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Many types of the coastal land use in Banten Bay have been assessed in order to know the change as evidently detected by Landsat imagery in 1994 and 2001. Image processing such as, supervised classification and various change detection techniques are performed to the satellite images. Red Green method showed the best result for detecting the coastal land use change. This method is suitable for detecting the increasing areas of the paddy fields and settlement. Image Differencing method is capable to detect the increasing areas in agriculture, decreasing in fishponds and natural areas. Image Ratioing method can be considered suitable for detecting the increasing area of fishponds, decreasing of paddy fields and agriculture areas. Each coastal land use type has increased, except for the natural area/brushwood. Most of agriculture and paddy fields areas have been converted to fish ponds. Key words: coastal land use/land cover, change detection method, remote sensing

This research aimed to understand the use of air gliding arc (GA) plasma to generate plasma-activated water (PAW) for fixing nitrogen in water and the chemical properties of PAW on the germination of rice seeds. The N2, NO, and OH molecules in GA plasma led to NO3−, NO2−, and H2O2 formation in the PAW. The NO3−, NO2−, and H2O2 contents in PAW rapidly decreased after 5 days of storage. The experiment was arranged in a completely randomised design using GA plasma discharged above the surface of deionised (DI) water with different airflow rates (2, 3, 4, 5, and 6 L/min) compared to the control (DI water). The NO3− and NO2− contents increased, resulting in an increase in total nitrogen (N) and gibberellic acid (GA3) accumulation in rice seeds. The PAW at an airflow rate of 5 L/min was optimal for enhancing radicle emergence at 48 and 72 h, germination, germination index, shoot length, fresh weight, and dry weight of seedlings. Therefore, air GA plasma to generate PAW is an efficient method for producing nitrogen in a soluble form, which can support the germination processes and early growth of rice seedlings.

Indoor exposure to heavy metals poses human health risks worldwide, but study reports from Thailand are still limited, particularly in rural and urban areas. We measured the heavy metals in a hundred indoor household dust samples collected from urban and rural areas in Chiang Mai and Lamphun provinces and found a significantly higher concentration of As in rural areas and Cd in urban areas with industrial activities. The source identification of the heavy metals showed significant enrichment from traffic emissions, paint, smoking, and mixed sources with natural soil. From health risk assessment models, children were more vulnerable to noncarcinogenic risks (HI = 1.45), primarily via ingestion (HQ = 1.39). Lifetime cancer risks (LCRs) due to heavy metal exposure were found in adults (LCR = 5.31 × 10−4) and children (LCR = 9.05 × 10−4). The cancer risks from As were higher in rural areas via ingestion, while Cr and Ni were higher in urban areas via inhalation and ingestion, respectively. This study estimated that approximately 5 out of 10,000 adults and 9 out of 10,000 children among the population may develop cancer in their lifetime from exposure to indoor heavy metals in this region.

This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.

In the Lubumbashi charcoal production basin (LCPB) in Southeastern DR Congo, agricultural and charcoal production activities regularly give rise to fires that lead to considerable degradation of the miombo open forest. This study analyzes the drivers of the spatiotemporal distribution of active fires and burnt areas in the LCPB by processing MODIS and Landsat data. In addition, a kernel density analysis method (KDE) was used to estimate fire risk, while the effect of the road network and dwellings on vegetation fires was highlighted in areas between a 0 and 3000 m radius. The obtained results revealed that fires in the LCPB generally occur between April and November, mainly during the day, between 11 a.m. and 12 p.m. These fires are concentrated in the central and southwestern part of the LCPB, more specifically in the savannahs and near roads. From 2002 to 2022, an average of 11,237 active fires and an average of 6337 km2 of burnt areas were recorded in the LCPB. Each year, these fires peak in August, and despite their steady decline, the few fires that have affected the forests have caused more devastation (more than 2790 km2/year) than those observed in the fields and savannah. These figures highlight the imperative need to put in place fire prevention and management measures in the LCPB, with particular emphasis on awareness, monitoring, and fire-fighting measures.

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications.

The rapid population growth in sub-Saharan Africa requires regular monitoring of the spatial expansion of cities in order to facilitate efficient urban planning. In this study, we quantified the dynamics of urban and peri-urban areas in the city of Kisangani from 1987 to 2021, based on morphological criteria. Results demonstrate continuous urban and peri-urban growth, with respective average annual change rates of 8.2% and 7.6%. The urban core area expanded from 13.49 km2 to 100.49 km2, resulting from an alternating process of diffusion and coalescence. Peri-urbanization indexes developed to assess the trend of the decline in urban densities indicate a phase of urban densification over the period 1987–2010 succeeded by a decline in urban density over the period 2010–2021 that is characterized by a large expansion of the peri-urban area. However, despite this trend observed between 2010 and 2021, the decrease in urban density was not effective between 1987 and 2021 in Kisangani, as the fraction of peri-urban area observed in 1987 remains equivalent to that observed in 2021. This suggests a continuity of urban densification despite increasing peri-urbanization.

AbstractThailand is among high-ranking coffee-exporting countries in the world. This study investigated, analyzed and proposed ideas for improved production of coffee to meet the demands from the local and international market. Using data collected from site visits, observation and interviews, value stream mapping analysis was used to identify the activities in the coffee production process. Automated machine deployment was used to improve the performance of coffee production. From the findings, it was shown that the total cycle time could be reduced by more than 82 %, the profit of sorted cherry coffee could be increased by 80%, and the processing time of green bean coffee could be reduced by almost 97%. An automated color sorting machine was deployed to improve the production process. With integrated lean and automated mechanization techniques, farmers can gain several benefits and become more responsive to future customer demands.