: The increased concern about rising global temperatures and the availability of abundant solar energy have driven worldwide research into solar space-heating applications. Extensive research has been carried out on solar air-heating technologies (both in patents and research papers) because of their environmentally friendly nature, and there is a need to assess global research trends on solar heating technology based on publications, author and national collaboration, keyword co-occurrence, and related factors. A brief trend of patents granted is studied and summarized; however, for a rigorous analysis of the large volume of scientific data on solar air heating, a bibliometric analysis of the literature has been conducted over the last six decades. : The analysis broadly focuses on global publication trends, prominent authors, geographical contributions to publications, citation patterns, and keyword analysis. In the last five years, more than 100 articles have been published each year on SAH, resulting in a substantial increase in overall publications. Indian scholars contributed 40% of the world’s publications, placing them ahead of the other 66 nations publishing on the same topic. The most frequently occurring keyword is artificial roughness, and among all other possible shapes or configurations, V-shaped roughness with multiple symmetric gaps provides the best performance parameters. For low Reynolds numbers (4000 to 8000), dimple-shaped roughness performs the best.
Introduction/Objective: Slag-based geopolymer slurry (GS) often suffers from rapid setting and limited fluidity, which are primarily attributed to its high CaO content and large specific surface area. These drawbacks restrict its workability and practical application in ambient-cured systems. To address these challenges, this study introduces fly ash as a co-precursor to modulate both the replacement ratio and the initial chemical composition of GS. Such adjustment simultaneously alters the physicochemical environment of the activating system, thereby influencing the balance between hydration and geopolymerization reactions. By clarifying this dual physical–chemical regulation, the present work not only provides fundamental insights into the synergistic reaction mechanism but also establishes a basis for the rational design of high-performance and potentially patentable geopolymer formulations. Methods: Composite precursors were prepared by replacing slag with 5-70 wt% fly ash, while dynamically adjusting the activator dosage to control the initial chemical composition (SiO2/Al2O3 and CaO/Al2O3 ratios). Fresh behavior tests, compressive strength measurements, and product characterization techniques, including XRD, FTIR, and SEM-EDS, were employed to evaluate the macroscopic properties and microstructural evolution of GS under ambient curing. Results: The introduction of fly ash improved fluidity, prolonged setting time, and optimized strength development through simultaneous effects of particle packing and chemical composition adjustment. A fly ash content of 30 wt% (S70F30) achieved the highest compressive strength (48.4 MPa at 3 days and 64.3 MPa at 28 days), attributed to balanced hydration–geopolymerization reactions and the formation of dense (C, N)-A-S-H gels. In contrast, excessive fly ash replacement reduced reactive components, weakened gel formation, and deteriorated matrix densification. Discussion: The improvement in GS performance results from dual mechanisms: (i) physical lubrication by spherical fly ash particles, enhancing packing uniformity, and (ii) chemical regulation of initial molar ratios, which shifts the reaction pathway and gel products. This interplay ensures stable strength development when the fly ash proportion and initial chemistry are both appropriately optimized. Conclusion: Incorporating 30 wt% fly ash with adjusted initial chemical ratios substantially improves the fresh properties and long-term strength of slag-based GS. These findings highlight the potential of composite precursor design in developing high-performance and sustainable geopolymer binders.
Introduction: Remote sensing satellite imagery has become an important tool in change detection and is essential for monitoring the environment, urban development, and land use. Conventional approaches often fail to reflect the subtle geospatial and temporal changes that comprise dynamic landscapes. The availability of satellite and aerial imagery has revealed the need for sophisticated algorithms. Recent deep learning developments have potential for satellite image analysis because they can extract features and recognize patterns. Methods: The proposed early fusion framework incorporates a UNet++ architecture with a Reg- NetY_016 backbone, reinforced with SCSE attention mechanisms in the decoder block. By learning hierarchical features, the proposed model effectively captures fine spatial details and subtle variations. Results: Comprehensive testing on Szada/1 and Tiszadob/3, on standard splits used for comparison with existing state-of-the-art methods, achieves F1-scores of 75.54% and 92.68%, respectively. The method also outperforms with an average F1-score of 87.87% ± 4.30% (Szada) and 93.72% ± 1.55% (Tiszadob) under 5- fold cross-validation on the full datasets across different data splits. Discussion: Ablation experiments and computational complexity analysis validate that both nested skip connections and the SCSE attention module boost the detection performance with minimal computational overhead. Cross-region experiments further confirm the proposed model’s transferability across distinct datasets. Conclusion: These results show that deep learning architectures coupled with specialized attention mechanisms are viable approaches for robust performance on change detection tasks in bi-temporal satellite images.
Introduction: Against the backdrop of climate change and accelerating urbanization, cities are increasingly exposed to extreme precipitation events, posing substantial threats to sustainable urban development and macroeconomic stability. Using the 2012 “7.21” Beijing rainstorm as a case study, this study assesses its economic consequences and evaluates the relative suitability of Input- Output (IO) and Computable General Equilibrium (CGE) models for disaster loss assessment. Methods: This study proposes an integrated modeling framework that combines IO and CGE models. Specifically, the IO model is used to estimate direct and sectoral indirect losses, while the CGE model simulates broader macroeconomic impacts and post-disaster recovery trajectories. In addition, resilience is incorporated into the CGE framework to capture the adaptive capacity of the economic system following a disaster. Results: The event caused substantial direct losses in the agricultural and transportation sectors, while associated supply-chain disruptions transmitted indirect shocks throughout the broader economy. IO models are well suited to the rapid estimation of short-term impacts, whereas CGE models account for endogenous price adjustments and medium- to long-term recovery trajectories. Incorporating resilience into the CGE framework substantially improves the accuracy of recovery estimates. Discussion: These findings establish resilience as an essential dimension of disaster impact assessment and post-disaster economic recovery analysis. Enhanced resilience management can reduce losses in disaster-vulnerable sectors, particularly transportation and agriculture, whose production and cost structures are especially susceptible to external shocks. The comparative analysis further indicates that model selection should be guided by assessment objectives, temporal scope, and the relevant policy context. Conclusion: This study incorporates resilience into an integrated IO-CGE analytical framework, thereby strengthening the assessment of disaster-related economic impacts. The proposed framework provides a more accurate basis for estimating economic losses from extreme rainstorms and offers theoretical support for formulating post-disaster recovery strategies and urban disaster management policies.
Introduction: Farm productivity largely depends on soil conditions, choosing the right crops, and using fertilizers in a balanced way. This work presents a multi-stage, AI-driven decision support system that integrates quantum computing with classical machine learning, leveraging quantum circuits for increased feature representation. To address the challenges of accurate soil analysis and crop selection, an intelligent agricultural decision support system is developed to classify soil categories from images and provide suitable crop and fertilizer recommendations. Multiple machine learning models were integrated into a single web application, enabling real-time analysis and decision support for farmers. Methods: The HQCNN (Hybrid Quantum Convolutional Neural Network) model classifies soil types from input images in this system. It processes soil images using CNN layers for feature extraction and a quantum layer for advanced feature transformation. By combining CNN with quantum learning, it accurately predicts soil type. The XGB Classifier model acts as the crop recommendation engine in this system. It takes soil nutrients, climate parameters, and encoded soil type as input and predicts the most suitable crop. Learning patterns from historical data using gradient boosting provides accurate multi-class crop classification based on environmental conditions. Random Forest (RF) Classifier serves as the fertilizer recommendation engine in this system. It takes inputs such as moisture, recommend crop type, NPK values, encoded soil type, temperature, and humidity to predict the most suitable fertilizer. Results: The HQCNN model achieved 97.62% training accuracy, 98.83% validation accuracy, and 98.25% test accuracy. Additionally, the model demonstrated excellent classification capability with a micro-average ROC (AUC = 1.00) and macro-average ROC (AUC = 1.00) score, indicating strong generalization performance. Discussion: High accuracy validates that HQCNN performs efficiently in soil classification. Combining it with XGBClassifier and RF enhances prediction capability. The system has more reliable crop and fertilizer recommendations. It provides clear insight into soil nutrient status. Web application integration improves usability for real-world farming use. Conclusion: The proposed system demonstrated high accuracy and practical applicability, offering a valuable tool for farmers to optimize crop selection and improve productivity. The work is potentially useful for ML-based soil classification and crop recommendation.
