Eurasia Journal of Science and Technology
Eurasia Journal of Science and Technology (EJST) (Print ISSN: 2663-1024; Online ISSN: 2663-1016) is an international, peer-reviewed, open access journal published by Upubscience Publisher House. It covers a broad range of topics in science and technology, including engineering, computer science, environmental science, and applied physics. The journal publishes original research articles, reviews, and short communications.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
AERIS: ADAPTIVE EXECUTOR-SCOPED RESOURCE MANAGEMENT FOR PRIVACY-PRESERVING DATA PROCESSING IN APACHE SPARK
Privacy-preserving Spark pipelines introduce executor-local costs that conventional resource policies do not model: cryptographic context reconstruction, enlarged protected working sets, and encrypted spill. This paper presents AERIS, an adaptive executor-scoped controller that couples task memory grants with soft affinity to reusable privacy state. AERIS operates only on coarse metadata—partition size, operator class, key domain, and executor load—and therefore does not inspect plaintext, keys, or differentially private outputs. Its cost estimator is calibrated by real local microbenchmarks of AES-GCM, HMAC-SHA256, bounded Laplace-noised aggregation, scrypt context setup, and encrypted temporary-file spill. We evaluate the design with an event-driven prototype aligned with Spark stage, task, barrier, and executor semantics. The prototype uses the real Wisconsin Diagnostic Breast Cancer dataset and deterministic bootstrap expansion to generate balanced, skewed, and bursty traces. Across ten paired seeds, AERIS reduces makespan by 9.3%, 16.6%, and 10.4% relative to fixed uniform executor budgets. In the two memory-stressed workloads it reduces encrypted spill volume by 62.7% and 64.4%. Against a stronger reactive load-aware baseline, AERIS is 1.5% faster on balanced traces, 0.6% slower on skewed traces, and 5.5% slower on bursty traces, while reducing encrypted spill by 74.4% and 81.0% and lowering p95 service time by 1.0%–3.7%. Queue-inclusive p95 completion latency worsens under skew because longest-first dispatch and state reuse delay short tasks; this limitation is reported rather than hidden. The results support executor-scoped privacy state as a useful scheduling dimension, while also showing that spill avoidance and completion-tail objectives must be balanced explicitly. The artifact is a measurement-backed, trace-driven prototype, not a claim of measurements from a patched Spark cluster.
Copyright (c) 2023 YiNuo Wang; ChangHao Zhang https://creativecommons.org/licenses/by-nc/4.0
AN AI-DRIVEN INTELLIGENT TRANSACTION EXECUTION FRAMEWORK FOR INTERNET FINANCE PLATFORMS: COST-SENSITIVE REAL-TIME INTEGRITY INTERDICTION
Internet finance platforms must decide, in real time and for every incoming transaction, whether to approve it, subject it to additional verification, or decline it. Most machine-learning research on transaction Integrity stops at producing a Integrity score and is judged with threshold-independent ranking metrics, leaving a gap between the score and the concrete execution action, and ignoring that different transactions carry very different monetary stakes. This paper presents IntelliExec, an AI-driven intelligent transaction execution framework that closes this gap. A class-weighted gradient-boosting model produces a Integrity probability that is calibrated with Platt scaling so it can be treated as a true probability; the calibrated probability and the transaction amount are then mapped to one of three execution actions—approve, step-up (one-time-password / 3-D Secure challenge), or decline—by minimising an example-dependent expected cost. Because the decision is derived analytically from a cost model, it needs no threshold tuning and adapts the operating point to each transaction's amount. On a public benchmark of 284,807 real card transactions (0.172% Integrity) evaluated under a strict temporal split, the framework lowers total expected cost by 71.8% relative to approving every transaction and by 45.4% relative to the best cost-aware global-threshold baseline, while hard-declining only three legitimate customers (versus thirty-seven for a binary cost-sensitive rule) by routing borderline cases to the low-friction step-up tier. The gradient-boosting scorer runs in under one millisecond per transaction, and a sensitivity analysis confirms the cost advantage is robust to the choice of cost parameters.
Copyright (c) 2022 YuXuan Qin; JiTong Zou; Minjae Rhee https://creativecommons.org/licenses/by-nc/4.0
HIROTA METHOD FOR SOLVING THE 2+1-DIMENSIONAL S-INTEGRABLE EXTENDED DYM EQUATION
The construction of soliton solutions to nonlinear partial differential equations is a central topic in nonlinear science. As a high-dimensional integrable equation, the 2+1- dimensional extended Dym equation is of great theoretical and practical significance. In this paper, we employ the Hirota method to transform the original equation into its bilinear form via appropriate variable substitutions. We construct one-soliton, two-soliton solutions and derive the general form of N-soliton solutions, analyzing their structural properties and physical implications. Results show that the equation is fully integrable and that soliton collisions are elastic. This work provides a theoretical reference for further studies on similar high-dimensional nonlinear equations.
Copyright (c) 2026 CaiHong Tian; Liang Qin https://creativecommons.org/licenses/by-nc/4.0
EFFICACY OF A THORACOLUMBAR ACTIVE RESISTANCE REHABILITATION TRAINER VERSUS TRADITIONAL BRIDGE EXERCISES IN SPINAL CORD INJURY: A RANDOMIZED CONTROLLED TRIAL
Objective: A thoracolumbar active resistance rehabilitation trainer was developed, aiming to verify its activation effect on the core muscles of the lumbar and abdominal regions in spinal cord injury (SCI) cases and explore the difference in therapeutic efficacy between this trainer and traditional bridge exercises. Methods: A prospective, assessor-blinded randomized controlled trial of 40 SCI inpatients. Participants were allocated to two groups, both alongside conventional rehabilitation. The experimental group received 8 weeks of training with the trainer, while the control group underwent 8 weeks of bridge exercises training. The assessment indicators include surface electromyography, Spinal Cord Independence Measure-III, Walking Index for Spinal Cord Injury, and Japanese Orthopaedic Association scores. Results: The experimental group demonstrated superior abdominal muscle activation versus controls at 8 weeks. Clinically, the experimental group showed greater improvement in functional independence (t=2.599, P=0.019, Cohen'd=1.225), exceeding the minimal clinically important difference (MCID). Both groups achieved MCID in walking ability (WISCI) and lumbar function (JOA), with no significant between-group differences. The trainer was safe and rated higher for convenience and usability. Conclusion: The trainer is safe and more effective than bridge exercises for functional independence in SCI rehabilitation, offering practical advantages for diverse settings.
Copyright (c) 2026 MeiLing Cheng; FangYong Wang https://creativecommons.org/licenses/by-nc/4.0
THE INFLUENCE MECHANISM OF VIRTUAL SIMULATION TECHNOLOGY ON INTERMEDIATE FINANCIAL ACCOUNTING LEARNING OUTCOMES: A COGNITIVE PSYCHOLOGY PERSPECTIVE
This study investigates the cognitive mechanisms through which virtual simulation technology influences learning outcomes in Intermediate Financial Accounting. Drawing on Cognitive Load Theory, Embodied Cognition Theory, and the Cognitive-Affective Theory of Learning with Media, we propose and empirically test a dual-pathway mediation model. A randomized controlled experiment was conducted with 286 undergraduate accounting students from four Chinese universities, randomly assigned to a VST-enhanced learning group and a traditional lecture-based group. Using a mixed-methods design combining pre- and post-knowledge assessments, eye-tracking metrics, EEG-based cognitive load measurement, and semi-structured interviews, we found that VST significantly improved procedural knowledge acquisition and conceptual understanding compared to traditional instruction. Structural equation modeling revealed that spatial ability enhancement and cognitive load optimization served as dual mediating mechanisms. Notably, germane cognitive load mediated the relationship between VST and learning outcomes, while extraneous cognitive load showed a suppression effect. These findings contribute to the theoretical understanding of technology-mediated learning in accounting education and provide evidence-based guidelines for designing effective VST interventions in professional accounting programs.
Copyright (c) 2026 YongHui Li; HaiBo Zhang https://creativecommons.org/licenses/by-nc/4.0
STABILITY ANALYSIS OF WATER QUALITY MONITORING VEHICLE
This study takes the water quality monitoring vehicle refitted from the Ford V363 four-wheel drive chassis as the research subject. Aiming at potential safety hazards caused by changes in vehicle load and gravity center after equipment installation, vehicle load characteristics and driving stability are comprehensively investigated. The mass moment method is adopted to conduct load calculation and layout optimization. The optimized axle load and gravity center parameters fully comply with the requirements specified in GB 7258-2017, and the relative deviation between experimental measurements and theoretical calculations is less than 0.3%. Vehicle dynamic theories are applied to calculate key indicators including roll stability angle and braking offset. All tested stability indicators satisfy relevant national standards, with the maximum relative error between experimental and theoretical results limited to 3.8%. The results verify that the proposed modification scheme is safe and feasible. The modified vehicle can operate reliably under diverse complex working conditions, and the research findings can provide technical references for the design of similar refitted special vehicles.
Copyright (c) 2026 ZhongXing Cao https://creativecommons.org/licenses/by-nc/4.0
DESIGN AND IMPLEMENTATION OF AN INTELLIGENT CHILD FALL PREVENTION SYSTEM FOR PARALLEL-PUSH WINDOWS
Traditional parallel-push windows employ simple handle-locking mechanisms that young children can easily unlock, whereas permanently locking these windows restricts ventilation and obstructs emergency egress. To address these issues, a dual-controller intelligent parallel-push window system based on the STM32F407ZGT6 microcontroller and the K230 visual AI module is designed and implemented. The K230 module runs a YOLO-based object detection model to recognize children approaching the window frame and estimate their distance, transmitting a window-closing command to the STM32 microcontroller upon detecting a potential hazard. Serving as the central hub, the STM32 manages multi-source environmental sensor data acquisition and actuation mechanism control, forming a closed-loop control system in conjunction with limit switches. The software architecture adopts a hybrid approach, combining external interrupts for immediate response to emergency events with dynamic polling within the main loop for routine monitoring. Furthermore, the system supports dual-channel operation via 4G remote control and a local touchscreen interface; in the event of a network outage, the STM32 can execute safety protocols independently. Performance tests demonstrate that the system automatically closes and locks the window when a child approaches, and responds similarly to environmental anomalies such as strong winds, heavy rain, or combustible gas leaks. The system effectively meets domestic fall-prevention requirements, exhibiting significant practical value and potential for widespread application.
Copyright (c) 2026 YuanJu Zhou; YongKang Nie; XiaoSong Nie; JiangLin Huang https://creativecommons.org/licenses/by-nc/4.0
PLASMA PROTEIN LEVEL RATIOS IDENTIFY NOVEL CIRCULATING TARGETS ASSOCIATED WITH STROKE RISK
Previous studies have nominated individual plasma proteins as therapeutic targets for stroke. Whether ratios of protein levels, which can capture relative pathway activity, are causally related to stroke remains unknown. We evaluated the potential causal associations between plasma protein level ratios and stroke risk. We conducted bidirectional, two-sample Mendelian randomization (MR) to estimate the associations between circulating protein level ratios and overall stroke and stroke subtypes. Pathway enrichment and protein–protein interaction (PPI) analyses were used to explore potential mechanisms. Forward MR indicated that a higher TGFBR2/THBD ratio was associated with increased risk of any ischemic stroke (AIS) (OR = 1.15; 95% CI, 1.09–1.21; p = 4.07e-7). A higher GP1BA/SDC4 ratio was associated with increased risk of small-vessel stroke (SVS) (OR = 1.25; 95% CI, 1.14–1.37; p = 2.25e-6), whereas a higher SDC4/SEMA4D ratio was associated with lower SVS risk (OR = 0.82; 95% CI, 0.76–0.89; p = 1.18e-6). Reverse MR found no evidence that AIS or SVS causally influenced these ratios, supporting the inferred direction of effect. Sensitivity analyses supported the robustness of the estimates. Pathway enrichment and PPI analyses implicated TGF-β signaling and coagulation in AIS pathobiology, and platelet activation, extracellular matrix organization, and axon guidance in SVS. Plasma protein level ratios may help identify biologically relevant pathways and therapeutic targets for stroke prevention and treatment.
Copyright (c) 2026 QianHui Wang; TianYu Jin; JunJie Gu; LiXu Liu https://creativecommons.org/licenses/by-nc/4.0
THE APPLICATION OF VISUAL-BASED ACUPOINT POSITIONING TECHNOLOGY IN ACUPOINT ROBOTS
Acupuncture efficacy depends on the precision of acupoint localization. Based on prior research, this paper proposes a technical framework for automatic facial acupoint positioning and robotic acupuncture: facial information is captured using RGB-D vision, and an improved YOLOv8 model detects 27 index acupoints across 15 categories, enabling precise three-dimensional coordinate mapping for localization. A specialized end-effector integrating needle exchange, holding, and rotation functions converts lifting, thrusting, twisting, retention, and withdrawal into linear and rotational motions. An enhanced APF-RRT* algorithm is proposed to achieve obstacle-avoidance path planning during continuous multi-acupoint needling. Experiments conducted with UR5e, Kinect V2, and a simulated human head demonstrate that the system achieves average errors of 0.54 mm, 0.50 mm, and 0.66 mm in the three dimensions, achieving millimeter-level accuracy in acupoint localization.
Copyright (c) 2026 MengDi Wang; DianHao Wu; Yi Wang; Qiao Fang; ZhenBiao Chen; KunLin Xie https://creativecommons.org/licenses/by-nc/4.0
INTELLIGENT SCREENING AND UNCERTAINTY QUANTIFICATION OF FUNDUS OCT LESIONS BASED ON AMSF-CPNET
In response to the prominent challenges in primary-level fundus screening—including high missed diagnosis rates, opaque decision-making of deep learning models, and uncalibrated probabilistic outputs—this study develops an adaptive multi-scale statistical fusion network integrated with conformal prediction (AMSF-CPNet). The framework leverages multi-branch convolutional neural networks to capture fine-grained local features and Swin Transformer to model long-range global dependencies, enhanced by channel-spatial adaptive attention for optimal feature integration. Conformal prediction is introduced to provide statistically rigorous uncertainty quantification with guaranteed coverage rates, while a concept-guided interpretability module establishes links between model decisions and clinical semantics. Evaluated on the public OCT2017 dataset and external OCTDL dataset, AMSF-CPNet achieves an accuracy of 96.8% and an AUC of 99.1%. At α = 0.05, the conformal prediction coverage reaches 94.7% with an average prediction set size of 1.23. Cross-domain validation demonstrates strong generalization under device shifts, with fine-tuned accuracy reaching 92.4%. This work offers a reliable, interpretable, and robust AI solution for grassroots ophthalmic screening, supporting the advancement of tiered healthcare systems.
Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by-nc/4.0
RESEARCH PROGRESS ON CORROSION BEHAVIOR OF METALLIC URANIUM IN HUMID ATMOSPHERES
The corrosion behavior of metallic uranium in humid environments is directly related to the safety and lifespan of nuclear facilities, but its microscopic mechanism has long been constrained by the spatiotemporal scale limitations of traditional density functional theory simulations. This paper systematically reviews the research progress of uranium corrosion in pure water, water-oxygen coexisting, and hydrogen environments. It points out that, limited by computational costs, traditional DFT struggles to reproduce the complex networks involving multi-molecule participation, multi-step reactions, and dynamic surface reconstruction during actual corrosion. This “common scale bottleneck” directly leads to controversies and blind spots regarding the core mechanisms in three key environments: the initial dissociation pathway of water molecules in pure water remains unclear; the “catalytic” acceleration effect of water in water-oxygen environments lacks support from microscopic kinetic landscapes; and the dynamic regulation mechanism of local strain on hydriding kinetics in hydrogen corrosion environments remains to be clarified. Combined with the latest theoretical simulation progress in the uranium-hydrogen system, this paper discusses how Machine Learning Interatomic Potentials, as a transformative tool, provides a new paradigm for bridging this scale gap by merging quantum mechanical accuracy with molecular dynamics efficiency. Finally, the paper looks to the future, pointing out that constructing high-fidelity MLIP for U-water and U-H systems to simulate the entire dynamic corrosion process under realistic surface morphologies and variable stress fields will be the key path to clarifying the controversial mechanisms and achieving a fundamental transition in uranium corrosion research from “static observation” to “dynamic prediction.”
Copyright (c) 2026 YiLi Sun; Fei Wang https://creativecommons.org/licenses/by-nc/4.0
METHODS TO IMPROVE THE PROCESSING CAPACITY OF THE VIBRATING SCREENING DEVICE FOR BALLAST CLEANING VEHICLE
With the global development of high-speed and heavy-haul railways, the demand for operational efficiency of large-scale railway maintenance machinery is increasing. As a core equipment, the processing capacity of the screening device in a ballast cleaning vehicle directly limits the overall efficiency. To address the limitations of traditional linear vibrating screens in processing railway ballast, such as screen clogging, thick material bed, and a limited effective screening area, this paper proposes an innovative solution integrating tri-axial elliptical vibration, double-layer ballast feeding, and multi-section variable-inclination screen surfaces. By innovatively utilizing the underutilized triangular space within the vehicle frame, the solution achieves simultaneous double-layer feeding and an increased screen area without enlarging the overall layout, applying the thin-layer screening theory. Additionally, the multi-section variable-inclination screen optimizes material conveyance and screening. Experimental results from a physical prototype show a processing capacity of 800 m³/h, which is over 23% higher than the traditional solution (650 m³/h), with significantly improved screening efficiency. This study provides a technically valuable pathway for overcoming the performance bottlenecks of ballast cleaning vehicles under axle load and clearance constraints.
Copyright (c) 2026 YongPing Yang https://creativecommons.org/licenses/by-nc/4.0
TOWARDS FOUNDATION MODELS FOR LIDAR SEMANTIC SEGMENTATION IN AUTONOMOUS DRIVING
LiDAR semantic segmentation (LSS), which assigns semantic labels to each point in a 3D scan, is a core perception task in autonomous driving. Over the past decade, fully supervised methods have achieved remarkable progress, with benchmark performance on SemanticKITTI improving from 14.6 mIoU in 2017 to over 75 mIoU in recent state-of-the-art models. Despite these advances, conventional supervised paradigms remain constrained by three fundamental limitations: dependence on large-scale dense annotations, restricted closed-set semantic understanding, and limited robustness under domain shifts and adverse environments.Recent advances in foundation models—including vision-language models, self-supervised pretraining frameworks, and segmentation foundation models—have opened a new direction for LiDAR perception by enabling transferable, label-efficient, and open-vocabulary 3D understanding. Motivated by this paradigm shift, this survey provides a systematic review of LiDAR semantic segmentation from supervised learning to foundation-model-driven approaches. We organize existing methods into five representative paradigms: cross-modal 2D-to-3D feature distillation, Segment Anything Model (SAM)-guided segmentation, open-vocabulary vision-language learning, LiDAR-specific self-supervised pretraining, and generalized 3D foundation models.Beyond taxonomy and benchmark comparison on SemanticKITTI and nuScenes, we further examine practical deployment factors—including inference latency, edge-device efficiency, robustness in adverse weather, and multimodal sensor fusion—that remain insufficiently captured by standard evaluation protocols. Finally, we identify six open research challenges and argue that the field is undergoing a fundamental transition: from adapting 2D foundation priors to developing native 3D LiDAR foundation models for autonomous driving.
Copyright (c) 2026 YiFan Zhao; ZiWei Huang https://creativecommons.org/licenses/by-nc/4.0
POST-TREATMENT STRATEGY: MOLECULAR SIEVE-CONFINED BIFUNCTIONAL METAL-SITE CATALYSTS FOR HYDROGEN PRODUCTION FROM FORMIC ACID DECOMPOSITION
Formic acid, as an important liquid organic hydrogen carrier, is considered a highly promising chemical medium for hydrogen storage due to its high hydrogen storage density, safety, non-toxicity, and ease of storage and transportation. It can also catalytically decompose to release hydrogen under mild conditions. However, traditional supported noble metal catalysts face issues such as metal nanoparticle migration, agglomeration, and leaching during the reaction process, leading to reduced catalyst activity and instability. To address these challenges, this thesis utilizes the microporous confinement effect of molecular sieves to design and prepare a series of Pd-based catalysts confined within molecular sieves. The catalytic performance, kinetic behavior, and cycle stability of these catalysts were systematically investigated. Various characterization techniques were used to elucidate the relationship between catalyst structure and performance. Using a commercial Beta molecular sieve as the carrier, acidic sites were removed through aluminophosphoric acid treatment. Then, highly dispersed Pd nanoclusters confined within the molecular sieve pores were successfully synthesized using a combination of ligand protection and hydrogen reduction after calcination. A second metal, Ce, was subsequently introduced to create the PdCe-Beta-DeAL-C-H bimetallic catalyst. Characterization results showed that aluminophosphoric acid treatment and the addition of Ce effectively improved the dispersion and stability of the Pd particles. The Pd nanoparticles had a small size of 2–5 nm and were uniformly distributed on the surface of the carrier and near its pores. Ce was in close contact with Pd, and the interfacial interactions altered the surface electronic structure of Pd, thereby promoting the formation and stabilization of metallic Pd⁰. Catalytic tests demonstrated that the optimal Pd₁.₀Ce₀.₂-Beta-DeAL-C-H catalyst achieved a conversion rate of 3199 molH₂·molPd⁻¹·h⁻¹ at 60 °C during formic acid decomposition. This value was approximately 127% higher than that of the single-metal catalyst. Additionally, the catalyst exhibited excellent cycle stability: after five cycles, its conversion rate remained above 1800 molH₂·molPd⁻¹·h⁻¹. Kinetic studies revealed that the apparent activation energy for formic acid decomposition over this catalyst was 55.93 kJ·mol⁻¹, significantly lower than that of the catalyst without Ce (84.5 kJ·mol⁻¹). Moreover, the catalyst performed well in the reaction of CO₂ hydrogenation to formic acid, with a reaction rate of 62 molformate·molPd⁻¹·h⁻¹. This study demonstrates that the incorporation of Ce through post-treatment effectively enhances the performance of Pd-based catalysts for formic acid decomposition. It provides valuable insights for the design of efficient bimetallic catalysts.
Copyright (c) 2026 LeBin Mi; Ning Wang; Tao Cai https://creativecommons.org/licenses/by-nc/4.0
PREPARATION OF BIOCHAR FROM WASTE NUTSHELLS AND ITS APPLICATION IN NITROGEN-CONTAINING WASTEWATER TREATMENT
Due to their unique pore structures and physicochemical properties, agricultural nutshell wastes serve as ideal precursors for the preparation of biomass biochar. However, traditional nutshell-derived biochars exhibit limitations in adsorption selectivity and capacity, showing poor removal efficiency for nitrate and nitrite nitrogen from rural domestic wastewater. In this study, walnut shells were used as feedstock to prepare pristine biochar via carbonization and physical activation. Subsequently, the biochar was modified through oscillatory impregnation with acid, alkali, and Fe³⁺ loading. The effects of modification conditions, adsorbent dosage, and adsorption time on the removal efficiency of the target nitrogen pollutants were investigated. The results indicated that the biochar achieved the optimal removal efficiency for the target nitrogen pollutants under the following conditions: a carbonization temperature of 700°C, Fe³⁺ modification, an Fe³⁺ loading ratio of 20%, an adsorbent dosage of 2.0 g, and an adsorption time of 60 min. Featuring simple operation and controllable costs, this process achieves the dual goals of “high-value utilization of waste” and “water pollution control”.
Copyright (c) 2026 Bin Han; JingYue Bao; Hao Li; HaiTao Liu; YanWen Guan https://creativecommons.org/licenses/by-nc/4.0
A NUMERICAL STUDY ON THE LOWER BOUNDS OF THE CLASSICAL AND REFINED HEISENBERG UNCERTAINTY PRINCIPLES
In Fourier analysis and quantum mechanics, the Heisenberg uncertainty principle stands as a fundamental result. Its classical version provides a product lower bound for the variances of position and frequency. More recently, for vector-valued functions, Dang, Deng and Qian introduced an improved inequality featuring an extra covariance term derived from the current density. The present work performs a one-dimensional numerical comparison of the two lower bounds using four test functions: a pure Gaussian, an asymmetric sum of two Gaussians, a linear chirp, and a vector-valued signal with spatially separated orthogonal components. For the Gaussian, the covariance term vanishes up to machine precision, making the two bounds identical. For the asymmetric sum, the refined bound exceeds the classical one by about three percent. The chirp signal yields a dramatically larger refined bound, approximately sixteen times above the classical one. For the vector-valued case with each component a real Gaussian, the refined bound is about eight times higher. These numerical outcomes confirm the validity of the refined inequality and highlight the sensitivity of the covariance term to phase structure and vectorial coupling.
Copyright (c) 2026 LuHao Yang; QiZhi Zhang; XueXin Li https://creativecommons.org/licenses/by-nc/4.0
BIOMECHANICAL ANALYSIS OF UPPER LIMB REHABILITATION DEVICE FOR GUIDING MOVEMENTS SELECTION
Purpose: The selection of appropriate rehabilitation movements is critical for the effective design of upper limb rehabilitation devices. Conventional approaches primarily rely on mechanical degrees of freedom, often neglecting quantitative biomechanical evaluation, which may lead to mismatches between device motion and human physiological characteristics. This study proposes a biomechanics-driven framework to guide rehabilitation movement selection. Methods: Four representative upper limb rehabilitation movements were selected based on the Brunnstrom recovery stages. Joint range of motion (ROM) data were collected from ten healthy subjects using clinical measurement methods. These kinematic data were imported into the OpenSim musculoskeletal model (DAS3) to simulate muscle forces of the deltoid, biceps brachii, and triceps brachii. Root mean square (RMS) values of muscle forces were calculated and statistically analyzed using one-way ANOVA and Tukey’s HSD post-hoc tests. Results: Significant differences in muscle activation were observed among the four movements. Horizontal adduction/abduction and flexion/extension exhibited significantly higher RMS muscle forces, particularly in the deltoid and triceps (p < 0.05). These movements demonstrated both higher peak forces and sustained muscle activation levels. Conclusion: The findings indicate that rehabilitation movements with higher biomechanical activation potential can be effectively identified through musculoskeletal simulation. Horizontal adduction/abduction and flexion/extension are recommended as optimal guiding movements for upper limb rehabilitation device design. This study provides a quantitative and physiologically grounded approach to improve the compatibility between rehabilitation devices and human motion.
Copyright (c) 2026 HaoYu Han; Li Feng https://creativecommons.org/licenses/by-nc/4.0
ENZYME-FREE COLOURIMETRIC SENSOR BASED ON ZIRCONIUM-BASED MOF FOR THE DETECTION OF MALATHION
To address the issues of enzyme inactivation, complex procedures, and dependence on large-scale equipment in traditional organophosphorus pesticide detection, we developed an enzyme-free colourimetric sensor employing Hemin@UiO-66 composite nanomaterials for the rapid and accurate detection of malathion. The UiO-66 metal-organic framework was synthesised using the solvothermal method, while the Hemin@UiO-66nanoenzyme was produced through post-modification with hemin chloride (Hemin). The structural characteristics of the material were analysed using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS).Thisnanoenzyme demonstrated remarkable peroxidase-like activity, effectively catalysing the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to blue-coloured oxidised TMB (oxTMB) in the presence of hydrogen peroxide (H₂O₂). Malathion interacts with the active sites of the material via Zr-O-P coordination, which significantly inhibits the catalytic activity of thenanoenzyme. As a result, the colour of the system gradually diminished with increasing malathion concentration. Under optimised conditions, a strong linear correlation was established between the change in absorbance of the sensor and malathion concentration, spanning from 5 to 270 ng/mL, with a detection limit as low as 4.23 ng/mL (3σ) and a detection time of 20 minutes. This method exhibited excellent selectivity, stability, repeatability, and anti-interference capabilities. The recovery rates of malathion in spiked real samples ranged from 95.3% to 104.7%, accompanied by a relative standard deviation (RSD) of less than 4.2%. By eliminating the need for biological enzymes, this sensor facilitates straightforward operation and rapid response, thereby providing an innovative approach for the on-site rapid detection of malathion residues in food.
Copyright (c) 2026 Rui Liu; WenJia Chen; JunXian Li; Run Wang; Liang Dong https://creativecommons.org/licenses/by-nc/4.0
AUTOMATIC SLEEP STAGE CLASSIFICATION USING MULTI-PHYSIOLOGICAL SIGNALS BASED ON PCBSLEEPNET
Automatic sleep staging, a key technology in sleep medicine, faces feature selection challenges due to multi-modal physiological signal heterogeneity. This study establishes a framework to evaluate bioelectrical signals' impacts on deep neural network feature representation, aiming to identify optimal integration paradigms.We propose a multi-branch hybrid neural network with: 1) a three-channel parallel convolutional module incorporating cascaded residual and dilated convolutions for time-frequency feature extraction, and 2) bidirectional long short-term memory (BiLSTM) layers for temporal dependency modeling. We systematically analyzed multi-channel electroencephalogram (EEG), single-channel physiological signals, and their combinations covering both single-channel and multi-channel configurations of EEG, electrooculogram (EOG), and electromyogram (EMG). Results demonstrate that six-channel EEG configurations outperformed single/dual-channel implementations. In the single- and dual-channel EEG settings, the C3-M2 and C4-M1 + C3-M2 channels exhibited superior performance. EEG maintained superior classification capability over EOG/EMG. The tri-modal fusion (EEG+EOG+EMG) achieved peak performance (accuracy: 89.5%, Cohen’s κ: 0.85). This demonstrates EEG's central role in sleep staging, while EOG/EMG synergistically enhance model performance. The findings reveal heterogeneous bioelectric signal interactions in deep neural networks, providing foundations for optimizing signal fusion strategies and model architectures.
Copyright (c) 2026 YuLi Yang; Ying Liu; HaoWei Zhang https://creativecommons.org/licenses/by-nc/4.0
NON-DESTRUCTIVE TESTING METHOD FOR THE WIRE ROPE WITH DIFFERENT SURFACE WEAR
Steel wire ropes, as key load-bearing components, suffer from significant wear and broken wire damage during their service period, which poses a serious threat to operational safety. Addressing the issues such as the single excitation method in traditional eddy current detection methods, the large noise interference from the phase wave, and the low accuracy of damage quantitative identification, this paper proposes a new non-destructive testing method that combines a dual-level excitation structure with signal processing. Firstly, a dual-level excitation structure composed of three asymmetric magnetic poles on the left, middle, and right sides was designed. By using differentiated permanent magnet grades (N35/N52/N52) and a 60 mm magnetic pole spacing, two independent intervals with significantly different excitation intensities (up to 7.7%) were formed within the steel wire rope, effectively reducing mutual interference. Subsequently, through finite element simulation analysis, the influence of magnetic pole parameters on the excitation effect was investigated, and a signal processing method based on offset superposition and sliding average filtering was proposed to effectively suppress periodic phase wave noise. Experimental results show that this method established a high-precision cubic polynomial quantitative model between the number of broken wires and the peak-to-peak value of the eddy current signal (coefficient of determination R² = 0.995), and compared with existing detection devices, the error rate of broken wire identification was significantly reduced from 51.3% to 13.3%. This research provides an effective technical means for the precise quantitative identification of wire rope wear and broken wire damage, especially composite damage.
Copyright (c) 2026 LiShuo Wu; Kun Huang; GongNing Li; XiangDong Chang; LiQun Xiong; Zhou Zhou https://creativecommons.org/licenses/by-nc/4.0
EXPERIMENTAL DATA FITTING AND INFLUENCING FACTORS ANALYSIS OF TEA WITHERING
With the continuous progress of tea factory intelligence and modernization, the construction of tea withering mode plays the important role on the product quality in the process of tea withering. It is very important to analyze the construction of tea withering model and the analysis of influencing factors for the improvement of product quality in the process of tea withering. In this paper, the weight of each factor of tea withering on the trend and time of water loss and weight loss of the whole withering was successfully analyzed by means of multiple regression analysis. When withering to 50 % weight loss, the weight of each factor from large to small is temperature, humidity, tea green layer number, tea green density. At the same time, multiple regression analysis was used to predict the trend of withering water loss and weight loss under different conditions. The model data fitting can better control the withering time, and has positive significance for the post-mortem investigation of the withering process.
Copyright (c) 2026 ZhenHua Shao; GuoShi Li; JiaLiang Ye; JiaCheng Ye; YuQin Liu; MeiXue Lin; PengFu Ye https://creativecommons.org/licenses/by-nc/4.0
WATER DAMAGE RESISTANCE OF LOW-TEMPERATURE MODIFIED ASPHALT MIXTURE
This paper analyzes the influence law of water damage on the strength and stability of low-temperature modified asphalt mixture, and discusses the action mechanism of raw material characteristics, mix proportion design, construction technology and environmental factors on water stability. The research shows that the water damage of low-temperature modified asphalt mixture is mainly caused by asphalt film peeling, aggregate softening and internal structure damage due to water intrusion; the addition of modified components such as rubber powder and polymer resin can significantly improve the water damage resistance of the mixture; optimizing the asphalt-aggregate ratio, improving the aggregate gradation and adopting reasonable construction temperature control measures can effectively reduce the risk of water damage.
Low-temperature modified asphalt mixture
Copyright (c) 2026 XiaoPeng Tong https://creativecommons.org/licenses/by-nc/4.0
HYBRID FEATURE-ENHANCED 4D GAUSSIAN SPLATTING FOR DYNAMIC SCENE RECONSTRUCTION
Real-time modeling of dynamic scenes is a pivotal challenge in computer vision and graphics. Methods employing canonical space deformation with 3D Gaussians have achieved compelling speed, but a fundamental limitation persists: their feature representation often fails to capture the complex interplay of spatial, temporal, and multi-scale information in dynamic settings. This paper introduces a hybrid feature enhancement framework that systematically addresses this core issue. Our key idea is to forge a powerful and adaptive feature representation through the synergistic co-design of three modules: a Spatial Relation Module that explicitly encodes geometric context, a Dynamic Feature Adapter that employs gating for temporal conditioning, and a Multi-scale Integration Module that dynamically aggregates features across scales. The primary contribution of our work is this unified architecture designed to robustly enhance the feature backbone of deformation-based dynamic Gaussian representations. Extensive experiments on major benchmarks, including D-NeRF and HyperNeRF, demonstrate that our framework consistently elevates reconstruction quality, achieving superior performance over state-of-the-art methods on key metrics like PSNR and SSIM, thereby validating its general effectiveness.
Copyright (c) 2026 Sha Li; WanXiang Qin https://creativecommons.org/licenses/by-nc/4.0
DESIGN AND IMPLEMENTATION OF A KA-BAND SILICON-BASED 3D-HETEROGENEOUS-INTEGRATED 8-CHANNEL TRANSMIT MICROSYSTEM
This paper presents the design and implementaion of a Ka-band Silicon-Based 3D-Heterogeneous-Integrated 8-channel transmit microsystem. By introducing capacitive and inductive transition structures in the 3D passive interconnections of the microsystem, up to 40GHz broadband impedance matching is realized. Meanwhile, a three-layer Cu-Sn bonded silicon interposer architecture and its corresponding fabrication process are developed for the implementation of the two stacked sub-modules. And sub-modules stacking and fan-out of ball grid array (BGA) pins are achieved using solders and solder balls with differentiated temperature gradients. The fabricated 8-channel transmit microsystem features 1 input port and 8 output ports, with an operating frequency band over 27–32 GHz, a saturated output power of 23 dBm, an overall dimensions of 9.1 mm × 8.5 mm × 3.25 mm, and a weight of less than 0.1 g per channel.
Copyright (c) 2026 Ming Wei https://creativecommons.org/licenses/by-nc/4.0
CAN CONSUMER’S IDENTITY MANAGEMENT AFFECT PLATFORM’S BEHAVIOR-BASED PERSONALIZED PRICING?
With the development of big data and platform economy, platforms can easily gather consumer information through first-period purchase and exercise personalized pricing. When consumers realize it, they may engage in identity management to bypass the platform’s attempt to price discriminate. Therefore, it is significant to clarify the impact of identity management on platform’s behavior-based price discrimination, and whether platforms continue to compete consumers if they realize that consumers may engage in identity management. We introduce the identity management into the standard framework of behavior-based price discrimination, then we analyze and compare equilibrium results with and without identity management. The main findings are as the follows: (i) when consumers are active in identity management, the competition between platforms is intensified in the information collection stage, but it is softened in the information application stage, and platforms can exercise perfect price discrimination to consumers. (ii) The consumer identity management leads to lower consumer surplus and higher social welfare. When consumers become farsighted, platforms can be better off. (iii) platforms still have incentives to compete with consumers and gather consumer information, even though platforms predict that consumers are active in the identity management. (iv) Compared to one-sided market, the consumer identity management in two-sided market makes platforms more willing to implement perfect price discrimination.
Copyright (c) 2026 KaiHua Bao; ZhiQiang Zhang; Ting Zhang https://creativecommons.org/licenses/by-nc/4.0
RESEARCH PROGRESS ON HIGH-NITROGEN AUSTENITIC STAINLESS STEEL
Advancements in metallurgical technology, coupled with the ever-growing industrial demand, have continuously driven the development of new materials. High-nitrogen austenitic stainless steel (HNASS), a resource-saving material, has been extensively investigated and rapidly developed owing to its high strength, high plasticity, and excellent corrosion resistance. This paper chronologically outlines the development of Cr–Mn–N series HNASS, which has evolved primarily through nickel-saving high-manganese austenitic stainless steel (such as AISI 200 series stainless steel), low-carbon molybdenum-containing austenitic stainless steel (such as AL6XN), and nickel-free HNASS (such as P900, developed by the VSG company of Germany). The development of HNASS is constrained by advancements in smelting technology. This paper outlines the current smelting methods for high-nitrogen steel, including atmospheric pressure smelting and high-pressure smelting, and summarizes the issues encountered during the smelting process. Welding technology is another key factor limiting the industrial application of HNASS. Extensive research has been conducted to reduce the number of welding defects in high-nitrogen steel. By optimizing welding processes and improving welding filler materials, welding joints with good performance can be obtained. However, welding technology for HNASS with universal applicability and industrialization potential still warrants further research.
High-nitrogen austenitic stainless steel
Copyright (c) 2026 YanYan Hong; XiaoPing Huang; Di Lu https://creativecommons.org/licenses/by-nc/4.0
OPTIMAL CROP PLANTING STRATEGIES BASED ON INTEGER LINEAR PROGRAMMING AND MONTE CARLO MODELS
This study investigates optimal crop planting strategies for a mountainous village in North China during 2024–2030 to maximize profits. Two optimization models were developed for different market scenarios. First, assuming stable market and production parameters, an integer linear programming model is developed to maximize profits while incorporating core agricultural constraints: field type compatibility, crop rotation to avoid replanting, and legume rotation. This model solves for optimal planting schemes under two scenarios: (1) surplus production leading to unsold waste, and (2) surplus sales at a 50% discount. Second, to address uncertainties and potential risks in projected sales volume, yield per mu, planting costs, and sales prices over the coming years, a Monte Carlo model was introduced for analysis. This method simulates fluctuations in parameters (e.g., annual sales growth rate of 5%–10%, yield variation of ±10% per mu) through random sampling, calculates maximum average returns, and thereby determines the optimal planting area allocation scheme with risk resilience in uncertain environments.
Copyright (c) 2025 CongCong Wang; YuLu Ding; XuZhe Chen; WeiHao Zhou; TianLe Cheng https://creativecommons.org/licenses/by-nc/4.0
NEURAL-NETWORK-ASSISTED MODEL PREDICTIVE CONTROL FOR ACTUATORS IN NUCLEAR POWER PLANT DRIVE SYSTEMS
This study develops a unified dynamic modeling framework for nuclear-grade actuators, such as fans, pumps, and valves, critical for reactor cooling, feedwater regulation, and ventilation. These actuators exhibit nonlinearities, cross-coupling, and operational constraints, challenging conventional controllers to maintain performance and safety under fluctuating conditions. To address this, a neural-network-assisted model predictive control (NN-MPC) architecture is proposed. The framework incorporates a mask-based input encoding strategy to represent all actuators within a single model, and a fully differentiable neural network is trained to approximate actuator dynamics. A bias-compensation mechanism is introduced to address multi-step prediction drift caused by process variations and aging. The resulting nonlinear MPC integrates actuator constraints, output limitations, rate restrictions, and real-time optimization, ensuring millisecond-level performance required in nuclear applications. The implementation workflow includes real-time execution analysis, anomaly detection, safety fallback logic, and hardware-in-the-loop validation. Experimental results demonstrate that the NN-MPC framework provides accurate modeling, high-performance control, and compliance with nuclear safety requirements. This research offers a robust pathway for advancing intelligent, data-driven control systems in next-generation nuclear power plants.
Copyright (c) 2025 Yuan Zhang; YanKun Li; Chao Si https://creativecommons.org/licenses/by-nc/4.0
DEEP LEARNING IN STROKE REHABILITATION COMPENSATORY MOVEMENT DETECTION: FROM LABORATORY BENCHMARKS TO CLINICAL REALITY
Stroke rehabilitation is going through some pretty major changes right now. We're seeing a gradual move away from those subjective clinical scales toward more objective, automated assessments—though it's not happening as fast as some people hoped. Traditional methods like the Fugl-Meyer Assessment (FMA) are still considered the gold standard in clinics, but they've got some real problems. They're time-consuming, different raters often give different scores, and they tend to hit ceiling effects that miss subtle improvements patients are making.This review looks at important studies from 2016 to 2025. We trace how automated detection systems evolved from those lab-based depth sensors to RGB cameras you can use at home and wearable IMUs. We compare different sensing technologies and look at public datasets. There's also this "sim-to-real" gap issue - basically, models trained on healthy actors often don't work well with real patients, which is a big problem. The algorithms have changed a lot too, going from traditional machine learning to foundation models. Clinical metrics have also expanded beyond simple detection to things like severity grading.At the end, we talk about what this all means for tele-rehabilitation and why getting clinicians to actually adopt these technologies is still really challenging. We suggest a roadmap that focuses on privacy-preserving collaboration and long-term validation studies, though honestly implementing all of this won't be easy.
Copyright (c) 2026 Xiao Xu; HaoNan Qin; LanShu Zhou https://creativecommons.org/licenses/by-nc/4.0
RAIL DAMAGE DETECTION SYSTEM BASED ON FUSION OF PLANAR CAPACITIVE SENSING AND VISION
This research addresses the specific and crucial application scenario of railway track damage detection by designing and implementing a comprehensive intelligent detection system solution. At the hardware architecture level, the system meticulously selects the high-performance STM32F103 microcontroller as the core control unit. This unit offers fast processing speed, low power consumption, and high reliability, laying a solid foundation for the system's stable operation. Centered around this core control unit, the system integrates various functional modules, including high-precision detection modules like planar capacitive sensors for real-time and accurate collection of track status data. Simultaneously, the OpenMV visual processing module is utilized to achieve precise machine vision recognition of track surface defects, significantly enhancing the intuitiveness and accuracy of detection. Additionally, the system is equipped with a motor drive module to enable precise movement of the detection device. The system not only features excellent hardware configuration but also possesses powerful data processing capabilities, enabling real-time and efficient analysis and processing of the collected massive data, thereby significantly improving detection accuracy and timeliness. In terms of material selection, the system innovatively adopts new energy-saving and environmentally friendly materials, which not only substantially reduce the overall energy consumption of the system but also effectively extend the equipment's service life, markedly enhancing its durability. This system integrates numerous advantages such as efficient detection, energy saving, environmental protection, and low-cost operation, demonstrating broad application prospects and significant market promotion potential in the field of railway operation and maintenance. It is expected to provide a solid and powerful guarantee for the safe and stable operation of railways.
Copyright (c) 2025 YuYang Xia; ZhongFu Liu https://creativecommons.org/licenses/by-nc/4.0