Search

This research aims to analyze and develop a theoretical framework, built with a performance model influenced by the work environment and entrepreneurial orientation, and mediated by organizational commitment. This research used a quantitative approach as the methodology. The survey was distributed using a questionnaire instrument. The population in this study were 6,708 MSME owners in Brebes Regency, Central Java, Indonesia, and the sample in this study were 377 MSME owners, which was determined using the Slovin formula. The sampling technique in this research used a non-random sampling with proportional sampling. Simultaneously, the statistical method used to test the hypotheses in this study is a multivariate Structural Equation Modelling (SEM). Based on the results of statistical tests, it can be known partially that 1) Work environment has a significant effect on the performance of MSMEs, 2) Entrepreneurial orientation has a significant effect on the performance of MSMEs, 3) Organizational commitment has a significant effect on the performance of MSMEs. 4) Work environment has a significant effect on organizational commitment, 5) Entrepreneurial orientation has a significant effect on organizational commitment, 6) Work environment has a significant effect on the performance of MSMEs through organizational commitment, and 7) Entrepreneurial orientation has a significant effect on the performance of MSMEs through organizational commitment. The novelty in this study is that the empirically constructed model of organizational commitment has proven to be significant as a mediation of the work environment and entrepreneurial orientation towards performance. with the strongest influence of work environment and entrepreneurial orientation on organizational commitment.

Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a "box map" based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a "box map". In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research. [ABSTRACT FROM AUTHOR]

The Red Sea has a special geographical feature because it is situated in the tropical and subtropical zones. The uniqueness of the Red Sea makes a variability of oceanography data (Chlorophyll-a (Chla), current, Salinity, etc.) and meteorological data (Precipitation, Wind Speed, etc.). However, investigations of differences interaction of oceanography and meteorology are less studied in of the Red Sea. Satellites and modeling make it possible to observe oceanographic and meteorological data over a long period of time. This study first demonstrates that there are three major areas along the Red Sea that represent the different Chl-a concentrations during the rainy season. The month of July has the highest Chl-a concentration in the south (1.3 mg/m³) and the lowest Chl-a concentration in the north (0.18 mg/m³). The southern part of the Red Sea has a different generation mechanism from the northern part of the Red Sea in terms of increasing and decreasing Chl-a concentrations. The existence of surface runoff in this area may result in the supply of anthropogenic organic compounds and fresh water to coastal waters. This may increase the supply of nutrients at the peak of the rainy season, and finally increase the concentration of Chl-a in the southern part. In the middle part, the variability of Chl-a is mainly affected by wind speed. Meanwhile, the high salinity in the northern part may limit the growth of phytoplankton and keep the Chl-a concentration low. [ABSTRACT FROM AUTHOR]

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution's work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straightforward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel's location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of data. [ABSTRACT FROM AUTHOR]

Electricity theft has caused massive losses and damage to electricity utilities. The damage affects the electricity supply's quality and increases the generation load. The losses happen not only for the electricity utilities but also affect the legitimate users who have to pay excessive electricity bills. That is why the method to detect electricity theft is indispensable. Recently, machine learning algorithms have been used to develop a model for detecting electricity theft. However, most algorithms have problems due to imbalanced data, overfitting issues, and lack of data. Therefore, this paper proposes a solution that implements the oversampling technique to address the problems and increase the developed model's accuracy. It is used to perform oversampling on the imbalanced dataset. Our proposed method consists of a pre-processing step to remove empty values and extract several parameters. After that, the oversampling technique is performed on the result of the pre-processing step. The logistic regression model combined with the oversampling techniques shows the best performance results on the developed model of electricity theft detection based on the state electricity company customers. The experiment shows that the proposed method, logistic regression combined with the synthetic minority oversampling technique, shows superior performance in terms of the accuracy of the training data and data testing, precision, recall, and F1-scores of 98.97%, 98.7%, 95%, 99%, and 97%, respectively. Moreover, the experiment also shows that the proposed solution outperforms existing methods. [ABSTRACT FROM AUTHOR]

Problem statement: The capability of performing autonomously a contour following task of a shape object is required for an industrial robot manipulator. These tasks include welding, sealing, grinding, painting and many more. These applications are very tedious and time consuming if done by manual contour following system. Approach: This study proposed the development of dual sensor logic algorithm for contour following task in order to automate the manual programming process. Results: The outcome of this study was to make industrial robot capable of doing self learning to follow the contour with minimal human involvement. The algorithm had been developed and tested using Adept Selective Compliant Assembly Robot Arm (SCARA) robot. Conclusion: The result showed its possibility could be used as automated contour following task for real industrial robot. Key words: Dual sensor logic, contour following, adept SCARA robot, INTRODUCTION The current trend towards industrial robotics requires the implementation of simple programming techniques. Programming by demonstration has emerged as one of the most promising solutions for effective programming of [...]