In Japan, the rapid expansion of solar farms since the Feed-In-Tariff (FIT) policy in 2012 has raised environmental and hazard-risk concerns, necessitating a comprehensive PV inventory for quantitative assessment of those challenges. This study presents a national-scale PV map created through the application of machine learning to remote-sensing imagery, enabling a thorough assessment of environmental changes and hazard risks of existing PV sites. By applying the trained XGBoost model on Sentinel-2 imagery acquired from September to October 2022, we calculated the likelihood of solar PV presence at each pixel followed by noise reduction with morphological filtering. The final solar PV map was produced by applying a threshold on the probabilistic output, and it was further converted into polygons representing solar PV perimeters. The resulting PV map efficiently reduces false positives and could be used to update existing databases both made from manual and machine-learning methods. Comparison with land-cover data revealed significant land-use changes due to PV installations, particularly in forested and agricultural areas. Hazard-risk assessment identified 30.0% of solar farms in flood-prone areas and 3.2% in landslide-prone zones. This study underscores the need for environmental protection and hazard mitigation measures to further advance solar power in Japan through remote-sensing-based national PV mapping and GIS analysis.

Satellite observations of nighttime light (NTL) play a vital role in monitoring urban dynamics. As NTL is observed from varying angles on a day-to-day basis, daily NTL data exhibits periodic fluctuations known as the angular effect. This phenomenon is believed to be closely linked to the three-dimensional (3-D) distribution of light sources. Therefore, our objective was to develop a model that can estimate 3-D urban structure based on NTL data by investigating the relationship between the angular effect and urban morphology. Firstly, we classified urban geometries using 3-D building model data and street data in Tokyo. Then, we compared these categorized urban structures with the observed angular effects. Our findings indicate a correlation between angular effects and specific urban structures, particularly in residential areas and districts characterized by dense high-rise buildings. The findings of this study provide a basis for using the angular effect to estimate urban structure. Expanding similar analyses to other cities will enable us to build a comprehensive model for urban structure estimation using NTL data. Such a model could provide more in-depth insights into urban land use, beyond the capabilities of traditional land cover classifications, especially in data-scarce regions.

A pivotal component of the construction industry's transformation involves the utilization of laser scanners to produce highly accurate point clouds, providing an unparalleled representation of real-world structures. Apple's LiDAR technology enables the capture of 3D point cloud data through the handy iPhone LiDAR Sensor.

This study addresses the challenge of enhancing the construction accuracy of utility duct installation with reference to a power cable installation project in Japan. The transition from overhead power lines to underground systems is a significant endeavor, and ensuring precise pipe installation is a complex task. Manually checking the layout of utility ducts and pipes for each step, along with their documentation, requires a significant amount of time, labor, and is susceptible to errors, further exacerbated by limited project timelines and labor shortages in the construction industry.

Our research focuses on developing an automated algorithm to detect and measure the 3D dimensions of boxes, positions, and lengths of pipes on construction sites utilizing iPhone LiDAR technology. In the methodology, site-captured point cloud data of utility ducts using iPhone LiDAR technology is processed to separate the excavation part through plan fitting. Sidewalls of the utility duct are separated from the excavation using normal-based K-means clustering. The bottom slab of the utility duct is removed from the pipes based on color analysis and 3D volume masks. The accuracy of pipe extraction is 83% based on tested data. In the next step, the extracted pipe point cloud is preprocessed through voxel-based down-sampling and then clustered using the DBSCAN clustering tool. The candidate clusters, found after clustering, have been post-processed to segment the point cloud for each pipe. As of now, we have achieved successful segmentation of straight pipes point cloud only.

Our proposed algorithm bridges the construction accuracy gap for utility ducts, promotes digital transformation in the industry, and offers a user-friendly, on-site, mobile phone-operated solution with low computation.

Road cracks are an important damage for road administrators to maintain the road condition. Deep learning (DL) is common for detecting cracks in road surface images considering its classification accuracy. Previous research works focused on convolutional neural networks (CNNs) without non-crack features or crack analysis with limited accuracies. This study incorporates background classification into CNNs. Background image features are extracted in an unsupervised way by a deep convolutional autoencoder (CAE). A self-organizing map (SOM) map clusters features to obtain background categories. By increasing the number of non-crack categories, CNNs are motivated to learn non-crack features. The proposed method is validated using common road crack datasets. Modified deep CNN models significantly improved accuracy by 1 % - 4 % and f-measure by 3 % - 8 % compared to previous models. The modified VGG16 showed the top-level performance, 96 % accuracy and 84 % - 85 % f-measure.

Climate change is one of the most severe challenges for a sustainable future. Resource recovery and recycling of organic waste is a burning question in recent times. To prevent global warming and climate change, green energy should be introduced. Microbial fuel cell is an innovative technology in which bioelectricity can be generated with the metabolic activities of the bacteria while degrading the organic substances. This research uses microbial fuel cells to generate green energy from organic waste and living plants. The by-products of bioelectricity generation from organic waste can be used as a soil conditioner or compost in the field. The various plants can use this compost to generate bioelectricity by using plant microbial fuel cells. This system can enhance the resource recovery option from organic waste and at the same time increase the use of living plants for bioelectricity generation for the future.

Urban expansion is a pivotal issue in emerging markets and developing countries, with Ulaanbaatar, Mongolia's capital, exemplifying this challenge. Home to half of the country's population, Ulaanbaatar faces significant pollution and overcrowding issues, largely due to the ger district, characterized by traditional Mongolian tent houses ('Gers') and insufficient infrastructure. This research uses various data sources to analyze the city’s historical morphology and population changes from the 1990s to 2020, with a particular focus on the ger district. Using the Global Human Settlement Layer (GHSL), crowd-sourced data, and governmental census data, we analyze Ulaanbaatar's settlement expansion and population changes from 1990 to 2020. Our approach introduces city-wide and 'Ger District' comparisons, supplemented by district-level analysis to capture the city's heterogeneity. We employ the United Nations (UN) Sustainable Development Goals (SDG) 11.3 index, specifically the Land Consumption Rate to Population Growth Rate (LCRPGR), to assess these changes. Our findings indicate that Ulaanbaatar's overall population growth peaked from 2000-2010, but the ger district's growth rate saw a surge from 2010-2020. This increase significantly expanded the city's total settlement footprint, leading to high land consumption rates (LCR) during this period. The low-density expansion is notably reflected in the city-wide LCRPGR index, with values exceeding 1 from 2010-2020, a significant rise from earlier periods. Our study highlights the necessity for a nuanced approach to urban expansion analysis, considering both city-wide and district-level dynamics. The insights gained are invaluable for urban planning and sustainable development strategies in Ulaanbaatar and other rapidly growing cities.

Seismic safety is one of the important aspects to be addressed while designing buildings in earthquake prone regions. The paper presents a systematic analysis and design methodology for earthquake resistant school buildings in Nepal. Linear dynamic analysis (Response Spectrum analysis) has been conducted to study the performance of these buildings under earthquake loading. Structural design has been carried out as per the Limit State Design Method. Structural analysis and design methodology is done abiding by the provisions enshrined in the Indian standards and codes, making the resultant school building structures sustainable and resilient to earthquake. The paper through a case study attempts to provide a guiding light to different stakeholders, especially in Indian subcontinent, involved in designing, planning, and execution of Earthquake-Resistant Design (EQRD) Structures. By adopting the methodology, 70 schools and 1 Library building have been constructed in earthquake affect regions of Nepal.

This paper provides an insight to the research program, wherein Plastic Cement Bag Mesh (PCBM) is explored for the retrofitting of masonry. PCBM was obtained using ordinary empty cement bags (made up of plastic), which were modified into the mesh for the purpose of retrofitting. The PCBM was evaluated on the basis of compressive strength, shear strength and retrofitting cost performed on masonry prisms and wallets. The PCBM retrofitting technique was carried out on the previously tested damaged confined masonry building. Retrofitted confined masonry building (CM_RET) and its original counterpart (CM) were compared to evaluate the effectiveness of retrofitting technique. The comparative results in terms of strength, stiffness, ductility, energy dissipation showed excellent enhancement upon retrofitting using PCBM on damaged CM building. It is expected that experimental investigation on retrofitting of masonry and its effectiveness will be useful to practicing engineers to choose suitable material for a seismic resistant and cost-effective effective retrofitting technique.

A small-scale trial system for energy saving is installed in a food processing factory in Vietnam. The system utilizes unused coldness of wastewater released from a poultry factory in Hanoi. The installed system is “pre-cool system” with a polyethylene made heat exchange system called “G-HEX”. As G-Hex is made of polyethylene, which is strong against chemical reaction, the system can be used in a bad water quality. The food processing factory in Vietnam generates a cold water (0 ℃) from an industrial water (30 ℃) by an electrical chiller system for their operation. As the electricity cost for the current cooling system is expensive, 45,000 US dollars per year, the pre-cool “G-Hex” system is expected to lower the electricity cost. After installation of the system, the water temperature and flow rate at the inlet and outlet of each component were closely monitored. The temperature and the flowrate have fluctuated slightly during the operation. Energy savings are calculated based on the monitored data. The existing chiller system on the left in Fig.4 required “64.9 kW” of energy to cool water to 0 ℃. In contrast, for the chiller with the pre-cooled G-Hex system on the right, “53.6 kW” was consumed reducing electricity consumption by 18% in total. Same system is applicable for a hot wastewater, too. There is a possibility of introducing the system to companies in the fisheries and textile industries with appropriate pretreatment and adjustment in the operation. Consequently, the pre-cool G-Hex system was successfully installed to reduce for saving electricity by 18% in the trial study in Vietnam. As the system can be used for hot condition and is strong against the chemical reaction, the system is applicable for a wide variety of industries, such as fisheries or textile industry etc.

Therapeutic perspectives of medicinal plants have been considered as a very important aspect in today’s public health research. Researchers focusing on traditional medicine, elucidates various kinds of common ailments and their treatment process through commonly available medicinal plants. However, this practice is ancient but for past two decades it has gained acceleration in biomedical science. The implementation of medicinal plants in medicine is considered as a valuable resource and a real alternative of primary healthcare for economically developing countries, especially in rural areas. We have done a survey-based study to assess the prevalence of common medicinal plants in a random population in West Bengal, India. Our findings broadly highlight the fact that Tulsi (Ocimum tenuiflorum), Neem (Azadirachta indica), Aloe (Aloe vera) are the most used as raw traditional medicine for treating several clinical issues like immunity boosting, cough and cold, dermatological problems etc. These plants have the high growth rate in Indian subcontinent and are easy to maintain in kitchen gardens. We predict that a regulated chronic usage of these medicinal plants can be beneficial for domestic healthcare perspectives and will help to prevent several common diseases.