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This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning. In our proposed method, a robust background subtraction (BG) for extracting motion, indicating the location of attention regions is employed. The resulting regions are finally fed into a three-dimensional Convolutional Neural Network (3D CNN). Specifically, by taking advantage of C3D (Convolution 3-dimensional), to completely exploit spatiotemporal relation, a deep convolution network is developed to distinguish normal and anomalous events. Our system is trained and tested against a large-scale UCF-Crime anomaly dataset for validating its effectiveness. This dataset contains 1900 long and untrimmed real-world surveillance videos and splits into 950 anomaly events and 950 normal events, respectively. In total, there are approximately ~ 13 million frames are learned during the training and testing phase. As shown in the experiments section, in terms of accuracy, the proposed visual attention model can obtain 99.25 accuracies. From the industrial application point of view, the extraction of this attention region can assist the security officer on focusing on the corresponding anomaly region, instead of a wider, full-framed inspection.

This paper proposes an Artificial Neural Network (ANN) model using a Multi-Stage method to optimize the configuration of an External Lightning Protection System (ELPS) and grounding system. ELPS is a system designed to protect an area from damage caused by lightning strikes. Meanwhile, the grounding system functions to direct excess electric current from lightning strikes into the ground. This study identifies the optimal protection system configuration, reducing the need for excessive components. The ELPS configuration includes the number of protection pole units and the height of the protection poles. In contrast, the grounding system configuration consists of the number of electrode units and the length of the electrodes. This study focuses on the protection system configuration at a Photovoltaic Power Station, where the area is highly vulnerable to lightning strikes. Several aspects need to be considered in determining the appropriate configuration, such as average thunderstorm days per year, ELPS efficiency, total area of photovoltaic module, area to be protected, soil resistivity, electrode spacing factor, and the total required electrode resistance. The proposed multi-stage ANN model consists of three processing stages, each responsible for handling a portion of the overall system tasks. The first stage is responsible for determining the protection pole configuration. In the second stage, the Lightning Protection Level (LPL) classification is performed. Then, in the third stage, the process of determining the grounding configuration is handled. The analysis results show that the Multi-Stage ANN model can effectively determine the configuration with a low error rate: MAE of 0.265, RMSE of 0.314, and MPE of 9.533%. This model can also explain data variation well, as indicated by the high R2 value of 0.961. The comparison results conducted with ATP/EMTP software show that the configuration produced by ANN results in fewer protection pole units but with greater height. Meanwhile, ANN produces a configuration with shorter electrode lengths but fewer units in the grounding system.

This study aims to evaluate the effectiveness of Trichoderma asperellum and Azotobacter chroococcum in providing nutrients and nutrient uptake after treatment inarching grafting on cocoa trees' rehabilitation efforts. The Split Plot Design, with two factors, namely the application of T. asperellum and A. chroococcum were repeated three times and continued using ANOVA and Tukey HSD at a 5%. Rehabilitation of cocoa plants that are relatively old age can be done without the need to cut down a cocoa tree, by way of modification Inarching grafting with environmentally friendly farming systems, so that farmers do not need a long time to produce cocoa beans. This research concluded that the inarching grafting method can be used to rehabilitate cocoa plants of relatively old ages and damage, with applications, T. asperellum (4 g/l) and A. chroococcum (4 × 108 CFU/ml) the dose of 40 ml per plant with a frequency of twice application to each plant. This treatment is the best interaction that can reduce the number of young fruits falling (74.15%), boost the number of flowers (264.71%), the number of young fruits formed (271.65%), and the number of surviving fruits (117%) and production.

Providing access to non-confidential government data to the public is one of the initiatives adopted by many governments today to embrace government transparency practices. The initiative of publishing non-confidential government data for the public to use and re-use without restrictions is known as Open Government Data (OGD). Nevertheless, after several years after its inception, the direction of OGD implementation remains uncertain. The extant literature on OGD adoption concentrates primarily on identifying factors influencing adoption decisions. Yet, studies on the underlying factors influencing OGD after the adoption phase are scarce. Based on these issues, this study investigated the post-adoption of OGD in the public sector, particularly the data provider agencies. The OGD post-adoption framework is crafted by anchoring the Technology–Organization–Environment (TOE) framework and the innovation adoption process theory. The data was collected from 266 government agencies in the Malaysian public sector. This study employed the partial least square-structural equation modeling as the statistical technique for factor analysis. The results indicate that two factors from the organizational context (top management support, organizational culture) and two from the technological context (complexity, relative advantage) have a significant contribution to the post-adoption of OGD in the public sector. The contribution of this study is threefold: theoretical, conceptual, and practical. This study contributed theoretically by introducing the post-adoption framework of OGD that comprises the acceptance, routinization, and infusion stages. As the majority of OGD adoption studies conclude their analysis at the adoption (decisions) phase, this study gives novel insight to extend the analysis into unexplored territory, specifically the post-adoption phase. Conceptually, this study presents two new factors in the environmental context to be explored in the OGD adoption study, namely, the data demand and incentives. The fact that data providers are not influenced by data requests from the agency’s external environment and incentive offerings is something that needs further investigation. In practicality, the findings of this study are anticipated to assist policymakers in strategizing for long-term OGD implementation from the data provider’s perspective. This effort is crucial to ensure that the OGD initiatives will be incorporated into the public sector’s service thrust and become one of the digital government services provided to the citizen.

The development of transgressive segregant (TS) selection on convergent breeding populations of S4 maize is a concept that is rarely applied. However, the development of TS is necessary to accelerate maize breeding pipelines. Therefore, the objectives of this study were (1) to develop the concept of TS selection and (2) to select S4 TS maize to be developed as hybrid cross parents. The study was carried out using two experiments. The first experiment was designed with an augmented design of 6 replications for control genotypes. This design is just one factor focused on maize genotypes. However, it was divided into two sets: non-replicated of 32 TS lines and replicated of four check hybrid varieties. The second experiment focused on validation using a three-way cross. The experiment used a randomized complete block design with three replications. Based on the resulting study, the combination of ratio analysis, path analysis, best linear unbiased prediction, relative fitness, and selection indices is an objective approach to assessing the genetic potential of the S4 TS. The selection index formed was 0.53 ear weight + 0.24-grain yield percentage + yield. The index selection resulted in 11 S4 TS lines being further evaluated for their hybrid potential, with the TS line CB2.23.1 being the best. However, these TSs are expected to focus on identifying and combining ability through diallel crosses in the future. Furthermore, the three-way cross hybrid lines assessment also revealed SG 3.35.12 × JH37 F1 and CB 2.23.1 × JH37 F1 to be promising hybrid lines.

Wind power, solar power and water power are technologies that can be used as the main sources of renewable energy so that the target of decarbonisation in the energy sector can be achieved. However, when compared with conventional power plants, they have a significant difference. The share of renewable energy has made a difference and posed various challenges, especially in the power generation system. The reliability of the power system can achieve the decarbonization target but this objective often collides with several challenges and failures, such that they make achievement of the target very vulnerable, Even so, the challenges and technological solutions are still very rarely discussed in the literature. This study carried out specific investigations on various technological solutions and challenges, especially in the power system domain. The results of the review of the solution matrix and the interrelated technological challenges are the most important parts to be developed in the future. Developing a matrix with various renewable technology solutions can help solve RE challenges. The potential of the developed technological solutions is expected to be able to help and prioritize them especially cost-effective energy. In addition, technology solutions that are identified in groups can help reduce certain challenges. The categories developed in this study are used to assist in determining the specific needs and increasing transparency of the renewable energy integration process in the future.

Class imbalance poses a significant challenge in water quality classification, often leading to biased predictions and diminished accuracy for minority classes. This study introduces SMOTE-PCA-HDBSCAN, a novel oversampling framework that integrates the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples, Principal Component Analysis (PCA) to enhance data separability, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to remove synthetic data noise. The cleaned synthetic data is then merged with the original dataset to form a balanced, noise-reduced training set. Comparative evaluations against SMOTE, SMOTE-DBSCAN, SMOTE-PCA-DBSCAN, SMOTE-ENN, and SMOTE-Tomek Links reveal that SMOTE-PCA-HDBSCAN consistently improves sensitivity for minority classes (Clean: 4.76% to 28.57%; Polluted: 38.09% to 61.90%) while maintaining high accuracy for the majority class. These results demonstrate the robustness of SMOTE-PCA-HDBSCAN in addressing class imbalance, offering a valuable tool for enhancing predictive models in environmental monitoring and other domains with imbalanced datasets.

Multiple-input multiple-output (MIMO) relaying system has attracted the attention of cooperative network researchers, due to its advantage over the conventional single antenna system, in terms of system capacity and spatial diversity. Precoder design is a processing scheme implemented at a source and relay node to improve system performance. We propose a linear precoder design for non-orthogonal amplify-and-forward MIMO relaying systems based on the minimum mean square error (MMSE) criterion. We analyze an upper bound of MMSE using a convenient expression to determine the structure of precoding matrices using the singular-value decomposition technique. Simulation results demonstrate that the proposed precoded scheme outperforms both unprecoded and existing precoded schemes.

Abundant potential of renewable energy (RE) in Indonesia is predicted to replace conventional energy which continues to experience depletion year by year. However, until now, the use of RE has only reached 2% of the existing potential of 441.7 GW. The main overview of this work is to investigate the availability of RE that can be utilized for electricity generation in Indonesia. National energy demand and targets in the long run during the 2017–2050 period are also discussed. Besides, government policies in supporting RE development are considered in this work. The results show that the potential of RE in Indonesia can be utilized and might replace conventional energy for decades. The use of RE for electricity generation can be achieved by employing a government policy that supports the investor as the executor of RE development. The selling price of electricity generated from RE is cheaper than electricity generated from fossils; this makes economy is more affordable for people. Finally, the target set by the government for utilizing RE as the main energy in Indonesia can be done by implementing several policies for the RE development. Thus, greenhouse gas emissions and the use of petroleum fuels can be reduced.

Good power quality and high-power efficiency are important aspects of power system management. Harmonic, as an unwanted frequency component in electrical signals, can interfere with power quality and reduce electrical efficiency. A passive harmonic filter is a device used to reduce or filter harmonics in electrical power systems. This research proposes technology Internet of Things with an automatic control system that moves the step filter to solve the harmonic problem, with the aim of improving the quality of power and electrical efficiency. The step filter functions to identify and isolate the harmonic, thus enabling appropriate controls to suppress the impact of the harmonic. The proposed system uses sophisticated control algorithms using sensors that can read current, voltage, frequency, and power factor to adjust the step filter parameters dynamically, following changes in load conditions and harmonizing. Simulation of the control network using the proteus software is subsequently undertaken in further research to design the control device. Using an automatic control set in the control simulation results reduced the need for manual intervention and let the system adjust the step filter automatically in response to changing network conditions. This showed that this method worked to lower harmonic distortion, increases the power factor through the filter used, and make the system more energy efficient overall. The study emphasizes the importance of applying sophisticated control strategies to manage harmonics efficiently, which ultimately paves the way to a cleaner and more reliable power grid.