Uttaradit Rajabhat University

The construction sector significantly contributes to environmental degradation due to intensive resource consumption and large volumes of waste generation. Rapid urbanization further increases the need for sustainable construction waste management strategies. This study evaluates the environmental performance of construction waste management systems in the lower northern region of Thailand by integrating Circular Economy (CE) strategies within a Life Cycle Assessment (LCA) framework. Two scenarios were analyzed: a Business-as-Usual (BAU) system representing conventional waste handling practices and a CE scenario incorporating enhanced recycling, material recovery, and energy recovery processes. The analysis covered six major construction waste streams—concrete, steel, wood, glass, paper, and mixed waste—and assessed environmental impacts across eighteen midpoint indicators, including climate change, resource depletion, land use, water consumption, and pollution-related impacts. Results indicate that the CE scenario significantly improves environmental performance compared with the BAU system, particularly through reductions in greenhouse gas emissions, resource depletion, land occupation, and ecotoxicity due to increased material recovery and improved recycling technologies. However, CE implementation also introduces trade-offs, including higher transportation-related emissions and particulate matter formation associated with expanded recycling logistics. These findings highlight the need for optimized waste transportation systems, decentralized recycling infrastructure, and low-emission technologies. Overall, the study provides a region-specific LCA framework and empirical evidence to support circular economy implementation in construction waste management, contributing to climate change mitigation and the achievement of the Sustainable Development Goals (SDGs).

Impulsive acoustic event classification based on spectrogram representations remains challenging due to the non-stationary and broadband characteristics of transient signals. While convolutional neural networks (CNNs) have been widely adopted for spectrogram-based classification, their reliance on local convolutional receptive fields may limit the modeling of long-range spectro–temporal dependencies. This study investigates the architectural scalability and attention stabilization properties of Swin Transformer V2 for robust impulsive acoustic spectrogram classification. A comprehensive experimental framework is developed using twelve time–frequency representations, including Bark, Chromagram, Cochleagram, Constant-Q Transform (CQT), Delta, Delta-Delta, Log-Mel, Mel, MFCC, Spectral Contrast, Short-Time Fourier Transform (STFT), andWavelet spectrograms. Multiple Swin Transformer configurations are evaluated and compared with widely used CNN baseline architectures, including ResNet50, ResNet101, EfficientNet-B0, and InceptionResNetV2, under a unified training protocol with five-fold stratified cross-validation. Experimental results demonstrate that Swin Transformer V2 consistently achieves higher classification accuracy and macro-F1 scores than CNN baselines across diverse spectrogram representations. In addition, the hierarchical attention mechanism provides improved stability across data partitions and enhanced robustness under spectrogram resolution variability. These findings highlight the effectiveness of transformer-based architectures for modeling complex spectro–temporal structures in impulsive acoustic signals and provide insights into the architectural behavior of Swin Transformer models for spectrogram-based acoustic classification.

Purpose The purpose of this study was to design, deploy, and evaluate a universal access–oriented emergency healthcare system that reduces barriers related to age, digital literacy, infrastructure limitations, and organizational fragmentation in rural settings. The research question addressed how a human-centric cyber-physical-human Internet of Things (IoT) platform can support equitable access to emergency healthcare for older adults in low-resource communities. Methods A Smart Information Management System and Emergency Medical Call System (SIMS–EMCS) was developed using a user-centered co-design approach. The platform integrates structured elderly health data management with real-time emergency communication through a widely used social messaging interface. The system was deployed in a rural subdistrict in Thailand and evaluated through real emergency use and controlled drills. Usability and system performance were assessed using a structured questionnaire administered to healthcare personnel, rescue teams, and community volunteers. Results The system achieved a 99.2% end-to-end communication success rate and reduced average emergency response time by 35% compared with conventional phone-based reporting. Usability evaluation yielded a “Good” overall score (mean 4.01 ± 0.65), indicating broad acceptability across users with diverse digital literacy levels. Conclusion The findings demonstrate that effective emergency healthcare access can be achieved without assuming high technical expertise or continuous connectivity, supporting core principles of universal access. SIMS–EMCS provides a replicable, human-centric model for inclusive emergency healthcare delivery in low- and middle-income country contexts.

In this study, we performed a comprehensive statistical analysis and a detailed survey of carbohydrate ligands bound within the active sites of carbohydrate-active enzymes (CAZymes) from the CAZy database (www.cazy.org). Specifically, we analyzed 3D structures of CAZyme-carbohydrate ligand complexes across 184 glycoside hydrolase families. By utilizing only coordinate files, we were able to retrieve, analyze, and interpret key structural and functional features. These include previously underexplored aspects, such as Cremer-Pople and Altona-Sundaralingam analyses of sugar ring conformations at the subsite -1 sugar ring, identification of catalytic residues (acid/base and nucleophile), and population trends. This study provides structural insights into catalytic residue patterns and ring conformational preferences in CAZymes, thereby supporting future efforts in functional analysis, mechanistic interpretation, and elucidation of catalytic itineraries.

There is an urgent need for innovative approaches to forest management given the increasing incidence and intensity of wildfire due to climate change. The impact of interactingprescribed burning, fuel reduction and natural fires on tree structure in Ban Pak Thap community forest; Thailand Twelve, 40 m x 40 m, experimental plots were studied for the ecological effects offour different management practices. The findings demonstrate that, not only do prescribed burning and fuel treatments significantly improve forest regeneration by decreasing inter-tree competition, enhancing nutrient recycling and encouraging superior sapling growth but also they also help to conserve species diversity as well as biomass accumulation. However, natural burning led to extensive reductions in tree density, species richness and total biomass. Extensive statistical analysis showed high relationships of the management practices tested on several ecological variables, and this demonstrated well the varying effects of these interventions. This result emphasizes the (compared to non-managed burns) advantageous effect ofmanaged fire on other hand important information for sustainable development of management strategies concerning tropical forest ecosystems. This study highlights the potential benefit of adaptive management in reducing negative impacts of climate change on forest health and resilience.