Scheduling Of Applications Research Articles

Perancangan Aplikasi Jadwal Liputan Berbasis Web Di Bidang Berita TVRI Dengan Metode Prototype

The absence of an application to assist in the scheduling of the Communication and Informatics department's duties at TVRI has resulted in ineffective schedule distribution, causing a TVRI staff member to receive two coverage schedules simultaneously, while others may not receive any schedule at all. The purpose of this final report is to create an Application for Coverage, Announcers, and Editorial Schedules at TVRI using the PHP programming language with the Laravel framework and MYSQL database, and designing its interface using Bootstrap. The issue faced by TVRI's Communication and Informatics Department (Diskominfo) is the operational data processing of broadcasting schedules using Microsoft Excel, which poses several challenges including insufficient data security, unorganized data storage making it difficult to search, and the risk of losing previously saved data, thereby slowing down TVRI's reporting process. This application comprises three User levels: admin, User, and TVRI head. The research adopts the Prototype development method to create the Coverage, Announcers, and Editorial Schedule Application at TVRI. Through the Prototype, the development team can quickly visualize the application's design and functionality, gather early User feedback, and address potential issues or design improvements before full implementation. The research outcome, in the form of the application, provides a concrete solution to the challenges of scheduling effectiveness at TVRI, enhancing efficiency, addressing data security concerns, ensuring organized data storage, and improving workflow speed.

Effect of conjoint application schedules of organic and inorganic sources of nutrients on growth, yield and economics of rice

A field experiment was conducted during (kharif) summer season of 2019 and 2020 at Crop Research Centre, Sardar Vallabh Bhai Patel University of Agriculture and Technology, Modipuram, Meerut, Uttar Pradesh to study the effect of integrated use of chemical nitrogen fertilizer and green manure on performance of rice (Oryza sativa L.). The experiment consists of fourteen treatment combinations of conjoint application of organic and inorganic sources of nutrients was laid-out in randomized block design with three replications. The results showed that the significant highest dry matter accumulation (1,205.90 g/m2 ) at harvest stage, leaf-area index (4.18), crop growth rate (16.34 g/m2 /day), relative growth rate (7.09 mg/g/day), grain yield (3.88 t/ha) and straw yield (6.64 t/ha) recorded in the 25% recommended N through dhaincha incorporated + 75% N through chemical fertilizer was found at par with RDF (120N : 60P : 50K) and 25% recommended N through vermicompost incorporated 10 days before transplanting + 75% N through chemical fertilizer and it was significantly superior over rest of the treatment combinations. The substitution of 25% N through incorporated dhaincha at planting was found most promising in terms of productivity, profitability and performed better than other treatments and may be recommended for farmers of Uttar Pradesh and similar agro-eco regions.

Application Scheduling With Multiplexed Sensing of Monitoring Points in Multi-Purpose IoT Wireless Sensor Networks

Application Scheduling With Multiplexed Sensing of Monitoring Points in Multi-Purpose IoT Wireless Sensor Networks

Resource allocation and aging priority-based scheduling of linear workflow applications with transient failures and selective imprecise computations

Resource allocation and aging priority-based scheduling of linear workflow applications with transient failures and selective imprecise computations

Penerapan Metode Waterfall dalam Pengembangan Aplikasi Schedule Maintenance Alat Produksi

PT. Shiroki Indonesia Cikarang is moving on the field of automotive spare parts manufacturing such as window regulator, seat recliner, sear track as well as floor lock. The activity uses a tool/machine as a support to produce spare parts that have good quality. With so many of these machines, then the company needs to do the maintenance and also the maintenance of the machine well. Scheduling of maintenance and maintenance of machinery production equipment still uses the record on board and requires information from the operator to be submitted to the technician. Therefore, it is necessary to create a web-based maintenance schedule application that can facilitate the performance of the staff in making the maintenance schedule of the production equipment more effective and efficient as well as the availability of reports from the maitenance. The research method used is the waterfall method which has five stages namely needs analysis, design, program code making, testing and support or maintenance. The schedule maintenance application was created using the PHP programming language using the CodeIgniter framework and the MySql database.

Optimization of social welfare in P2P community microgrid with efficient decentralized energy management and communication-efficient power trading

The primary objective of implementing a demand response (DR) program is to actively involve participants in maximizing social welfare by effectively reducing electricity demand, optimizing cost savings, and facilitating peer-to-peer (P2P) trading of surplus power between prosumers and consumers residing within a community. In this paper, a novel approach is proposed to optimize the scheduling of home-controlled appliances, minimize overall electricity costs of the community, and prioritize participants' privacy by integrating non-cooperative energy game theory under time-of-use (TOU) tariffs and incentive schemes. The proposed Segmentation Based Distributed Alternating Direction Method of Multipliers (SBD-ADMM) is a novel algorithm that efficiently addresses the social welfare and participants' privacy problem in a decentralized distribution of power. It introduces a communication-efficient approach to optimize power trading while maximizing collective welfare, without relying on a central hub or authority. Node coloring is used in the formulation of the algorithm, in which every node just needs to trade with its neighbors in order to attain the entire optimum solutions. The suggested P2P trading algorithm is assessed on a broad range of scenarios, demonstrating its efficiency as well as fast convergence. The P2P trading algorithm provides similar optimum results with considerably a lesser number of communications and iterations as compared to other contemporary decentralized techniques. The suggested strategies for DR program and P2P trading are more feasible and privacy-preserving compared to other modern strategies. Finally, numerical results demonstrate that demand response and P2P trading can reduce overall costs for consumers by up to 30.23 % and for prosumers by up to 44.34 %.

FPT algorithms for a special block-structured integer program with applications in scheduling

FPT algorithms for a special block-structured integer program with applications in scheduling

Energy Management Scheme for Optimizing Multiple Smart Homes Equipped with Electric Vehicles

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38% was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV.

Enhancing Transplant Aman Rice Productivity through NPK Fertilizer Split Application Scheduling Under Zero Till Non-Puddled Condition

This experiment was conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh to evaluate the varietal response of split application of NPK fertilizer application schedule on transplant aman rice under zero till non-puddled condition. The experiment comprised three rice varieties viz. Bashiraj (local), BRRI dhan49 (inbred), Dhanigold (hybrid); and seven NPK fertilizer application schedules. The experiment was laid out in a randomized complete block design with three replications. The effect of variety, NPK fertilizer split application schedule and their interaction showed significant variation on the yield attributes and yield of aman rice. In interaction, the highest number of effective tillers hill-1 (11.66), total spikelets panicle-1 (164.00), grains panicle-1 (151.22), 1000-grain weight (38.70 g), grain yield (5.67 t ha-1) and harvest index (60.36%) were obtained from hybrid variety Dhanigold when fertilized following 3-splits of N [1/3 at 14 days after transplanting (DAT) + 1/3 at maximum tillering (MT)+ 1/3 at panicle initiation (PI)] and P (1/3 as basal + 1/3 at MT + 1/3 at PI ), and single split of K as basal (full dose). Same treatment combination also provides the highest yield of BRRI dhan49, but the local Bashiraj gave the highest yield at recommended practice where only N was top-dressed at 3 splits. Based on this result, it may be concluded that for better productivity hybrid Dhanigold and inbred BRRI dhan49 might be fertilized with 3-splits of N (1/3 at 14 DAT + 1/3 at MT + 1/3 at PI) and P (1/3 as basal + 1/3 at MT + 1/3 at PI) and full dose of K as basal during aman season under zero till non-puddled condition. On the other hand, for local variety Bashiraj 3-spilts of N either [½ as basal + ¼ at active tillering (AT) + ¼ at PI] or N (1/3 at 7 DAT + 1/3 at MT + 1/3 at PI) and P, K as single split as basal appeared as the best application schedule.

Optimization of Home Energy Management Systems in Smart Cities Using Bacterial Foraging Algorithm and Deep Reinforcement Learning for Enhanced Renewable Energy Integration

This paper presents a pioneering exploration into the optimization of Home Energy Management Systems (HEMS) through the novel application of the Bacterial Foraging Metaheuristic Optimization (BFMO) algorithm and Deep Reinforcement Learning (DRL). The study systematically addresses the pressing challenge of enhancing residential energy efficiency, focusing on dynamic appliance scheduling within HEMS. A robust methodology is established, encompassing data collection from smart homes, implementation details of the BFMO algorithm, DRL techniques, and a comprehensive evaluation framework. The unique contribution of this research lies in the effective integration of the BFMO algorithm and DRL to orchestrate energy‐conscious scheduling of home appliances within HEMS. The BFMO algorithm demonstrates its adaptability to fluctuating energy costs and consumption patterns by simulating the foraging behaviour of bacteria. At the same time, DRL enhances the system’s ability to learn and optimize scheduling decisions over time, showcasing their combined efficacy in real‐world scenarios. The algorithms’ iterative application of chemotaxis, reproduction, elimination‐dispersal, swarming, and learning consistently yields optimized appliance schedules. The main focus of this study resides in the evaluation metrics illustrating the tangible benefits of BFMO and DRL compared to traditional HEMS. Significant reductions in total energy consumption and cost, accompanied by improved peak demand management, exemplify the algorithms’ impact. Furthermore, the study delves into enhancing user comfort, integrating renewable energy sources, and the overall robustness of HEMS, all demonstrating the multifaceted advantages of the BFMO and DRL approaches. This research contributes methodologically by introducing and detailing these algorithms and provides a valuable dataset and evaluation metrics for future research in the domain. The findings underscore the immediate and long‐term relevance of optimizing HEMS with BFMO and DRL, catering to researchers, practitioners, and policymakers involved in advancing smart grid technologies and sustainable residential energy management. In summary, this work establishes the BFMO algorithm and DRL as pioneering and versatile tools for energy‐conscious appliance scheduling in HEMS, offering a substantial leap forward in the quest for efficient and sustainable residential energy management.

Cost-Centric Energy Management of a Test Grid System Considering Load Flexibility for DSM

Demand-Side Management (DSM) adjusts load demand by rebuilding the load demand model of a distribution system and thus reduces operating costs. One potential approach to achieving cost savings is the redistribution of flexible loads to time periods characterized by lower utility expenses per unit, or alternatively, the elimination of nonessential loads. The primary objective of this research is to examine the use of DSM approaches in the scheduling of domestic appliances. The aim is to effectively minimize operating expenditures, the peak-to-average demand ratio (PADR), and any associated problems. Domestic appliances may be categorized into three distinct types based on their ability to adjust to variations in time, temperature, and light: time-flexible, temperature-flexible, and light-flexible. The second step of the study focuses on modelling the power consumption scheduling issue for demand-side clients. This is done by including operating cost priorities and modes of operation while taking into account both demand and supply factors. This research optimizes an IEEE 33 bus test grid system using load scheduling, fossil fuel generators, and renewable energy. The primary objective of this optimization is to minimize operating costs. In order to optimize the load model, it was necessary to restructure it according to the DSM participation level. After considering the restructured load demand models, distributed load scheduling ideas are disseminated to reduce test grid system power distribution costs.

Notice of Violation of IEEE Publication Principles: Scientific Workflow Partitioning and Data Flow Optimization in Hybrid Clouds

Notice of Violation of IEEE Publication Principles “Scientific Workflow Partitioning and Data Flow Optimization in Hybrid Clouds” by Rubing Duan, Rick Siow Mong Goh, Zheng Qin and Young Liu in Pre-Prints for IEEE Transactions on Cloud Computing After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper and it should not be used for research or citation The scheduling and execution of workflow applications are complex issues because of the dynamic and heterogeneous nature of distributed computing environments like hybrid clouds. While hybrid cloud computing environments provide good potential for achieving high performance and low economic cost, it also introduces a broad set of unpredictable overheads. This paper describes a novel approach to refine workflow structure and optimize intermediate data transfers without changing the scale of scheduled cloud service for large-scale scientific workflows containing thousands (or even millions) of tasks. These methods include pre- and post-partitioning of workflows, data flow optimization, static and dynamic optimization, as well as virtual single execution environment incorporated into a workflow management system.We demonstrate the effectiveness of our methods on the three real applications, Montage, Broadband, and WIEN2k, executed in a real hybrid cloud.

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.

An Improved Dynamic Robust Optimization Algorithm and Its Application in Optimal Scheduling of Integrated Energy System Considering Carbon Emission Reduction

An Improved Dynamic Robust Optimization Algorithm and Its Application in Optimal Scheduling of Integrated Energy System Considering Carbon Emission Reduction

Legal regulation and application of electricity blackout schedules in Ukraine

Legal regulation and application of electricity blackout schedules in Ukraine

An explainable multi-agent recommendation system for energy-efficient decision support in smart homes

Abstract Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing and implementing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbors, extreme gradient boosting, adaptive boosting, Random Forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches local, interpretable, model-agnostic explanation and SHapley Additive exPlanations as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations.

Heuristics for Home Appliances Scheduling Problems with Energy Consumption Boundsp, Li { White-Space: Pre-Wrap; }

Heuristics for Home Appliances Scheduling Problems with Energy Consumption Boundsp, Li { White-Space: Pre-Wrap; }

Modeling techno-economic multi-objectives of smart homes considering energy optimization and demand management

The research suggests an approach that prioritizes customer needs and aims to reduce energy expenses while safeguarding customer privacy. Furthermore, it is recommended that smart homes incorporate a home energy management system to optimize appliance energy consumption. Conversely, the introduction of demand-side management addresses the energy management challenges faced by smart households. The main goal of this approach is to decrease energy usage and electricity costs for customers. Moreover, it enhances user satisfaction while waiting at common intervals. The primary emphasis of this study is on a smart residence furnished with energy management technology and smart home gadgets capable of supplying electricity to the grid. These objectives are considered distinct aspects in the multi-objective optimization issue stemming from this approach. The study utilizes the grasshopper optimization algorithm (GOA) to optimize battery and home appliance scheduling in smart homes with flexible devices. The goal is to reduce the overall cost of microgrid systems through demand-side management implementation. This comparison highlights the superiority of the proposed method in optimizing energy consumption and reducing carbon emissions in a variety of scenarios. By achieving lower energy costs and carbon emissions while maintaining a comfortable indoor environment, the proposed method proves to be a highly effective and sustainable solution for energy management in buildings. These simulation results provide strong evidence of the method’s potential to significantly impact energy efficiency and environmental sustainability in real-world applications. Furthermore, the consistent minimization of the discomfort index showcases the method’s ability to prioritize occupant comfort while still achieving significant energy savings and emissions reductions. Overall, the comparison with other algorithms solidifies the effectiveness and practicality of the proposed method in addressing the complex challenges of energy management and sustainability in smart homes.

Triplex real-time qPCR for the simultaneous detection of Botryosphaeriaceae species in woody crops and environmental samples.

Species of Botryosphaeriaceae fungi are relevant pathogens of almond causing trunk cankers, extensive gumming, necrosis of internal tissues and plant dieback and dead, threatening almond productivity. A novel triplex quantitative real-time PCR (qPCR) assay was designed for the simultaneous detection and quantification of Neofusicoccum parvum, Botryosphaeria dothidea and the Botryosphaeriaceae family. The method was validated in symptomatic and asymptomatic almond, avocado, blueberry and grapevine plants and in environmental samples, such as cropping soil and rainwater and in artificially inoculated trapped spores, demonstrating the same performance on several matrices. The limit of detection of the triplex qPCR was 10 fg of genomic DNA for the three fungal targets, with high correlation coefficients (R2) and amplification efficiencies between 90 and 120%. Although the triplex qPCR demonstrated to be more sensitive and accurate than the traditional plate culturing and further sequencing method, a substantial agreement (kappa index = 0.8052 ± 0.0512) was found between the two detection methods. The highly sensitive qPCR assay allows for accurate diagnosis of symptomatic plants and early detection of Botryosphaeriaceae fungi in asymptomatic plants (rootstocks and grafting scions from almond nurseries). Furthermore, the triplex qPCR successfully detected Botryosphaeriaceae fungi in environmental samples, such as cropping soils and rainwater. It was also capable of detecting as few as 10 conidia in artificially inoculated tapes. Therefore, the triplex qPCR is a valuable tool for accurate diagnosis, aiding in the implementation of suitable control measures. It enables preventive detection in asymptomatic samples, helping to avoid the introduction and spread of these pathogens in production fields. Moreover, it assists in identifying inoculum sources and quantifying inoculum levels in crop environments, contributing to a precise phytosanitary application schedule, thereby reducing production costs and preserving the environment.

Fuzzy Logic Project Scheduling Application with Limited Resources on Development Irrigation Project on Java Island

Fuzzy Logic Project Scheduling Application with Limited Resources on Development Irrigation Project on Java Island