Cities attract people by holding many events such as seasonal product sales, festivals, exhibitions, and other trendy events. Information on the various events spreads on the internet by the media. This suggests that media articles can be real-time outputs of the city's transition associated with events. However, the accumulation of information in the media, which is updated daily, has not been sufficiently managed, and the city has not been evaluated from the perspective of events held there. This study aims to examine the transition or flow of events extracted from media articles by analyzing an online database of event announcements.

In this study, we analyze articles published on FASHION PRESS. We construct a spatio-temporal directed graph by using the data of the coordinates and dates of events in Tokyo. The node of the graph represents an event. The weight of each edge is given by considering the Euclidean distance, the difference in dates, and the similarity of the article texts between two events. We constructed the Minimum Spanning Tree (MST) of the graph to visualize the transition of events in a city. We change the way to assign the weights to each edge according to the difference in the place and date of the events parametrically and compare the output graphs. By analyzing these MSTs, we evaluate the image of the city in terms of changes in the content of events, such as the occurrence of trends and seasonal changes.

By comparing the urban image obtained from media articles with the actual city and its concrete urban planning, we can extend the image of the city obtained through conventional fieldwork to that incorporating information space. Such an extension will allow us to propose a new perspective of urban planning considering digital urban space.

Aquaculture plays a crucial role in global food supply, but its environmental implications, especially in marine settings, remain a concern. While terrestrial aquaculture monitoring is robust, offshore facilities have not received similar scrutiny. Our study leverages the C-band wavelength of Sentinel-1 to detect and map offshore aquaculture facilities in Vietnam, a country where the ubiquitous presence of aquaculture activities pose an environmental risk. Using the current Sentinel-1 dataset, we were able to characterise unique backscattering patterns from fish cages and aquaculture boats. To distinguish aquaculture facilities from other structures in the sea, we conducted an object-based image analysis to highlight the distribution pattern of aquaculture facilities as opposed to other objects such as oil rigs or bridges. Next, we manually created a training dataset for a supervised classification, revealing a notable presence of these facilities in calm, bay-sheltered seas. Our findings underscore the need for enhanced marine aquaculture regulation and set the stage for exploring its environmental ramifications, particularly concerning water quality and pollution.

The oil palm is a globally vital crop for vegetable oil production, particularly crucial for Southeast Asia's economy. However, the sustainability of oil palm plantations in this region is under significant threat from Ganoderma disease, or Basal Stem Rot. The mechanisms driving the spread of this disease remain poorly understood, and existing management methods have proven ineffective. Consequently, there is a pressing need for monitoring and implementing effective disease management techniques in oil palm plantations. The aim of this study was to utilize time series of ALOS2 PALSAR2 dual polarization SAR imagery over 9 years to identify Ganoderma infected oil palm plants incorporating the XGBoost machine learning model. This study proposes a pipeline starting from: collecting geospatial information from field observations of infected plants in oil palm plantations using a smartphone-based application; examine the potential backscatter variables for infected plants using time series of PALSAR2 measurements, utilizing SAR imagery and the XGBoost machine learning model to predict the Ganoderma-infected plants; and visualizing all the field observations and machine learning-based predictions through a web GIS-based dashboard. The data collected from the smartphone-based application were used as ground truth data to train the machine learning model. It comprises information on 138 trees, consisting of 73 healthy trees and 65 infected trees. The results proved that Horizontal transmit and Vertical receive (HV) component provides the highest accuracy which is 76.2% for identifying infected plants and 70.8% for identifying healthy plants. In conclusion, this study examines the capacity to detect infected plants using ALOS2 PALSAR2 SAR imagery and enhance the visualization of results, facilitating a clearer understanding of disease dynamics within large-scale plantation areas.

The collection of information following large-scale natural disasters is of paramount importance for emergency response and recovery efforts. Synthetic Aperture Radar (SAR) sensors can be used around the clock, making them suitable for emergency data acquisition. In recent years, airborne SAR systems have also been developed and utilized during experimental phases. Pi-SAR2 is an airborne SAR system installed on aircraft, capable of detecting objects as small as 30 centimeters from altitudes exceeding 10,000 meters, even in adverse conditions such as nighttime, inclement weather, or volcanic eruptions. This enables rapid situational awareness in emergency situations and holds promise for providing robust support in formulating response strategies. During the 2016 Kumamoto earthquake in Japan, which caused extensive damage including building collapses and landslides due to strong tremors, the National Institute of Information and Communications Technology (NICT) conducted observations of the affected areas using the airborne X-band synthetic aperture radar sensor Pi-SAR2 shortly after the earthquake. In this study, we utilized the fully polarimetric (HH, HV, VV, and VH) Pi-SAR2 data acquired during the 2016 Kumamoto earthquake to investigate the capabilities of X-band airborne SAR data in damage assessment in urban areas. Employing the four-component covariance matrix decomposition method, we decomposed the original fully polarimetric Pi-SAR2 data into four scattering powers, namely double-bounce scattering (Pd), volume scattering (Pv), surface scattering (Ps), and helix scattering (Pc). We explored the post-disaster backscattering characteristics of urban areas affected by the catastrophe.

Flood waters impose great health and economic risks such as contaminated water & food and property losses. The loss of shelter during flood leaves people susceptible to insect contact, heat and other environmental hazards. Medical care is decisive in areas where little medical care exists. After typhoons, heavy rains and flooding, the impending dangers of diseases increase, such as water-borne diseases (e.g. Typhoid, Leptospirosis, Jaundice, Hepatitis, Skin allergy, etc.) and vector-borne diseases (e.g. Malaria, Dengue etc.). Flooding events including tsunamis can present a potentially hazardous public health risk. Foodstuff in the affected areas may turn out to be contaminated and as a result, it turns out unsafe for human consumption. To put off food-borne diseases, end users should pay additional attention. The effects of a disaster last for a long time. There is a greater need for financial as well as material assistance in the following months subsequent to a disaster and actions such as surveys, medical facilities and supply of medicines, restoration of services and helping the community in recovering to normalcy when the crisis has subsided. The paper presents the required post-flooding measures in terms of precautions, medicines, facility management, and social & physiological assistance.

The fragile landscape of hilly regions of India is susceptible to several disasters like earthquakes, landslides, snow avalanche and flash floods. Landslide disaster is one of the major recurrent concerns in Himalayan terrain. The highly sensitive hilly regions have also been subjected to enormous amount of pressure, primarily due to the escalating population thereby leading to overexploitation of natural resources. Thus, the amalgamation of ecological disruption either due to natural or anthropogenic activities is making the hill slopes fragile and thus the infrastructures unsafe. Such disasters pose risk to lives and all sorts of vital infrastructures such as human settlements, roads, rails, tunnels, hydro-power projects etc. The most recent example is the Joshimath landslide-subsidence incident which aggravated in the month of January 2023 affecting thousands of human settlements and other infrastructural facilities.

Researchers and scientists working in this domain have conducted several studies and devised numerous technological interventions to reduce the impact of such hazard and make it safe for the community. Nevertheless, unplanned & uncontrolled construction activities, overseeing of building bylaws, using inappropriate building materials, poor workmanship, constructing buildings in the vulnerable zones and unawareness of the local community are some of the most common challenges in making the infrastructures safe and sustainable with reference to landslide disaster. Since building represents safety and security of the residents along with their emotional attachment after investing all their hard-earned money, it acts as the backbone of human-coping capacity. But when the buildings are vulnerable to disasters like landslides, it needs to be addressed on priority basis, in order to avert adverse situations. All the existing and upcoming infrastructures in the hilly terrain need proper attention and scientific efforts towards landslide risk reduction with advanced technological interventions.

The present paper on landslide disaster risk reduction stresses upon some of the pertinent developments and issues on vulnerability-risk assessment, debris flow hazard assessment and fore warning system for better preparedness. This will help the society in reducing landslide risk with a vision of sustainable infrastructural development in difficult terrains.

Background: Despite the significance of internal migration as an important social determinant of health that could potentially affect the utilization of maternal healthcare services, the magnitude of this relationship is yet to be fully explored in the context of different types of migration streams in Bangladesh. This study aims to examine the effects of internal migration on antenatal and institutional delivery care service utilization in Bangladesh.

Methodology: Analysing data from the 2017-18 Bangladesh Demographic and Health Survey, this study has examined a group of 3453 migrant women between the ages of 15-49 who sought maternal healthcare services after migrating and gave birth within the last three years, as well as 554 non-migrant women who also gave birth within the same time frame. Chi-square test as well as simple and multiple binary logistic regression is conducted to quantify the effects of migration on maternal healthcare service utilization (MHCSU). Two indicators of MHCSU, at least 4 ANC utilization (≥4 ANC) and institutional delivery (ID), are used in this study.

Result: Both of the MHCSU indicators differ among migrants and non-migrants, particularly when different categories of migration streams are considered, where the rural non-migrants are the most disadvantaged group. Urban to urban migrants (ID=OR=2.247, p<0.001; ≥4 ANC=1.866, p<0.01; ID=OR=2.247, p<0.001) and migrants from urban areas (ID=OR=1.689, p<0.05; ≥ 4 ANC= 1.24, p>0.05) are found to be utilizing more maternal healthcare than rural non-migrants. However, when compared against the urban non-migrants, all migrants of all streams are found to be utilizing less maternal healthcare.

Conclusion: The findings contribute to a comprehensive understanding of migration by stream on maternal healthcare services utilization in the context of Bangladesh. To develop effective maternal healthcare service utilization programs, it is essential to account for the variations in usage of health services by migration stream which will help programs to be designed to effectively cater to the unique needs and challenges faced by migrant populations.

In the wake of centuries of global battle against contagious diseases, the COVID-19 pandemic emerged as a tough enemy, profoundly impacting lives worldwide, including Bangladesh. Many have faced short-term and long-term complications, as evidenced by numerous studies. This study investigates the physical and mental complications experienced by post-COVID-19 patients in Bangladesh. Utilizing a mixed-method approach, which includes both statistical analysis and the examination of secondary and primary data, we assess the prevalence and impact of these complications. Physical complications include hair loss, dizziness, palpitations, and loss of appetite, suggesting a lasting impact of COVID-19 on physical health. Mental complications, such as depression, anxiety, and memory problems are prevalent. Importantly, mental complications appear to affect post-COVID-19 patients more than physical complications. These findings emphasize the need for comprehensive post-recovery care that addresses both physical and mental health. Public awareness campaigns and mental health support resources should be prioritized. Further research is essential for the effective management of post-COVID-19 complications.

In the Democratic Republic of Timor-Leste, rainfall during the rainy season has been increasing in recent years due to the effect of climate change. As a result, flood damage has occurred such as flood of houses and road collapse. In April 2021, Cyclone Seroja caused small and medium size rivers to overflow, and the capital city of Dili was flooded. Flooded roads caused the road network to be cut off, restricting movement within the Dili. Although the same level of disaster is expected in the future, no analysis has been conducted on the time required for evacuation and getting to hospitals in the event of flood. In this study, a network analysis was conducted to determine the degree of delay in time to major facilities such as airports and hospitals in the city. We assumed a flooding damage equivalent to the heavy rainfall in 2021, and that the road traffic network was blocked. Through the analysis of this study, it is clear that the delay time to destination varies depending on the density of the road in Dili.

The Democratic Republic of Timor-Leste is one of countries on the Pacific ring of fire, and according to European commission, it might be vulnerable and struggle during disaster. In fact, this country has experienced some serious disaster, especially Cyclone Seroja in April 2021. The cyclone caused significant damage and the river in the capital city, Dili, has been in a state of collapse. In this study, we conducted a questionnaire survey to educational institutions in October. As a result, we found out East Timorese’s disaster risk perception and real disaster education situation. They had a fear of earthquake, flood, tsunami, and landslide. However, they didn’t have a fear of heavy rain which might strike those disasters. In addition, approximately 80% of respondents expressed an interest in learning about earthquake, flood, tsunami, and landslide in school. This result suggests that disaster education in the Republic of Timor-Leste may be insufficient and in need of improvement.