Academic Journal of Emerging Technologies
Academic Journal of Emerging Technologies ISSN Print: 3104-4417 ISSN Online: 3104-4425 CD ISSN: 3104-4433
Uncovering Influencing Mechanisms and Practical Strategies for Teaching Effectiveness of College Mathematics General Education Courses: Evidence from SEM-fsQCA Hybrid Analysis
As a core foundational course in higher education, college mathematics general education courses play a crucial role in fostering students' logical thinking and supporting their subsequent professional learning. However, current teaching practices face challenges such as insufficient student engagement and significant disparities in learning outcomes. Traditional evaluation methods struggle to reveal implicit correlations among multiple variables, making it difficult to provide targeted optimization strategies. This study adopted a mixed‑method approach combining Structural Equation Modeling (SEM) and fuzzy‑set Qualitative Comparative Analysis (fsQCA) to evaluate and optimize the teaching effectiveness of college mathematics general education courses at University A. A total of 572 valid samples were collected from freshmen of Grade 2025 using a structured questionnaire. SEM results indicated that teaching methods had the strongest direct effect on learning outcomes (standardized coefficient = 0.362, p < 0.001). Meanwhile, teachers’ literacy and teaching resources exerted indirect effects on learning outcomes through teaching methods, with mediating effect ratios of 81.8% and 55.7%, respectively. For three groups of majors with different mathematical needs (high, moderate, and low), fsQCA identified three distinct configurational paths leading to high learning outcomes: (1) For majors with high mathematical needs, the optimal path was “teaching resources + high school mathematics foundation + teaching methods”; (2) For majors with moderate mathematical needs, the effective path was “teaching content + teaching methods + teachers’ literacy”; (3) For majors with low mathematical needs, the key path was “teachers’ literacy + teaching methods”. This study addresses the limitations of single quantitative or qualitative research by integrating SEM and fsQCA. The proposed optimization strategies, including hierarchical teacher training, diversified collaborative teaching teams, and targeted student support, provide practical references for improving the teaching quality of college mathematics general education courses and realizing personalized teaching in similar universities.
College Mathematics General Education Courses
Fuzzy-Set Qualitative Comparative Analysis (fsQCA)
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
Consistency Margin Adapter for Few-Shot Medical Anomaly Detection
Vision-language models such as CLIP have become a versatile foundation for zero and few-shot medical anomaly detection, yet most adapter designs still rely on shal-low projections between visual tokens and handcrafted textual prompts. We introduce a Consistency Margin Adapter that aligns multi-layer visual representations with normal prompts via learnable directional shifts, semantic margin objectives, layer-wise consistency, and entropy regularization. The adapter produces reliable heatmaps from only k-shot support samples and integrates seamlessly with existing CLIP+CoOp pipelines. Extensive experiments on BUSI, BrainMRI, and CheXpert demonstrate superior image and pixel-level AUROC over prior adapters, particularly in the low-shot regime.
Evaluation and Prediction of Factory Sewage Discharge Based on ARIMA Model — Incorporating Biological Water Purification Factors for Model Optimization
This study focuses on the evaluation and short-term prediction of factory sewage discharge. Based on the detected water quality data, we quantified the temporal characteristics of sewage discharge indicators, defined a comprehensive pollution index, and established a prediction model by integrating methods such as Pearson correlation analysis, standardization, entropy weight method, and ARIMA model. Finally, the model was further optimized by introducing biological factors affecting water purification. For the temporal characteristics analysis (Problem 1), we verified the strong correlation between pollutant concentrations (COD and ammonia nitrogen) and their emissions using Pearson correlation analysis, and revealed intraday (7:00-11:00 and 17:00-20:00 as peak discharge periods) and daily variation trends through line charts, heatmaps, and statistical analysis. For the pollution index definition (Problem 2), pH, COD concentration, and ammonia nitrogen concentration were selected as evaluation indicators; data were standardized with reference to national emission standards, and indicator weights were determined by the entropy weight method to calculate the comprehensive pollution index (values >1 indicate excessive pollution). For the prediction model (Problem 3), the ARIMA model was established after stationarity testing, difference processing, and ACF/PACF analysis, which achieved reliable prediction results for future pollutant indicators and comprehensive pollution index (verified by RMSE and MAPE). The optimized model incorporating biological purification factors (microorganisms and phytoplankton) is more in line with the actual dynamic changes of aquatic ecosystems.
Terahertz on-chip surface plasmon polaritons modulation devices
This Surface plasmon polaritons (SPPs) refer to a special electromagnetic wave mode that exists at the interface between a metal and a dielectric, holding significant application prospects in on-chip devices. However, relevant on-chip modulation devices are quite scarce, especially in the terahertz (THz) band. In this paper, by integrating metasurfaces with photoresist, a terahertz on-chip modulation device is designed, enabling the control of the propagation of surface plasmon polaritons. This terahertz on-chip modulator provides a reference for the design of novel surface plasmon polariton devices and exhibits broad application prospects in future photonic integrated chips.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
A Pipe Sticking Prediction Method Based on Random Forest
This study aims to address the challenge of predicting pipe sticking accidents in petroleum drilling engineering. In response to the high blindness of tradi-tional empirical qualitative operations, the difficulty of mathematical models in accurately reflecting drilling laws, and their poor operability, machine learning methods such as neural networks (NNs), support vector machines (SVM), and random forests are introduced for pipe sticking prediction re-search. Adhering to the principles of sample representativeness, category balance, and diversity, 9 drilling parameters including depth, drilling speed, weight on bit (WOB), torque, friction coefficient, outlet temperature, rota-tional speed, inlet flow rate, and outlet flow rate were selected. Drilling data from 30 wells in the Yan'an area were collected. Measurement data within a certain period before pipe sticking were used as pipe sticking samples, which were cross-arranged with normal samples to construct the training dataset. By comparing the performance of SVM (linear kernel, radial basis kernel) and random forest models, the results show that the random forest performs the best. In its confusion matrix, the number of correctly predicted samples for true labels 0 and 1 is 190 and 207 respectively, with only 9 and 5 misclassifi-cations. The accuracy rate reaches 0.966. In summary, this study confirms that the random forest has significant advantages in balancing "accuracy and efficiency" in pipe sticking prediction tasks, providing a reliable early warning model and technical support for safe drilling operations.
Research on Torque Prediction Method of Drill String Based on TCN-KAN
To accurately predict drill string torque, enhance drilling efficiency, and reduce operational costs, this paper addresses the issues of limited generalization capability and high complexity in torque prediction models during horizontal well drilling by conducting research on intelligent prediction methods based on deep learning. An intelligent torque prediction model based on TCN-KAN is proposed. Multi-source characteristic variables such as drilling depth, rotary speed, and weight on bit are selected for model training, and a TCN comparative model is established for experimental validation. The results show that the TCN-KAN model outperforms the comparative model in evaluation metrics such as root mean square error and mean absolute error, achieving a prediction accuracy of 98.58%. It effectively captures the complex nonlinear relationships between torque and drilling parameters. This model is suitable for drilling torque monitoring, intelligent feed optimization, and drilling decision support, providing reliable technical support for the automation and intelligence of drilling operations.
Intelligent drilling optimization Component
Static and Dynamic Analysis of Injection -Production Tubing Under Alternating Loads
Underground gas storage serves as a critical facility for natural gas peak shaving and strategic reserves. The injection-production string faces safety risks such as buckling, erosion, and vibration under cyclic loads, temperature variations, and complex wellbore conditions. Based on traditional dynamic analysis, this paper focuses on the dynamic effects of alternating loads by establishing a three-dimensional string dynamics model to systematically investigate vibration characteristics, stress responses, and fatigue life. The research findings aim to provide a theoretical basis for the safety evaluation and parameter optimization of gas storage injection-production strings.
Research and Design of HIRFL SSC High Frequency Broadband Matching Network
This paper analyzes the causes of the low beam extraction efficiency of the Separated Sector Cyclotron (SSC) in the Heavy Ion Research Facility in Lanzhou (HIRFL), and explains the main problems that hinder the increase of the Dee voltage of the SSC radio frequency (RF) cavity. A broadband matching network for the SSC RF system is proposed, designed and successfully developed. Through tests and actual beam operation, the design of the broadband matching network is verified, and the beam extraction efficiency of the SSC RF cavity is effectively improved.
PTL-HAR: A Framework for Few Shot Activity Recognition
With the popularity of wearable devices and IoT technology, sensor-based human activity recognition is valuable in fields like smart healthcare. However, traditional deep learning requires large labeled datasets and struggles with new categories, users, or environments where data is scarce. To address this, we propose a Few-Shot Transfer Learning method for Human Activity Recognition (FTL-HAR). It leverages pre-trained models to transfer knowledge, enabling adaptation to new categories with minimal data. Experiments on public datasets (PAMAP2, OPPORTUNITY) under 1-shot and 5-shot settings show that FTL-HAR significantly outperforms traditional methods by effectively utilizing pre-trained features for rapid fine-tuning.
Research on water injection development mode and technical countermeasures of bottom water reservoir
Bottom water reservoir is an important reservoir type in China, and its efficient development is of great significance for increasing reserves and production. In view of the problems of rapid energy attenuation, water flooding caused by bottom water coning and low recovery rate in the development of such reservoirs, this paper summarizes the principles and applications of conventional water injection and formation gravity water injection. On this basis, the technical countermeasures with the core of optimizing water injection parameters, reasonably selecting water injection mode and real-time dynamic adjustment are put forward to supplement formation energy, inhibit water coning and improve oil displacement efficiency. The case analysis shows that the implementation of targeted water injection technology can effectively enhance oil recovery and prolong the economic life of the oilfield. This paper summarizes the relevant research results, and points out that it is necessary to continue to deepen the innovation and research of water injection development technology in the future, so as to cope with the increasingly complex challenges in the middle and late stages of development and provide support for the efficient development of bottom water reservoirs.
Research on the Measurement of Intermediate Mass Fragments Based on a Silicon Telescope System
Intermediate Mass Fragments (IMFs) produced in heavy-ion collisions at Fermi energies are crucial observables for investigating reaction mechanisms, nuclear matter properties, and multifragmentation dynamics. Their production is closely related to the nuclear equation of state, liquid-gas phase transition, and the compression-epansion process of excited nuclear matter. Reliable measurements of IMF charge, mass, energy, and angular distributions are essential for understanding the thermodynamic evolution and dynamical behavior of nuclear systems under intermediate-energy collisions. In this study, a multi-layer silicon telescope system composed of four silicon detectors and a CsI(Tl) scintillator was employed to measure IMFs emitted from the Sn reaction. Positioned at relative to the beam direction, the telescope covers two angular ranges and provides adequate solid-angle coverage for fragments detection. Clear ΔE-E correlation spectra were obtained, enabling isotopic identification from He (Z = 2) up to Ca (Z = 20). Up to 6-8 isotopic lines were resolved for each element, demonstrating excellent particle identification and energy resolution. After detector calibration and linearization, energy spectra and fragment yields were extracted. The experimental data exhibit characteristic multifragmentation features commonly observed in the Fermi-energy domain. The obtained IMF distributions provide meaningful constraints for nuclear reaction models and transport simulations. This work demonstrates the capability of the Si-CsI telescope system for IMF measurements and provides a robust experimental foundation for future studies of reaction mechanisms, thermal properties of nuclear matter, and isotopic scaling behaviors in intermediate-energy heavy-ion collisions.
Research on Multi-source Video Streaming Vehicle Object Detection and Parallel Statistics Based on YOLO-OBB
Based on the YOLO-OBB rotating object detection model, a statistical analysis system for multi-intersection vehicles is proposed. The system realizes the accurate analysis of vehicles running in complex intersection scenarios by analyzing the characteristics of the vehicle's driving trajectory and the characteristic change law of the steering angle in the intersection area, and integrating the rotating bounding box detection technology and the innovative dual-threshold steering recognition algorithm. The system can not only accurately identify the entrance direction of the vehicle, but also judge the steering behavior and complete the model classification in real time. To improve efficiency, the system adopts a multi-threaded parallel architecture that can analyze 9 high-definition video streams simultaneously, and establishes an intuitive visual interface through the PySide6 framework. The experimental results show that on the 1920×1080 resolution video stream, the vehicle recognition accuracy of the four intersections is 92.2%, and the accuracy of vehicle and steering judgment is also more than 80%, which can meet the demand for real-time and accurate vehicle flow data in actual scenarios, and provide data support for decisions such as signal light optimization and road planning.
statistical analysis of multi-intersection vehicles
dual-threshold steering recognition algorithm
Research Progress on Phalaenopsis Diseases and strategy of Green Control
Phalaenopsis spp., known for its gorgeous morphology, bright and showy colors, and long-lasting blooms, is often referred to as the “Queen of Orchids” and owns high ornamental and economic value. However, some diseases in the Phalaenopsis orchids severely affect the flower quality, plant growth, ornamental and economic value. This paper reviews the current research progress of common Phalaenopsis diseases and subsequent chemical and biological control methods, which provides comprehensive understand of disease prevention and control strategies in Phalaenopsis orchids.
Deep Learning-Based Multi-Modal Sensor Fusion for Real-Time Environmental Monitoring and Intelligent Decision Making
Environmental monitoring systems require sophisticated integration of heterogeneous sensor data to provide accurate real-time assessment and intelligent decision support. This paper presents a novel deep learning framework that combines multi-modal sensor fusion with advanced image processing techniques for comprehensive environmental monitoring. Our approach leverages a hybrid neural architecture incorporating Transformer-based attention mechanisms and Graph Convolutional Networks (GCN) to process data from various sensors including LiDAR, hyperspectral cameras, air quality sensors, and meteorological stations. The proposed Multi-Modal Environmental Fusion Network (MEFN) achieves superior performance with 96.7% accuracy in environmental anomaly detection and 89.3% precision in predictive analytics compared to existing methods. Experimental validation across three different environmental settings demonstrates the system's robustness and scalability for large-scale deployment with a total project budget of $40,000.
Retraction: A Solar Collector-Based Atmospheric Water Harvesting Device Integrated with Silica Gel Adsorbents
At request of the authors, this article has been retracted by Erytis Publishing Limited in light of clear evidence that the results and conclusions are no longer valid. We thank the authors for notifying us so that the publication record can be amended accordingly. Retraction published: November 08, 2025.
Optimization of Microhole Morphology in Electrolytic Abrasive Jet Machining
To address issues such as thick recast layers, high pore wall roughness, and dif-ficult-to-control geometric accuracy during micro-hole machining of nick-el-based high-temperature alloy 718, a laser-electrochemical hybrid processing method assisted by abrasive jet is proposed. By establishing a synergistic “electrolyte abrasive jet-nanosecond laser” processing system, combined with multiphysics coupling simulation and experimentation, this study system-atically investigates the influence mechanisms of parameters such as jet veloci-ty, jet angle, abrasive particle size, and voltage on microhole roundness, taper, wall roughness, surface morphology, and recast layer formation. Results indi-cate that moderate jet and electrical parameters significantly enhance orifice morphology and wall quality while effectively suppressing microcracks and re-cast layers. Orthogonal experiments optimized the following optimal process parameters: jet velocity 8 m/s, jet angle 60°, abrasive particle size 15 μm, voltage 15 V, and current 3 A. Under these parameters, the micro-hole round-ness, surface morphology, and taper achieved optimal values, while hole wall roughness was significantly reduced and surface quality markedly improved.
The Synergistic Effect of Waterjet Assistance and a Novel Line-Delay Strategy on Precision Laser Drilling of Inconel 718
This study investigates the optimization of process parameters for waterjet-assisted nanosecond laser drilling of Inconel 718, with a focus on improving microhole quality through pulse delay strategies and defocus control. Experi-ments were conducted using concentric circular scanning under both underwa-ter and in-air conditions, with emphasis on the effects of delay methods—specifically face-delay and line-delay—on geometric accuracy and morphologi-cal integrity. Results indicate that the line-delay approach significantly reduces entrance diameter, taper, microcracks, and oxidation compared to non-delayed and face-delayed conditions. The ablation threshold increased from 12.58 J/cm² in air to 22.12 J/cm² underwater due to energy attenuation effects. Optimal machining quality was achieved with a defocus distance of –0.5 mm, corre-sponding to focal positioning near the workpiece center. The study demon-strates that waterjet-assisted laser drilling with adaptive pulse delay effectively enhances machining precision and surface quality, showing great potential for applications in high-temperature component manufacturing.
Experimental Study on Laser Pulse Delay Machining of IN718 Assisted by Chemical Liquid
IN718, a typical nickel-based superalloy, is widely used in critical components such as aero-engine turbine blades and combustion chambers due to its excel-lent high-temperature strength, creep resistance, and corrosion resistance. However, the material's high-temperature strength deficiency, low thermal conductivity, and poor plasticity lead to low efficiency in conventional ma-chining processes and severe tool wear. Laser machining, with its advantages of high energy density, non-contact processing, and flexibility, is regarded as an effective method for machining micro-holes in superalloys. Nevertheless, laser micro-hole machining in ambient air still faces challenges such as oversized hole diameters, rough hole walls, wide heat-affected zones, and thick oxide layers. To address these issues, this study proposes the use of a chemical-assisted machin-ing environment combined with a pulse delay technique, aiming to thoroughly investigate its mechanism and effectiveness in the micro-hole machining of IN718 alloy.
Comparison and Study of Various Degreasing Test Methods for Monel Alloy
The primary purpose of alloy degreasing is to eliminate oil stains and impurities, enhance alloy purity, and ensure processing quality and alloy performance. This article mainly compares various degreasing processes for Monel alloy pipelines. By comparing the reaction results of degreasing fluorescent agents, the most effective degreasing process is selected to improve the coating quality of the alloy and provide a quality foundation for subsequent alloy applications.
Reservoir Characteristics of the Chang 63 Sub-member in the Daluogou Area
The Chang 6 reservoir is characterized by "five lows and one strong": low porosity, low permeability, low temperature, low pressure, low oil saturation, and strong heterogeneity. With the continuous and in-depth development of the tight oil reservoir in the Chang 6 formation of the Dagugou area, the natural energy depletion development method has led to severe formation energy deficit, rapid production decline, and difficulties in maintaining stable production, which have become "bottlenecks" restricting the sustainable development of this area. Deepening the understanding of the Chang 6 formation in the Dagugou area through reservoir characteristic research is an effective approach to break through the development bottlenecks and achieve increased and stable production. This study utilizes scanning electron microscopy, cast thin sections, high-pressure mercury intrusion, physical property analysis, oil-water two-phase flow experiments (unsteady-state), and nuclear magnetic resonance movable fluid experiments to investigate diagenesis, reservoir physical properties, and pore structure. It analyzes the microscopic pore structure characteristics of different parts of the sand body and different sand belts, examines the controlling factors influencing reservoir physical properties, conducts a comprehensive reservoir evaluation, and maps the planar distribution of reservoir physical properties to determine the distribution patterns of high-quality reservoirs. This provides theoretical guidance for the subsequent development of the tight oil reservoir in the Chang 6 formation of the Dagugou area.
Research on Route Optimization of Fresh Food Distribution to Residential Areas in Emergency Scenarios
When cities are hit by sudden public safety incidents such as natural disasters, toxic gas leaks, extreme air pollution and epidemics, the daily supply of fresh food for residents cannot be guaranteed. This paper constructs a multi-center demand-splittable model for residents' material distribution in emergency scenarios, aiming to solve the problem of distributing materials from urban emergency warehouses to residential areas in controlled zones. The comprehensive objectives of this model are to minimize the total distribution time and make full use of the service capabilities of each distribution center. Through case analysis, an improved genetic algorithm is used to solve the proposed model. The results show that the distribution scheme can comprehensively utilize the service capabilities of various distribution centers, ensure the efficient delivery of fresh food, and thus has practical significance.
Causal Meta-Learning for Few-Shot Threat Detection in NIDS
In the realm of Network Intrusion Detection Systems (NIDS), detecting novel threats like zero-day attacks or variants poses significant challenges due to the scarcity of labeled samples and the difficulty in rapid model adaptation. This paper introduces a causal meta-learning framework based on Model-Agnostic Meta-Learning (MAML) to enable few-shot threat detection. The core innovation lies in incorporating causal invariance constraints into both the inner and outer loops of MAML, allowing the model to learn how to adapt quickly using causally invariant features that remain stable across varying threat distributions. By leveraging counterfactual reasoning and invariant risk minimization, the model disentangles causal factors from spurious correlations, enhancing robustness and generalization. Experimental evaluations on benchmark datasets, such as NSL-KDD and CIC-IDS2017, demonstrate superior performance in few-shot scenarios, achieving up to 10% higher detection accuracy compared to standard meta-learning baselines. This approach holds promise for real-time NIDS in dynamic environments, paving the way for more resilient cybersecurity defenses.
Seed germination characteristics of Phoebe sheareri (Hemsl.) Gamble
The germination characteristics of fresh seeds of Phoebe sheareri (Hemsl.) Gamble was investigated under three different treatments (gibberellin treatment, stratification treatment, seed removal treat-ment). Observations: after soaking seeds with different concentrations of gibberellin solution (0mg/L, 500mg/L, 1000mg/L, 1500mg/L) for 24h, the seed germination experiment showed that 1500mg/L gib-berellin treatment resulted in the best germination for P. sheareri (Hemsl.) Gamble seeds, with germi-nation percentage and germination potential (average of three replicates) reaching 82% and 71% re-spectively, among the highest across all treatments. Among the three stratification treatments (room temperature stratification, 4℃low temperature stratification, and 25℃to 5℃temperature stratifica-tion), the 25℃to 5℃ temperature stratification treatment significantly promoted P. sheareri (Hemsl.) Gamble seed germination, with a seed germination percentage reaching 34% within half a month, far higher than the germination percentages of other stratification treatments. The seeds of P. sheareri (Hemsl.) Gamble without seed shells did not germinate on the 20th day of the germination experiment, and the experiment also indicated that the water The permeability of the seeds was better, so the seed shell had no effect on the germination of the seeds.
Superhydrophobic HWCNTs/EP composite coating with anti-icing and photothermal deicing properties
Photothermal superhydrophobic surfaces have garnered significant attention for their exceptional anti-icing and photothermal deicing capabilities. However, the shortcomings of stability and fabrication complexity limit their practical application. This work designed and fabricated a HWCNTs/EP composite coating that combines anti-icing/deicing performance, and facile preparation. During fabrication, the HWCNTs and EP ratio was systematically optimized by evaluating wettability, photothermal conversion capability, and anti-icing/deicing performance. The composite coating in the ratio of HWCNTs/EP 1:1 exhibited a water CA of 156.7°. Photothermal testing revealed a maximum temperature increase of 69 ℃ (reached in 200 s) under one sun intensity. The composite coating demonstrated a prolonged droplet freezing time of 1026 s at −15 ℃. Photothermal deicing testing achieved complete ice melting and detachment within 44 s. This work provides novel insights for designing stable photothermal superhydrophobic coatings with anti-icing/deicing functionality, contributing to the development of energy-efficient ice mitigation technologies.
Research on Mineral Inversion Method Based on Elemental Logging
The elemental data and mineral data obtained from elemental logging and X-ray diffraction were used to invert the mineral content through five methods. The oxide closure model method is based on the theoretical stratigraphic model, and the core condition of setting the sum of the mass percentages of clay minerals, carbonates, and the quartz-feldspar-mica mixture (QFM) to 100% is used for modeling and solving. The non-negative least squares method, truncated singu-lar value decomposition method, and linear programming method are based on the conversion relationship model between elements and minerals proposed by Herron. Through the analysis of the lithological characteristics of the block, the element content inversion mineral content model is established based on the matrix solution method. The XGBoost machine learning algorithm automatically learns and captures the complex relationships between samples from the data samples to solve the problem. Taking well x as an example, the mineral content was calculated through the five inversion methods. The comparison of the cal-culation results with the whole rock analysis results shows that the variation trends of each method with the depth of the stratum are basically consistent with the whole rock analysis results. Among the four mineral inversion meth-ods, the processing result of the XGBoost machine learning algorithm is the best, with a mean square error of 0.91; the processing effects of the non-negative least squares method and the truncated singular value decomposition method are second, with mean square errors of 0.88 and 0.81, respectively; the mean square error of the linear programming method is relatively low, only 0.79.
Preliminary study of leaf structure and histochemistry of Cynodon dactylon
Cynodon dactylon (L.) Pers. has strong adaptability to drought and other stresses. In order to clarify the mechanism of its adaptation to adversity ecolo-gy, this study started with morphology and microstructure, and studied the bo-tanical morphological characteristics, anatomical structure and histochemistry in two different habitats by setting two different habitat conditions of flooding and non-flooding.
Studies on cuttings propagation of Ilex cassine ‘angustifolia’
This study explored the effects of different rooting hormones on cutting rooting and physiological indices of Ilex cassine‘angustifolia’. Cuttings were treated with KIBA, IBA, NAA, IAA (1000 mg/L each) and CK (water control) via 20-second quick-dip. Results showed KIBA 1000 mg/L as optimal, with 67.6% survival rate, 56.25% rooting rate, and significantly higher root activity, soluble protein, and soluble sugar content. IBA 1000 mg/L followed (54.4% survival rate), while CK and IAA had low rooting rates (12.50% and non-significant, respective-ly). Conclusion: KIBA 1000 mg/L is optimal for promoting rooting and growth of Ilex cassine‘angustifolia’ cuttings.
Target Detection Algorithm for UAV Aerial Images Based on IRE-YOLO
To address challenges in UAV aerial images such as a high proportion of small objects, complex backgrounds, significant scale variations, and limited computing resources, this study proposes an improved object detection algorithm based on YOLOv11n—namely, IRE-YOLO. In the backbone network, the algorithm introduces the inverted residual mobile block (iRMB) attention mechanism to enhance the model's ability to extract features of small objects in complex backgrounds. It replaces some standard convolutions with depthwise separable convolutions (DSConv) to reduce the number of parameters while maintaining feature expression capability. In the neck network, an efficient upsampling block (EUCB) is designed to recover detailed information through hierarchical feature reconstruction, thereby improving the representation quality of small objects' edges and textures. Experimental results on the DOTA dataset show that IRE-YOLO outperforms mainstream lightweight models in key metrics including precision (P), recall (R), mAP@50, and mAP@50:95, achieving 69.4%, 59.0%, 61.0%, and 34.2% respectively. Meanwhile, the model's parameter count is reduced to 8.7MB, realizing an effective balance between accuracy and lightweight design. The research demonstrates that IRE-YOLO has high practical value in small object detection tasks for UAV aerial images and provides a feasible solution for real-time deployment on embedded platforms.
Research Progress on Ultra-High-Speed Laser Cladding Technology and Its Coating Wear Resistance
Ultra-high-speed laser cladding is an advanced surface coating method, which is widely used due to its characteristics of fast processing speed, small heat-affected zone, dense formed coating, and adaptability to multiple materials. This paper first briefly introduces the characteristics and advantages of ultra-high-speed laser cladding compared to traditional laser cladding technology, analyzes the processing principles of ultra-high-speed laser cladding technology, and combines the latest research to analyze the unique characteristics of ultra-high-speed laser cladding technology. Furthermore, the wear resistance of the coating produced by ultra-high-speed laser cladding technology is analyzed, and finally, based on the current research status at home and abroad, the shortcomings of ultra-high-speed laser cladding technology are discussed, and the development trend of ultra-high-speed laser cladding technology is proposed.
Design of a tandem ankle rehabilitation robot
Impaired ankle function resulting from various diseases and sports-related inju-ries is a prevalent clinical concern, significantly affecting patients’ mobility and quality of life. In response to the growing demand for effective rehabilita-tion solutions, this paper presents a novel serial-structured ankle rehabilitation robot (ARR) based on emerging robotic technologies and existing rehabilitation products. Taking into account the anatomical movement patterns and physio-logical range of motion of the ankle joint (AJ), a three-dimensional mechanical model was carefully designed to closely mimic natural joint kinematics. A func-tional prototype was developed, and experimental tests were conducted to evaluate key performance metrics, including speed response and positioning accuracy. The results demonstrated that the robot exhibits stable and responsive motion control with high precision. Furthermore, the Anybody simulation soft-ware was employed to assess the activation levels of major muscles involved in ankle movement during robotic-assisted training. The data revealed significant engagement of the targeted musculature, indicating effective muscle stimula-tion. Overall, the proposed ARR shows great potential to fulfill the essential re-quirements of ankle joint rehabilitation training.