A Novel Hybrid ViT-CNN Approach for Pneumonia and Lung Opacity Detection in X-Ray Images

In order to automatically classify chest X-ray pictures into three diagnostic categories—Normal, Lung Opacity, and Viral Pneumonia—this study presents a novel hybrid deep learning architecture that combines the Vision Transformer (ViT) with a Convolutional Neural Network (CNN). The suggested model successfully addresses the drawbacks of single-architecture systems by fusing the ResNet-18 CNN's expertise in local texture analysis with the ViT's global feature representation capability. According to experimental assessments, the hybrid ViT-CNN architecture outperforms the state-of-the-art methods, achieving 94.2% classification accuracy with precision, recall, and F1-scores continuously above 94% for the majority of categories. Even in complicated situations where traditional methods usually falter, like distinguishing between lung opacity and normal patients, the model exhibits strong performance. Additionally, it performs well in discrimination, with AUC values above 0.95 in every class. The system is ideal for real-time clinical deployment because it maintains a high computational efficiency, generating conclusions in about 0.0012 seconds per image. Grad-CAM visualization makes it evident which areas of the image are important for making diagnostic decisions, hence validating the model's interpretability. All things considered, this work establishes a new benchmark for chest X-ray classification performance and offers a useful foundation for automated diagnostic assistance in resource-constrained healthcare settings

A Data-Driven SPC Framework for Monitoring and Forecasting Global Temperature Trends

This study aims to develop an enhanced Statistical Process Control (SPC) framework for the effective monitoring and forecasting of global temperature trends, addressing the limitations of traditional control charts in handling autocorrelated environmental data. The primary objective is to construct a modified Exponentially Weighted Moving Average (EWMA) chart capable of detecting both abrupt and gradual shifts in time series exhibiting seasonal and stochastic dependencies. The proposed approach models global temperature data using a Seasonal Autoregressive Moving Average process with exponential white noise (SARMA(1,1)L) to capture temporal patterns and residual variability. An analytical formulation for evaluating the one sided Average Run Length (ARL) is derived, enabling quantitative assessment of chart performance. The simulation analysis demonstrates that, by applying an optimized smoothing parameter, the proposed EWMA control chart outperforms the traditional EWMA and CUSUM charts, particularly in detecting small shifts with significantly lower ARL1 values. The findings confirm that the proposed model effectively tracks and predicts global temperature anomalies with high sensitivity and accuracy. The novelty of this research lies in integrating SARMA-based modeling with SPC design to improve detection reliability under autocorrelated and nonstationary conditions. This data-driven framework offers a promising tool for real-time environmental monitoring and climate forecasting applications

Multi-Dimensional Digital Twin Modeling for Fault Diagnosis of Industrial Centrifugal Pump

Industrial centrifugal pumps operating under complex conditions frequently encounter failures, yet existing diagnostic methods face challenges in effectively fusing multi-source sensor data with physical mechanisms. To address these limitations, this paper proposes a multidimensional digital twin modeling approach that establishes a deep bidirectional synergy between data-driven perception and mechanism-based simulation. First, a Dynamic Sparse Spatio-Temporal Graph Attention Network (DS-STGAT) is designed to capture dynamic local and global dependencies among multi-source signals. Second, unlike conventional unidirectional methods, a novel data-mechanism collaborative adaptive mechanism is introduced. This creates a closed-loop pathway of “data-guided → simulation-refined → data-enhanced,” where the perception model retroactively optimizes simulation parameters (e.g., stiffness coefficients) via consistency constraints, while the simulation model provides physical priors to guide graph construction. Experimental results on multiple bearing datasets and real-world pump conditions demonstrate that the proposed method outperforms baseline models in accuracy, physical consistency, and robustness. Notably, the approach achieves high fault identification rates even in zero-shot scenarios, validating its effectiveness and scalability for the intelligent fault diagnosis of complex rotating machinery

Advanced Microstructural Engineering of Gas Diffusion Layers for Synergistic Electrochemical Performance and Durability

The microstructure of gas diffusion layers (GDLs) is a critical factor in determining the electrochemical behaviours and durability of the proton exchange membrane fuel cells (PEMFCs). The paper will assess the ability of engineered GDL microstructures to improve the synergistic behavior of electrochemical performance. The 3D multi-physics numerical model is created, which considers the anisotropic porous transport, water capillary behavior, electrochemical reaction, and thermal effects, and is tested in accordance with known reference data. Using four GDL architectures, which include a traditional GDL, horizontally aligned, vertically, and an X-pattern GDLs, four GDL architectures are compared. The findings demonstrate that directional microstructural engineering produces a strong influence on performance characteristics. Whilst the GDLs that are horizontally and vertically aligned have a selective effect on in-plane and through-plane transport, respectively, the X-pattern GDL has a more balanced nature and exhibits a 35, 40, 28 and 45% reduction in oxygen transport resistance, a reduction in liquid water saturation, an increase in peak power density, and a 45% reduction in voltage degradation rates, respectively, than the conventional design. The originality of the given work is in showing that multidirectional GDL engineering provides an opportunity to synergize electrochemical performance and durability

A Real-Time IoT-Enabled Machine Learning for Quality Prediction of Perishable Beef Product

The cold chain industry in Indonesia is experiencing rapid growth, especially for perishable products such as beef. Ensuring product quality during distribution requires accurate monitoring of storage environmental factors, including temperature, humidity, and gas exposure. This study aims to develop an IoT-based quality monitoring system for perishable beef products and implement a machine learning approach for quality prediction. An IoT-enabled e-Sense device was developed to collect real-time environmental parameters and RGB colour information as quality parameter from tenderloin beef samples. The collected data were analysed using three regression-based machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Support Vector Regression (SVR). Data preprocessing and hyperparameter tuning were applied to improve model performance. The results show that SVR consistently outperformed RF and DT in predicting RGB colour in presenting beef quality based on prescribed parameters. SVR achieved an R² of 0.973 for RED and 0.992 for both GREEN and BLUE channels. These findings confirm the effectiveness of integrating IoT technology with machine learning for real-time perishable products quality prediction. This research contributes to combining real-time multi-sensor IoT data with regression-based models to provide improved continuous quality monitoring compared to previous single-parameter or offline approaches

Product Usability Mediates Cognitive-Purchase Relationship in Elderly Consumers: Urban-Rural Differences

Objective: This study investigates how cognitive ability affects elderly consumers' purchase decisions for home products, examining product usability as a chain mediator and urban-rural differences as a moderator. Methods: A stratified sampling survey was conducted among 1,247 adults aged 60 and above across 28 Chinese provinces. Structural equation modeling and multi-group analysis were employed to test the proposed model. Findings: Results demonstrate that cognitive ability significantly influences purchase decisions. Product usability serves as a significant chain mediator between cognitive ability and purchase decisions. Notably, urban-rural differences moderate this mechanism: urban elderly rely more on product usability when making purchase decisions, while rural elderly's cognitive ability directly influences their purchasing behavior. The indirect effect accounts for 78.6% of the total effect, with the chain mediation effect being statistically significant (95% CI [0.35, 0.49]). Contributions: This study extends cognitive processing theory by revealing the threshold activation and cascading amplification characteristics in elderly decision-making. The findings provide practical implications for designing elderly-friendly home products and developing differentiated marketing strategies for urban and rural markets

Optimizing Oxidative Roasting of Low-Grade Molybdenum Intermediates on Phase-Controlled Process Parameters

Processing low-grade molybdenum intermediates demands oxidative-roasting regimes that maximize Mo recovery and resource efficiency. This work proposes a phase-oriented optimization approach for a 22.23% Mo intermediate, grounded in the multifactor Protodyakonov-Malyshev model and tabular nomographs as a practical parameter-selection tool. Control experiments varied temperature to 700 °C with roasting limited to 20 min and bed height to 0.010 m. Phase evolution during roasting and Mo recovery after alkaline leaching were quantified from integrated mineralogical-chemical data. A robust process window was identified at 550 °C, achieving 97% Mo recovery. Increasing temperature above 600 °C initiates a mechanism shift that forms MoO₂, reducing selectivity. Using the developed nomographs enables 20 min roasting while favoring MoO₃ formation (88%) and minimizing undesired phases, thereby improving overall resource efficiency for low-grade feed. The study’s novelty is the adaptation of the Protodyakonov-Malyshev model to phase-composition control during roasting, steering the transformation from MoS₂ to MoO₃, and proposing a resource-efficient alternative to conventional regimes by exploiting an exothermic reaction that supplies heat and requires no additional energy input. The results support the development of technological regulations, furnace design and operation, and industrial trials at copper-molybdenum plants for producing molybdenum products (CaMoO₄)

Aerodynamics Analysis of a Novel Multirotor Structure Derived from Generative Design Using Computational Fluid Dynamics

This study investigates whether generative design structures can enhance the aerodynamic efficiency and payload capacity of multirotor UAVs compared with conventional stacked-rotor layouts. The methodology integrated structural generative design in Autodesk Fusion 360 and evaluated hover aerodynamics using computational fluid dynamics (CFD) analysis in SimScale, employing a steady, pressure-based incompressible RANS solver and Multiple Reference Frame (MRF) zones to model hover at 4500 rpm. Three configurations were evaluated: a baseline “Initial Model” and two generative-design outcomes (Model 1 and Model 3). The results show that Model 1 provides negligible improvement, whereas Model 3 increases total thrust to 42.36 N, corresponding to a 9.6% gain relative to the initial configuration, and raises the system-average figure of merit by approximately 65%. Flow-field analyses indicate that Model 3 promotes cleaner inflow to the upper tier and a more coherent downwash, with reduced recirculation and interference-related losses. The novelty lies in coupling generative structural synthesis with CFD-based aerodynamic screening to demonstrate that frame geometry can actively enhance stacked-rotor hover performance

Project Management Strategies for Smart City Initiatives: A Framework for Sustainable Urban Development

Rapid advances in artificial intelligence (AI), the Internet of Things (IoT), and digital government platforms are accelerating the transformation of urban systems toward smart and sustainable cities, creating new challenges for managing complex urban development projects. This study proposes an integrated project management framework that aligns digital transformation technologies with sustainability principles to enhance smart city implementation in emerging economies. A qualitative research design combining systematic literature analysis and comparative case study evaluation was employed, examining smart city initiatives in Aqkol, Nur-Sultan (Astana), and Almaty, Kazakhstan. The analysis investigates how technological integration, governance mechanisms, stakeholder collaboration, and sustainability objectives interact within adaptive project management environments. Results indicate that successful smart city development depends on coordinated alignment across these four dimensions, supported by flexible governance models and data-driven decision processes. The findings further demonstrate that AI-enabled digital twin technologies and participatory digital governance significantly improve infrastructure optimization, urban decision-making efficiency, and resilience outcomes. The proposed framework contributes a structured evaluation and implementation model that bridges project management theory with smart city digitalization practices. The novelty of this research lies in integrating sustainability-driven project governance with emerging digital technologies into a unified operational framework, providing practical guidance for policymakers, urban planners, and project managers seeking scalable and resilient smart city solutions in rapidly evolving technological contexts

Tourism Ecological Environment Quality Assessment Using Principal Component Analysis and Remote Sensing Data

Current methods for assessing tourism ecological environment quality suffer from insufficient analysis of complex environmental impact factors and low assessment accuracy. In response to this situation, this study proposes a method for assessing tourism ecological environment quality that takes into account principal component analysis and remote sensing data. The study first combined remote sensing data and principal component analysis to establish an evaluation index system, and then introduced the grey-scale correlation method to rank and assign weights to key factors affecting ecological environment quality. Based on the results of principal component analysis and the grey-scale correlation method, the study constructed a comprehensive assessment model. Subsequently, dynamic simulation and prediction were achieved by combining the meta-cellular automata model and the Markov model. The experimental results indicated that in the ecological assessment of different seasonal environments, the Kappa coefficient and overall accuracy of the proposed method reached 0.93 and 0.93, respectively. Furthermore, the coefficient of determination and mean absolute percentage error were 0.91 and 0.08, respectively, and the assessment accuracy reached 93.88%. The tourism ecological environment quality assessment method proposed in this study can effectively address challenges in different environments. It maintains high-precision assessments and dynamic predictions, providing a reliable basis for decision-making in ecological environment protection