Search Results (41)

Fashion products are typically sold through both online and offline channels during two distinct phases: the launch and markdown period. Pricing strategies present significant challenges for manufacturers, particularly as consumers increasingly adopt strategic purchasing behaviors. Key factors, including product fashion utility, purchase timing, and consumer characteristics, complicate manufacturers’ channel selection, pricing decisions, and service strategy formulation—necessitating deeper investigation. This paper establishes a two-echelon supply chain model featuring a fashion manufacturer and a retailer to determine optimal channel, pricing, and service strategies across both selling periods amid strategic consumer behavior. We examine four channel strategies: (1) the MM strategy: the manufacturer operates both channels (online and offline channels) during both periods (launch and markdown period); (2) the MR strategy: the manufacturer operates both channels during the launch stage, and the retailer sells online during the markdown period; (3) the RR strategy: the manufacturer sells offline, and the retailer operates the online channel during both stages; (4) the RM strategy: the manufacturer sells online during both stages, and the retailer sells through the offline channel. Our analysis yields critical insights: When off-season discounts are limited, the manufacturer should maintain direct control of both channels. However, when the off-season discount is significant, the manufacturer needs to set the channel strategy according to the fashion utility. If the fashion utility is small, direct sales through offline channels during the launch period, while entrusting the retailer to distribute in online channels during both periods, should be adopted. If the fashion utility is large, a dual-channel, two-stage, entirely direct sales strategy should be adopted. This study elucidates the optimal manufacturer channel and pricing strategy options and provides some theoretical contributions and practical implications. Full article

Accomplishing China’s national targets of carbon peaking and carbon neutrality necessitates proactive solutions, hinging critically on fundamentally transforming rural construction models. Current construction practices in rural areas are characterized by inefficiency, high resource consumption, and reliance on imported materials. These shortcomings not only jeopardize the attainment of climate objectives, but also hinder equitable development between urban and rural regions. Using the Digital Industrial Park in Yongtai County, Fuzhou City, as a case study, this study focuses on prefabricated public buildings in regions with extreme hot–humid climate, and innovatively integrates BIM (Building Information Modeling)-driven carbon modeling with the Gaussian Two-Step Floating Catchment Area (G2SFCA) method for spatial accessibility assessment to investigate the carbon emissions and economic benefits of prefabricated buildings during the embodied stage, and analyzes the spatial accessibility of prefabricated building material suppliers in Fuzhou City and identifies associated bottlenecks, seeking pathways to promote sustainable rural revitalization. Compared with traditional cast-in-situ buildings, embodied carbon emissions of prefabricated during their materialization phase significantly reduced. This dual-perspective approach ensures that the proposed solutions possess both technical rigor and logistical feasibility. Promoting this model across rural areas sharing similar climatic conditions would advance the construction industry’s progress towards the dual carbon goals. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

We present a comprehensive structural and thermomechanical investigation of N-salicylideneaniline, a Schiff base derivative that exhibits remarkable thermosalient phase transition behavior. By combining variable-temperature X-ray powder diffraction (VT-XRPD), differential scanning calorimetry (DSC), hot-stage microscopy, and Hirshfeld surface analysis, we reveal two distinct thermosalient mechanisms operating in different polymorphic forms. Form I displays pronounced anisotropic thermal expansion with negative strain along a principal axis, culminating in a sudden and explosive phase transition into Form IV. In contrast, Form III transforms more gradually through a microstrain accumulation mechanism. Fingerprint plots and contact evolution from Hirshfeld surface analysis further support this dual-mechanism model. These insights highlight the importance of integrating macro- and microscale structural descriptors to fully capture the mechanical behavior of responsive molecular solids. The findings not only enhance the fundamental understanding of thermosalience but also inform the rational design of functional materials for actuating and sensing applications. Full article

Phyllosticta fragaricola-induced angular leaf spot causes substantial economic losses in global strawberry production, necessitating advanced severity assessment methods. This study proposed a dual-phase grading framework integrating deep learning and computer vision. The enhanced You Only Look Once version 11 (YOLOv11) architecture incorporated a Content-Aware ReAssembly of FEatures (CARAFE) module for improved feature upsampling and a squeeze-and-excitation (SE) attention mechanism for channel-wise feature recalibration, resulting in the YOLOv11-CARAFE-SE for the severity assessment of strawberry angular leaf spot. Furthermore, an OpenCV-based threshold segmentation algorithm based on H-channel thresholds in the HSV color space achieved accurate lesion segmentation. A disease severity grading standard for strawberry angular leaf spot was established based on the ratio of lesion area to leaf area. In addition, specialized software for the assessment of disease severity was developed based on the improved YOLOv11-CARAFE-SE model and OpenCV-based algorithms. Experimental results show that compared with the baseline YOLOv11, the performance is significantly improved: the box mAP@0.5 is increased by 1.4% to 93.2%, the mask mAP@0.5 is increased by 0.9% to 93.0%, the inference time is shortened by 0.4 ms to 0.9 ms, and the computational load is reduced by 1.94% to 10.1 GFLOPS. In addition, this two-stage grading framework achieves an average accuracy of 94.2% in detecting selected strawberry horn leaf spot disease samples, providing real-time field diagnostics and a high-throughput phenotypic analysis for resistance breeding programs. This work demonstrates the feasibility of rapidly estimating the severity of strawberry horn leaf spot, which will establish a robust technical framework for strawberry disease management under field conditions. Full article

Topside choking is a common way to eliminate severe slugging flow in pipeline riser systems in offshore oil and gas fields. However, a lack of fundamentals in two-phase flow gives rise to difficulty in the model selection of valves and the effective control of the valves. In this study, the prediction of the valve opening required for slugging control based on measurable parameters is investigated experimentally and theoretically. It is found that the resistance coefficient factor of the valve is almost the same for pipeline risers and simple vertical pipes when severe slugging is eliminated. Therefore, fluid parameters can be approximated by the conditions of a simple vertical pipe. The target of control is to achieve dual-frequency fluctuation, and it is quantitatively converted to the pressure drop of the valve. Based on these two empirical enclosures, the valve opening can be worked out by using the gas fraction model and the theoretical model of valve flow resistance. The non-slip model is found to be better than the drift-flux model in the final prediction of the optimal valve opening. An explicit model for the calculation of the optimal resistance factor and the corresponding valve opening is established, making it more convenient to select the valve in the design stage of offshore oil and gas exploitation. The average absolute error of the proposed model is +0.01%, which is smaller than the simulation performed by OLGA 7.0 software (+4.91% before tuning and +0.08% after tuning). A field case with a flexible S-shape riser proves the good applicability of the model (with a deviation smaller than ±2%). The applications of the prediction model in the model selection of the valve and uncertain factors in the operation are also discussed. Full article

In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeling and a Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy optimization (GPTD3), aiming to enhance adaptability and multi-objective optimization. Initially, GPs are utilized to model the uncertain dynamics of agents based on sensor data, providing a stable and noiseless training virtual environment for the first phase of TD3 strategy network training. Subsequently, a TD3-based compensation learning mechanism is introduced to reduce consensus errors among multiple agents by incorporating the position state of other agents. Additionally, the approach employs an enhanced dual-layer reward mechanism tailored to different stages of learning, ensuring robustness and improved convergence speed. Experimental results using a differential drive robot simulation demonstrate the superiority of this method over traditional controllers. The integration of the TD3 compensation network further improves the cooperative reward among agents. Full article

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the impact of regulated deficit irrigation strategies on spring wheat production, with the dual objectives of enhancing water conservation and optimizing yield–quality synergies. Through a two-year field experiment (2020~2021), four irrigation regimes were implemented: rain-fed control (W0), single irrigation at the tillering–jointing stage (W1), dual irrigation at the tillering–jointing and heading–flowering stages (W2), and triple irrigation incorporating the grain-filling stage (W3). A comprehensive analysis revealed that an incremental irrigation frequency progressively enhanced plant morphological traits (height, upper three-leaf area), population dynamics (leaf area index, dry matter accumulation), and physiological performance (flag leaf SPAD, net photosynthetic rate), all peaking under the W2 and W3 treatments. While yield components and total water consumption exhibited linear increases with irrigation inputs, grain yield demonstrated a parabolic response, reaching maxima under W2 (29.3% increase over W0) and W3 (29.1%), whereas water use efficiency (WUE) displayed a distinct inverse trend, with W2 achieving the optimal balance (4.6% reduction vs. W0). The grain quality parameters exhibited divergent responses: the starch content increased proportionally with irrigation, while protein-associated indices (wet gluten, sedimentation value) and dough rheological properties (stability time, extensibility) peaked under W2. Notably, protein content and its subcomponents followed a unimodal pattern, with the W0, W1, and W2 treatments surpassing W3 by 3.4, 11.6, and 11.3%, respectively. Strong correlations emerged between protein composition and processing quality, while regression modeling identified an optimal water consumption threshold (3250~3500 m³ ha⁻¹) that concurrently maximized grain yield, protein output, and WUE. The W2 regime achieved the synchronization of water conservation, yield preservation, and quality enhancement through strategic irrigation timing during critical growth phases. These findings establish a scientifically validated framework for sustainable, intensive wheat production in arid irrigation districts, resolving the tripartite challenge of water scarcity mitigation, food security assurance, and processing quality optimization through precision water management. Full article

The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their malicious goals, highlighting the urgent need for robust detection strategies to secure IoT networks. To overcome these obstacles, this research presents an innovative anomaly-driven intrusion detection approach specifically tailored for IoT networks. The proposed model employs an advanced hybrid architecture that seamlessly integrates convolutional neural networks (CNN) with multilayer perceptron (MLP), enabling precise detection and classification of both binary and multi-class IoT network traffic. The CNN component is responsible for extracting and enhancing features from network traffic data and preparing these features for effective classification by the MLP, which handles the final classification task. To further manage class imbalance, the model incorporates the enhanced hybrid adaptive synthetic sampling-synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification, advanced ADASYN for multiclass classification, and employs edited nearest neighbors (ENN) alongside class weights. The CNN-MLP architecture is meticulously crafted to minimize erroneous classifications, enhance instantaneous threat detection, and precisely recognize previously unseen cyber intrusions. The model’s effectiveness was rigorously tested using the IoT-23 and NF-BoT-IoT-v2 datasets. On the IoT-23 dataset, the model achieved 99.94% accuracy in two-stage binary classification, 99.99% accuracy in multiclass classification excluding the normal class, and 99.91% accuracy in single-phase multiclass classification including the normal class. Utilizing the NF-BoT-IoT-v2 dataset, the model attained an exceptional 99.96% accuracy in the dual-phase binary classification paradigm, 98.02% accuracy in multiclass classification excluding the normal class, and 98.11% accuracy in single-phase multiclass classification including the normal class. The results demonstrate that our model consistently delivers high levels of accuracy, precision, recall, and F1 score across both binary and multiclass classifications, establishing it as a robust solution for securing IoT networks. Full article

Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters’ (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and increase costs, thereby limiting applications. Therefore, there is still a lack of cost-effective and sensorless thermal monitoring techniques. This paper proposes a high-efficiency datasheet-driven method for sensorless estimation utilizing the third-quadrant characteristics of MOSFETs. Without changing the existing hardware, the closure degree of MOS channels is controlled through a dual-gate bias (DGB) strategy to achieve reverse conduction in different patterns with body diodes. This method introduces a MOSFET operating current that TSEPs are equally sensitive to into the two-argument function, improving the complexity and accuracy. A two-stage current pulse is used to decouple the motor effect in various conduction modes, and the TSEP-combined temperature function is built dynamically by substituting the currents. Then, the junction temperature is estimated by the measured bus voltage and current. Its effectiveness was verified through spice model simulation and a test bench with a three-phase inverter. The average relative estimation error of the proposed method is below $7.2\%$ in centigrade. Full article

The early identification of forest wood-boring pests is essential for effective pest management. However, detecting infestation in the early stages is difficult, as larvae, such as the emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), usually feed inside the trees. Acoustic sensors can detect the pulse signals generated by larval feeding or movement, but these sounds are often weak and easily masked by background noise. To address this, we propose a dual-branch time-frequency multi-dilated dense network (DBMDNet) for noise reduction. Our model decouples two denoising training objectives: a magnitude masking decoder for coarse denoising and a complex spectral decoder for further magnitude repair and phase correction. Additionally, to enhance global time-frequency modeling, we use three different multi-dilated dense blocks to effectively separate clean signals from noisy data. Given the difficult acquisition of clean larval activity signals, we describe a self-supervised training procedure that utilizes only noisy larval activity signals directly collected from the wild, without the need for paired clean signals. Experimental results demonstrate that our proposed approach achieves the optimal performance on various evaluation metrics while requiring fewer parameters (only 98.62 k) compared to competitive models, achieving an average signal-to-noise ratio (SNR) improvement of 17.45 dB and a log-likelihood ratio (LLR) of 0.14. Furthermore, using the larval activity signals enhanced by DBMDNet, most of the noise is suppressed, and the accuracy of the recognition model is also significantly improved. Full article

The Transductive Support Vector Machine (TSVM) is an effective semi-supervised learning algorithm vulnerable to adversarial sample attacks. This paper proposes a new adversarial attack method called the Multi-Stage Dual-Perturbation Attack (MSDPA), specifically targeted at TSVMs. The MSDPA has two phases: initial adversarial samples are generated by an arbitrary range attack, and finer attacks are performed on critical features to induce the TSVM to generate false predictions. To improve the TSVM’s defense against MSDPAs, we incorporate adversarial training into the TSVM’s loss function to minimize the loss of both standard and adversarial samples during the training process. The improved TSVM loss function considers the adversarial samples’ effect and enhances the model’s adversarial robustness. Experimental results on several standard datasets show that our proposed adversarial defense-enhanced TSVM (adv-TSVM) performs better in classification accuracy and adversarial robustness than the native TSVM and other semi-supervised baseline algorithms, such as S³VM. This study provides a new solution to improve the defense capability of kernel methods in an adversarial setting. Full article

Acid fracturing is essential in enhancing recovery efficiency, especially within carbonate reservoirs. Although extensive studies have been conducted on hydraulic fracturing, understanding the intricate dynamics between acid–rock reactions and fracture propagation in heterogeneous layered reservoirs remains limited. This study employs a comprehensive coupled hydro-mechanical-chemical flow framework to investigate acid fracturing processes in layered geological formations. The model incorporates a two-stage homogenization approach to account for rock heterogeneity, a dual-scale continuum framework for fluid flow and acid transport, and a phase field method for examining fracture propagation. We thoroughly examine how treatment parameters, particularly acid concentration and injection rate, affect fracture propagation modes. The analysis identifies three distinct propagation patterns: crossing, diversion, and arresting. These are influenced by the interplay between pressure buildup and wormhole formation. Initially, higher acid concentration aids in fracture crossing by lowering the peak pressure required for initiation, but excessive concentration results in arresting because it causes extensive wormhole development, which reduces fluid pressure. Similarly, the injection rate plays a crucial role in fracture movement across layer interfaces, with moderate rates optimizing propagation by balancing pressure and wormhole growth. This comprehensive modeling framework serves as a valuable prediction and control tool for acid fracture behavior in complex layered formations. Full article

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the ‘Many’ class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice. Full article

The rapid increase in urbanization is accompanied by the evolution of land use structure and function. Since its reform and opening up, China has entered a stage of rapid urbanization, which has brought about higher requirements in terms of rational allocation within land use structure and the optimization of land use function. However, most existing studies have evaluated the structure and function of land use separately, resulting in a decoupling of the two, and have not accurately depicted the spatiotemporal characteristics of the evolution of land use. Here, based on statistical data and remote sensing image data, we constructed a dual evaluation index system for land use structure and function which uses the characteristics of land use structure to evaluate the property of land use function directly. We used the entropy weight method to characterize the spatiotemporal evolution of urbanization and land use structure and applied a land use function deviation degree model to discuss the evolution path for land use function. Our results showed that the dominant dimension of urbanization changed from eco-environmental urbanization to economic urbanization in the rapid economic development stage. In terms of quantity within land use structure, urban-agricultural-ecological spaces have developed in a synergistic direction. Regarding the quality of land use structure, its development level exhibited an upward trend in Beijing and Hebei, while Tianjin demonstrated a U-shaped development trajectory. With urbanization development, the dominant function of regional land use has evolved to a higher level of synergy in the Beijing–Tianjin–Hebei region. These results offer inspiration for formulating regional dynamic land use policy and phased planning of urbanization development in rapidly urbanizing regions. Full article