Academic Journal of Applied Sciences (AJAS) is an international, peer-reviewed journal. The mission of the journal is to provide an academic platform for scholars and practitioners to discuss the issues of applied sciences across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Real-time Monitoring of Coaxial Fluid Reaction Rates and Analysis of Influencing Factors
To enable precise control of the cross-linking process in coaxial reactive flows, this study presents an in-situ method for online measurement of the reaction rate and systematically elucidates the governing factors that influence it. The Ca²⁺ -sensitive chromogenic property of murexide is utilized to create a clear color contrast between the sol and gel layers. After elution to reveal the actual boundary of the cross-linked layer, the reaction front is superimposed on the captured flow images, and its grey-scale intensity is extracted pixel-wise. This protocol allows in-line acquisition of the cross-linked thickness at any axial position and, consequently, the calculation of the mean reaction rate. Results demonstrate that the thickness of the cross-linked layer obtained by the proposed on-line protocol deviates by only 1.09 %, fully satisfying engineering accuracy requirements. Both reactant concentrations exert a positive, co-directional influence on layer thickness and mean reaction rate, with calcium chloride concentration offering superior regulation efficacy. When the total flow rate of the coaxial reactive stream is increased while maintaining a constant flow-rate ratio, the cross-linked thickness decreases whereas the mean reaction rate rises. Conversely, extending the co-flow distance enlarges the layer thickness yet reduces the mean reaction rate.
Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
Linking Iron Supply, Microbial Mineralization and Environmental Control in the Formation of Ferruginous Stromatolites
Ferruginous stromatolites are microbially laminated sedimentary structures in which iron enrichment is integrated into stromatolitic growth, stabilization and preservation. They are important archives for reconstructing early iron cycling, shallow-water redox structure and the ecological role of microbial communities in Precambrian oceans. This review synthesizes recent English-language literature on the formation mechanisms of ferruginous stromatolites from a source-to-sink perspective. Rather than using any site-specific quantitative dataset, the review focuses on four linked dimensions: conceptual definition and diagnostic criteria, iron source pathways, microbial mediation of iron mineralization, and environmental controls on growth and preservation. Current evidence indicates that no single process can explain all ferruginous stromatolites. Continental weathering, groundwater seepage, hydrothermal supply and volcaniclastic input may all contribute iron, but their relative roles are filtered by basin restriction, upwelling, water-column redox stratification and sea-level change. Microorganisms mediate enrichment through oxygenic oxidation, anoxygenic photoferrotrophy, microaerophilic Fe(II) oxidation, and extracellular polymeric substance adsorption that traps or templates poorly crystalline Fe(III) phases within laminae. Subsequent early diagenesis converts unstable precursors into hematite, magnetite, Fe-silicates or siderite, thereby controlling what is ultimately preserved in the rock record. The review emphasizes that ferruginous stromatolites should be interpreted as coupled products of iron supply, microbial processing and environmental focusing. A multi-proxy framework combining textures, mineralogy, isotopes and sedimentary context is therefore essential for distinguishing source models and for evaluating the significance of ferruginous stromatolites in Earth's oxygenation history.
Calcium Alginate Hydrogel Filtration Membrane Reinforced by Vacuum Drying and Its Separation Performance for Dye
Calcium alginate (CaAlg) hydrogel membranes have demonstrated great application potential in the separation field owing to their excellent antifouling properties, yet their inherent insufficient mechanical strength has severely restricted their large-scale applications. To address this issue, in this study, CaAlg hydrogel membranes were prepared via the ion crosslinking method, and a vacuum drying-rehydration process was adopted for post-treatment, achieving the goal of reinforcing the membrane's mechanical properties while preserving its separation efficiency. Low-field nuclear magnetic resonance (LF-NMR) analysis revealed that free water and bound water inside the membrane were gradually lost during the drying process, and this change was directly correlated with the reduction of membrane thickness and the improvement of tensile strength. Performance test results showed that the optimized membrane exhibited stable rejection performance against various dye pollutants under low-salt conditions, with a rejection rate higher than 95% and a water permeation flux ranging from 8.5~17.5 L·m⁻²·h⁻¹. The flux was jointly affected by the operating pressure and the molecular weight of the dye. This study revealed the balancing mechanism between mechanical reinforcement and separation performance of CaAlg hydrogel membranes, providing new insights and technical support for the development of sustainable membrane materials in the fields of wastewater treatment and carbon capture.
Mineralogical Characteristics and Metallogenic Significance of Ore-Bearing Quartz Veins in Baimiaogou Molybdenum Deposit, East Qinling Molybdenum Metallogenic Belt
The mineralogical analysis of ore-bearing quartz veins in molybdenum deposits is the basis for studying the formation mechanism of molybdenum deposits. This study takes the ore-bearing quartz veins of Baimiaogou molybdenum deposit in the world 's largest molybdenum metallogenic belt-East Qinling molybdenum metallogenic belt as the research object. On the basis of field geological survey and petrographic observation, combined with laser Raman spectroscopy in situ test, the mineral composition and fabric of ore-bearing quartz veins are analyzed, and their significance for mineralization is discussed. The results show that the ore-bearing quartz veinlets mainly occur in altered andesite. The main mineral composition is quartz, grade I gray to grade I yellow, with a content of about 90% and a particle size of 0.5mm ~ 60mm. There are residual potassium feldspar cleavage and scattered argillization materials inside, and a small amount of potassium feldspar with strong argillization between quartz minerals. It shows that quartz in quartz veins is secondary quartz, which is the product of strong silicification of felsic veins, and with the enhancement of silicification, the particle size of quartz minerals gradually increases. The ore-bearing quartz veinlet contains about 5% potassium feldspar, grade I ash, and remains between quartz particles with different degrees of silicification. In the same ore-bearing quartz vein composed of secondary quartzite, with the evolution from early to late silicification, sulfide mineralization has the characteristics of transforming from pyrite and chalcopyrite to molybdenite. Molybdenite is mainly euhedral-subhedral leaf-shaped, columnar, and agglomerated with good crystallization, and a small amount is semi-euhedral-allotriomorphic grain-like structure. It is mostly associated with pyrite and chalcopyrite. It is mainly developed in potassium feldspar, which is strongly residual after strong silicification, epidotization and chloritization in ore-bearing quartz veins, and is distributed in the contact area of early and late silica fossils.
east Qinling molybdenum metallogenic belt
A Multivariate Statistical Modeling Approach for Non-Invasive Prenatal Testing
The accuracy of non-invasive prenatal testing (NIPT) is significantly influenced by the fetal fraction and maternal physiological indicators. This study aims to enhance the precision of fetal health assessment by constructing multivariate statistical and machine learning models to optimize clinical decision-making. First, to analyze the correlation between maternal factors and Y-chromosome concentration, One-way ANOVA and Ordinary Least Squares (OLS) regression were employed; results indicated that gestational age and BMI are key predictors, with Random Forest Partial Dependence Plots (PDP) utilized to interpret non-linear relationships. Second, to address BMI-based stratification and timing, the Jenks Natural Breaks method was applied to divide BMI into eight groups, identifying an optimal testing window of 11–13 weeks, validated through Monte Carlo simulations to minimize detection risks. Third, a multi-factor optimization was achieved using an improved K-means clustering algorithm integrating height, weight, and age, which identified five refined BMI categories with an optimal testing point around 14 weeks. This model quantifies the contribution of technical errors through a weighted risk assessment. In conclusion, this integrated framework shifts NIPT from single-factor analysis to multi-dimensional optimization. Future work will incorporate multi-center data and Long Short-Term Memory (LSTM) networks to predict dynamic trends in fetal DNA concentrations.
U-Net-Based Semantic Segmentation and Porosity Analysis of C/GFRP CT Images
Accurate identification of internal voids and clarification of their evolution remain challenging in carbon/glass fiber hybrid composites (C/GFRP). In this study, interlaminar hybrid C/GFRP laminates were investigated using in situ tensile X-ray computed tomography (CT) to perform automatic void segmentation and quantitative analysis. CT images acquired at different loading stages were first preprocessed, and Gaussian, median, and non-local means (NLM) filters were compared, with NLM selected for denoising. Based on a finely annotated manual dataset, a U-Net semantic segmentation model was then established to automatically identify void regions and material-layer structures. On this basis, slice-wise layer-porosity variations along different section directions were analyzed, and the evolution of voids and cracks during loading was investigated in conjunction with three-dimensional reconstruction. The results show that the proposed segmentation workflow can stably identify voids and layer structures in C/GFRP CT images. Dice coefficients of 0.976 and 0.989 were obtained on the validation set for CFRP void segmentation and GFRP layer segmentation, respectively, indicating high accuracy and good generalization. The slice-wise layer porosity exhibits pronounced spatial non-uniformity in different section directions, and high-porosity regions persist during loading and correspond spatially to the final fracture location. Three-dimensional evolution results further reveal a damage process transitioning from dispersed voids to local concentration and then to crack coalescence and propagation. Damage in the GFRP layers propagates significantly faster than in the CFRP layers, indicating that the GFRP layers are the weak region governing progressive failure of the hybrid laminate. These findings provide methodological support for intelligent identification of internal defects, characterization of damage evolution, and correlation analysis of mechanical behavior in C/GFRP composites.
Research on the Optimization Scheduling of Integrated Energy System Considering Heat Recovery from Data Center
With the explosive growth of data centers, a large amount of waste heat resources generated by their operation need to be efficiently utilized. In order to improve the energy utilization efficiency of the integrated energy system and reduce the operating cost, an optimal scheduling model of the integrated energy system considering the waste heat recovery of the data center is proposed. Firstly, a data center integrated energy system framework including wind power, photovoltaic, gas turbine, electric boiler, heat pump and absorption chiller is constructed, and a data center waste heat recovery model is established. The heat dissipation is upgraded by heat pump and connected to the system heating network and absorption chiller. Secondly, with the goal of minimizing the sum of total operating costs, considering the power balance of electricity, heat and cold and the constraints of equipment output, an economic dispatch model is formed. Simulation verification is performed by setting a comparison scenario with or without waste heat recovery. The results show that after the waste heat recovery is enabled, the total cost of the system is reduced by 7.95%, and the cost of gas purchase is reduced by 16.86 %. It effectively replaces the gas heating and drives the absorption refrigeration, and realizes the cascade utilization of thermal energy and the consumption of renewable energy.
An Improved Mean Teacher Framework for Robust Domain Adaptive Foggy Object Detection
To further address the limitations prevalent in existing domain adaptation methods for foggy object detection — namely, insufficient robustness against noise and interference, slow convergence during training, susceptibility to gradient oscillations and fluctuations, training instability, and inadequate extraction and utilization of small-object features — we propose a novel algorithm termed An Improved Mean Teacher Framework for Robust Domain Adaptive Foggy Object Detection (IMT-Det). First, the Mean Teacher paradigm is incorporated to alleviate the convergence difficulties inherent in adversarial training for domain-adaptive foggy object detection. The framework comprises a teacher model and a student model. During training, a dynamic smoothing coefficient is devised, through which the teacher model's parameters are updated via Exponential Moving Average (EMA) of the student model's parameters. This smooth training strategy mitigates convergence instability during adversarial optimization and, compared to a single-model baseline, renders the EMA-updated teacher model considerably less sensitive to noise and perturbations, thereby avoiding the oscillatory behavior that may arise from directly optimizing adversarial objectives. Subsequently, a weighted teacher loss mechanism is introduced, which dynamically adjusts loss weights to achieve fine-grained refinement of the training process and to resolve the problem of excessive regression errors under foggy scene conditions. Finally, a FEUCB upsampling module is designed to address the challenges posed by low contrast in foggy images and significant variations in object scale, thereby enhancing detection capability in foggy scenarios.
Latent Cognitive Reinforcement for Anti-Backdoor Strategy
With the widespread deployment of deep neural networks in sensitive applications, backdoor attacks have become a serious security concern. Such attacks enable adversaries to manipulate the prediction results without affecting the normal classification of clean samples by embedding hidden triggers in the model. Aiming at the limitations of existing defense methods in terms of adaptive attacks and generalization ability, this paper proposes the Latent Cognitive Reinforcement (LCR) backdoor defense strategy. The method utilizes a multi-layer feature representation of the intermediate and output layers to extract the cognitive patterns of the model, thereby identifying and eliminating potential backdoor trigger signals. Compared with traditional methods such as anomaly detection, neuron pruning, or adversarial training, LCR is more generalized and robust. Experiments on two datasets, CIFAR-10 and GTSRB, and five mainstream model architectures prove that LCR achieves an average of 91.80% in detection performance (AUROC), which significantly outperforms the existing state-of-the-art defense techniques and demonstrates good cross-model and cross-attack scenario adaptability.
Green Power and Chemical Foundations: Evolution and Bottlenecks of Core Materials for Solar Electric Vehicles
Solar electric vehicles (SEVs) depend on material breakthroughs in three core components: photovoltaic conversion, energy storage batteries, and lightweight bodies. This paper systematically analyzes the material evolution and key bottlenecks of these three technical routes. In photovoltaics, perovskite single-junction cells have exceeded 26% in efficiency, whereas large-area fabrication and long-term stability remain major barriers to industrialization. For power batteries, solid-state electrolytes are promising for improving the safety of liquid systems, yet solid-solid interfacial impedance and process compatibility are still unresolved. In lightweighting, carbon fiber composites significantly reduce weight, whereas thermosetting matrices are difficult to recycle and thermoplastic matrices often exhibit weak interfacial bonding. These bottlenecks differ in nature: some arise from intrinsic material properties, others from process scaling. Therefore, differentiated R&D strategies must be formulated accordingly.
Miniaturization Improvement and Application Research of Organic Synthesis Experiments
The miniaturization of organic synthesis experiment is very beneficial for education and research laboratory. The effect on 4 main reactions has been studied here, those are esterification, aldol condensation, nucleophilic substitution, and oxidation. With the reduction of reagents from 10 - 50 mL(conventional)to 0.1 - 2.0mL(miniaturized),four comparison experiment were done on 120 students in 2 semester by miniaturized method. I gathered quantitative info on reaction yields, time efficient, reagents and students: The smaller protocol was 78.4%, the normal sized protocol was at an average of 74.6%. And it consumed 91.3% less solvent, and the cost per sample is down 68.5%. Students were more engaged and had better operations. From the results we can see that Organic synthesis miniaturization is green chemistry and undergrad lab improvement.
Corrosion Inhibition Performance of Syringaldehyde–1,4-Phenylenediamine Schiff Base on Mild Steel in Different Acidic Media
To investigate the corrosion inhibition performance of syringaldehyde–1,4-phenylenediamine Schiff base on mild steel in different acidic media, syringaldehyde and 1,4-phenylenediamine were used as raw materials to prepare syringaldehyde–1,4-phenylenediamine Schiff base (SPSB), and its structure was characterized by nuclear magnetic resonance. Electrochemical methods (electrochemical impedance spectroscopy and potentiodynamic polarization curves) and the static weight loss method were employed to study the corrosion inhibition behavior of this Schiff base on mild steel in four acidic media (4.44 M HCl, 2.22 M H₂SO₄, 1 M HCl, 0.5 M H₂SO₄) at 25 °C. The results show that, under the same hydrogen ion concentration, the inhibition efficiency of SPSB for mild steel in 4.44 M HCl is higher than that in 2.22 M H₂SO₄, and the inhibition efficiency in 1 M HCl is higher than that in 0.5 M H₂SO₄; within the same acid medium, the inhibition efficiency in high-concentration acid systems is significantly higher than that in low-concentration systems, i.e., the inhibition efficiency in 4.44 M HCl is higher than that in 1 M HCl, and that in 2.22 M H₂SO₄ is higher than that in 0.5 M H₂SO₄.
Nighttime image dehazing is a core technology for improving the performance of all-weather visual systems, and its key challenge lies in overcoming the degradation of image quality caused by the coupling of multiple factors including haze degradation, inhomogeneous illumination, multi-light source interference and low signal-to-noise ratio (SNR). Traditional physics-based methods have inherent limitations under the complex illumination conditions of nighttime, while deep learning and multimodal fusion technologies have brought new breakthroughs to this field. The integration of data-driven approaches and physical constraints has significantly improved dehazing performance and scene adaptability. This paper systematically reviews the recent research advances in this field: firstly, it elaborates on the particularity of nighttime image degradation; secondly, it classifies and analyzes the mechanisms of existing methods; furthermore, it compares the performance of typical algorithms through subjective and objective experiments. Current research still faces challenges such as the scarcity of real-world data, insufficient model generalization ability and low computational efficiency. In the future, developing efficient, robust and physically interpretable algorithms, constructing high-quality datasets, and promoting end-to-end optimization with high-level vision tasks will become important research directions in this field. This paper aims to provide a systematic reference and technical outlook for subsequent research in this area.
Clinical Value Study of Artificial Intelligence-Assisted Gastrointestinal Ultrasound Contrast Imaging in the Diagnosis of Gastric Mucosal Lesions
Evaluating the diagnostic significance of AI-assisted contrast-enhanced ultrasonography in diagnosing gastric mucosal lesions among 200 cases diagnosed with suspected gastric mucosal diseases through conventional imaging and subsequently confirmed via histopathology under MRI guidance was determined to be an adequate method for comparison. Diagnosis by this system achieved a score above 83 percent, which is statistically significantly lower than 73 per cent for regular ultrasound examination results (p <0.01). Using AI algorithms to precisely define lesions and microcirculatory vessels to reduce interscanner variability. According to the above research results, an artificial intelligent assist gastrointestinal CEUS examination system has already been developed; therefore, its clinical diagnosis applications are highly necessary.
A Unified Battery Discharge Prediction Framework Based on Electro-Thermal Coupling Model and Markov-Modulated Monte Carlo Algorithm
This paper addresses the modeling of dynamic characteristics during the discharge process of smart device batteries by constructing a continuous-time equivalent model that integrates electrical and thermal coupling mechanisms. At the electrical level, an improved first-order Thevenin equivalent circuit is introduced to uniformly model open-circuit voltage, ohmic internal resistance, and polarization effects. Combined with a constant-power load constraint, this model characterizes the nonlinear feedback behavior where voltage drop triggers current amplification; At the thermal level, a temperature evolution model is established based on the equilibrium between Joule heating and convective cooling, while a temperature-dependent internal resistance expression based on the Arrhenius equation is introduced to achieve a bidirectional electro-thermal coupling description. On this basis, the system is transformed into a state-space form to uniformly characterize the cooperative evolution of the charge state, polarization voltage, and temperature. Furthermore, to address random load fluctuations and performance degradation in real-world usage scenarios, a Monte Carlo simulation framework based on continuous-time Markov processes is proposed. This framework incorporates Gaussian perturbations to model load uncertainty and introduces a health state parameter to unify the modeling of capacity degradation and internal resistance increase. Simulation results demonstrate that this method effectively captures discharge behavior and lifespan trends under multi-factor coupling, exhibiting good generalization capability and predictive stability. It provides a unified modeling foundation for battery performance evaluation and energy management.
Olympic Medal Prediction Method Based on Random Forest and Multilayer Perceptron Models
This paper constructs a medal count prediction model based on historical Olympic data, utilizing the Random Forest algorithm to model nonlinear relationships within complex datasets. First, the raw data undergoes feature selection and correlation analysis to extract key input features from variables such as athlete potential, host country factors, number of events, historical medal records, and gender. The statistical correlations among these features are verified using a correlation matrix. Building on this foundation, multiple training subsets are generated using Bootstrap sampling. A Random Forest prediction model is constructed through a multi-decision tree ensemble mechanism, and model parameters are optimized by adjusting the number of nodes and decision trees. To validate the model’s performance, multi-layer perceptron, multiple linear regression, and support vector regression models are also constructed for comparative experiments. Comprehensive evaluation is conducted using metrics such as mean squared error, root mean square error, mean absolute error, and coefficient of determination. The experimental results indicate that the Random Forest model outperforms the other models in terms of prediction accuracy and stability. It effectively captures the nonlinear characteristics within complex data and achieves accurate predictions of medal distributions, demonstrating good generalization ability and practical value.
Research on Stereo Matching Method for Ultra-Thin Strip-Shaped Materials of Power Battery Covers Based on Multi-Algorithm Fusion
To address the problems of incomplete feature extraction and high mismatch rates in traditional stereo matching of ultra-thin strip materials for power battery top covers, a stereo matching method integrating edge enhancement and robust matching is proposed. First, the Canny edge detection algorithm is employed to accurately extract the edge contours of the power battery top cover, suppressing image noise while ensuring edge continuity and local feature integrity. Then, the SIFT algorithm is used to achieve cross-scale matching of feature points between the left and right images, enhancing robustness against deformation and rotation. Finally, the RANSAC algorithm is applied to eliminate mismatched points and construct a high-precision stereo matching model. Experimental results show that compared with the traditional NCC algorithm, the proposed stereo matching method improves the matching rate by 2.7% and reduces the matching time by 12.4 seconds; compared with the SGM algorithm, it increases the matching rate by 4.68% and shortens the processing time by 9 seconds, significantly optimizing the stereo matching performance for ultra-thin strip materials.
A Bearing Fault Diagnosis Method Based on Adaptive Graph Structure Sparse Coding and Bayesian Probabilistic Neural Network
Modern complex industrial systems have put forward unprecedented requirements for accuracy and reliability in the early fault identification and precise diagnosis of critical rotating machinery. Although current mainstream diagnostic methods have enhanced recognition performance with deep learning models, they are generally trapped in the "representation bottleneck" and "black-box dilemma". On the one hand, end-to-end learning neglects the explicit modeling of fault physical mechanisms, leading to insufficient interpretable structural priors for feature representation. On the other hand, deterministic models cannot quantify prediction uncertainty, which limits their application in supporting trustworthy decision-making for high-risk scenarios. This study aims to construct a novel diagnostic paradigm integrating high discriminability, strong interpretability, and reliable uncertainty quantification. The core contributions are summarized as follows. First, an Adaptive Graph Structure Sparse Coding (AGSC) method is proposed. It innovatively embeds multi-domain features of vibration signals into a dynamic graph structure space. By jointly optimizing feature representation and graph topology, the non-Euclidean manifold correlations among features are explicitly modeled, realizing discriminative representation learning that fuses physical mechanism inspiration and data-driven learning. Second, a Bayesian Probabilistic Neural Network (BPNN) is developed. Through constructing a hierarchical probabilistic model of kernel widths and a variational inference framework, the empirical parameters of traditional probabilistic neural networks are converted into learnable probability distributions. Thereby, confidence estimation can be synchronously output in classification decisions, which enables the diagnostic results to achieve uncertainty awareness.
Federated Graph Learning Enables Decentralized Disease Surveillance Across Health Systems
The emergence of federated graph learning (FGL) offers a transformative paradigm for disease surveillance across distributed health systems, enabling collaborative model training without centralizing sensitive patient records. Conventional disease monitoring pipelines require aggregating clinical data into unified repositories, which raises significant concerns related to patient privacy, regulatory compliance, and institutional data governance. This paper presents a decentralized surveillance framework that integrates graph neural network (GNN) architectures with federated optimization protocols, allowing geographically dispersed hospitals, public health agencies, and community clinics to jointly learn disease propagation dynamics from locally retained data. The proposed system constructs patient-population interaction graphs at each participating node and trains a spatiotemporal GNN under a privacy-preserving federated aggregation scheme. Empirical evaluations using simulated multi-site infectious disease datasets demonstrate that the federated model achieves detection accuracy within 3.2% of a centralized training baseline while substantially reducing inter-institutional data exposure. This work advances the intersection of distributed machine learning and public health informatics, providing a principled and scalable foundation for real-time epidemic intelligence systems that respect institutional data sovereignty.
Research on Selection of Engineering Coordinate System and Coordinate Transformation Method for High-Speed Railway
In view of the engineering characteristics of high-speed railway long and narrow linear projects, such as large span, significant topographic relief, and strict control of projection deformation, the traditional national unified coordinate system can hardly meet the accuracy requirement that the projection length deformation is ≤ 2.5 cm/km. Taking high-speed railway engineering survey as the research background, this paper sorts out the core theories and selection principles of engineering coordinate systems, constructs mathematical models including ellipsoid transformation and Bursa seven-parameter transformation, and carries out projection deformation calculation and coordinate system optimization design with examples of Taibai-Fengxian Expressway and typical mountainous railway sections. The results show that by customizing the compensation elevation plane and adjusting the position of the central meridian, the elevation deformation and Gauss projection deformation can be effectively compensated, and the global deformation value can be controlled within 1/43000, which meets the accuracy specifications for high-speed railway engineering survey. The coordinate transformation method based on the seven-parameter model enables the accurate connection between the national coordinate system and the independent engineering coordinate system. The coordinate system selection strategy and transformation method proposed in this paper can provide practical references for the coordinate system design of similar linear projects.
SFENet: A Real-Time Semantic Segmentation Network Based on Selective State Models and Frequency-Edge Enhancement
Existing real-time semantic segmentation models have achieved notable progress. However, they still suffer from feature redundancy and insufficient capability in capturing spatial details. To address these issues, this paper proposes a real-time semantic segmentation network based on selective state models and frequency-domain spatial detail enhancement. Specifically, a Selective Redundancy Suppression Module (SRSM) is designed based on a selective scanning mechanism. This module retains informative features while suppressing redundant ones through selective scanning. In addition, a Frequency Edge Enhancement Module (FEEM) is constructed. It combines the Fast Fourier Transform (FFT) with an attention mechanism to enhance high-frequency edge information. SFENet is evaluated on two datasets. On the Cityscapes dataset, it achieves 77.3% mIoU, outperforming the baseline by 1.4% mIoU. On the CamVid dataset, it achieves 77.1% mIoU. Experimental results demonstrate that SFENet effectively preserves critical semantic features and accurately segments object boundaries.
Fusing Optical Remote Sensing and SAR Garlic Planting Structure Extraction Method
Obtaining crop planting structure information is an important basis for agricultural production management and policy formulation. As an important economic crop in China, high-precision identification of garlic planting distribution is of great significance for production management, optimization of planting structure, and yield assessment. Addressing the problems of mixed pixels and spectral confusion caused by the overlapping growth periods of garlic and winter wheat, and the small and scattered planting plots, this study takes Kaifeng City in Henan Province as the research area, integrating Sentinel-2 optical images and Sentinel-1 radar data, and conducts garlic planting distribution identification based on phenological features. The main results are as follows: (1) A multimodal time-series vegetation index dataset for the entire growth period of garlic was constructed, and after smoothing by Savitzky–Golay filtering, a continuous and stable vegetation index variation curve was obtained to identify key turning points for dividing critical phenological periods. (2) To address the problem that optical data is easily affected by weather, a method integrating optical and SAR data was proposed, extracting optical and radar features at different phenological periods to construct optimal recognition features. Classification results show that after integrating optical and SAR data with phenological information, the overall accuracy reached 96.27%, with a Kappa coefficient of 0.95. This method effectively overcomes the problems of spectral confusion and mixed pixels in garlic identification, significantly improves classification accuracy and spatial consistency, and provides effective technical support for the accurate acquisition of garlic planting structure information.
Analysis of road bridge inspection technology research
Highway bridges are an important part of transport, and their safety and reliability are crucial to ensure smooth traffic flow. The development of highway bridge inspection technology is of great significance for the timely detection of structural damage and potential safety hazards of bridges. This paper discusses the classification and characteristics of non-destructive and destructive inspection techniques through the overview and analysis of road bridge inspection techniques
Semi-Supervised Learning Method for Drill Pipe Detection
In underground coal mine drilling operations, complex environmental factors such as low illumination, dust interference, and severe occlusion significantly degrade the performance of visual perception systems. Meanwhile, large-scale surveillance video data generated in mining environments are difficult to fully annotate, leading to insufficient scene coverage in labeled datasets and consequently limiting the generalization capability of detection models. To address these challenges, this paper proposes a semi-supervised object detection method with a multi-strategy pseudo-label optimization mechanism based on a teacher–student framework, aiming to improve drill-related object detection performance under low-labeling conditions. Specifically, in the pseudo-label generation and filtering stage, a dual-teacher consistency verification mechanism is introduced, along with a scene-aware noise evaluation factor to adaptively adjust pseudo-label selection thresholds, thereby reducing the impact of noisy labels in complex environments. Furthermore, a pseudo-label localization optimization module is designed from the perspective of regression stability, where the spatial consistency of multiple regression predictions is exploited to evaluate localization quality and eliminate unreliable pseudo-labels with large deviations. In addition, an uncertainty-aware weighting strategy is proposed to dynamically adjust training loss by jointly modeling pseudo-label confidence, environmental noise, and localization quality, enabling more effective utilization of high-quality pseudo-labels while suppressing the influence of low-quality samples. Experiments are conducted on a self-constructed underground drilling dataset under different labeling ratios. The results demonstrate that the proposed method significantly improves detection accuracy in low-label scenarios and exhibits strong robustness and generalization capability in complex underground environments. This study provides an effective solution for leveraging large-scale unlabeled data in intelligent visual monitoring for coal mine applications and offers valuable insights for semi-supervised object detection in complex industrial scenarios.
Concepts Classification and Development of Self-Centering Structures
Self-centering structures have emerged as a transformative solution for enhancing seismic resilience by minimizing residual deformations after earthquakes. This paper presents a comprehensive state-of-the-art review of self-centering structural systems, focusing on conceptual foundations, classification frameworks, and development trajectories. The historical evolution from ancient rocking structures to modern applications is traced. A systematic classification is proposed based on mechanical principles and structural typologies, including moment frames, braced frames, shear walls, bridge piers, and mass timber systems. Key enabling technologies—post-tensioning, shape memory alloys, and hybrid systems—are critically analyzed. Research gaps and future directions are identified to advance broader implementation of self-centering technologies in resilient infrastructure.
Liver Tumor Segmentation via State Space Modeling and Multi-Scale Detail Enhancement
Accurate liver tumor segmentation from CT images is challenging because tumors present blurred boundaries, irregular shapes, and substantial scale variation. To address these issues, we propose a liver tumor segmentation network that combines state-space modeling with multi-scale detail enhancement (SSMDENet). SSMDENet follows an encoder-decoder architecture and introduces a Global-Local Modeling (GLM) block as the basic feature extractor. Within GLM, the Spatial Dependency Modeling (SMD) module captures long-range spatial dependencies to encode global anatomical context, while the Multi-scale Feature Enhancement (MFE) module uses parallel depthwise convolutions and channel recalibration to strengthen local boundary and texture information. In this way, the proposed network jointly models global semantics and local details. Experiments on the LiTS2017 and 3DIRCADB-01 datasets demonstrate the effectiveness of the method. SSMDENet achieves Dice scores of 85.79% and 84.12% on LiTS2017 and 3DIRCADB-01, respectively, outperforming several representative segmentation methods. Ablation studies further confirm the complementary benefits of the SMD and MFE modules. These results indicate that SSMDENet is an effective and robust solution for liver tumor segmentation.
3D Borehole to Surface Electromagnetic Inversion Based on Upscaling network
Borehole-to-surface electromagnetic method, due to its transmitter source being located in the borehole, can more directly excite electromagnetic responses from deep formations compared to surface electromagnetic methods, effectively improving the detection resolution of deep targets. However, for steel-cased wells, the casing possesses extremely high electrical conductivity, and the casing effect cannot be ignored in the data processing of borehole-to-surface electromagnetic surveys. Direct discrete modeling of slender hollow steel casings is both difficult and unfavorable for subsequent inversion calculations. Therefore, this paper proposes a three-dimensional borehole-to-surface electromagnetic inversion method based on an upscaling network. First, through unsupervised deep learning, the method establishes a nonlinear mapping between the electromagnetic response of fine-grid models containing casings and equivalent coarse-grid models, training an upscaling network from fine-scale grids to coarse-scale grid electrical structures. Second, the upscaled coarse-grid information is used as the initial model to perform three-dimensional borehole-to-surface electromagnetic inversion. Finally, the accuracy and feasibility of the proposed method are verified through three-dimensional borehole-to-surface electromagnetic inversion examples, and the inversion capability for deep targets is analyzed through synthetic data models, examining sensitivity regions, single-transmitter wells, and dual-transmitter wells.
Personalized Hierarchical Federated Learning Algorithm Based on Adaptive Differential Privacy
This paper proposes ADP-PHFL, an Adaptive Differential Privacy-based Personalized Hierarchical Federated Learning framework. It integrates hierarchical federated learning with personalized training via knowledge transfer, mitigating performance degradation caused by directly applying a global model while reducing communication overhead. An adaptive differential privacy mechanism is introduced to allocate privacy budgets more effectively, avoiding the drawbacks of uniform noise injection and enhancing privacy protection against data leakage. Theoretical analysis establishes the convergence and privacy guarantees of the proposed method. Experimental results demonstrate that ADP-PHFL achieves improved communication efficiency and higher model accuracy.
The integration of Ultra-High-Performance Concrete (UHPC) with 3D printing technology provides innovative solutions for the field of digital construction, combining ultra-high mechanical performance with design freedom. However, there is an inherent contradiction between the characteristics of UHPC—such as its low water-to-binder ratio and high fiber content—and its printability, which constitutes the core challenge in this research area. This article systematically reviews the current research status of 3D printed UHPC (3DP-UHPC), summarizing the latest achievements from the perspectives of material rheological property control, fiber reinforcement mechanisms, characteristics of mechanical anisotropy, progress in large-scale applications, and green, low-carbon development. Studies show that through nanomaterials modification, mixture proportion optimization, and printing process control, a synergistic improvement in the printability and mechanical properties of UHPC can be achieved. While the extrusion process-induced directional alignment of fibers can enhance strength in specific directions, it also exacerbates the structural anisotropy. The development of low-cement-content UHPC and geopolymer systems provides new pathways for the sustainable development of 3DP-UHPC. Existing research still faces challenges such as a lack of standardization, weak interlayer bonding, and insufficient long-term performance data. Future efforts should focus on establishing dedicated standard systems, developing intelligent process control methods, and deepening multi-scale performance research.
Response Surface Optimization of Compressive Strength for Fluorogypsum-Based Cementitious Materials under Multi-Factor Interactive Effects
The massive accumulation of fluorogypsum (FG) poses severe environmental challenges. To address the limitations of previous single-factor studies, this research employed the Box-Behnken design and response surface methodology (BBD-RSM) to investigate the interactive effects of FG, ground granulated blast-furnace slag (GGBS), and sodium sulfate (Na₂SO₄) on the compressive strength of a novel solid waste-based cementitious material. Analysis of variance confirmed the high fitting accuracy of the established quadratic regression model. Results indicated that the significance of factors on compressive strength ranks as: GGBS > FG > Na₂SO₄. The optimal mix proportion for maximizing compressive strength was determined as 65.39% FG, 15% GGBS, 14.61% fly ash (FA), and 5% ordinary Portland cement (OPC), with an external addition of 1.47% Na₂SO₄. Under this formulation, the measured 28-day compressive strength reached 36.51 MPa, yielding a relative error of only 1.4% compared to the predicted value. This study provides robust theoretical guidance for the large-scale application of FG in green building materials.