Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

Payment for forest ecosystem services (PFES) policy is a prevalent strategy designed to establish a marketplace where users compensate providers for forest ecosystem services. This research endeavours to scrutinise the impact of PFES on households’ perceptions of forest values and their behaviour towards forest conservation, in conjunction with their socio-economic circumstances and their communal involvement in forest management. By incorporating the social-ecological system framework and the theory of human behaviours in environmental conservation, this study employs a structural equations model to analyse the factors influencing individuals’ perceptions and behaviours towards forest conservation. The findings indicate that the payment of PFES significantly increases forest protection behaviour at the household level and has achieved partial success in activating community mechanisms to guide human behaviour towards forest conservation. Furthermore, it has effectively leveraged the role of state-led social organisations to alter local individuals’ perceptions and behaviours towards forest protection.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.

Given the eclectic and localized nature of environmental risks, planning for sustainability requires solutions that integrate local knowledge and systems while acknowledging the need for continuous re-evaluation. Social-ecological complexity, increasing climate volatility and uncertainty, and rapid technological innovation underscore the need for flexible and adaptive planning. Thus, rules should not be universally applied but should instead be place-based and adaptive. To demonstrate these key concepts, we present a case study of water planning in Texas, whose rapid growth and extreme weather make it a bellwether example. We review historic use and compare the 2002, 2007, 2012, 2017 and 2022 Texas State Water Plans to examine how planning outcomes evolve across time and space. Though imperfect, water planning in Texas is a concrete example of place-based and adaptive sustainability. Urban regions throughout the state exhibit a diversity of strategies that, through the repeated 5-year cycles, are ever responding to evolving trends and emerging technologies. Regional planning institutions play a crucial role, constituting an important soft infrastructure that links state capacity and processes with local agents. As opposed to “top-down” or “bottom-up”, we frame this governance as “middle-out” and discuss how such a structure might extend beyond the water sector. 

Transportation projects are crucial for the overall success of major urban, metropolitan, regional, and national development according to their capacity by bringing significant changes in socio-economic and territorial aspects. In this context, sustaining and developing economic and social activities depend on having sufficient Water Resources Management. This research helps to manage transport project planning and construction phases to analyze the surface water flow, high-level streams, and wetland sites for the development of transportation infrastructure planning, implementation, maintenance, monitoring, and long-term evaluations to better face the challenges and solutions associated with effective management and enhancement to deal with Low, Medium, High levels of impact. A case study was carried out using the Arc Hydro extension within ArcGIS for processing and presenting the spatially referenced Stream Model. Geographical information systems have the potential to improve water resource planning and management. The study framework would be useful for solving water resource problems by enabling decision makers to collect qualitative data more effectively and gather it into the water management process through a systematic framework.

Liquid Metal Battery (LMB) technology is a new research area born from a different economic and political climate that has the ability to address the deficiencies of a society where electrical energy storage alternatives are lacking. The United States government has begun to fund scholarly research work at its top industrial and national laboratories. This was to develop Liquid Metal Battery cells for energy storage solutions. This research was encouraged during the Cold War battle for scientific superiority. Intensive research then drifted towards high-energy rechargeable batteries, which work better for automobiles and other applications. Intensive research has been carried out on the development of electrochemical rechargeable all-liquid energy storage batteries. The recent request for green energy transfer and storage for various applications, ranging from small-scale to large-scale power storage, has increased energy storage advancements and explorations. The criteria of high energy density, low cost, and extensive energy storage provision have been met through lithium-ion batteries, sodium-ion batteries, and Liquid Metal Battery development. The objective of this research is to establish that Liquid Metal Battery technology could provide research concepts that give projections of the probable electrode metals that could be harnessed for LMB development. Thus, at the end of this research, it was discovered that the parameter estimation of the Li//Cd-Sb combination is most viable for LMB production when compared with Li//Cd-Bi, Li-Bi, and Li-Cd constituents. This unique constituent of the LMB parameter estimation would yield a better outcome for LMB development.