From an environmentally conscious and ecological perspective, the sustainability of cities within the effects of climate change are closely related to the wise use of resources and modifications in the ecological status of the environment. In terms of the ecological environment, the sustainability of smart cities entails meeting present and future societal demands for the environment of the water, land, and air, among others. Environmental and the ecological concerns that arise from rapid climate change and monetary developments are shown in the inconsistency between ecological assets, environmental pollution, and the destruction of nature. In this study, the authors aim to develop a strategy to deal with climate change in urban areas using Remote Sensing and the Driver-Pressure-State-Impact-Response (DPSIR) Framework with a case study in Jakarta Smart City. The DPSIR framework, which will be developed and implemented in the city of Jakarta, is a smarter and more sustainable framework that is evaluated through a systematic evaluation of sustainability with quantitative research using the entropy weight method and Partial Least Square-Structural Equation Modeling (PLS-SEM). These methods evaluate 58 representative elements of environments at the urban level, including the shortcomings of earlier research such as data availability, spatial and temporal constraints, and several related ecological indicators, such as soil pH, wind speed, air quality index as well as land changes in the spatial (spatiotemporal) time series. The results of the study show that in the metropolitan city of Jakarta, the Drivers that are related to climate change are the rate of population growth and the rate of industrial growth which, although increases people’s income and GRDP in Jakarta; it also creates Pressures, namely an increase in the amount of water consumption and in the amount of wastewater. Based on these pressures, the environmental conditions (State) of Jakarta city have undergone several environmental changes, such as loss of water supply, changes in wind speed, changes in rainfall, and increasing concentrations of the Air Pollutant Standard Index. The Impact of these three elements resulted in the increase in household and industrial water consumption, an increase in annual electricity consumption, and deteriorating air quality. Hence, the Response to these four interrelated causal variables is that the Jakarta Provincial Government must increase annual funds for the construction of urban community facilities, increase the production capacity of clean water supply, build environment-friendly wastewater treatment facilities, increase the capacity of waste processing infrastructure and transportation fleets, and educate people to use water wisely to reduce the level of water use.

Artificial Intelligence has been widely used for object detection in various applications such as text recognition and wildlife detection. Although it can detect an object at a certain level of confidence, the accuracy can be affected by the object's background, lighting condition, or when the object is moving. In this paper, a machine learning algorithm is applied to develop a traffic sign detection and recognition system (TSDR) using Convolution Neural Network (CNN) to build a localised traffic sign model using a novel multi-objects suppression approaches. The constructed model is tailored for specific country's traffic signs and can be installed on a portable microcontroller with GPU capability, to provide a real-time traffic sign recognition system. The proposed system can update the traffic sign model online or offline. Extensive experimental evaluations have shown that the traffic sign's model can be customised using multi-objects suppression with CNN and the accuracy of the model increases with the number of training samples.

Activity recognition approaches have been applied in home ambient systems to monitor the status and wellbeing of occupant especially for home care systems. With the advancement of embedded wireless sensing devices, various applications have been proposed to monitor userâĂŹs activities and maintain a healthy lifestyle. In this paper, we propose and evaluate a Smart Medication Alert and Treatment Electronic Systems (SmartMATES) using a non-intrusive wearable activity recognition sensing system to monitor and alert an user for missing medication prescription. Two sensors are used to collect data from the accelerometer and radio transceiver. Based on the data collected, SmartMATES processes the data and generate a model for the various actions including taking medication. We have evaluated the SmartMATES on 9 participants. The results show that the SmartMATES can identify and prevent missing dosage in a less intrusive way than existing mobile application and traditional approaches.

According to the United Nations Food and Agriculture Organization, global aquaculture production reached a record high in 2022, surpassing capture fisheries for the first time. In Brunei Darussalam, the fisheries sector has exhibited steady growth over the past two decades. From an energy consumption perspective, the aquaculture industry is among the electricity-intensive sectors, with demand varying according to the size, layout, and number of ponds in operation. Aeration of aquaculture ponds, particularly intensive ponds, is a critical requirement, and while it is essential to achieve expected production levels, it also represents a major component of operational cost. Aerators are the most power-consuming equipment in aquaculture farms, and paddle wheel aerators generally operate continuously (24 h/day). The monthly energy consumption of a two-impeller paddle wheel aerator is 537.120 kWh. This paper discusses the techno-economic and environmental feasibility of a solar PV-powered energy supply system for paddle wheel aerator operation in aquaculture farms. A case study conducted at an operational aquaculture farm equipped with a hybrid PV system for aeration is analysed in detail. Furthermore, the economic performance of the PV system is evaluated at different penetration levels (10%, 25%, and 50%). The results indicate that solar PV integration provides substantial electricity cost savings, with increasing benefits at higher penetration levels. The payback period (PB) is 8.3 years for a 12.16 kWp (10%) system and 6.9 years for a 60.81 kWp (50%) system, with corresponding returns on investment (ROI) of 2.80% and 3.65%, respectively. The levelised cost of electricity is BND 0.036/kWh at 25% penetration (30.4 kWp) and BND 0.034/kWh at 50% penetration, representing 71.4% and 73.01% reductions, respectively, compared to grid electricity tariffs. At 50% PV penetration, monthly CO₂ emissions are reduced by 6242.71 kg, with a lifetime reduction of 1872.813 tonnes.