Scheduling Of Applications Research Articles

Legal regulation and application of electricity blackout schedules in Ukraine

Legal regulation and application of electricity blackout schedules in Ukraine

EffiESGen: A User-centric Web Application for Efficient Exam Scheduling and Collaboration in Education

EffiESGen: A User-centric Web Application for Efficient Exam Scheduling and Collaboration in Education

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

Implementasi Sistem Penjadwalan Mata Kuliah Menggunakan Metode Algoritma Genetika Berbasis Web

The process of preparing the lecture schedule at Bina Insan University is still done semi-manually with the help of Microsoft Excel and takes days, even weeks, whereas making the schedule must be done optimally and quickly because the schedule will be used for lecture activities each semester, so that the scheduling process can be carried out effectively and efficiently, so we need an application that can simplify the scheduling process, namely a scheduling application and applying the right algorithm, one of the algorithms that can be used in scheduling applications is the Genetic Algorithm. Course scheduling is a process of allocating courses, lecturers, space and time by matching predetermined rules so that all these components can be fulfilled. Genetic Algorithms are search algorithms that are based on natural selection mechanisms and natural genetics. The results of the research are in the form of a schedule that is prepared automatically without any more clashes between times and classes to be used

Environmental Monitoring and Agricultural Insights: Analysis of Cotton Crop Using PowerBI

To understand the variables affecting Pakistan's cotton output, our research paper study focuses on applying the PowerBI tool to analyze data related to the cotton crop. We gathered data from two fields in Rahimyar Khan and Shah Alam Shah, Matiari, Sindh, to analyze the soil moisture content, availability of fertilizer, and environmental factors to improve agricultural practices and increase crop yields. The dataset contains data from monitoring dates and factors like temperature, humidity, soil moisture content, and signal intensity. We forecast cotton crop output, improve planting schedules, and foresee possible issues like bug outbreaks using predictive analytics. The study offers practical suggestions for decision-making procedures about fertilizer application schedules, irrigation schedules, and the sustainability of cotton crops. Limitations include data quality and scalability challenges, and future research will focus on improving agricultural techniques for better cotton growing.

Alfalfa (Medicago sativa L.) Nitrogen Utilization, Yield and Quality Respond to Nitrogen Application Level with Center Pivot Fertigation System

Nitrogen (N) application with a center-pivot fertigation system is commonly used for alfalfa production in the North China Plain, with its barren soil, but improper N application has resulted in lower N use efficiency and even negative effects on yield and quality. The effects of N application levels on soil NO3−-N content, nitrogen utilization, yield and quality of alfalfa at various cuttings and established years were evaluated under sprinkler fertigation to optimize the N application schedule. Four N application levels of 0, 12.5, 25 and 37.5 kg N ha−1 (N0, N1, N2 and N3) for each cutting were applied to alfalfa at the early vegetative stage from the first to third year after establishment. The results showed that the variation in soil NO3−-N content was mainly concentrated in the topsoil (0–40 cm) after N application via sprinkler fertigation. N uptake amount was dramatically improved through the N application for one-year-old alfalfa. Compared to the yield under the N0 treatment, the N application significantly improved the yield of alfalfa at the first two cuttings in the first year. The N use efficiency generally reached a high value with a low N application level. N application had an insignificant effect on the alfalfa quality classification during the three years. For obtaining high yield, quality and N use efficiency of alfalfa planted in the North China Plain, the N application level is recommended as 12.5 kg N ha−1 at the first cutting and 37.5 kg N ha−1 at the second cutting for one-year-old alfalfa.

Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments

Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes.

Energy cost optimization through load shifting in a photovoltaic energy-sharing household community

Home energy management systems are essential for the optimization of resources in complex demand scheduling problems that require energy efficiency in homes. This can be achieved through the use of Renewable Electricity Sources (RES), for cleaner and more sustainable energy generation, as well as participation in Demand Response (DR) programs, for lower energy prices. The problem becomes more complex when it is considered a community of households. This paper aims to address individual and community household participation in DR programs and RES sharing while considering constraints imposed on the operation schedule of appliances, through load shifting optimization. For that, a Genetic Algorithm is proposed, implemented, and validated, which focuses on minimizing energy costs. It takes into account dynamic pricing, distributed generation, and household community energy sharing. Using real household workload data, two case studies are presented, one for the cost optimization of an individual household and another for a photovoltaic energy-sharing household community containing twenty houses. Both case studies represent five days of scheduling, where each house can have up to five appliances able to shift. The business as usual costs are 16.74 EUR and 269.99 EUR for the individual and community case studies, respectively. Results show improvements of up to 24.3% (12.67 EUR in the optimized schedule, 4.07 EUR in savings) for the individual case study and 11.8% (238.21 EUR in the optimized schedule, 31.78 EUR in savings) for the household community. In a community, households can expect cost reductions of 1.5%–26.8% when compared to individual scheduling.

Detecting interference between applications and improving the scheduling using malleable application clones

This paper presents a novel feature for improving the scheduling process based on the performance prediction and the detection of CPU and I/O interference between applications. This feature consists of using malleable synthetic benchmarks – called clones – that reproduce the behaviour of applications executed in a cluster. These proxies can be used with two objectives: to build large and representative datasets that can be used to train the machine learning algorithms for modelling the platform workload, and to evaluate in advance if two executing applications can potentially produce contention hazards related to the shared use of the system resources like CPU, cache memory or I/O bandwidth. The proposed framework generates application clones based on generic-purpose performance information collected from monitoring. Unlike other works based on the use of micro-architectures or metrics obtained from simulators, in the approach presented in this work, the application clones generate similar behaviour without extracting data from the binaries, avoiding the necessity of managing code or data from the applications. One advantage of this approach is that they can be shared securely because they have not been generated using any piece of the original code. In addition, the proposed clones are malleable, so they also able to model the application behaviour under a different number of processes. In this work, we show how the use of clones contributes to improving the application scheduling by reducing the number of evaluations that is necessary to perform, and to detect performance degradation (interference) without the necessity of involving the execution of the actual applications. We evaluate the proposed clone generation approach on two sets of benchmarks (CPU and I/O oriented) and several applications. We also compare the performance obtained during the execution of the proxies and the applications to show their similarity. Finally, we include an evaluation of the interference detection using this novel approach.

Spinosad‐infused biodegradable hydrogel beads as a potential organic approach for argentine ant, Linepithema humile (Mayr) (hymenoptera: Formicidae), management in California citrus orchards

AbstractArgentine ant, Linepithema humile (Mayr), disrupts biological control services in California citrus orchards, leading to economically damaging densities of honeydew‐producing hemipteran pests. With the elimination of chlorpyrifos, the sole registered insecticide for L. humile control in California citrus, the development of effective alternatives is necessary. One alternative is aqueous sugar baits laced with minute concentrations (0.0001%) of insecticide. Field trials were conducted to assess the efficacy of spinosad and thiamethoxam‐infused alginate hydrogel beads (HGBs) for L. humile control. Thiamethoxam‐infused HGBs have previously shown high efficacy against L. humile in citrus. Spinosad is approved for organic use, while thiamethoxam, a neonicotinoid, along with other neonicotinoid insecticides, might soon face deregistration in California, underscoring the need for alternative insecticide chemistries for ant control. Therefore, this study aimed to optimize spinosad‐loaded HGBs by identifying efficacious concentrations, application rates, and deployment frequency, and to compare with thiamethoxam‐infused HGBs, the standard reference treatment. Results showed that spinosad‐infused HGBs significantly reduced high‐density L. humile populations in citrus orchards. Notably, the highest spinosad concentration tested (0.01%) demonstrated comparable control to both 0.0001% and 0.01% thiamethoxam concentrations. A tri‐weekly application schedule for spinosad‐infused HGBs provided best control. Trees treated with 125 or 250 g of hydrogels infused with 0.01% spinosad at weeks 0, 3, and 6 displayed significantly reduced ant activities compared to plots treated solely during weeks 0 and 6, as well as control plots lacking HGB applications. Spinosad‐infused HGBs may offer an effective alternative to thiamethoxam‐infused HGBs for control of L. humile in California citrus orchards.

Penerapan Algoritma Genetika Untuk Penjadwalan Mata Pelajaran Di SMK Negeri 1 Kota Tambolaka

consider other things such as avoiding the same or repeated schedules in one day, the number of teaching staff involved, and practicum subjects which in their implementation require a laboratory. The subject scheduling process at SMK Negeri 1 Tambolaka City is still carried out manually by Deputy Head 1 for the curriculum field, who currently uses Microsoft Excel software, which has the potential for errors and often requires correction before the schedule is implemented. Because the subject scheduling process is very important, a solution is needed in making subject schedules. Therefore, this research aims to optimize course schedules by applying a genetic algorithm to the course scheduling application. In this research, the process of preparing rosters for applied subjects was carried out at school using a web-based genetic algorithm method so that schedule collisions did not occur and the process was more optimal. In the experiment, the results of this subject scheduling application system consisted of several forms and menus, including a login form, department data menu, subject data, class data, teachers, hour ranges, special schedules, and the scheduling menu. Creating a teacher's teaching schedule using a genetic algorithm can produce a schedule automatically and systematically, thereby displaying the schedule for the day and time of each teacher's teaching schedule. The application of genetic algorithms in scheduling teacher teaching at SMK Negeri 1 Tambolaka City is a very optimal solution for scheduling, so that the possibility of similarities in the same teacher's teaching time and hours is very small.

Enhanced planning and scheduling in building construction projects: an innovative approach to overcome scheduling challenges

This article introduces an Enhanced Planning and Scheduling (EPS) approach for complex construction projects with multiple sections, offering an innovative solution to tackle existing planning and scheduling challenges. This research presents a systematic and computational approach to optimize project duration and enhance schedule reliability and productivity. The goal is to overcome the inherent limitations of traditional scheduling methods. The proposed methods have the potential to be integrated with emerging cross-industrial project management strategies to enhance planning and scheduling processes in construction. The EPS method is executed in three stages: the enhanced Milestone, Phase, and Production schedules. Fundamental elements of this methodology encompass work areas, activities categories, labor hours estimation, and planning indicators. Drawing on combinatorial optimization concepts, this study offers industry practitioners an effective, easily-implementable solution. A case study was conducted to assess the application and effectiveness of the Milestone schedule. The results showed a 40-workday reduction in project duration compared to the traditional scheduling methods in Table 4.

Vehicle Routing Problem with Time Windows to Minimize Total Completion Time in Home Healthcare Systems

We propose a vehicle routing problem with time windows (VRPTW) with compatibility-matching constraints and total completion time as the objective function, with applications in home healthcare routing and scheduling. Mixed integer linear programming is provided with total completion time minimization as the objective function. The solution approach has two objectives, total completion time (primary objective) and total distance (secondary objective). A heuristic is proposed comprising three phases: initializing to find an initial feasible routing (inserting the procedure with a modified K-means algorithm), swapping and moving the procedure to find a local optimal routing, and shooting the procedure to move away from the local optimum. Proof of feasibility for the inserting procedure is provided to prevent unnecessary insertions. Phases 2 and 3 will be repeated as needed to ensure solution quality. Solving our model with the proposed heuristic algorithm increases the total distance by 90.00% but reduces the total completion time by 25.86%. To test our model and heuristic, we examined a system with 400 home-healthcare cases in Chiang Mai. The heuristic quickly solved the problem. When total completion time is minimized, some caretakers serve up to twice as many patients as their coworkers; when total distance is minimized, workload discrepancies can increase up to seven-fold.

Multi-coordinated scheduling application of small and medium-sized source network, load and storage system under blockchain technology

Abstract This paper firstly analyzes the small and medium-sized source-grid-load-storage system and mathematically models the system structure based on the power supply model, the grid model, the load model and the energy storage model. Secondly, based on blockchain technology, the multivariate coordinated scheduling objectives and constraints of small and mediumsized source-grid load-storage systems are modeled through the Cosmos platform, and the model is optimized and solved by the Gray Wolf optimization algorithm. Finally, the impact of multivariate coordinated scheduling on small and medium-sized source-network load storage systems is examined using the parameters of the example and the micro-network design. The results show that compared to the total operating cost before optimization, the total operating cost decreased by 1163.7 yuan. In terms of the peak-valley difference of load electricity consumption, the peak-valley difference before the implementation of the demand response is 0.48, the peak-valley difference of load after the implementation of the demand response is 0.35, and the real-time residual capacity of the energy storage device is always maintained between 60 and 250 kW. The multivariate dispatch model based on this study has a positive impact on small and medium-sized distribution grids with source-grid load-storage systems.

An Evaluation of Online Training on Pre-service Special Education Teachers’ Case Application of Visual Schedules for Individuals With Intellectual and Developmental Disability in China

Multiple evidence-based practices (EBPs) were identified in education, but relatively little attention has been paid to implementation. Special education teachers in the Chinese mainland faced great challenges in terms of a lack of knowledge and skills regarding EBPs. Asynchronous online training has been successfully provided for pre-service professionals on instructional methods or strategies. The present study evaluated the effects of self-paced online training on pre-service special education teachers’ case application of visual schedules to support individuals with intellectual and developmental disabilities. A pre/post-test control group design was utilized. Seventy-seven undergraduate students (mean age 20.5 years) who majored in special education participated in this study. Results indicated that the self-paced online training was effective in improving pre-service special education teachers’ case application of the visual schedules strategy. In addition, participants were in agreement on the social acceptability of the online module of visual schedules. The results are discussed from a perspective of effective and socially valid online training, along with limitations and future implications.

Stabilized urea for maize grown on an Amazonian Cerrado soil

AbstractUrea splitting and the use of stabilized ureas in corn (Zea mays L.) crops are management strategies that increase urea efficiency and reduce nitrogen (N) losses by volatilization and leaching. This study aimed to evaluate the effects of urea stabilized with urease inhibitor (UI) and nitrification inhibitor (NI) compared to conventional urea applied at three different schedules, on corn grain yield and the dynamics of inorganic nitrogen on an Amazonian Cerrado soil, Brazil. Two experiments were carried out, one in 2019 and the other one in 2021, in a randomized block design with four replications. Treatments were arranged in a factorial scheme (3 × 3), with three types of urea (urea, U; UI; and NI) and three application schedules (100% at planting, 30% at planting and 70% as topdressing, and 30% at planting and two topdressings with 35% N). In 2019, compared to U and UI, NI increased corn yield when 100% of urea was applied at planting. Regardless of the splitting management, NI ensured the same yields. In 2021, urea splitting was required to improve corn yields, and UI promoted higher yield in all urea application schedule. In 2019 and 2021, both NI and UI, respectively, ensured higher inorganic N levels in the soil, especially after the first topdressing. These N contents are reflected in yield gains. Urea with nitrification or urease inhibitor may be a relevant option for managing nitrogen fertilization in corn crops in the Amazonian Cerrado.