dataset was preprocessed through characteristic parameter extraction, extended physical parameter calculation, data splicing and sliding window operation, and the LSTM network structure was optimized by adjusting hyperparameters such as the number of hidden layers and neurons. Experimental results show that the proposed algorithm achieves a Mean Absolute Error (MAE) of 31.89 and a Physical Inconsistency of 0.1723 on the test set, with MAE reduced by 14% and Physical Inconsistency reduced by 7.5% compared with traditional machine learning models such as Random Forest. Ablation experiments verify that the introduction of physical mechanisms can improve the prediction accuracy of the model by about 25%. This algorithm makes up for the defects of traditional prediction algorithms, has good generalization ability and physical consistency, and provides an effective method for the prediction of engine exhaust plume temperature distribution. Full article

Multimodal Named Entity Recognition (MNER) aims to integrate textual and visual information to identify entities with specific semantic categories. However, existing methods often suffer from insufficient intra-modal semantic modeling, coarse cross-modal alignment, and vulnerability to noisy or ambiguous expressions in social media. To address these challenges, we propose a Visual–Semantic Guided Interaction Network (VSGN), which improves multimodal representation learning from both semantic and structural perspectives. Specifically, we first design an adaptive visual–semantic fusion module that incorporates visual descriptions as semantic guidance, enabling more informative cross-modal interactions. To further enhance feature quality, we introduce a deviation-aware channel-wise inhibitory routing (CIR) mechanism, which jointly models channel importance and distributional deviation to suppress noisy or redundant visual signals. In addition, we propose a visual–semantic guided graph structure learning module (VSG), which explicitly captures structural dependencies across modalities. By enforcing distribution-level alignment between textual and visual graph representations, the model achieves structure-aware cross-modal interaction and reduces modality inconsistency. Extensive experiments on the Twitter-2015 and Twitter-2017 datasets demonstrate the effectiveness of the proposed method, achieving F1 scores of 76.72% and 87.86%, respectively. The results show that jointly modeling semantic enhancement and structural alignment leads to more robust and discriminative multimodal representations. Full article

Underwater multi-class object detection in nearshore waters is essential for intelligent cleaning operations and ecological monitoring. However, strong reflection and scattering interference, color attenuation, frequent occlusion, and non-rigid deformation often cause fine-grained information loss and feature misalignment in conventional detectors, leading to missed and false detections. To address these challenges, we propose an enhanced YOLOv11 framework integrating FasterNet and attention mechanisms. Specifically, we include FasterNet to replace the YOLOv11 baseline backbone to improve fine-grained feature preservation while reducing computational redundancy. Furthermore, a Deformable Underwater Attention Module (DUAM) is introduced to capture local texture variations and deformation-aware features, enhancing discrimination among heterogeneous categories. Additionally, a Submerged Occlusion-Aware Head (SOAH) is designed to recalibrate features based on occlusion visibility, improving the detection of small-scale and partially occluded objects in the high-resolution P2 layer. Performance gains mainly stem from the recalibration strategy and its synergy with multi-scale optimization objectives. Experiments on a nearshore underwater multi-class dataset (8610 images across 40 classes) show that the proposed method increases mAP from 66.9% to 82.3%, achieving a 15.4-point improvement over baseline YOLOv11, with superior robustness under complex backgrounds. Full article

This communication discusses the problem of depositing equiatomic metal alloy films. It is shown that this problem can be solved using a magnetron equipped with a target constructed using a new “multilayer target” paradigm. This target, sputtered in an argon environment, consists of several parallel metal plates mounted on the magnetron axis. A method based on the equality of the sputtered fluxes generated by the plates is proposed for calculating the geometric dimensions of the plates. This equality leads to a system of algebraic equations, which are proposed to be solved under the assumption of a uniform discharge current density distribution in the sputtering region of the target. The communication describes two types of targets in which the plates have slots of different shapes. In one case, the slots are shaped as sectors of a ring with a given angle. In the other, the plates are shaped as rings. As examples, the geometric dimensions of targets for a balanced magnetron system intended for the deposition of films of equiatomic Ti_0.33Ta_0.33Nb_0.33 and Ti_0.25Ta_0.25Nb_0.25Mo_0.25 alloys are calculated. The presentation is accompanied by the results of individual experiments. This report is preliminary in nature; experimental verification is ongoing. The application of the new paradigm in magnetron target design facilitates the fabrication of films of nanostructured medium- and high-entropy alloys with specified chemical compositions, which is the central theme of the Special Issue devoted to functional nanomaterials. Full article

This paper considers the incentive-based demand response (IDR) economic dispatch problem (EDP) in smart grid while preserving the privacy of sensitive information, where the sensitive information is the consumers’ electricity consumption. The incentive-based demand response (IDR) optimization objective function as an EDP is established. A novel state decomposition-based privacy-preserving distributed consensus algorithm (SDecPP-DC) is designed to address this EDP, where the state decomposition mechanism is proposed to preserve the privacy of sensitive information. The feedback gains in the SDecPP-DC algorithm for the mismatch variables are non-coordinated and constant. The convergence of the proposed SDecPP-DC algorithm is theoretically proved by using multi-parameter perturbation theory. It is shown that the SDecPP-DC algorithm can deal with the directed network topology with a row-stochastic matrix, and the convergence point is the optimal solution of EDP. Finally, the correctness and effectiveness of SDecPP-DC are confirmed by the experiments. Full article

The ability to continuously learn over time by incorporating new information while holding onto previously acquired expertise is known as incremental learning (IL). Although this concept is fundamental to human learning, existing machine learning techniques have a significant propensity to forget prior experience by overwriting previously learned patterns from classes. The continuous learning of new information in K-nearest neighbor (KNN) with lazy learning strategies compounds to loss of old knowledge upon learning new information and stability-plasticity dilemma. The change in new data points and data distributions in unforeseen ways impacts KNN’s ability to adapt to changes in class label distribution, leading to concept drift. This experiment models a hybrid 3WDKNN-based incremental learning algorithm (ILA) designed for application in a heterogeneous and dynamically changing environment. This model addresses the limitations of KNN by overcoming computational costs and inefficiencies associated with loss of information in classes, while facilitating incremental learning to attain high predictive accuracy in crop yield datasets. The algorithm employs weighted voting to identify optimal assigned classes for the nearest neighbor and uses memory reconstruction strategy for class incremental learning until the memory is full without forgetting. Using weighted voting for the best assigned classes for the nearest neighbor, the algorithm uses a local mean vector to determine the best distances for the shortest-term incremental learning to achieve the highest performance accuracy in a concept drift environment. The hybrid 3WDKNN_ILA was developed and evaluated alongside advanced algorithms within the same dataset context. The model improves performance in incremental learning contexts by utilizing current concepts and minimizing errors on both current and recent data to avoid parameterization. The model achieves optimal efficient incremental learning by mitigating intentional loss and minimizing errors associated with valuable class information derived from aggregated mean values through class rectification and transfer. The hybrid model achieves the best efficient performance accuracy in all the tested weighted averages of 200W, 500W, and 1000W with tested set K values of 5, 9, and 13K. This hybrid model demonstrates performance accuracy of 97% at a value of 13K, whereas 3WD_KNN achieves 96% at 9K, HoKNN attains 89% at 13K, and 1IKNN reaches 88% at 9K accuracy, respectively. The integrated novelty in the hybrid 3WDKNN_ILA proves superior in terms of computational efficiency, accuracy, and high-level incremental performance and learning in comparison with other tested models of algorithms. Full article

This study investigates the soundscape of Taoist temples and its influence on visitors’ worship experiences, integrating auditory perception, visual landscape evaluation, and emotional and experiential responses into a comprehensive analytical framework. Based on questionnaire surveys conducted in multiple Taoist temples, the study examines how different sound sources affect soundscape evaluation and how this evaluation shapes perceptual and experiential outcomes. The results indicate that Taoist ritual sounds (e.g., ritual music and chanting) play a significant positive role in shaping visitors’ soundscape evaluation, whereas artificial sounds related to general human activities show a negative effect. Soundscape evaluation is found to significantly influence visual landscape evaluation and emotional perception, and further contributes to visitors’ overall temple experience. Visual landscape evaluation is found to partially mediate the relationship between soundscape evaluation and emotional perception, while emotional perception further mediates the relationship between soundscape evaluation and temple experience. A comparison across sensory dimensions suggests that soundscape evaluation exerts a relatively stronger influence on temple experience than visual landscape evaluation, highlighting the important role of auditory experience in religious and cultural environments. The study also identifies a synergistic interaction between auditory and visual evaluation, indicating that multisensory integration can enhance the overall experiential quality of Taoist temples. Overall, this research provides empirical insights into the role of soundscapes in religious spaces and offers practical implications for the design, management, and optimization of multisensory environments in Taoist temples. Full article