Land use or land cover (LU/LC) mapping serves as a kind of basic information for land resource study. Detecting and analyzing the quantitative changes along the earth’s surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, economic and industrial growth. The LU/LC pattern in Madurai City, Tamil Nadu, has undergone a significant change over the past two decades due to accelerated urbanization. In this study, LU/LC change dynamics were investigated by the combined use of satellite remote sensing and geographical information system. To understand the LU/LC change in Madurai City, different land use categories and their spatial as well as temporal variability have been studied over a period of seven years (1999-2006), by analyzing Landsat images for the years 1999 and 2006 respectively with the help of ArcGIS 9.3 and ERDAS Imagine 9.1 software. This results show that geospatial technology is able to effectively capture the spatio-temporal trend of the landscape patterns associated with urbanization in this region.

The hopes and aspirations of Law No. 6/24 on Village autonomy has faced several problems and challenges. These problems and challenges arose when the village government had to undertake various delegated tasks assigned by the regency, provincial, and central governments. As a result, the village is preoccupied with delegated tasks assigned by supra-village authorities, straining its resources and budget. The shift in focus resulted the village government are unable to perform their main tasks and responsibilities. This situation is akin to the Village Head functioning as a state employee. Stunting is one of the assignment programs that causes various problems and instrumentalizes villages. This process involves mobilizing village institutions, human resources, and budgets to ensure the program’s success. This study employed exploratory-qualitative approach to investigate the challenges arising from the stunting program’s implementation in Ngargosari Village. The research informants included the village head, village officials, posyandu cadres, community leaders, and program beneficiaries. The data were gathered through in-depth interviews were validated and reconfirmed using Focus Group Discussions. Furthermore, an in-depth analysis was carried out to obtain findings related to village instrumentalization in the stunting program. The findings revealed that the stunting program’s implementation involved mobilizing village institutions, resources, and budgets. The village government lacked bargaining power against supra-village policies, despite their alignment with local values and wisdom. The central government dictated the system, procedures, mechanisms, and methods for handling stunting in a centralized manner, disregarding local wisdom and the authority of village governments as outlined in Law Number 6 of 2014 on Villages. Consequently, the stunting program represents a form of village instrumentalization akin to the New Order era, with centralistic initiatives that relegate village heads to the role of state employees.

The regulation of compressor extraction and energy storage can improve the performance of gas turbine energy system. In order to make the gas turbine system match the external load more flexibly and efficiently, a gas turbine cogeneration system with solar energy coupling compressor outlet extraction and energy storage is proposed. By establishing the variable condition mathematical model of air turbine, waste heat boiler and solar collector, we use Thermoflex software to establish the variable condition model of gas turbine compressor outlet extraction, and analyze the variable condition of the coupling system to study the changes of thermal parameters of the system in the energy storage, energy release and operation cycle. Taking the hourly load of a hotel in South China as an example, this paper analyzes the case of the cogeneration system of solar energy coupling compressor outlet extraction and energy storage, and compares it with the benchmark cogeneration system. The results show that taking a typical day as a cycle, the primary energy utilization rate of the system designed in this paper is 3.2% higher than that of the traditional cogeneration system, and the efficiency is 2.4% higher.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.

This study aims to identify and the implementation of ASN Management policies on career development aspects based on the merit system in the West Java Provincial Government and 6 Regency/City Governments in West Java Province. The failure of the institutionalization of the meritocratic system in ASN career development is partly triggered by the symptoms of the appointment or selection of officials in the central and regional levels not based on their professionalism or competence except for subjective considerations, political ties, close relationships and even bribery. This study uses a qualitative method with a descriptive approach. The operationalization concept in this study uses Merilee S. Grindle's Policy Implementation theory which consists of dimensions of policy content and its implementation context. The factors that cause the implementation of the policy to be less than optimal include: 1. Uneven understanding of meritocracy; 2. Slowness/unpreparedness in synchronizing central and regional rules/policies; 3. The information integration system between the center and regions has not yet been implemented; 4. Limited supporting infrastructure; 5. Limited permits for related officials; 6. Transparency; 7. Collaboration across units/agencies; 8. External intervention; 9. Use of information systems/technology. To optimize these factors, an Accelerator of Governmental Unit's Success (AGUS) model was created, which is a development of the Grindle policy implementation model with the novelty of adding things that influence implementation, including top leader's commitment and wisdom, effectiveness of talent placement, on-point human development, technology savvy, cross-unit/agency collaboration, and monitoring and evaluation processes.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.