Flood damages have increased in recent years; while this is partly due to anthropogenic climate change, it has been exacerbated by population growth and economic development taking place in regions of high vulnerability. Onrush water flow from upstream rivers is also responsible for flash floods in these regions. In particular, the north-eastern wetland region of Bangladesh is extremely vulnerable to flash floods, which damage crops, property, and infrastructure every year. The flash flood of 2022 alone has surpassed most of the previous records. The main purpose of this study is to develop and evaluate a methodology for the delineation of flash flood hazard zones considering the recent year flash flood of 2022, 2020 and 2018. After reviewing relevant literature, flood affected area and delineation of flood hazard zone in the study region is investigated through remote sensing approach. The Sentinel-01 SAR images were used to generate inundation extents and SRTM data, JRC and Landsat satellite images were used for flood hazard classification using Google Earth Engine (GEE) and ArcGIS Pro. The findings identified 56% of the Sylhet division as inundated with significant inter-district variability in flood hazards: Sunamganj (82%), Sylhet (62%) and Moulavi Bazar (33%) districts emerged as affected. In addition, having 57% affected area, Habiganj have 30 union which was flashed more than 80% of its land. At Sunamganj, there was 301405.4 Hectare area flashed and 1374939 female population was affected which represents more than 50% of total population of Sunamganj district. With the onslaught of climate change, the risk and uncertainty of flash floods will increase. As such, this paper identifies zones of high risk to inform future regulations of spatial planning and interventions. The findings will be useful for involved authorities and policymakers while taking explicit and efficient flood damage reduction strategies in the future.

Dengue fever (DF) is very dangerous to people's health in many places around the world. Dengue fever has become more common around the world over the past few decades. The goal of this study was to find out how climate factors affect the number of dengue cases in the Dhaka City Area. We got monthly counts of dengue cases in the Dhaka City Area from 2008 to 2022 from Bangladesh's Ministry of Health and Family Welfare for the study. From 2008 to 2022, the Bangladesh Meteorological Department (BMD) kept records of the maximum and minimum temperatures (◦C), humidity (g.kg−1), rainfall (mm), sunshine hours (in average number of hours per day), and wind speed (knots (kt)) at Dhaka Station. The Poisson regression model was used first because the data we have is count data. Model 2 (Negative Binomial regression model) was then used. The Akaike Information Criterion (AIC) was used to rate how well the models fit. In the end, the model with the lowest AIC value was picked (Negative Binomial Regression). The average number of dengue cases goes up when the maximum temperature, minimum temperature, wind speed, and rainfall all go up. An analysis shows that the number of dengue cases will rise by 3.844 and 4.23 times for every 1 degree Celsius rise in the maximum and minimum temperatures. Again, every 1 mm of extra rain will cause.006 times as many dengue cases. Instead, when relative humidity goes up by one unit, the average number of dengue cases goes down by 0.081 cases. As a result of this study, officials in Bangladesh will be able to come up with a new climate-based alert system and take proactive steps to deal with the Dengue problem.

One of the major global environmental problems is human and ecological exposure to
hazardous wastes from agricultural, industrial, military, mining activities and
anthropogenic processes. These wastes often include heavy metals, hydrocarbons,
biological matters and other organic chemicals. The waste heat that increases the urban
heat. Findings reveal that landfills are situated very close to residential areas, water
bodies and agricultural lands, exposing them to various health and environmental
hazards. Improper solid waste management practices of the landfills cause adverse
environmental effects. Remote imaging method can be very useful for the detection and
remediation of hazardous wastes. Aerial photography helps to determine waste site
evaluations. Satellite systems have been successfully employed in both spectral and
morphological analysis of hazardous wastes on the landscape. Besides Satellite image
helps to emerge hyperspectral sensors have permitted determination of the specific
contaminants by processing strategies using the acquired wavelengths in the solar
reflected or thermal infrared parts of the electromagnetic spectrum. Two Dhaka,
Bangladesh, landfills are explored to understand how management practices impact
environmental quality and public health in the surrounding areas. Dhaka’s existing waste
management practices should be organized to address these concerns. Sources of these
waste reduction must be implemented to ensure the reduction of disposed waste at
landfills.

During the past few years, rail infrastructures in Bangladesh had been expanded not only in developing new rail routes but also in rehabilitation and modernization of existing tracks. Previous studies on subsidence identified Bangladesh and the Ganges-Brahmaputra Delta (GBD) region as a high-risk zone of land subsidence resulting from sea-level rise and climate change (Steckler et. al., 2022). This regional subsidence will affect the stability of linear rail infrastructure. The possible effect of this subsidence on rail infrastructure health was not scientifically examined before. Dhaka-Kasiani-Gopalganj Railway, which connects the capital city Dhaka to Gopalganj city in the southern part of Bangladesh, is chosen as case study. This study aims to evaluate the ground subsidence rate along railway lines using remote sensing techniques for prioritization of inspection and maintenance activity. As the analysis can be done remotely, this technology posed a solution for decision makers in low-resource settings for resilient transport infrastructure. The annual ground deformation rate will be estimated from Interferometric Synthetic Aperture Radar (InSAR) time-series analysis. Sentinel-1A data in ascending orbit from September 2016 to August 2023 will be used and global navigation satellite system (GNSS) data will be integrated for accurate measurement. Capabilities and limitations of using InSAR techniques with Sentinel-1A data for instability mapping of railway line will also be discussed.

Biomedical decisions were effectively taken by clinicians which were supported by the respective hospital management during COVID-19 pandemic. For the proper executions of the suitable foundation to serve the patients along with life-saving support structure and critical care system, the management implemented the needful. Despite of the problems in the health economic system, due to the pandemic, the management had to take vital decisions for allotting the limited funds for both human and consumable resources, as well as maintaining the life-saving support in the hospital for the treatment of huge, infected population. The care unit administration efficiently manifested networking with the pharmaceuticals, and other medical-allied industries, stakeholders and governmental authorities. In fact, effective healthcare management protocols in respect to regional, national and global healthcare systems, were applied for running the care facility where networking took a very crucial role. Our research concentrates on the alignment between healthcare system and associated units for care delivery to different population. Business Analytics (BA) played a critically important role in the assessment of the demand-supply data across any region. Both commercial and the clinical data were sorted out to take necessary decisions to tackle clinical and associative non-clinical (administrative or financial) challenges.

The shared cycle system, which originated in the Netherlands, is now being used in Japan as well.
However, since local governments are the main operators in Japan, it is necessary to evaluate the implementation of the program.
Therefore, we focused on Nerima Ward, Tokyo, and used actual usage data, which is a record of actual demand, to conduct a simulation, which will enable us to evaluate the potential demand.
Based on the above, a queueing network system was constructed and an evaluation of the system was attempted.

Since the 2011 magnitude 9 off the Great East Japan earthquake, Japan is said to have entered a period of seismic activity. When a magnitude 9-class earthquake occurs, volcanic activity usually increases in the surrounding area, and in Japan, volcanic eruptions have become more active since 2011.In light of this situation, if an eruption and ash fall of the same magnitude as the eruption of Mt. Fuji that occurred approximately 300 years ago(1707) during the Edo Period were to occur today, the impact, damage, and countermeasures would be incomparably more difficult than in the Edo Period.

　Mt. Fuji eruption during the Edo Period the continued intermittently for about two weeks, causing ash fall damage not only to the area around Mt. Fuji but also to the town of Edo with the final ash fall estimated to have been 3 to 10 cm. However, damage to houses, roads and water sources, no major damage occurred except some damage to crops in the fields.

One of the major differences between the Edo period and modern society is the vastly different structure and character of the various types of living infrastructure. Especially transportation infrastructure such as roads, railroads, and airports; and daily life infrastructure such as electricity, communications, water, sewage, and gas; supply of food and other essential goods; and the nature of businesses, schools, and hospitals are very different between the Edo period and today. Therefore, the impact of ash fall on society is expected to be incomparably more damaging than it was then.

Analyzing trajectory data within buildings provides valuable insights for enhancing environmental design and habitability. To gain a comprehensive understanding of how architectural structures affect human behavior, it is necessary to collect extensive spatial-temporal trajectory data from various building types. Therefore, we need methods that can precisely estimate trajectories using sensors that are easy to install. However, indoor location sensor data tends to be both sparse and noisy. Conventional route estimation models face difficulty in effectively applying to this situation. Our study aims to obtain detailed, temporally, and spatially rich trajectory data from this compromised sensor information. We achieve this by interpreting trajectories as continuous stay points. To facilitate this, we introduce a building corridor network that conceptualizes buildings as a series of points. This network enables the consideration of stay point relationships and those between stay points and responding beacons over the full data length. Routes are inferred using a sequence estimation model applied to this network. This approach employs spring dynamics, which balance the resistance to staying with the attraction to specific beacons. Their competing forces are modeled as a mathematical optimization problem, balancing the costs of both travel and beacon. The travel cost controls the relationship between the stay points at each time, while the beacon cost forms the framework to fit the reaction of the beacons. To evaluate the accuracy of this estimation method, experiments are performed. The evaluation experiment compares the trajectory paths estimated by our model using the obtained beacon responses with the actual walking paths. Notably, our model can deduce a trajectory of 131 points from only 15 beacons with, an accuracy rate of 87%. Our method presents a promising avenue for capturing extensive route data.

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.