Search Results (11,028)

With the widespread adoption of virtual fitting technology in e-commerce and fashion, optimizing user experience through interface design has become increasingly critical. However, research on the usability of virtual fitting interaction interfaces remains limited. Current interfaces frequently suffer from disorganized information layouts and ambiguous auxiliary instructions, reducing efficiency and immersion. This study systematically investigates the effects of information layout (matrix layout, list layout, horizontal layout) and auxiliary instruction display mode (positive polarity: dark content on light background; negative polarity: light content on dark background) on user task performance and subjective experience. A between-subjects experiment was conducted with 60 participants across six conditions. Participants performed a series of tasks, and data were collected on task completion time, subjective ratings, and Technology Acceptance Model responses. Analyses were conducted using two-way ANOVA. The main findings were as follows: (1) The matrix layout demonstrated higher efficiency in multi-target search and complex decision-making tasks, and also received higher subjective ratings for perceived ease of use. (2) The positive polarity display mode demonstrated better performance in single-information search and cognitively intensive tasks, coupled with higher subjective ratings for interface rationality and information clarity. (3) A significant interaction effect was identified between information layout and display mode. The matrix layout combined with positive polarity improved efficiency, whereas the list layout with negative polarity impaired task performance. The horizontal layout was also rated lower for operational fluency. These findings provide practical guidance for designing virtual fitting interfaces that enhance both performance and subjective user experience. Full article

This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate of 91.6%, largely due to delays and inaccuracies in manual ore loading and accounting. To resolve these challenges, an automated system was developed using a bin and conveyor mechanism integrated with a suite of industrial automation components, including a programmable logic controller (PLC), stepper motors, hydraulic cylinders, ultrasonic sensors, and limit switches. The system is designed to transport ore from the draw point, halt when one ton is detected, and activate the hoisting process automatically. Digital simulations demonstrated that the automated system reduced loading time by 12% and increased utilization by 16.6%, particularly by taking advantage of the 2 h post-blast idle period. Financial evaluation of the system revealed a positive Net Present Value (NPV) of $1,019,701, a return on investment (ROI) of 69.7% over four years, and a payback period of 2 years and 11 months. The study concludes that the proposed solution significantly improves operational efficiency and recommends further enhancements to the hoisting infrastructure to fully optimize performance. Full article

Static mixers are commonly used for process intensification in a wide range of industrial applications. For the design and selection of a static mixer, an accurate prediction of the hydraulic performance, particularly the pressure drop, is essential. This experimental study examines the pressure drop for turbulent single-phase and gas–liquid two-phase flows through a Komax triple-action static mixer placed on a horizontal pipeline. New values of friction factor and z-factor are reported for fully turbulent liquid single-phase flow (11,700 ≤ Re_L ≤ 18,700). For two-phase flow, the pressure drop for stratified and intermittent flows (0.07 m/s ≤ U_L ≤ 0.28 m/s and 0.46 m/s ≤ U_G ≤ 3.05 m/s) is modeled using the Lockhart–Martinelli approach, with a coefficient, C, correlated to the homogenous void fraction. Conversely, the analysis of power dissipation reveals a dependence on both liquid and gas superficial velocities. For conditions corresponding to intermittent flow upstream of the mixer, flow visualization revealed the emergence of a swirling flow in the Komax static mixer. It is interesting to note that an increase in slug frequency leads to an increase, followed by stabilization of the pressure drop. The results offer valuable insights for improving the design and optimization of Komax static mixers operating under single-phase and two-phase flow conditions. In particular, the reported correlations can serve as practical tools for predicting hydraulic losses during the design and scale-up. Moreover, the observed influence of the slug frequency on the pressure drop provides guidance for selecting operating conditions that minimize energy consumption while ensuring efficient mixing. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

Background/Objectives: Pharmaceutical preparation technologies can enhance the bioavailability of poorly water-soluble drugs. Ursolic acid (UA) has been found to possess anti-cancer and hepatoprotective properties, demonstrating its potential as a therapeutic agent; however, its hydrophobicity and low solubility present challenges in the development of drug formulations. This study investigates the preparation of a nano-UA suspension by wet grinding, researches the influence of process parameters on particle size, and explores the rules of particle breakage and agglomeration by combining model fitting. Methods: Wet grinding experiments were conducted using a laboratory-scale grinding machine. The particle size distributions (PSDs) of UA suspensions under different grinding conditions were measured using a laser particle size analyzer. A single-factor experimental design was employed to optimize operational conditions. Model parameters for a population balance model considering both breakage and agglomeration were determined by an evolutionary algorithm optimization method. By measuring the degree to which UA inhibits the colorimetric reaction between salicylic acid and hydroxyl radicals, its antioxidant capacity in scavenging hydroxyl radicals was indirectly evaluated. Results: Wet grinding process conditions for nano-UA particles were established, yielding a UA suspension with a D50 particle size of 122 nm. The scavenging rate of the final grinding product was improved to three times higher than that of the UA raw material (D50 = 14.2 μm). Conclusions: Preparing nano-UA suspensions via wet grinding technology can significantly enhance their antioxidant properties. Model regression analysis of PSD data reveals that increasing the grinding mill’s stirring speed leads to more uniform particle size distribution, indicating that grinding speed (power) is a critical factor in producing nanosuspensions. Full article

Background: Robotic liver surgery is emerging as a key advancement in minimally invasive techniques, though it still faces several challenges. Meanwhile, colorectal cancer (CRC) continues to be a leading cause of cancer deaths, with liver metastases affecting 25–30% of patients. These metastases significantly burden healthcare systems by raising costs and resource demands. Methods: A narrative literature review was performed, resulting in the inclusion of 14 studies in our analysis. Fourteen studies met the inclusion criteria and were analyzed with attention to patient characteristics, surgical details, perioperative outcomes, and reporting limitations. For consistency, simultaneous robotic-assisted resection (RAR) refers to cases in which the colorectal primary and liver metastasectomy were performed during the same operative session. Results: The 14 studies included a total of 771 patients (520 males and 251 females), aged between 31 and 88, undergoing simultaneous robotic-assisted resection (RAR). Most were affected by rectal cancer (76%) and unilobar liver metastases (82%). All surgeries using the DaVinci system are represented by 62% wedge resection and 38% anatomical (21.39% major and 16.61% minor). Patients’ BMI ranged from 19.5 to 40.4 kg/m², the average blood loss was 181.5 mL (30–780), the median hospital stay was 7 days (range 2–28), and the mean operative time ranged from 30 to 682 min. Data on POLF (postoperative liver failure) are reported in two studies: Rocca et al., 1/90 patients; Marino et al., 1/40 patients. Biliary leak is reported in one case by Marino et al., while Winckelmans et al. reported a 2.6% incidence of biliary leak in the laparoscopic group and 3.4% in the robotic group. Conclusions: As research advances and new therapies emerge for colorectal liver metastasis (CRLM), surgery remains the mainstay of treatment. However, evidence is limited by small sample sizes, heterogeneous study designs, inconsistent reporting of perioperative chemotherapy, timing of surgery, metastasis localization, and complications. Robotic liver surgery has become a well-established technique and possibly represents the future for managing colorectal liver metastases. Further prospective and comparative studies with standardized outcome reporting are needed to define optimal patient selection and long-term effectiveness. Full article

In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy. Full article

Biobanks rely on ultra-low-temperature (ULT) storage for irreplaceable specimens, where precise 3D temperature field reconstruction is critical to preserve integrity. This is the first study to apply geostatistical methods to ULT field reconstruction in cryogenic biobanking systems. We address critical gaps in sparse-sensor environments where conventional interpolation fails due to vertical thermal stratification and non-stationary trends. Our physics-informed universal kriging framework introduces (1) the first domain-specific adaptation of universal kriging for 3D cryogenic temperature field reconstruction; (2) eight novel lag-binning methods explicitly designed for sparse, anisotropic sensor networks; and (3) a leave-one-out cross-validation-driven framework that automatically selects the optimal combination of trend model, binning strategy, logistic weighting, and variogram model fitting. Validated on real data collected from a 3000 L operating cryogenic chest freezer, the method achieves sub-degree accuracy by isolating physics-guided vertical trends (quadratic detrending dominant) and stabilising variogram estimation under sparsity. Unlike static approaches, our framework dynamically adapts to thermal regimes without manual tuning, enabling centimetre-scale virtual sensing. This work establishes geostatistics as a foundational tool for cryogenic thermal monitoring, with direct engineering applications in biobank quality control and predictive analytics. Full article

To foster innovation in stored energy solutions and advance the development of green energy, this work presents a novel energy storage patented technology which involves storing energy in subsurface fractures through pumping. A new mechanical model was established to examine how variations in fracture size and operating parameters (i.e., injection and flow-back rates) modulate the scale and efficiency of energy storage under various geological conditions, and an optimized design scheme is proposed. The study demonstrates that both the scale and efficiency of energy storage are influenced by geological conditions. Selecting reservoirs with greater fracture toughness or lower permeability can achieve higher efficiency. Additionally, increasing reservoir fracture toughness also significantly enhances the scale of energy storage. Variations in geological conditions have a small impact on the optimal design of fracture size and injection/flow-back rate. Whether dealing with shallow penny-shaped fractures or deep elliptical fractures, using a moderate injection/flow-back rate in larger fractures is the optimal approach. The model presented in this paper is essential for tackling design challenges and interpreting data in subsurface pumping energy storage field applications. Full article

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article

Electric submersible pumps (ESPs) are among the most widely used artificial lifting systems, and their operational stability is crucial to the production capacity and lifespan of oil wells. However, during the operation of ESP systems, they often face complex flow issues such as gas lock and insufficient liquid carry. Traditional control strategies relying on liquid level monitoring and electrical parameter alarms exhibit obvious latency, making it difficult to effectively guide the adjustments of key operating parameters such as pump frequency, valve opening, and on/off strategies. To monitor the flow state of ESP systems and optimize it in a timely manner, this paper proposes an innovative profile recognition method based on multiphase flow fitting in the wellbore, aimed at reconstructing the flow state at the pump’s intake. This method identifies flow abnormalities and, in conjunction with flow characteristics, designs targeted operating parameter optimization logic to enhance the stability and efficiency of ESP systems. Research shows that this optimization method can significantly improve the pump’s operational performance, reduce failure rates, and extend equipment lifespan, thus providing an effective solution for optimizing production in electric pump wells. Additionally, this method holds significant importance for enhancing oil well production efficiency and economic benefits, providing a scientific theoretical foundation and practical guidance for future oil and gas exploration and management. Full article

To address the uncertainty in selecting the optimal spherical base curvature radius (SR) for tapered roller bearings, this study develops dynamic and friction torque models under combined loading conditions to evaluate three SR configurations—0.85ρ_p, 0.90ρ_p, and 0.95ρ_p, where ρ_p represents the curvature radius of the inner rib—in terms of load capacity, friction losses, and operational precision. The results indicate that (1) the 0.85ρ_p configuration minimizes friction by optimizing the contact zone’s f_V parameter under combined loads, making it ideal for low-friction applications; (2) the 0.95ρ_p design achieves superior operational accuracy; (3) the intermediate value of 0.90ρ_p offers an optimal compromise, balancing friction torque reduction with operational precision. These findings establish quantitative guidelines for SR selection based on specific bearing performance requirements. Full article

The rapid advancement of kinetic pursuit technologies has significantly increased the difficulty of evasion for high-speed UAVs (HSUAVs), particularly in scenarios where two collaboratively operating pursuers approach from the same direction with optimized initial space intervals. This paper begins by deriving an optimal initial space interval to enhance cooperative pursuit effectiveness and introduces an evasion difficulty classification framework, thereby providing a structured approach for evaluating and optimizing evasion strategies. Based on this, an intelligent maneuver evasion strategy using semantic classification progressive learning with twin delayed deep deterministic policy gradient (SCPL-TD3) is proposed to address the challenging scenarios identified through the analysis. Training efficiency is enhanced by the proposed SCPL-TD3 algorithm through the employment of progressive learning to dynamically adjust training complexity and the integration of semantic classification to guide the learning process via meaningful state-action pattern recognition. Built upon the twin delayed deep deterministic policy gradient framework, the algorithm further enhances both stability and efficiency in complex environments. A specially designed reward function is incorporated to balance evasion performance with mission constraints, ensuring the fulfillment of HSUAV’s operational objectives. Simulation results demonstrate that the proposed approach significantly improves training stability and evasion effectiveness, achieving a 97.04% success rate and a 7.10–14.85% improvement in decision-making speed. Full article