World Journal of Engineering Research (WJER) is an international peer-reviewed, open access journal.
STICKY RICE SLURRY-MODIFIED LIME-METAKAOLIN STABILIZATION OF SILTY CLAY: MECHANICAL PERFORMANCE AND MICROSTRUCTURAL CHARACTERIZATION
Lime-stabilized silty clays are widely used in subgrade construction and earthen heritage conservation, but their relatively slow strength development, susceptibility to wet-dry deterioration and limited long-term durability still constrain their broader engineering applications. In this study, a ternary binder consisting of sticky rice (SR) slurry, metakaolin (MK) and hydrated lime was formulated to stabilize a low-plasticity silty clay, with the aim of exploiting both the pozzolanic reactivity of calcined kaolin and the biomolecular templating effect of amylopectin. A systematic series of unconfined compressive strength (UCS) tests was conducted on lime-soil (LS), metakaolin-lime-soil (ML) and sticky rice-metakaolin-lime-soil (SM) specimens with varying lime contents, metakaolin dosages, sticky rice concentrations and water-to-solid ratios, cured for 7, 28 and 90 days. The microstructural and mineralogical evolution of the stabilized matrix was characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD), and the internal water retention behavior was tracked throughout the curing period. The experimental results indicate that the optimum binder composition corresponds to 13% lime, 10% metakaolin and 3% sticky rice at a water-to-solid ratio of approximately 0.34, beyond which strength either plateaus or declines because of dilution or excess organic phase. Compared with conventional LS and ML systems, the SM specimens develop a denser and more homogeneous gel-rich microstructure, exhibit a more pronounced consumption of portlandite and a stronger amorphous hump associated with C-S-H and C-A-H, and retain internal moisture more effectively during curing. The synergetic interaction between the biopolymer network of sticky rice and the pozzolanic products of the lime-metakaolin system is identified as the principal mechanism underlying the strength enhancement, providing a sustainable and historically inspired stabilization route for silty clay.
Copyright (c) 2026 Tao Jiang; ChunLong Liu; ZhiQiang Zhang https://creativecommons.org/licenses/by/4.0
FATIGUE LIFE PREDICTION AND PERFORMANCE EVALUATION OF SMALL MECHANICAL TRANSMISSION SHAFTS BASED ON 3D SIMULATION AND LSTM MODELING
This study proposes a fatigue life prediction and performance evaluation method for small mechanical transmission shafts by integrating 3D simulation with LSTM modeling. First, a three-dimensional finite element model of the transmission shaft is established, and structural and fatigue simulations are performed under different loading conditions to obtain key response features, including equivalent stress, deformation, fatigue life, and safety factor. The simulation results indicate that the shoulder fillet and keyway root are the main fatigue-critical regions, where local stress concentration significantly reduces fatigue life. Then, the operating parameters and structural response features obtained from finite element simulation are constructed into a time-series dataset, and an LSTM model is developed to learn the nonlinear dynamic relationship among loading history, stress response, and fatigue life. The results show that the LSTM model achieves high prediction accuracy and outperforms BP neural network, SVR, Random Forest, and GRU models. Feature sensitivity analysis further demonstrates that simulation-derived stress, strain, and damage features can substantially improve prediction performance. This study suggests that the integration of 3D simulation and LSTM modeling provides an effective framework for fatigue life assessment, structural optimization, and maintenance decision-making of small mechanical transmission shafts.
Copyright (c) 2026 HaoNan Yan https://creativecommons.org/licenses/by/4.0
ROUTE OPTIMIZATION AND SCHEDULING FOR WASTE SORTING TRANSPORTATION BASED ON PARTICLE SWARM ALGORITHM
Aiming at the low efficiency and high cost of classified municipal solid waste transportation in urban China, this paper proposes a constrained vehicle routing optimization model. The particle swarm optimization (PSO) algorithm with greedy initialization is adopted to solve routing and scheduling problems under single-vehicle, multi-vehicle coordination, transfer station and time-window scenarios. The single-vehicle scheme achieves a minimum daily travel distance of 1101 km with 15 vehicles. For classified multi-vehicle transportation, the optimized fleet allocation yields a minimum daily cost of 2869 yuan. This study integrates classification characteristics, load and time constraints to bridge the gap between traditional VRP and real-world waste logistics. It provides a quantitative decision support tool for urban sanitation management to reduce costs and improve efficiency.
Copyright (c) 2026 Yan Zhang; ZeYu Pan; SiChao Zhu https://creativecommons.org/licenses/by/4.0
SYSTEMATIC ANALYSIS OF DOMINANT FACTORS CONTROLLING PHOTONIC NANOJET PERFORMANCE FOR SUBSTRATE-EMBEDDED HIGH-REFRACTIVE-INDEX MICROSPHERES
Addressing the physical bottleneck that high-refractive-index materials struggle to overcome whispering-gallery modes (WGMs), this study systematically investigated the optical field evolution laws of silicon-based high-refractive-index microspheres in the 1550 nm band, evaluating the influence of four dimensions: environmental refractive index, substrate coupling, asymmetric substrate dimensions, and microsphere embedding depth. Research indicates that elevating the environmental refractive index to above 2.40 can effectively overcome the “focal retraction” phenomenon induced by extreme refractive index contrast. However, in practical on-chip integration applications, the introduction of the underlying substrate triggers a pronounced “substrate dragging effect,” leading to massive optical field energy dissipation and focal length degradation. Consequently, the microsphere must maintain a shallow embedding (depth less than 0.08 times the microsphere diameter) to sustain a far-field propagating state; simultaneously, asymmetric substrate dimensions exert a significant anisotropic influence on beam focusing. By constructing a global parameter phase diagram, this study identifies the optimal comprehensive optimization interval, providing engineering recommendations for the on-chip integration and structural design of high-refractive-index infrared micro/nano-detectors.
Copyright (c) 2026 HaoRun Li https://creativecommons.org/licenses/by/4.0
HOW TO PENETRATE THE "BLACK BOX" OF MULTI-FACTOR COUPLING IN CONTINUOUS-TIME MODELING OF LITHIUM-ION BATTERIES
Unstable battery life significantly limits user experience of smartphones. Based on electrochemical properties of lithium‑ion batteries, this study develops a continuous‑time mathematical model with strong multi‑scenario adaptability to realize state‑of‑charge (SOC) prediction, discharge time calculation and energy‑saving strategy design. A SOC evolution model with piecewise dynamic weighting and physical mechanism drive is proposed under the law of capacity conservation, integrating temperature and battery aging factors. A hybrid prediction model combining rolling‑window GM(1,1), Markov chain correction and ARIMA calibration is established for discharge time prediction, and the 95% confidence interval is quantified by normal distribution. Sensitivity of eight core variables is tested using a modified Moulton method with control variables, which verifies model robustness. An optimization strategy system is constructed via improved analytic hierarchy process (AHP). Results show that high CPU/GPU load and persistent GPS are main causes of shortened battery life. Combined strategies including intelligent GPU allocation and screen brightness reduction can extend battery life by 15%–20%. The model can be extended to tablets and smartwatches, and aging adaptation strategies increase endurance by 8% for devices with SOH = 0.7.
Copyright (c) 2026 YuXin Wang; Le Yu; MengHui Xu https://creativecommons.org/licenses/by/4.0
UAV SMOKE-SCREEN OBSCURATION EVALUATION AND STRATEGY OPTIMIZATION VIA TIME-STEPPING SIMULATION AND SIMULATED ANNEALING
This paper develops a simulation-based framework for evaluating and optimizing UAV-deployed smoke-screen obscuration in missile engagement scenarios. First, the UAV, smoke cloud, missile, and protected target are described through a unified kinematic model, and angle and distance criteria are used to determine whether the smoke cloud effectively blocks the missile-target line. The valid time steps are accumulated to obtain the total effective obscuration duration, which provides a quantitative basis for strategy comparison. Second, the deployment problem is formulated as a constrained optimization task with UAV flight direction, flight speed, release time, and detonation delay as decision variables. A simulated annealing algorithm, combined with neighborhood search, exponential cooling, and bounded L-BFGS-B local refinement, is used to maximize the effective obscuration duration. Numerical results show that the baseline strategy provides 1.420 s of effective obscuration, while the optimized strategy increases it to 4.60 s, demonstrating the feasibility of the time-stepping simulation and optimization pipeline for smoke-screen deployment design.
Copyright (c) 2026 TianLin Wang; Hao Zhang; TengKun Hou https://creativecommons.org/licenses/by/4.0
POWER BALANCE MODEL FOR GRID-CONNECTED GREEN POWER-TO-HYDROGEN-TO-AMMONIA PARKS
Against the background of large-scale development of green hydrogen and ammonia industries, green power directly connected parks face prominent challenges in operational compliance and economic performance, due to the inherent conflict between renewable volatility and rigid industrial loads. Existing studies mostly rely on low-temporal-resolution models or focus on single optimization objectives, lacking a systematic framework that uncovers fine-grained power interaction mechanisms and quantifies the coupling of compliance and economy. To address this gap, this paper establishes a refined hourly power balance model that systematically integrates wind-photovoltaic output, conventional load, and rigid loads of electrolyzers and ammonia synthesis units. The core innovation lies in constructing a unified multi-dimensional quantitative framework that simultaneously realizes hourly power matching, green compliance assessment, and ammonia cost accounting, breaking the limitation of isolated single-objective research. Typical day calculation results show that the park’s renewable generation reaches 603.45 MWh, exceeding the total electricity consumption of 558.72 MWh. However, significant time mismatch between renewable output and rigid load leads to 172.04 MWh grid purchase and 216.77 MWh surplus feed-in. In terms of compliance, the green power proportion meets standards, while self-consumption rate and feed-in ratio fail policy requirements, with ammonia production cost at 4,287.78 yuan per ton. The findings confirm time mismatch as the core bottleneck. The proposed model provides an effective algorithmic tool and reliable quantitative reference for the operation optimization and policy formulation of grid-connected green power-to-hydrogen-to-ammonia parks.
Copyright (c) 2026 YuChang Wang; Fan Zhang; LiHang Luo https://creativecommons.org/licenses/by/4.0
IMOGWO-BASED STRUCTURAL OPTIMIZATION OF A DUAL-DRIVE FEED SYSTEM UNDER MULTI-FORCE COUPLING
This study presents an RSM- and IMOGWO-based structural optimization method for a dual-drive feed system worktable under multi-force coupling conditions. A finite element model is established, and three structural depths are selected as design variables to minimize mass and maximum coupling stress while maximizing the first-order natural frequency. A response surface surrogate model is constructed from orthogonal experiments, and the improved MOGWO is used for multi-objective optimization. The optimized design reduces mass by 6.58% and maximum coupling stress by 1.60%, while increasing the first-order natural frequency by 0.065%. Finite element validation verifies the reliability of the proposed method.
Copyright (c) 2026 FangYong Xu; JunShuo Cui; ZhuoYue Li; RunJia Yu; ZhenKai Luan https://creativecommons.org/licenses/by/4.0
THE IMPLICATION OF HUMAN CENTROID VARIATION DUE TO THE BENDING OVER OF A HUMAN BODY
Work at height is one of the construction activities, and a drying rack is one of the construction products that may need to be considered in the context of construction safety. Both of them involve people standing in a posture with inclining or bending their body out from a stable and safe point toward a place in the air. Conventionally, the construction industry concerns the height of the barrier, the parapet, and the window frame. However, people falling from height can happen when the centroid of the people moves out from the stable point due to the posture of inclining or bending. The study investigates the relationship between the inclining and bending posture of the human body with the variation of centroids and suggests a reference for the inclining and bending limit for designing the gap between the platform and the wall for scaffolding and the size of a drying rack. The reference supporting loadings required for the guard-rail design of scaffolding and the cantilever support design of drying racks are also suggested.
Height restriction of protection barrier
Copyright (c) 2026 Timothy Kwan-fai Chan; XiaoYing Lu; Alpha Chi Him Tsang https://creativecommons.org/licenses/by/4.0
CNN-GRU-BASED DEFORMATION PREDICTION METHOD FOR SUBWAY EXCAVATIONS
To address the issues of decreased deformation prediction accuracy and early warning lag caused by missing monitoring data during subway foundation pit construction, this paper proposes a deformation prediction method based on the CNN-GRU neural network. First, a convolutional neural network (CNN) is used to impute missing data in the original monitoring sequence. Second, wavelet decomposition is applied to decompose the imputed sequence into a trend component and a noise component. Third, a gated recurrent unit (GRU) neural network is constructed to predict the trend component, while an ARMA model is used to fit and predict the noise component. Finally, the prediction results of the two parts are reconstructed to obtain the final deformation prediction value. This paper conducts an empirical analysis using the vertical displacement monitoring data of the wall top of No. 2 air vent at Terminal 3 Station of B Airport. The results show that with a missing rate of 5.8%, the proposed model achieves a mean absolute percentage error (MAPE) of 1.93% and a coefficient of determination (R²) of 0.95. Compared with GA-BP, LSTM, single GRU, and ARMA models, all error metrics are significantly reduced, and the prediction lag problem is effectively improved. This method provides an effective solution for deformation prediction of foundation pits under incomplete data conditions.
Copyright (c) 2026 ChenShu Meng; Hui Wang; YanHui Zhang; ZiJie Li; Yu Sun https://creativecommons.org/licenses/by/4.0
A MULTI‑MODE INTELLIGENT AQUARIUM MONITORING SYSTEM WITH IOT (INTERNET OF THINGS) REMOTE CONTROL
With the popularization of smart home technologies, traditional aquarium breeding suffers from issues including low monitoring accuracy, single control modes and inconvenient remote management. To address these drawbacks, this paper presents an intelligent ecological aquarium monitoring system based on STM32. Adopting the STM32F103C8T6 as the main control chip, the system integrates DS18B20, pH and TDS sensors to achieve real-time monitoring of water temperature and water quality parameters. It realizes automatic feeding, oxygenation and water circulation via servos and relays, and supports four operating modes: timing, manual, threshold and automatic, to meet diverse application requirements in different scenarios. An OLED screen enables on-site data display, while an ESP-01S Wi-Fi module uploads collected data to a mobile APP, allowing remote timing configuration, device control and threshold adjustment. Test results indicate that the system features high measurement accuracy, fast response and stable operation, and can greatly improve the management efficiency of aquarium environments. With low cost, compact size and easy deployment, this system offers an intelligent solution for household aquarium maintenance and small-scale aquaculture applications.
Copyright (c) 2026 WenXiu Liu https://creativecommons.org/licenses/by/4.0
MODELING AND EMPIRICAL STUDY OF 3D LOADING OPTIMIZATION FOR A SINGLE VEHICLE TYPE BASED ON MULTI-CONSTRAINT HEURISTIC ALGORITHMS
Addressing the complex demands for space utilization and safe loading in logistics transportation, this study focuses on optimizing 3D loading decisions for a single vehicle type. After systematically organizing eight key rigid constraints—spatial non-overlap, truck compartment boundaries, cargo support, load-bearing capacity per unit area, stacking stability, placement of special cargo, orthogonal placement, and total vehicle load—this paper constructs a single-vehicle loading maximization model with a weighted objective score based on space utilization and load utilization, as well as a scaled-up transportation model aimed at minimizing the total number of vehicles. To address the combinatorial explosion problem in large-scale cargo loading, this study introduces a grid-based dimensionality reduction strategy, transforming continuous spatial constraints into discrete grid load-bearing constraints, thereby significantly reducing the scale of the problem. At the algorithmic level, a multi-strategy greedy heuristic loading algorithm based on an extreme point generation mechanism was designed. By alternately testing various sorting rules—such as volume, weight, and priority—the algorithm achieves efficient verification of complex constraints and space filling. Empirical results show that Vehicle Type 2 outperforms Vehicle Type 1 in both single-vehicle loading performance and overall vehicle scheduling efficiency, with a maximum space utilization rate of 91.45%. This study provides theoretical support and algorithmic references for logistics enterprises to formulate scientific loading plans under stringent physical constraints.
Copyright (c) 2026 ShiChang Shang https://creativecommons.org/licenses/by/4.0
OPTIMAL DELIVERY STRATEGY OF SMOKE SCREEN JAMMING BOMBS BASED ON KINEMATICS AND PARTICLE SWARM OPTIMIZATION
Aiming at the problem of accurate interception and effective coverage in the process of smoke screen deployment, this paper establishes a complete three-dimensional kinematics model, which fully considers the air dynamic environment, spatial position constraints, and target movement characteristics, and includes the motion of carriers, the diffusion and drop of smoke screens, and the trajectory of moving targets. On this basis, a multi-objective optimization model is constructed with the goal of maximizing the effective interception time and the coverage rate. The model takes the flight parameters of carriers, the release timing of smoke screens, the detonation delay and the spatial constraints as the main decision variables, and introduces the environmental interference factors and dynamic constraint conditions to enhance the adaptability and robustness of the model. The intelligent optimization algorithm is used to solve the optimal deployment strategy, and the iterative optimization mechanism and global search ability are fully utilized to improve the convergence speed and optimization accuracy. Simulation results and case analysis show that the proposed method can significantly improve the continuous interception effect of smoke screens, and realize the autonomous and accurate deployment in the whole process. The model and method can provide theoretical support and engineering reference for the design of smoke screen interception, regional shielding and safety protection in complex environments.
Copyright (c) 2026 XingYu Liu; XueJiao Lu; JiaYing Wang; JiaYi Shao https://creativecommons.org/licenses/by/4.0
PROGRESSIVE MODELING FOR SILICON CARBIDE EPITAXIAL LAYER THICKNESS MEASUREMENT: FROM DUAL-BEAM THEORY TO MULTI-BEAM INTERFERENCE CORRECTION
Combining infrared reflection interferometry with experimental validation, this paper presents a layered structural modeling and inversion approach for measuring the thickness of silicon carbide (SiC) epitaxial layers. To overcome the challenges in directly acquiring critical parameters during measurement, an analytical relationship between the epitaxial layer thickness and the wavenumber, incident angle, refractive index, and interference order is established. This framework integrates geometric optics, Snell’s law, and the Fresnel equations within a dual-beam interferometry context. To handle the discrete and non-uniformly sampled nature of the measured reflectance spectra, the optimal interpolation method is selected based on cross-validation and the root mean square error (RMSE) criterion. Refractive index inversion is subsequently conducted via a golden section search, while the Brent method is employed to accurately locate interference extrema, enabling stable thickness inversion at incident angles of 10° and 15°. Furthermore, recognizing that multi-beam interference (MBI) typically dominates in practical experiments, this study investigates the coherent superposition of multiple reflected beams. A necessary condition for significant MBI (R²→1) is derived, and a comprehensive MBI model—incorporating complex amplitude reflection coefficients and phase correction terms—is developed. This research provides a robust theoretical and mathematical foundation for the high-precision, non-destructive measurement of SiC epitaxial layer thickness.
Copyright (c) 2026 QiMin Gao; YiYi Luo https://creativecommons.org/licenses/by/4.0
ANALYSIS AND OPTIMIZATION DESIGN OF SIGNAL INTEGRITY IN OPTOELECTRONIC INTEGRATED SYSTEMS
With the rapid development of high-speed communications, artificial intelligence computing, data centers, and advanced packaging technologies, optoelectronic integrated systems have shown great potential for high-bandwidth, low-power, and high-density interconnect applications. However, in practical systems, high-speed electrical signals still propagate through packaging interconnects, differential transmission lines, pads, vias, and electro-optic modulator interfaces, where they are susceptible to impedance discontinuity, insertion loss, signal reflection, crosstalk, and bandwidth limitation. These impairments can degrade the electrical eye diagram and further reduce the optical modulation quality. This paper investigates signal integrity issues in optoelectronic integrated systems by analyzing the main degradation mechanisms along the high-speed electrical interconnect path and at the electro-optic conversion interface. Optimization strategies are discussed from the perspectives of impedance matching, interconnect structure optimization, crosstalk suppression, and driver–modulator co-design. The results indicate that system-level signal integrity optimization can effectively reduce high-frequency loss and reflection, improve eye opening, and enhance the effective driving voltage at the modulator input as well as the optical output quality. This work provides useful guidance for the design of high-bandwidth, low-power, and highly reliable optoelectronic integrated systems.
Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by/4.0
THE PERFORMANCE OF COAL GANGUE SUBGRADE FILLER MODIFIED BY CEMENT-FLY ASH SYNERGY
To address the problems of performance discreteness, low strength, and insufficient water stability encountered in the resource utilization of coal gangue for road embankment filling, this study selected coal gangue samples from Meihuajing and Yangchangwan Mines in Lingwu City, Yinchuan, Ningxia. The coal gangue was compounded with typical fine-grained soil (Group C filler) at different proportions, and additives such as cement, fly ash, and lime were incorporated to systematically investigate the engineering properties of the mixtures. Through a series of laboratory tests, including particle size analysis, heavy compaction test, California Bearing Ratio (CBR) test, expansion ratio test, and resilient modulus test, the gradation characteristics, compaction behavior, strength, water stability, and deformation performance of the mixtures were comprehensively evaluated. The results show that blending coal gangue with Group C filler can significantly optimize the gradation, with the coefficient of uniformity (Cu) being greatly improved. The mixture with 3% cement and 8% fly ash exhibits the optimal performance: its maximum dry density reaches 2.16 g/cm³, the peak CBR value is 171.49%, and the resilient modulus exceeds 60 MPa. All these indices are far higher than the requirements specified in current specifications for subgrade filler used in the lower embankment of high-grade highways. In addition, the incorporation of cement and lime effectively inhibits the expansibility of the mixtures. This study verifies the feasibility of using coal gangue mixtures as subgrade filler and provides a theoretical basis and data support for the optimization of its engineering mix ratio.
Copyright (c) 2026 SiYuan Yang https://creativecommons.org/licenses/by/4.0
DESIGN AND IMPLEMENTATION OF A MULTIFUNCTIONAL 3D ROBOT FOR KITCHEN WASTE RECYCLING
Currently, household kitchen waste is commonly disposed of either by mixing with other garbage or by directly discharging it into sewer systems. Such practices lead to several environmental and sanitary issues, including contamination of recyclable materials, reduced efficiency of waste sorting, pipe blockages, increased load on sewage treatment facilities, and microbial growth. To achieve on-site, resource-oriented, and high-value utilization of kitchen waste, this paper proposes a multifunctional kitchen robot that integrates kitchen waste processing with 3D printing. The device can directly convert kitchen waste into practical items such as flowerpots, thereby realizing waste reuse and low-carbon environmental goals. Based on the principle of Fused Deposition Modeling (FDM) 3D printing, the robot features improved mechanical structures and feeding methods to enable the use of kitchen waste as a printable material. The integrated system combines automatic crushing, conveying, printing, and self-cleaning functions. The feeding system consists of an extrusion mechanism and a cleaning water pump. The printing unit adopts an MB structure to effectively reduce print bed vibration and improve printing accuracy. The nozzle uses a screw extrusion method, eliminating the traditional heating module and heated bed, thus significantly reducing energy consumption. A dual-chamber isolation design is implemented: the kitchen waste processing module crushes the waste into a slurry and extrudes it for printing. When printing high-structural components, additives can be introduced to enhance mechanical properties, and the printed objects achieve usable strength after natural drying. An independent food printing module, comprising a precise feeding mechanism and a mixing mechanism, enables quantitative proportioning and uniform mixing of ingredients such as flour and sugar for personalized 3D-printed cookies. The two systems operate independently, ensuring hygienic safety. The robot supports both automatic and manual control modes, with adjustable parameters such as crushing duration, water addition, and extrusion speed, offering convenient operation. This robot integrates kitchen waste valorization and multifunctional manufacturing, significantly reducing the volume of household waste requiring collection and transportation as well as associated environmental pollution. It is suitable for households, communities, and educational outreach scenarios such as campus science demonstrations, providing a novel technical solution for low-carbon kitchen waste treatment and home-based smart manufacturing.
Copyright (c) 2026 XiaoCui Tian https://creativecommons.org/licenses/by/4.0
EFFECT EVALUATION OF THE BOPPPS TEACHING MODEL BASED ON THE OBE CONCEPT AND SPOC PLATFORM ON HUMANISTIC CARE ABILITY AND NURSING COMMUNICATION COMPETENCE OF HIGHER VOCATIONAL NURSING STUDENTS
Objective: Higher vocational nursing education aims to develop students’ clinical competence, yet traditional teaching methods often fail to adequately cultivate humanistic care and nursing communication abilities. The BOPPPS model, combined with the OBE concept and SPOC platform, offers a student-centred, outcome-driven, blended learning approach. However, evidence of its effectiveness in Internal Medicine Nursing courses for vocational nursing students remains limited. Methods: A quasi-experimental study was conducted. The 2022 and 2023 higher vocational nursing students (hereinafter referred to as nursing students) from two parallel classes at a vocational college were enrolled. A total of 452 students participated: the class of 2022 (n=216) served as the control group (traditional teaching), and the class of 2023 (n=236) as the experimental group (BOPPPS–OBE–SPOC intervention). Humanistic care ability and nursing communication competence were measured using validated scales before and after the intervention. Results: Following the intervention, the experimental group taught with the BOPPPS model (based on the OBE concept and SPOC platform) exhibited significantly superior performance in humanistic care and nursing communication abilities relative to the control group (P < 0.05). Conclusion: The BOPPPS teaching model based on the OBE concept and SPOC platform significantly improves humanistic care and nursing communication abilities in higher vocational nursing students enrolled in the Internal Medicine Nursing course. This blended, student-centred approach can be considered an effective pedagogical strategy for nursing education.
Copyright (c) 2026 WenWen Shi; MengMi Lu https://creativecommons.org/licenses/by/4.0
PREDICTION OF THE INFLUENCE OF ROLLING PROCESS PARAMETERS ON THE MECHANICAL PROPERTIES OF RARE-EARTH-CONTAINING ALUMINUM ALLOYS
This study investigated the effects of rolling temperature and total reduction ratio on the mechanical properties of a rare-earth-containing Al-Mg-Si alloy, aiming to optimize the strength–ductility balance during thermomechanical processing. Rolling experiments were conducted under different temperatures and reduction ratios, and the resulting mechanical properties were evaluated by tensile testing and hardness measurements. Optical microscopy, scanning electron microscopy, and electron backscatter diffraction were employed to characterize microstructural evolution and to clarify the process–microstructure–property relationship. The results showed that increasing the total reduction ratio significantly improved yield strength, ultimate tensile strength, and hardness, but reduced elongation, whereas increasing the rolling temperature weakened the strengthening effect while enhancing ductility. The highest strength was achieved at 25 °C with a 70% reduction ratio, while the best elongation was obtained at 400 °C with a 30% reduction ratio. A favorable strength–ductility balance was realized at 200 °C and 50% reduction. In addition, quadratic regression and random forest models were developed to predict mechanical properties, and the random forest model exhibited higher accuracy. This work establishes a process–microstructure–property framework and highlights machine-learning-assisted prediction as a useful strategy for optimizing rare-earth-containing aluminum alloys.
Copyright (c) 2026 HongXu Tu; YuHan Gao; ShengEn Qiao https://creativecommons.org/licenses/by/4.0
PREPARATION OF STRAW-SLUDGE BASED ACTIVATED CARBON FOR THE TREATMENT OF PHOSPHORUS-CONTAINING WASTEWATER
In this study, rapeseed straw and dewatered sludge were used as raw materials and ZnCl₂ as an activator to prepare a straw-sludge based composite activated carbon via a mixed co-carbonization method. Single-factor and orthogonal experiments were conducted to investigate the effects of carbonization temperature, solid-liquid ratio, activation time, raw material ratio, and activator concentration on the iodine adsorption value of the composite activated carbon, aiming to optimize the preparation process. The results showed that the optimal preparation conditions for the composite activated carbon were: raw material ratio (sludge:straw) of 1:3, solid-liquid ratio of 1:3, activation time of 60 min, ZnCl₂ concentration of 3.5 mol/L, and carbonization temperature of 500 ℃. The composite activated carbon prepared under the optimal conditions was then utilized to adsorb PO₄³⁻ in water, and the effects of initial wastewater concentration, activated carbon dosage, and wastewater pH on the adsorption performance were investigated. The results demonstrated that under the conditions of an initial wastewater concentration of 40 mg/L, an activated carbon dosage of 40 g/L, and a pH of 7, the removal rate of PO₄³⁻ exceeded 93%, with an adsorption capacity of 1.17 mg/g.
Copyright (c) 2026 ZhuYun Li; YuLong Zhang; Fang Deng; ShiZhen Peng; ZiLing Huang; XiaoYan Gen; Wei Peng; Jun Dong https://creativecommons.org/licenses/by/4.0
ISOLATION AND IDENTIFICATION OF AN AMMONIA-NITROGEN-DEGRADING BACTERIUM BACILLUS STERCORISL1 AND DEGRADATION CHARACTERISTICS
This study aimed to screen efficient ammonia-nitrogen-degrading bacteria from natural environments and investigate their taxonomic status and degradation characteristics, thereby providing bacterial resources for the biological treatment of ammonia-nitrogen wastewater. Ammonia-nitrogen-degrading bacteria were enriched and isolated from soil samples collected around South Lake in Wuhan, using ammonium chloride as the primary nitrogen source. The bacterial strain was identified through morphological observation and 16S rDNA sequencing. The effects of initial ammonia-nitrogen concentration (40~120 mg/L), inoculum size (1%~10%, V/V), temperature (25~35 ℃), and initial pH (6.0~8.0) on the ammonia-nitrogen removal rate of the strain were investigated using single-factor experiments. A highly efficient ammonia-nitrogen-degrading bacterium, identified as Bacillus stercoris SL1 (16S rDNA similarity 99.65%), was isolated. The single-factor experiments revealed that under the conditions of an initial ammonia-nitrogen concentration of 60 mg/L, an inoculum size of 1%, and a pH of 7.0, the strain exhibited a removal rate of 61.50% at 30 ℃ over a 48-hour period. Notably, when the temperature was increased to 35 ℃, the removal rate significantly rose to 70.08% (P<0.05). Additionally, substrate inhibition was observed when the initial ammonia-nitrogen concentration was ≥80 mg/L. The highest removal rate (60.41%) was achieved with an inoculum size of 1% over a 48-hour period, while excessive inoculum (10%) reduced the rate to 50.03%. The strain exhibited high degradation activity within the pH range of 6.0~8.0. After culturing under optimal conditions for 48 hours, the nitrite nitrogen accumulation in the culture medium was only 0.016 mg/L, indicating minimal nitrite accumulation during the ammonia-nitrogen degradation process. The strain Bacillus stercoris SL1 has significant heat tolerance and efficient ammonia-nitrogen degradation ability, showing great potential in the biological treatment of medium and high temperature ammonia-nitrogen wastewater.
Copyright (c) 2026 YuJie Cai; Dan Zheng; JiaHong Feng; ZhiHong Liu; Yan Qian; JiaYi Peng; YuDie Fang; Tian Tian; Jun Dong https://creativecommons.org/licenses/by/4.0
DYNAMIC EARLY WARNING MODELING OF PASSENGER AGGREGATION RISK AT RAILWAY PASSENGER HUBS
Under conditions such as holiday travel peaks, concentrated train arrivals and departures, and operational disturbances, railway passenger hubs are prone to localized passenger aggregation in key areas such as security checkpoints, waiting halls, ticket gates, and platforms, which may further lead to congestion propagation and operational risk. To address the limitations of existing studies, including insufficient focus on railway passenger hub scenarios, inadequate dynamic characterization of risk identification, and limited management orientation of warning results, this study investigates the dynamic early warning of passenger aggregation risk at railway passenger hubs. First, the formation mechanism of passenger aggregation risk is analyzed from the perspectives of passenger demand fluctuations, facility service capacity constraints, and operational disturbance propagation, and a risk indicator system is established from three dimensions: passenger-state conditions, facility operations, and disturbance factors. On this basis, a dynamic early warning framework consisting of data input, state identification, risk assessment, and warning output is proposed to support graded identification and dynamic response for passenger aggregation risk in key functional areas. Finally, an illustrative case study is conducted to demonstrate the applicability of the proposed framework. The results show that passenger aggregation risk at railway passenger hubs exhibits significant time-varying, spatially heterogeneous, and propagative characteristics, while bottleneck areas such as security checkpoints and ticket gates are more sensitive in both risk formation and escalation. The proposed framework is capable of representing the evolution of risk and translating operational states into graded warning results with direct managerial relevance. This study provides theoretical support and methodological guidance for passenger flow safety management and operational optimization at railway passenger hubs.
Copyright (c) 2026 Yuan Fang; Zheng Han; XinShan An; Jie Liu; Liang Zhu https://creativecommons.org/licenses/by/4.0
CURRENT HARMONIC SUPPRESSION STRATEGY OF PERMANENT MAGNET SYNCHRONOUS MOTOR BASED ON MULTI-SYNCHRONOUS ROTATING COORDINATE SYSTEM
This paper focuses on the harmonic problems of stator current in permanent magnet synchronous motor (PMSM) caused by factors such as magnetic circuit saturation and inverter nonlinearity, and conducts research on harmonic suppression strategies based on multiple synchronous rotating coordinate systems. Firstly, the hazards of harmonics to motor efficiency, torque stability, and system reliability are analyzed, and the engineering significance of harmonic suppression technology is expounded. On this basis, a mathematical model of PMSM containing 5th and 7th harmonics is established, and a harmonic extraction and compensation method combining low-pass filtering and closed-loop PI control in multiple synchronous rotating coordinate systems is proposed. By constructing a coordinate system that rotates synchronously with the harmonics, the harmonic components are converted into direct currents for precise suppression, and the corresponding control structure is designed to achieve harmonic voltage compensation. Simulation results show that this strategy can effectively reduce the total harmonic distortion of stator current and improve the current waveform quality at different speeds of 500 r/min and 1500 r/min, verifying the effectiveness and practicality of the proposed method under different operating conditions, and providing a feasible control solution for high-performance and low-harmonic operation of PMSMs.
Multiple synchronous rotating coordinate systems
Copyright (c) 2026 Zhi Tan; QinFeng Hu https://creativecommons.org/licenses/by/4.0
INFRARED SPECTROSCOPY-BASED ONLINE MONITORING SYSTEM FOR MICROBUBBLE CONCENTRATION
To address the urgent need for the real-time and precise monitoring of microbubble concentrations in industrial fluids, an online monitoring system for microbubbles was developed based on the principles of infrared spectroscopy sensing and light intensity attenuation, featuring an architecture that integrates array-based infrared photoelectric sensing with cloud collaboration. A 'three-emitter, three-receiver' array-based infrared detection scheme is adopted to effectively mitigate the issue of poor sample representativeness associated with single-optical-path detection in complex fluid phases. Centered around a Raspberry Pi as the primary controller, the system incorporates precise photoelectric signal conditioning circuitry, high-resolution analog-to-digital conversion modules, and industrial communication links. A dual-channel data communication architecture based on the RS485 bus and the MQTT IoT protocol was established. Testing demonstrates that the system exhibits high real-time performance, strong anti-interference capability, and excellent digital management features, enabling both on-site real-time monitoring of microbubble concentrations and remote visualization via a cloud dashboard.
Copyright (c) 2026 Peng Liu; ChangCheng Wan; Long Xie https://creativecommons.org/licenses/by/4.0
HEALTH‑MONITORING‑BASED INVESTIGATION OF THERMAL EFFECTS ON DOUBLE‑RIBBED PLATE GIRDERS OF CABLE‑STAYED BRIDGES
To investigate the temperature-induced effects on double-ribbed plate girders in cable-stayed bridges, this study leveraged health-monitoring data from the Poyang Lake Bridge. The temperature field distribution was analyzed using ABAQUS, and a full-bridge model was established in Midas/Civil 2021 to examine the mechanical responses of the main girder under overall temperature differences and temperature gradients. The results showed that under the overall temperature drop of 19 °C, the main girder experienced a maximum tensile stress of 6.44 MPa, with extreme vertical and longitudinal displacements of 21.3 mm and 54.6 mm, respectively. Under the overall temperature rise of 21 °C, the maximum compressive stress reached -7.12 MPa, and the extreme vertical and longitudinal displacements were 23.5 mm and 60.4 mm, respectively. Under a positive temperature gradient, the maximum compressive and tensile stresses were -5.47 MPa and 3.52 MPa, respectively, with extreme vertical and longitudinal displacements of 55.1 mm and 26.5 mm. Under a negative temperature gradient, the maximum tensile and compressive stresses were 1.81 MPa and -3.03 MPa, respectively, with extreme vertical and longitudinal displacements of 32.8 mm and 13.5 mm. A comprehensive comparison reveals that the overall temperature difference exerts a more pronounced influence on longitudinal displacement, whereas the vertical temperature gradient, has a more dominant effect on vertical displacement.
Copyright (c) 2026 Bo Liu; XiaoQiang Xue; WenQiang Huang https://creativecommons.org/licenses/by/4.0
THE DYNAMIC MECHANISM OF HEAVE IN OSCILLATING BUOY-TYPE WAVE ENERGY DEVICES AND THE OPTIMIZATION OF ENERGY HARVESTING SYSTEMS
This paper presents a systematic study on dynamic modeling and parameter optimization to address the energy capture efficiency of wave energy conversion devices in the vertical oscillation mode. Based on Newton’s second law, the study establishes a coupled second-order nonlinear differential equation system describing the motion of the float and the embedded oscillator, comprehensively considering the interactions among wave excitation forces, wave-induced damping forces, hydrostatic restoring forces, and the energy output system. To solve this strongly coupled system of equations, the fourth-order Runge-Kutta method was employed to calculate time-domain responses, yielding precise dynamic characteristics of system displacement and velocity at extremely small time steps. For evaluating energy conversion efficiency, a single-objective optimization model was formulated to maximize steady-state average output power, utilizing Riemann integration and approximation methods to handle the discretized integration of instantaneous power. By introducing a Bayesian optimization algorithm, the study achieved efficient heuristic search within the complex damping parameter space. Experimental results show that under two typical operating conditions—constant damping and power-law damping—the system achieved the expected power capture targets through optimal damping coefficient configuration, with a maximum average power of 228W. These findings provide quantitative evidence for the structural optimization of wave energy devices and the formulation of PTO system strategies.
Copyright (c) 2026 YuHang Xiao https://creativecommons.org/licenses/by/4.0
UNMANNED LEAF SWEEPING AND CRUSHING VEHICLE
In urban and campus environments, fallen leaves accumulate extensively. The research team has developed an unmanned leaf sweeping vehicle to improve cleaning efficiency and reduce labor costs. The innovative features of this unmanned leaf sweeping vehicle lie in its unmanned driving software technology and built-in navigation function. The hardware design adopts corrosion-resistant hard materials, high-speed and energy-saving motors, adjustable cleaning brushes, vacuum cleaners, and large-capacity lithium batteries, enabling the sweeping vehicle to achieve intelligence, high performance, high efficiency, energy saving, and environmental protection. The core contents of the unmanned leaf sweeping vehicle include: control of driving speed and sweeping speed; calculations for movement and sweeping to minimize leaf sweeping time and movement path; mathematical model prediction; real-time sensor feedback; intelligent machine learning; and predicted movement path. The control system includes driving control, sweeping control, and crushing control. The motor provides power, the ball screw converts motion, and the sensor provides real-time feedback. Autonomous navigation technology and deep learning algorithms are introduced to enhance intelligence. Users can control the vehicle and realize remote monitoring through a mobile phone APP.
Copyright (c) 2026 ZhaoMing Li https://creativecommons.org/licenses/by/4.0
THE KEY TECHNOLOGIES FOR RISK IDENTIFICATION OF "TWO PASSENGER AND ONE HAZARDOUS" VEHICLES BASED ON MULTI-SOURCE DATA FUSION
"Two passenger and one hazardous" (TPOH) vehicles are primary targets of road traffic safety supervision. Jilin Province, located in the northeastern seasonal frozen region of China, experiences severe winter conditions, including icy and snow-covered roads and extremely low temperatures, which substantially increase the operational safety risks of these vehicles.This study proposes a risk identification framework based on multi-source data fusion. The framework integrates GPS positioning data, on-board OBD data, meteorological information, and road infrastructure data to construct a multidimensional risk identification model. The model captures five categories of abnormal driving behaviors: speeding, harsh acceleration, harsh braking, vehicle vibration, and abrupt lane changes.An improved sliding-window feature extraction method and a hybrid XGBoost–LSTM classification model are employed to achieve accurate vehicle risk identification under complex seasonal frozen conditions. In addition, a system management and control platform is developed to provide decision-support tools for the transportation authorities of Jilin Province.
Copyright (c) 2026 HengBo Zhang; WanMiao Yu; Long Peng; ChunFu Jia; ZhiGang Wei; Miao Li https://creativecommons.org/licenses/by/4.0
MONOCULAR 3D BINAURAL LOCALIZATION AND DYNAMIC TRACKING FOR EAR-SIDE ACTIVE NOISE CONTROL IN VEHICLE CABINS
Accurate three-dimensional ear-position tracking is a prerequisite for ear-side active noise control in vehicle cabins, because natural head motion can directly shift the control target region and degrade the spatial consistency between the actual ears and the modeled control points. To address this problem, this study proposes a monocular-vision-based method for binaural three-dimensional localization and dynamic tracking in cabin scenes. A unified mapping among the pixel coordinate system, camera coordinate system, and cabin coordinate system is first established through camera calibration and geometric modeling. Facial landmark detection is then used to infer the two-dimensional locations of the left and right ears, after which cabin-feature-constrained monocular depth estimation is introduced to recover ear-region depth in a spatially aligned manner. The binaural three-dimensional coordinates are further refined through head-pose compensation, temporal filtering, and short-term prediction, so that the final output can be directly used as a control-oriented ear-state sequence. Multi-condition experiments under different illumination, occlusion, and head-pose variations show that the proposed method maintains good localization accuracy and trajectory continuity in all six tested cases. The root-mean-square error of binaural three-dimensional localization ranges from 18.40 to 25.57 mm, while the mean interaural distance error remains within 0.37 to 3.33 mm. Even under adverse conditions such as weak illumination and partial occlusion, the processed ear-state output remains continuously available to the downstream active noise control interface. These results indicate that the proposed method provides a low-cost and practically deployable solution for monocular binaural ear tracking in vehicle cabins and can serve as an effective perception front-end for ear-side active noise control.
Copyright (c) 2026 Cong Zhang https://creativecommons.org/licenses/by/4.0
ENHANCING WIND TURBINE PERFORMANCE AND SUSTAINABILITY THROUGH 3D-PRINTED BLADE OPTIMIZATION: A STUDY ON MATERIAL AND SHAPE EFFICIENCY
The growing demand for sustainable energy solutions has driven innovation in wind turbine design, particularly in optimizing blade performance and environmental impact. We investigate the application of 3D printing technology to fabricate wind turbine blades, focusing on material selection and geometric design to balance efficiency and sustainability. Two widely used thermoplastics, polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS), are evaluated for their environmental footprint using Global Warming Potential (GWP), where PLA exhibits a significantly lower carbon footprint (500g CO₂ eq/kg) compared to ABS (2900g CO₂ eq/kg). The study employs a material extrusion-based 3D printer to produce four blade configurations, combining these materials with distinct shapes, and assesses their power generation efficiency through electrical power, thrust, voltage, and vibration measurements. Our experimental results demonstrate that PLA-based blades not only reduce environmental impact but also achieve competitive performance, suggesting their viability as a sustainable alternative. Furthermore, the flexibility of 3D printing enables rapid prototyping and iterative design improvements, which are critical for advancing wind energy technology. The findings highlight the potential of additive manufacturing to enhance turbine sustainability without compromising functionality, offering a practical pathway for greener energy systems. This work contributes to the broader discourse on renewable energy innovation by bridging material science, manufacturing technology, and environmental considerations.
Copyright (c) 2026 Chun-Kit He; Dawson Wai-Shun Suen; Chi-Ying Vanessa Li; Juan Zhang; Xuejuan Cui; Wei Liu; Chi-Wing Tsang; Xiao-Ying Lu https://creativecommons.org/licenses/by/4.0