Unprecedented clinical threats always creates abrupt physiological threat along with psychological trauma. COVID-19 pandemic situation has resulted in significant onset of mental stress in global population. The rationale behind the study substantiates the plausible major reasons and effects behind the inception of psychopathological stress in a population due to the COVID-19 pandemic circumstances. The research work is an evidence-based demographic study, conducted through psycho-clinical interviewing of random population during the pandemic (lockdown) in West Bengal, India. The study was designed to produce statistical evidence of mental unwellness due to COVID-19 ambience and understand the brain-behaviour circuit behind the psychopathological condition. Findings strongly indicate the prevalence of psychological instability in adults. The case-study reveals the fact that sudden pandemic outbreak created upsurge in the anxiety-like behavioural patterns along with situational depression, followed by abnormal sleeping patterns. Digital media were the prime source of pandemic-related neurobehavioural stress contents, which eventually resulted in significant expression of psychopathological manifestations which were analysed in terms of behavioural marker exhibitions. However, there were indications of remedial strategies to combat the mental stress in the population. The case-report unravels the fact that apart from the severity of COVID-19 biomedical issues, there were strong traumatic exposition in the global population which was initiated due to the abrupt severity of the disease and also due to the surge of huge negative data of the pandemic. Clinical psychologists are working on novel protocols to fight with future pandemic-related mental illness, which eventually affects the daily lifestyle of a population.

The world population growth is projected to greater than 9 billion in 2050, boosting the demand for palm oil with approximately more than double of the current world production. Malaysia is the second largest palm oil producer in the world. However, it has been experiencing the labor shortage in the last decade. Also, land expansion for newly established palm oil plantation is severely restricted to avoid further deforestation. In addition, the impact of climate change is ambiguous. These situations will limit the growth of palm oil production in the country. Therefore, the increase of efficiency of palm oil production within the limited resource under climate change is crucial for meeting the future demand. To improve the efficiency, prioritizing the potential area which is suitable for palm oil growth and stable under climate change is one of strategies. While several studies report the suitable area for palm oil in Malaysia with the prediction under climate change, the resilience of palm oil to climate change is understudied.

This study aims to investigate the response of palm oil growth to the climatology obtained from satellite from 2010 to 2021. Satellite data used in this study are precipitation from GSMap, 2m surface temperature from ECMWF, gloss primary product (GPP) from MODIS, evaporation and transpiration from GLEAM, soil moisture and vegetation optical depth (VOD) from SMOS. In addition, water use efficiency (WUE) are calculated from GPP divided by transpiration. Water balance (Balance) is also obtained from precipitation subtracted by evaporation and transpiration. Satellite data is calculated for 8-day sum for precipitation and 8-day average for other variables, and the time series trends by Seasonal-Trend decomposition using LOESS (STL) are analyzed. The region of interest are patches of 0.25-degree grids in which Man-Kendall analysis showed the significant change of temperature and the grids with less significant change.

The result showed lower WUE in the grids where temperature has risen but precipitation is unchanged than in the grid where precipitation has increased. The grid without significant change of temperature and precipitation showed the lowest WUE through the period. The result implies that the increase of both precipitation and temperature may partially have the positive impact on palm oil production, while the effect may vary in places. Further investigation is needed.

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.