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.
Research Progress on Algal-Bacterial Symbiotic Systems for the Treatment of Livestock Wastewater
Livestock wastewater is characterized by high organic loading, elevated concentrations of ammonia nitrogen and phosphorus, high suspended solids, and the coexistence of antibiotics, heavy metals, antibiotic resistance genes, and other emerging contaminants. Improper treatment may lead to eutrophication, ecological toxicity, and the dissemination of antimicrobial resistance in aquatic environments. Algal-bacterial symbiotic systems couple organic matter degradation, nitrogen and phosphorus transformation, pollutant adsorption and immobilization, and biomass valorization through the synergistic interaction between microalgal photosynthesis and bacterial heterotrophic metabolism, providing a promising pathway for low-carbon treatment and nutrient recovery from livestock wastewater. This review systematically summarizes the major pollution characteristics of livestock wastewater and the applicability of algal-bacterial systems, and discusses the key mechanisms involved in algal-bacterial symbiosis, including oxygen-carbon dioxide exchange, nutritional complementarity, extracellular polymeric substance-mediated floc formation, and nitrogen transformation driven by aerobic-anoxic microzones. Furthermore, recent progress in the removal of organic matter, nitrogen, phosphorus, antibiotics, antibiotic resistance genes, heavy metals, persistent organic pollutants, microplastics, and other emerging contaminants by algal-bacterial systems is reviewed. The treatment performance of algal-bacterial systems is mainly influenced by algal species and bacterial community composition, algal-bacterial inoculation ratio, light conditions, pH, temperature, dissolved oxygen, wastewater pretreatment, and pollutant loading. Future studies should focus on long-term operation under real livestock wastewater conditions, regulation of algal-bacterial communities, clarification of pollutant migration and transformation among the aqueous phase, biomass, and sediments, and the establishment of safe biomass valorization and comprehensive environmental benefit assessment frameworks. These efforts will promote the transition of livestock wastewater treatment from pollution reduction toward resource recycling.
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Simulation and Optimization of Structural Parameters of Nozzle for Abrasive Water Jet Stubble Breaking
Aiming at the demand for removing maize stubble in sticky soil areas of Southwest China, this study conducts simulation and optimization on nozzle structural parameters to improve the impact capacity and energy concentration of abrasive water jet. With garnet particles of 0.2 mm in diameter and an abrasive inlet velocity of 15 m/s as basic working conditions, the single-factor simulation method is adopted to investigate the influences of outlet length, convergence angle and mixing chamber length on jet velocity, beam convergence and energy distribution. The results show that the optimal parameter combination is determined as the outlet length of 20 mm, convergence angle of 30° and mixing chamber length of 30 mm. Under this structural configuration, the impact velocity of the jet reaches 180 m/s at a standoff distance of 200 mm. The jet features good beam convergence and slow kinetic energy attenuation along the path, which can effectively enhance the crushing performance of maize stubble. The research findings can provide theoretical references for the design and engineering application of nozzles used in agricultural abrasive water jet stubble breaking.
Design and Optimization of Feeding Device for Pneumatic Seeder of Legume-Gramineae Mixed Sowing
To address the problems of uneven velocity and seed separation during the mixed feeding of alfalfa and smooth brome, which have significant differences in physical properties, a dedicated feeding device was designed based on the Venturi principle. The EDEM-Fluent gas-solid two-phase flow coupled simulation method was adopted to explore the effects of nozzle cross-sectional size, nozzle length and air inlet velocity on seed motion characteristics and flow field distribution. Multiple sets of simulation tests were carried out with the outlet velocity difference between the two grass seeds and airflow energy loss as evaluation indexes. The results show that the optimal parameter combination is a nozzle cross-section of 26 mm × 16 mm, a nozzle length of 30 mm, and an air inlet velocity of 14 m/s. Under this working condition, the outlet velocity difference between alfalfa and smooth brome is only 0.29 m/s, featuring high airflow conveying efficiency, low pressure loss and excellent gas-solid mixing performance. The research results can provide a theoretical reference for the structural design and operating parameter matching of the feeding device of legume-gramineae mixed pneumatic seeders.
Research on a YOLOv8-Based Metal Residue Detection Algorithm
To address the recognition challenges caused by the tiny size of metal residues, high reflectivity, and severe background texture interference in industrial production environments, this paper proposes a YOLOv8-based metal residue recognition algorithm. First, to overcome the scarcity of high-quality samples in industrial sites, a metal residue dataset containing multiple complex backgrounds such as welds and oil stains is constructed by simulating actual working conditions. Targeted data augmentation techniques are employed to enhance the model's generalization ability in extreme environments. Second, to tackle the problems of extremely low pixel occupancy of metal targets and the difficulty of feature extraction, a lightweight coordinate attention mechanism is introduced into the backbone network. By capturing cross-channel directional and positional information, the model's perception and extraction capabilities of subtle metal edge features are strengthened. Experimental results show that the improved algorithm achieves a mean average precision (mAP) of 0.724 on the self-built dataset. While maintaining real-time inference speed, it can accurately identify metallic foreign objects with extremely small diameters, effectively reducing the miss rate and false alarm rate under complex working conditions, thus providing reliable technical support for the deployment of industrial precision inspection and automated rejection systems.
Research on Wind Energy Data Quality Control Methods in Qinghai Province on the Qinghai-Tibet Plateau (2019–2021)
This paper presents the construction methodology and quality assessment results of the “Qinghai-Xizang Plateau Multi-source Integrated Wind Energy Basic Dataset (V1.0)”. Addressing the challenges in wind energy resource assessment over the complex terrain of the Qinghai-Xizang Plateau, this study establishes a comprehensive data quality control system. The dataset spans from 2019 to 2021 and integrates observations from 12 meteorological towers and 53 national-level weather stations across Qinghai Province, along with multi-source fused analysis products at a spatial resolution of 5 kilometers. A tiered quality control process is applied, including range checks, temporal consistency checks, and vertical consistency checks. Additionally, dynamic threshold adjustment rules are designed based on historical climate extremes, and a multi-factor correlation logic incorporating temperature-humidity coupling is introduced, leading to an innovative algorithm for wind tower data quality control that significantly improves data quality in high-altitude regions of Qinghai Province. Assessment results show that the overall accuracy of the data after quality control reaches 93.28%, substantially higher than the national average. This dataset provides solid foundational support for wind energy resource evaluation, climate research, and low-carbon transition efforts in northwestern China, and demonstrates the effectiveness of multi-source data fusion techniques in enhancing the accuracy of meteorological observations in high-altitude areas.
Learning When to Reason: Gating LLM Inference for Cost-Efficient Serverless Function Scheduling at Scale
Serverless computing platforms face a fundamental tension when integrating large language model based scheduling agents: invoking full chain-of-thought reasoning for every function placement decision is computationally prohibitive, yet relying exclusively on lightweight heuristics cannot capture complex workload semantics. This paper presents GateLLM, a learned gating framework that selectively routes incoming serverless scheduling requests to either a lightweight fast-path handler or a deliberative large language model reasoning pipeline based on a compact feature vector extracted from request metadata. The gating classifier is a multilayer perceptron trained through a combination of offline oracle labeling and online reinforcement feedback derived from observed scheduling outcomes, enabling continuous adaptation to workload distribution shifts. Evaluated on a large-scale production function trace, GateLLM reduces total large language model inference cost by 61.3% relative to a full-reasoning baseline while incurring only a 2.1% degradation in average job completion time and a 0.25 percentage point increase in service level objective violation rate. Analysis demonstrates that over 68% of real-world scheduling events are structurally simple and resolvable without large language model involvement, establishing inference gating as an essential primitive for operationally viable intelligent schedulers in production cloud environments.
Robust Structural Response Prediction Under Coupled Thermo-Mechanical Effects via Hierarchical Attention-Based Sensor Fusion
Accurate prediction of structural responses under coupled thermo-mechanical loading remains a persistent challenge in structural health monitoring (SHM) and reliability engineering. Conventional approaches typically treat thermal and mechanical effects in isolation, failing to capture the nonlinear interactions that arise when temperature gradients and mechanical loads coexist. This paper introduces a hierarchical attention-based sensor fusion (HASF) framework that integrates multi-modal sensor data—encompassing strain gauges, thermocouples, and acceleration sensors—to deliver robust structural response predictions under such coupled conditions. The hierarchical attention mechanism operates at two levels: an intra-modal level that captures temporal dependencies within each sensor modality, and an inter-modal level that adaptively fuses information across modalities according to learned relevance weights. A physics-informed loss function is incorporated to enforce consistency with established thermo-mechanical governing equations, thereby improving generalization in data-sparse regimes. Validation on both a laboratory steel frame structure and a publicly available benchmark dataset confirms that HASF reduces root mean square error (RMSE) by 31.4% compared with state-of-the-art baselines, while achieving competitive inference speeds suitable for real-time monitoring. These findings establish HASF as a viable approach for structural prognosis and health monitoring across aerospace, civil, and mechanical engineering applications.
Study on Overlying Rock Deformation and Surface Subsidence Prediction under Layered Grouting Filling Conditions
To investigate the movement mechanism of overlying rock and surface under layered filling extraction conditions, scientifically evaluate the applicability of traditional prediction models under layered filling conditions, and improve the accuracy of surface subsidence prediction parameters, this paper takes the 1605 layered filling working face of Guhanshan Coal Mine as the engineering background. A comprehensive approach combining long-term field precise levelling measurements, non-contact microwave remote sensing (SBAS-InSAR) monitoring, and parameter optimisation inversion using the probability integral method was employed to conduct an in-depth study of overlying rock and surface movement patterns under layered filling. The study shows that under the disturbance of filling excavation, surface deformation exhibits distinct staged evolution characteristics of "grouting disturbance," "dynamic pressure balance," and finally "rheological stability." The high-pressure grouting filling area shows significant local atypical settlement different from the traditional caving method. SBAS-InSAR technology was used to obtain multi-dimensional surface deformation monitoring results for the entire field, achieving a leap from "point-line" unidirectional control to full "area" coverage in mining area settlement monitoring. On this basis, the limitations of the probability integral method in layered filling mining were explored, and an equivalent mining thickness correction model and optimisation fitting inversion algorithm were established. Engineering verification shows that the corrected probability integral model after parameter inversion can highly fit the measured scatter points, successfully achieving refined prediction of the atypical surface subsidence under layered filling, providing scientific data support for disaster prevention, loss reduction, and optimisation of filling parameters in mining areas.
Analysis of the Dilemmas and Causes of “Supervisor-Counselor” Collaborative Education in Universities in the Context of AI
The deep application of large language model (LLM) technology has injected new momentum into the reform of graduate education while also posing profound challenges to the traditional “supervisor-counselor” collaborative education model. Based on synergy theory and game theory, and drawing on existing literature and typical practical cases, this paper systematically analyzes the practical dilemmas and underlying causes of “supervisor-counselor” collaborative education in universities. The study finds that the current “supervisor-counselor” collaborative education is mired in a triple “zero-sum game” dilemma, in which conceptual gaming leads to goal fragmentation, responsibility gaming leads to boundary ambiguity, and evaluation gaming leads to dispersed motivation. This paper further analyzes the deep logic of dilemma formation from the institutional, technological, and cultural dimensions. Breaking the zero-sum game of “supervisor-counselor” collaboration and moving toward a technology-empowered positive-sum game has become an urgent task for enhancing the management and service capacity of graduate education.
Innovative Practice of Game Level Design and Scene Narrative Driven by AI Technology
As Artificial Intelligence Technology has advanced rapidly, a great deal of change has happened on the stage of Game Development concerning Level Design and Scene Narration. This paper systemically examines the new application scenarios of AI-powered technologies, including Procedural Content Generation (PCG), Generative Adversarial Networks (GANs), large language models (LLM), and dynamic difficulty adjustment (DDA) in game level Design and Narrative creation. Referring to verifiable market Data, peer-Reviewed Researches and documented Industry applicationss To identify The essential Technical Pathways Through Which A I Enhances Content Diverse City Playengement And Narrative Immersion. The global artificial intelligence (AI) in games' market is worth about $5.85 billion in 2024, expected to reach US$37.89 billion by 2034, showing its enormous Scale of Transformation. The limitations of current artificial intelligence techniques have been discussed; therefore, the Future Direction of Game Design Will Be Human-AI Collaboration That Preserves Authoritative Intent While Enhancing Scalability and Adaptation Abilities of The System.
Exploratory Research Based on AGIL Model: Application Path Practice of "Fengqiao Experience" in Improving College Students' Mental Health in Zhejiang Universities' "One-Stop" Student Communities
In the new era, the "Fengqiao Experience", with the core essence of "no conflicts handed over to higher authorities, no safety incidents, and no gaps in services", provides local practical guidelines and value guidance for the innovation of university "one-stop" student community governance. Based on Talcott Parsons' AGIL functional model and combined with empirical survey data from multiple undergraduate and vocational colleges in Zhejiang Province, this study systematically analyzes the mechanism of each functional dimension of the "one-stop" student community on college students' mental health. The research shows that there is a structural mismatch between resource input and education effectiveness in the current community construction: although considerable resources are invested in life services and ideological and political education, their efficiency in alleviating students' psychological anxiety is limited; cultural activities and organizational systems are the core key factors to resolve students' conflicts and relieve psychological pressure. Accordingly, this paper proposes an integrated education path of cultural infiltration – organizational collaboration – service sinking – source governance, promoting the in-depth implementation and precise empowerment of the "Fengqiao Experience" in the governance of university "one-stop" student communities, and comprehensively improving the quality and efficiency of college students' mental health education.
An Optimization Path for "Supervisor-Counselor" Collaborative Education in Universities Empowered by Large Language Models
The deep integration of large language model (LLM) technology offers a transformative opportunity to break the long-standing zero-sum game dilemma in graduate "supervisor-counselor" collaborative education. This paper proposes a value orientation grounded in the concepts of "precision, synergy, integration, and symbiosis," and guided by the practical principles of "technological support, multi-stakeholder cooperation, resource sharing, and dynamic adjustment," to construct an optimized path system for university "supervisor-counselor" collaborative education in the context of LLMs. On this basis, it proposes reshaping a positive-sum game pattern featuring the deep integration of "academic and ideological-political education" under a fourfold guarantee of institution, platform, capability, and culture, thus providing an operable practical scheme for enhancing the management and service capacity of graduate education.
From "Disciplinary Space" to "Life Harbor": Dilemmas and Path Optimization of the "One-Stop" Student Community Construction in Medical Universities Based on the "Fengqiao Experience"
Students in medical universities are confronted with heavy academic pressure, anticipated high-risk occupational exposure, and complex humanistic and ethical challenges. Their mental health is directly linked to the quality of the future medical and health workforce. At present, the construction of "one-stop" student communities in medical universities generally presents a structural dislocation of "valuing hardware over software, emphasizing management over education". Based on the governance logic of the "Fengqiao Experience" and combined with the psychological characteristics of medical students, this paper proposes to construct a four-dimensional education system of "life education – grid autonomy – precise service – source prevention and control", aiming to build the student community into a "life harbor" that safeguards the physical and mental health of medical students.
Lamb Waves in One-Dimensional Hexagonal Piezoelectric Quasicrystal Layered Plate‌s
Lamb wave propagation in one-dimensional hexagonal piezoelectric quasicrystal (PQC) layered plates is investigated. The coupled phonon, phason, and electric fields are considered simultaneously, and a multilayered plate model with traction-free and electrically open-circuited surfaces is established. By introducing rectangular window functions and expanding the field variables in each layer using Legendre polynomials, the governing wave equations are transformed into a matrix eigenvalue problem, from which the phase velocity dispersion characteristics are obtained. The effects of phonon–phason coupling, layer thickness, stacking sequence, and piezoelectric properties on Lamb wave behavior are systematically examined. The results show that the stacking sequence and layer thickness have a pronounced and mode-dependent influence on the dispersion curves, indicating that guided-wave characteristics can be effectively tailored through structural design. Moreover, the piezoelectric effect increases the phase velocities of both phonon and phason modes, while an increase in the dielectric constant weakens this effect. The dielectric constant of the middle layer plays a dominant role at low frequencies, whereas that of the surface layer becomes more influential at high frequencies. These findings provide theoretical guidance for the design of multifunctional PQC layered structures with controllable wave characteristics.
Quantitative Drawing of Seismic Calm Images Before the Main Earthquake in Wenchuan Earthquake Sequence
Using zmap grid technology, the temporal and spatial changes of Wenchuan earthquake, Lushan earthquake and Jiuzhaigou earthquake aftershock area and extended aftershock area were calculated. It was found that Lushan earthquake was calm before and after the earthquake, but the degree of calm was low. Jiuzhaigou is located in the Coulomb stress shadow area, and there was remarkable earthquake calm before the earthquake, which may be related to the self-excitation of small earthquakes in the extended aftershock area outside the aftershock area. In addition, the Lushan earthquake also played a certain unlocking role in Jiuzhaigou, because the change of seismic activity rate in the extended aftershock area of Jiuzhaigou after the Lushan earthquake showed a calm increase but a regional decrease.
Toward Proactive Overdose Prevention Through Spatiotemporal Cluster Prediction
Drug overdose mortality in the United States has reached unprecedented levels, with opioid-involved deaths surpassing 80,000 annually and continuing to escalate despite sustained public health investment. The reactive architecture of most existing prevention strategies—deploying naloxone, expanding treatment capacity, or routing outreach workers only after overdose clusters have already been documented—has proven insufficient to prevent loss of life at scale. This paper proposes and evaluates a proactive spatiotemporal cluster prediction (SCP) framework designed to shift overdose surveillance from retrospective hotspot mapping toward prospective risk forecasting. By integrating historical overdose death records, socioeconomic covariates derived from the American Community Survey (ACS), and space-time permutation scan statistics with Bayesian hierarchical regression, the proposed framework generates one-year-ahead census-tract-level predictions of overdose cluster probability. Applied to publicly available county and tract-level mortality data from 2015 through 2022, the model identifies the top 10% of predicted high-risk tracts as the locus of approximately 74% of observed overdose deaths in held-out evaluation years. Findings indicate that socially vulnerable, economically deprived, and historically clustered tracts account for a disproportionate share of forward-predicted high-risk areas, and that proximity to naloxone access points independently attenuates predicted cluster formation risk. The paper discusses implications for pre-positioning harm reduction resources, mobile health service routing, and policy-level allocation of overdose prevention funding.
Achieving Constant Memory Complexity in Long Context Transformers Through Linear Attention
The transformer architecture has emerged as the dominant paradigm for sequence modeling, yet its standard self-attention mechanism imposes quadratic time and memory cost with respect to sequence length, presenting a fundamental scalability barrier for long-context applications. This paper investigates linear attention as a principled mechanism for achieving constant memory complexity (KM) during autoregressive inference in transformer models. By replacing the softmax normalization in scaled dot-product attention with a kernel decomposition, the computation is restructured so that keys and values are combined before interacting with queries, yielding an equivalent recurrent form that maintains a fixed-size hidden state regardless of sequence length. We present a unified theoretical derivation of this parallel-to-recurrent equivalence, introduce a learnable positive feature map paired with data-dependent gating and structured state normalization, and evaluate the resulting architecture on long-context language modeling and downstream comprehension benchmarks. Empirical results confirm that peak GPU memory consumption remains constant at 2.1 gigabytes across context lengths from 1,024 to 131,072 tokens, achieving a 13.6× throughput advantage over standard softmax attention at length 65,536, with only a 4.8% relative perplexity increase on the Pile dataset. These findings establish constant-complexity linear attention as a viable deployment mechanism for memory-constrained long-context transformer inference.
Bridging Social Determinants and Opioid Risk via Heterogeneous Graph Learning
The opioid crisis continues to impose substantial public health burdens across the United States and beyond, necessitating novel computational frameworks capable of integrating multidimensional risk factors. Social determinants of health (SDOH), encompassing economic instability, housing insecurity, educational attainment, and social isolation, have emerged as critical yet underutilized predictors of opioid use disorder (OUD) onset and escalation. Conventional machine learning (ML) approaches largely treat these factors as independent variables, failing to capture the relational complexity embedded in real-world patient and community networks. This study proposes a heterogeneous graph learning (HGL) framework that encodes SDOH-related entities and their interactions through a multi-relational graph structure, enabling nuanced risk stratification at both individual and population levels. By integrating electronic health records (EHR), census-level socioeconomic data, and clinical outcome registries, the proposed model constructs heterogeneous graphs where nodes represent patients, communities, clinical events, and social variables, while edges encode diverse relationship types. Experiments conducted on a large-scale real-world dataset demonstrate that the HGL model outperforms state-of-the-art baselines by up to 14.3% in area under the receiver operating characteristic curve (AUROC), with notable improvements in identifying high-risk populations in socioeconomically disadvantaged areas. These findings underscore the potential of heterogeneous graph learning as a powerful tool for SDOH-informed opioid risk prediction, paving the way for targeted and equitable intervention strategies.
A GRU-Based SAT Phase Prediction Algorithm
In the field of computer science, the Boolean Satisfiability Problem (SAT) was the first problem proven to be NP-complete and has been widely applied in artificial intelligence, formal verification, and many other research domains. Most modern SAT solvers are built upon the Conflict-Driven Clause Learning (CDCL) framework, whose performance heavily relies on heuristic mechanisms such as variable branching decisions and phase selection strategies. Although graph neural network-based heuristic guidance has become a popular research direction, existing approaches still suffer from significant limitations when handling large-scale industrial instances, particularly in capturing long-range variable dependencies and achieving sufficient reasoning depth. To address these challenges, this paper proposes NeuroLogic-GSM, a SAT phase prediction model based on Gated Recurrent Units (GRUs). The proposed model reconstructs the traditional static convolution-stacking paradigm into a recurrent iterative reasoning architecture, where weight-shared logical processing units are employed to simulate unit propagation and conflict analysis mechanisms, while GRUs dynamically track the evolution of variable states to effectively capture long-range logical dependencies. The introduction of recurrent reasoning units not only enhances the logical reasoning depth of the model, but also improves parameter utilization efficiency and training stability. Experimental results on the main tracks of the SAT Competitions from 2023 to 2025 demonstrate that NeuroLogic-GSM achieves solver performance improvements of 2.5%, 1.77%, and 1.48%, respectively, compared with NeuroBack, validating its effectiveness in handling complex logical reasoning tasks.
The Application and Influence of Stratigraphy in Paleoenvironmental Evolution Research
As an important branch of geology, stratigraphy and its recent advances play a key role in research on paleoenvironmental evolution. Through high-resolution sedimentary stratigraphic analysis, scientists can reveal changes in climate, geomorphology, and biodiversity throughout Earth's history. Recent stratigraphic research no longer relies solely on traditional sediment analysis, but also incorporates advanced techniques such as geophysical logging and environmental magnetism, thereby improving the accuracy and depth of data acquisition. These advances provide an important basis for paleoclimate reconstruction. For example, data such as trace-element contents and isotopic compositions in sediments can be used to reconstruct patterns of paleoclimate change with relatively high precision. At the same time, stratigraphic research also promotes the understanding of paleogeomorphic evolution and reveals geological phenomena such as river migration and landscape change. In addition, fossil records preserved in strata provide valuable materials for studies of paleobiodiversity and promote the development of biogeography, paleoecology, and related fields. Recent advances in stratigraphy not only enrich our understanding of paleoenvironmental evolution, but also provide a scientific basis for addressing modern climate change, resource exploration, and natural-disaster prevention. Therefore, they have far-reaching applications and impacts.
A Unified Framework for Epilayer Thickness Inversion Based on Lorentz–Drude Modeling and Particle Swarm Optimization
To address the challenge of accurately inverting epitaxial layer thickness under complex conditions, this paper proposes a comprehensive computational framework that integrates physical modeling with intelligent optimization. Methodologically, a basic model is first established based on optical interference theory, and the Lorentz–Drude composite model is introduced to characterize the frequency-dependent properties of refractive index. This approach corrects the traditional assumption of constant parameters, thereby enhancing the model’s physical consistency and broadening its applicability. At the data processing level, sliding average filtering and differential screening strategies are employed to enhance the stability and noise resistance of spectral data. Furthermore, to address the nonlinear offset issues caused by multi-beam interference, a full-frequency-domain reflectance model is constructed, transforming the thickness estimation problem into a parameter optimization problem. By utilizing a particle swarm optimization algorithm for global search, the method effectively avoids the shortcomings of traditional gradient methods, such as sensitivity to initial conditions and the tendency to get stuck in local optima. Experimental results demonstrate that this method exhibits excellent stability and high accuracy under various incident conditions and across multiple datasets, significantly improving the consistency and reliability of thickness estimates. Overall, the proposed model integrates physical mechanisms with intelligent algorithms, possesses strong general applicability, and can be extended to other scenarios involving the inversion of complex optical parameters and big data modeling, thereby providing an effective analytical and optimization approach for related fields.
Pilot Study on the Treatment of Rural Domestic Wastewater with Low C/N Ratio Using the AO-MABR Process
To address the poor removal efficiencies of chemical oxygen demand (COD), ammonia nitrogen, and total nitrogen (TN) by existing treatment processes for rural domestic wastewater with low carbon-to-nitrogen (C/N) ratios, an integrated AO-MABR process was developed and evaluated in a field pilot-scale study. The results demonstrated that the optimal operational parameters of the process were as follows: hydraulic retention time (HRT) of 3.0 h, membrane aeration pressure of 20 kPa, and internal reflux ratio of 200%. Under these optimal conditions, when the influent C/N ratio ranged from 3 to 4, the average removal efficiencies of COD, ammonia nitrogen, and TN by the AO-MABR system reached 91.28%, 86.52%, and 86.67%, respectively, with corresponding effluent concentrations of 26.87 mg/L, 2.65 mg/L, and 4.98 mg/L. The integrated AO-MABR process exhibited superior pollutant removal performance compared with conventional treatment technologies.
An Investigation and Teaching Optimization Strategies of Humanistic Care Ability of Nursing Students in Higher Vocational Nursing Education
Objective: This study aims to investigate the level of humanistic care ability among Chinese vocational nursing students and propose teaching optimization strategies. Methods: This study adopts a quantitative research method and focuses on full-time first and second year nursing students from three representative vocational colleges in Shandong Province. A structured questionnaire was distributed using a combination of stratified cluster sampling and convenience sampling to investigate their humanistic care abilities. A total of 300 effective samples were collected, and an Outcome Based Education (OBE) intervention framework was constructed. Results: The results showed that vocational nursing students had a relatively high level of humanistic care ability (mean = 4.96, SD = 0.462), with a negatively skewed distribution. Students’ humanistic care competence was generally homogeneous and influenced by curriculum, clinical practice, and educational interventions. On this basis, an 8‑week OBE‑based intervention program named HEART+ (Humanistic Engagement and Resilience Training Program) was constructed, covering self‑efficacy enhancement, learning engagement, humanistic value integration, reflective practice, mentorship, and systematic evaluation. The program is expected to effectively improve students’ humanistic care ability and provide a theoretical basis and operational pathway for optimizing humanistic literacy courses and teaching strategies in vocational nursing education. Conclusion: Humanistic care ability of vocational nursing students can be effectively improved through targeted education and training. The HEART+ program provides a scientific and operable path for the construction of humanistic courses and the optimization of teaching strategies in vocational nursing education.
Susceptibility assessment of Landslide Disasters in Yunnan-Guizhou-Sichuan Region Based on GIS and RS
This paper takes landslides in the Yunnan-Guizhou-Sichuan region as the research object. Based on grid and slope units respectively, the analytic Hierarchy Process (AHP) and information model are used to evaluate the susceptibility of landslides. The results show that all four evaluation results pass the rationality and accuracy tests, and the accuracy of the raster cell information volume model is the best. The study area as a whole is mainly composed of low-susceptibility zones, with a relatively small proportion of high-susceptibility zones. Spatially, it is generally lower in the west and higher in the east, concentrated in the middle. The spatial differentiation of susceptibility among Yunnan, Guizhou and Sichuan provinces is significant. In Yunnan, the susceptibility increases from south to north. In Guizhou, it is higher in the south and lower in the north. In Sichuan, it is higher in the west and lower in the east. In addition, by adopting the integrated comprehensive evaluation of multiple classifiers and standardizing the superimposed classification of the results of a single model, the evaluation accuracy has been significantly improved.
Evaluation of susceptibility to landslide disasters Analytic Hierarchy Process Information volume model GIS
Mechanistic Insights into Interfacial Interactions and Fertilizer Efficiency of Modified Biochar-Based Controlled Release Fertilizers
To address the prominent issues associated with conventional chemical fertilizers, including rapid nutrient release, low utilization efficiency, and environmental pollution, and to promote sustainable agricultural development, modified biochar-based controlled-release fertilizers have emerged as a research hotspot due to their multiple advantages, such as nutrient slow-release, soil improvement, and carbon sequestration. This paper systematically reviews the preparation methods of modified biochar and the effects of modification on its physicochemical properties. It focuses on analyzing the interfacial interaction mechanisms between modified biochar and fertilizer nutrients, and explores the long-term fertilization mechanisms of modified biochar-based controlled-release fertilizers in soil environments. Based on relevant experimental findings, the key factors affecting long-term fertilizer efficiency are discussed. Current limitations in existing studies are identified, and future research directions are proposed, providing theoretical references and practical guidance for the development, optimization, and industrial application of modified biochar-based controlled-release fertilizers.
Characterization and Development Potential Evaluation of the No. 5 Coal Reservoir in the Shanxi Formation of a Block in Southeastern Ordos Basin
The southeastern margin of the Ordos Basin holds immense potential for coalbed methane (CBM) resources, yet its development faces severe challenges due to the extremely low permeability of the reservoirs. To thoroughly investigate the reservoir characteristics and development potential of the No. 5 primary coal seam in the Lower Permian Shanxi Formation within a specific block, this study systematically analyzed the spatial distribution, coal lithology and quality, gas-bearing properties, pore-permeability characteristics, and mechanical properties of the coal seam. The methodology integrated well logging interpretation, core observation, maceral identification, isothermal adsorption experiments, and rock mechanics testing. The results indicate that the No. 5 coal seam is controlled by a gently dipping monocline structure that is high in the east and low in the west, with a thickness ranging from 4.42 to 8.61 m (averaging 6.39 m). Three phases of tectonic fractures have developed: the Indosinian near north-south trending fractures are weakly developed, whereas the Yanshanian northwest-southeast trending and Himalayan northeast-southwest trending fractures are strongly developed. This leads to significant spatial variations in the in-situ stress field and fracture intensity, constituting the macroscopic geological background for reservoir heterogeneity. The coal seam is characterized by high rank, high vitrinite content, and low ash and moisture. The coal texture is predominantly primary and cataclastic. Gas content increases with burial depth, generally exceeding 15 m³/t in deep zones; however, the permeability is overall lower than 0.4 mD, exhibiting an ultra-low permeability feature. The maximum and minimum horizontal principal stresses and the breakdown pressure all increase with depth. The difference coefficient of in-situ stress is generally greater than 0.37, indicating that the reservoir is in a state of strong stress disequilibrium. A comprehensive evaluation suggests that the No. 5 coal seam in the study area has undergone multi-stage tectonic modifications. Despite its high resource abundance, it possesses extremely low permeability and exists in a state of strong stress disequilibrium. It is a typical reservoir type that requires large-scale hydraulic fracturing stimulation to achieve industrial productivity.
The southeastern part of the Ordos Basin
A Dynamic Grain and Semantic-FCM Model for Long-Term Multivariate PM2.5 Forecasting
This paper addresses long-term multivariate time series forecasting, where feature redundancy, noise interference, and complex inter-variable relationships pose significant challenges. A ridge regression model enhanced by Dynamic Granular Computing (DGC) and Fuzzy Cognitive Map (FCM) features is proposed for long-term PM2.5 concentration prediction. The DGC module transforms raw time series into granule-level features described by statistical attributes such as mean, slope, and standard devia-tion, enabling dimensionality reduction and noise mitigation in long-horizon sequences. Since statistical granules alone cannot capture latent relationships among granule-level concepts, an FCM based on nonlinear Hebbian learning is employed to extract cognitive features. These features are combined with statistical granule features and used as inputs to the ridge regression predictor. Experimental results show that granule-based models outperform baselines using raw sequences, and that FCM-enhanced models achieve higher R² values and more stable error performance across multiple monitoring sta-tions, demonstrating the effectiveness of the proposed approach.
Long-term Multivariate Time Series Forecasting
Perceived Challenges and Benefits of Implementing Hospital Information Systems
This study examined the growing impact of Hospital Information Systems (HIS) on nursing practice, with a focus on frontline nurses’ experiences, which remain underexplored. Using a descriptive qualitative design, semi-structured interviews were conducted with 15 registered nurses, and the data were analyzed following Colaizzi’s seven-step method. The study identified three key themes. First, HIS improved nurses' access to patient-specific information, database knowledge, patient workflow data, and management information. Second, nurses recognized that the functionality of HIS met their clinical nursing requirements. Third, major challenges included repetitive record-keeping, insufficient training, slow technical support response, and the emotional burden of system malfunctions. Overall, HIS contributes positively to nursing quality, patient safety, and nurses’ professional confidence. However, despite improving information accessibility, issues such as limited usability and increased implicit emotional labor persist, highlighting the need for improvements in both system design and organizational management to better support clinical nursing practice.
A Review of Soil Constitutive Model Research: From Classical Theories to Frontier Advances
Soil constitutive models are mathematical expressions that describe the stress-strain-strength-time relationships of soil mass, and are essential for accurately predicting the deformation behavior of soil under external loading. This paper systematically reviews the development history of soil constitutive models both domestically and internationally. Internationally, the Cambridge model proposed by Roscoe et al. in 1963 marked the beginning of the modern stage of soil mechanics. Domestically, the concept of constitutive models began to appear in the 1970s, and with the development of computer technology and the finite element method, research on such models has gradually gained practical significance. On this basis, this paper introduces various types of constitutive model advances, including the BBM model for unsaturated soils, and typical recent achievements: a mechanical model for frost heave of trapezoidal canals based on the two-parameter elastic foundation beam theory, a three-dimensional modified Cam-clay model for structured soils considering intermediate principal stress, a modified Yin Zongze model for biopolymer-improved expansive soil, and a nonlinear dynamic constitutive model for soils considering fluid-solid coupling effects and its implementation in FLAC3D. Finally, future directions for constitutive model development are discussed, emphasizing the integration of physical mechanisms, mathematical methods, and computer technology to improve model accuracy and applicability in practical engineering. This paper serves as a theoretical reference for research in soil mechanics.
Research Status of Termite Secretion Simulants
As a complex biochemical system, termite secretions exhibit unique functional properties in soil improvement and geotechnical engineering. This paper systematically analyzes the main chemical components of termite secretions, including formic acid, enzymes, polysaccharides, proteins, etc., and deeply explores their influence mechanisms on the physical, chemical and mechanical properties of rock and soil. Studies have shown that termite secretions significantly alter soil properties through multiple mechanisms such as biological cementation, acid-base regulation and enzymatic hydrolysis. The concentration of formic acid can reach 3.8%, and the secretions increase soil strength by 10 times with a compressive strength of up to 32 MPa. Based on bionic principles, this paper focuses on the widely used simulants in academia and engineering, including microbial-induced calcium carbonate precipitation (MICP) technology, chitosan and its derivatives, heshutin, and synthetic termite saliva. Through comparative analysis, it is found that MICP technology has the most significant effect in improving strength and reducing permeability, with the compressive strength of treated soil reaching 1.5-8.0 MPa and permeability reduced by 1-3 orders of magnitude; chitosan has advantages in simulating biocompatibility and pH regulation; heshutin performs prominently in water retention and nutrient release. These simulants show differentiated applicability in different soil types, providing important technical support for the application of biological materials in geotechnical engineering.