Scheduling Of Applications Research Articles

Optimizing the Scheduling of Teaching Activities in a Faculty

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates schedules that aim to be conflict-free and efficient, utilizing evolutionary algorithms along with multi-objective optimization. Resource usage, scheduling problems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.

The Impact of Artificial Intelligence on Project Management Efficiency

This research paper examines the influence of Artificial Intelligence (AI) on the efficiency of project management, concentrating on the ways in which AI tools and techniques improve different facets of project management. The study initiates with a comprehensive examination of project management and the increasing significance of AI in various domains, emphasizing the shortcomings of conventional methodologies and the capacity of AI to tackle these issues. The study seeks to evaluate the impact of AI on project processes, pinpoint effective AI tools, and investigate the advantages and obstacles associated with AI integration. A thorough examination of existing literature investigates AI tools, including machine learning, natural language processing, and predictive analytics, along with their applications in planning, scheduling, resource allocation, risk management, communication, and performance monitoring. Case studies and empirical evidence illustrate the beneficial effects of AI on project outcomes, while also highlighting existing gaps in the literature that warrant further investigation. The mixed-methods approach integrates qualitative and quantitative data sourced from surveys, interviews, and case studies, employing both thematic and statistical analysis. The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration. Nonetheless, factors such as initial investment, skill requirements, resistance to change, and ethical considerations are acknowledged. The discussion outlines actionable strategies for project managers, highlighting the importance of training, incremental integration, ensuring data quality, and promoting innovation. The study concludes by offering recommendations for future research regarding the long-term effects of AI, conducting cross-industry analyses, and creating AI tools specifically designed for project management. This approach aims to provide empirical evidence and strategic insights into the integration of AI for enhanced project outcomes.

Meta-heuristic-based home energy management system for optimizing smart appliance scheduling and electricity cost reduction in residential complexes

Meta-heuristic-based home energy management system for optimizing smart appliance scheduling and electricity cost reduction in residential complexes

Nitrogen fertilization management and seeding density differently affect net blotch incidence and grain yield in one two-row and one six-row cultivar of barley

A two-year field experiment was carried out on barley (Hordeum vulgare L.) to test the effect of cultivar, N fertilization management and seeding density on crop growth, net blotch (NB) disease occurrence and yield. The three variables were factorially combined in a split-split-plot design as follows: 1) two barley cultivars, one two-row (H. v. distichum, cv Callas) and one six-row (H. v. hexastichum, cv Azzurro); 2) four N application schedules differing for rates and/or timing of N supply: unfertilized control; fertilized with 35kgha-1 of N at BBCH29 plus 30kgha-1 of N at BBCH31; fertilized with 70kgha-1 of N at BBCH29 plus 60kgha-1 of N at BBCH31; fertilized with 130kgha-1 of N simultaneously at BBCH31; 3) four seeding densities: 100, 200, 400 and 800 viable seeds m2.The three variables affected crop growth and NB occurrence resulting in different grain yield and quality. Results varied with the seasonal trend, in particular with rainfall regime and air temperature, for their possible effect on soil N dynamics and the presence of NB, which resulted in different source capacity, and for their effect on grain setting and filling, i.e., the sink capacity. Both N fertilization and seeding density increased NB pressure, with seeding density affecting the disease even under unfavourable weather conditions for NB development. At the maximum N fertilization rate, delaying N application helped limit plant height and the related risk of lodging, as well as NB symptoms, resulting in greater hectolitre weight and protein content together with no appreciable decrease of grain yield, provided seeding density was close to optimal (i.e., 400 seeds m-2) or higher.

Optimizing Logistics: Developing An Efficient Tracking and Delivery System for Automotive Spare Parts

In the automotive spare parts industry, efficient supply chain management is the backbone of operational success. Amidst this complex ecosystem, companies face challenges in delivering fast, reliable, and competitive services, accommodating a constantly evolving consumer base with discerning preferences and rising expectations. This article aims to identify challenges in developing tracking and delivery scheduling applications for the automotive spare parts industry and improve system performance by introducing effective solutions. The research adopts a system development approach using the Waterfall method to produce a responsive and user-friendly web application, followed by functional testing using Black Box and evaluation using the System Usability Scale (SUS). Development of the tracking and delivery scheduling application using the Waterfall method resulted in a reliable and responsive application, with all system functionalities running smoothly. SUS testing indicated that the application has a good level of usability, with an average score of 77.5 out of 100. This study successfully provides relevant and efficient technological solutions to overcome operational barriers in the automotive spare parts industry, with the potential to enhance customer satisfaction and solidify the company's position as a pioneer in the automotive aftermarket sector. However, further improvements in application usability can be achieved through future iterations guided by user feedback and additional usability testing.

Exploring possibilities of broccoli (Brassica oleracea L. var. italica) production under organic management

Broccoli (Brassica oleracea L. var. italica) was introduced as part of the Indian government's efforts to diversify and modernize agriculture in the region. Its introduction has helped J&K farmers to increase their income through higher market prices and to gain access to new markets and trade opportunities. It being a nutrient-demanding crop, farmers use high doses of chemical fertilizers, pesticides and herbicides and if consumed as salad or half-boiled in many culinary dishes, these chemicals are likely to impact the health of consumers and affect the environment. Therefore, alternative usage to these chemicals using combinations of organic inputs was aimed at producing a healthy crop of broccoli. It was concluded that the application of FYM@ 25 t/ha as a basal dose in the presence of seedling dip treatment of biofertilizers (Azotobacter @ 0.5 kg/ha + Phosphate-Solubilizing Bacteria @l kg/ha + Potassium-Mobilizing Bacteria @ 750 ml/ha) along with the scheduled application of Neem oil +Baeuveria bassiana at 1, 3, 7 and 15 days of interval after disease symptoms was found superior in obtaining higher curd yield (109.57 q/ha) and seed yield (604.96 kg/ha).

Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment

Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment

Glyphosate-based herbicides reduced overwintering population and reproduction of agrobiont spiders

Spiders are important in ecosystem and serve as predators in the biological control of pest insects in agroecosystem, where they encounter various harsh challenges including pesticides and low temperature in winter. Glyphosate-based herbicides (GBH) are widely and frequently applied to diminish weeds, exposing spiders a disturbed habitat, especially to overwintering spiders. We conducted a study combining field surveys and lab assays, to assess the effects of a GBH on the overwintering of the agrobiont wolf spider, Pardosa pseudoannulata. The GBH significantly reduced the overall overwintering spider population by about 69 %, and reduced the number of vulnerable juveniles by about 80 %. The survivors exhibited substantial fitness costs such as reproductive dysfunctions and enhanced oxidative stress responses. We then mimicked the overwinter process in lab. We housed spiders on soil patches with and without weeds to examine whether weeds contributed to the GBH’s sublethal effects. Spiders overwintered independent of weeds when GBH was not applied. When GBH was applied before or during overwintering, juvenile spiders overwintered in weedy habitats exhibited reduced survival and fecundity, and increased oxidative stress compared to their counterparts in weed-free habitats. Therefore, GBH-containing weeds contributed to the persistent adverse effects of GBH on overwintering spiders. The findings revealed the cross-talk among weeds, herbicides, low temperature, and non-target organisms. The study provides novel information on the environmental risk assessment of pesticides and rational scheduling of pesticide application.

Overview of Embedded Rust Operating Systems and Frameworks.

Embedded Operating Systems (OSs) are often developed in the C programming language. Developers justify this choice by the performance that can be achieved, the low memory footprint, and the ease of mapping hardware to software, as well as the strong adoption by industry of this programming language. The downside is that C is prone to security vulnerabilities unknowingly introduced by the software developer. Examples of such vulnerabilities are use-after-free, and buffer overflows. Like C, Rust is a compiled programming language that guarantees memory safety at compile time by adhering to a set of rules. There already exist a few OSs and frameworks that are entirely written in Rust, targeting sensor nodes. In this work, we give an overview of these OSs and frameworks and compare them on the basis of the features they provide, such as application isolation, scheduling, inter-process communication, and networking. Furthermore, we compare the OSs on the basis of the performance they provide, such as cycles and memory usage.

The impact of micronutrients and their application schedule on the yield of carrots (Daucus carota L.) variety New Kuroda in Southern Telangana conditions

The impact of micronutrients and their application schedule on the yield of carrots (Daucus carota L.) variety New Kuroda in Southern Telangana conditions

Price-based demand response with renewable energy sources and peer-to-peer trading for residential microgrid with electric vehicle uncertainty

This article deals with the impact of demand response (DR) with peer-to-peer (P2P) energy trading for a residential smart home consisting of smart appliances, an electric vehicle, a battery energy storage system (BESS), and renewable energy-based distributed generation (DG) such as solar PV and wind. The proposed work deals with two stages, such as DR implementation and P2P energy trading. The first stage of implementation deals with the optimal scheduling of smart appliances in a residential home using a Binary Dragonfly Algorithm (BDA). Each appliance in the smart home is scheduled based on the slab tariff, Time of Day (ToD), and Real-Time Pricing (RTP) with DR to identify the most economical tariff structure for the residential home. Five different cases are examined to identify the best smart home for effectively utilizing DGs by reducing grid dependency and electricity costs.The RTP tariff is proved to be more beneficial than the slab tariff and ToD tariff because the electricity cost of the smart home is very minimal for all the cases with RTP, especially in case 5 cost is reduced by ₹12.0613 than slab tariff and ₹8.414 than ToD. Hence, the RTP tariff is chosen as the optimal tariff structure for the next stage of the proposed work.The second stage of implementation deals with the P2P trading between the smart homes with a proposed enhanced bidding strategy to reduce the grid dependency and electricity cost of the individual smart homes. The proposed enhanced bidding strategy involves a double auction mechanism to determine the trading decision and trading cost for the benefit of both consumers and prosumers. The electricity cost reduction achieved by consumer 1 and consumer 2 from the enhanced smart bidding strategy are ₹92.7615 and ₹15.1525, respectively. The results also proved that prosumer 1 and prosumer 2 obtained a profit of ₹78.2170 and ₹28.6970, respectively. The EV uncertainty analysis is also examined in smart homes to validate the effectiveness of the proposed bidding strategy. The simulation results proved that the proposed DR-based P2P trading with an enhanced bidding strategy reduces grid dependency and electricity costs, including EV uncertainty conditions.

Supply responsive scheduling for ethylene cracking furnace systems based on deep reinforcement learning

AbstractEthylene is one of the most important chemicals, and scheduling optimization is crucial for the profitability of ethylene cracking furnace systems. With the diversification of feedstocks and the high variability in prices, supply chain fluctuations pose significant challenges to the scheduling decisions. Dynamically responding to these fluctuations has become crucial. Traditional mixed integer nonlinear programming (MINLP) models lack the capability of supply chain response, while receding horizon optimization (RHO) models require parameter prediction and repeated optimization solving. To address this challenge, we propose a deep reinforcement learning‐based framework that includes an ethylene dynamic scheduling environment and a decision agent based on deep Q‐network. Across three test cases, compared to the MINLP and RHO models, this framework significantly minimizes losses caused by supply chain fluctuations, thereby increasing daily average net profits by 9%–27%, demonstrating its significant potential for application in responsive scheduling in the presence of supply chain fluctuations.

Effects of Paclobutrazol on Reproductive and Vegetative Traits of Phalaenopsis Join Grace 'TH288-4'.

Phalaenopsis is the most popular potted plant worldwide. However, its typically long stalks often lead to increased shipping costs and risks. This study investigates the effectiveness of varying the concentration, timing, and frequency of paclobutrazol (PP333) applications on shortening the stalk of Phalaenopsis Join Grace 'TH288-4'. Concurrently, it also examines the potential for producing visually appealing and single-flower potted phalaenopsis products by means of truncation. Mature phalaenopsis plants were moved to a cool room in the seventh week to induce flowering. Four experimental groups were established based on different PP333 application schedules: the control (CK) group, with reverse osmosis water application in the second week; the T2 group, with a single application in the second week; the T2T3 group, with applications in both the second and third weeks; and the T7T8 group, with applications in the seventh and eighth weeks. The PP333 concentrations used were 250, 500, 750, and 1000 mg·L-1, applied as foliar sprays. The results showed that the shortest stalks, measured from the base to the first flower, were observed in the T2 group treated with PP333 at 750 mg·L-1 and in the T2T3 group with PP333 at 500, 750, and 1000 mg·L-1. These treatments resulted in stalk lengths of 19.18-22.17 cm, which are 67.2-71.6% shorter than the controls. PP333 application had minimal effect on the stalk diameter, pedicel length, flower width, length, and length/width ratio. However, root diameter was thicker in plants treated with PP333 compared with the control plants. For producing single-flower phalaenopsis, a foliar spray of 750 mg·L-1 PP333 is recommended approximately a month before moving the plants to cooler conditions, followed by truncation, retaining only the first flower. As a result, this study establishes a PP333 treatment protocol for phalaenopsis, offering a strategy to effectively shorten the stalks.

Heuristics for home appliances scheduling problems with energy consumption bounds

In this paper we consider the problem of scheduling two types of home appliances: ones with fixed power and flexible operation time and others with fixed operation time and flexible power. We consider energy consumption bounds that depend on the time of the day, which modify considerably the computational complexity of the appliance scheduling problems. Additionally, we also take into account the possibility that appliances with flexible operation time could be interrupted. The goal of the decision-making process is to minimize the electricity bill paid by the users and the discomfort perceived by them, according to a desired-tradeoff, subject to the operation constraints involved.Since these problems turn out to be NP-hard, we describe several heuristics we designed, explaining their foundations. Finally, we analyze the computational results obtained for instances we generated and present the best algorithms to employ according to different priorities the users might have.

Machine learning‐driven implementation of workflow optimization in cloud computing for IoT applications

AbstractThe optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML‐driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large‐scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.

Integrating machine learning and change detection for enhanced crop disease forecasting in rice farming: A multi-regional study

Crop diseases are increasingly causing devastating yield losses each year, posing a significant threat to global food security. Currently, fungicide treatment is one of the most effective measures to mitigate these disease-induced yield losses. Accurately predicting disease occurrence, onset timing, and development is critical for optimizing fungicide application schedules to achieve high efficacy. In this study, we compiled weekly disease observations, crop management, and weather data from 207 locations across China, India, and Japan. We compared six machine learning (ML) models for their performance in simulating disease severity. By mimicking disease development curves and trends at early stages, we developed a novel change detection (CD) model, named rolling linear regression (RLR), and combined it with the best-performing ML model to predict the occurrence, severity, and onset dates of four major rice diseases: leaf blast (Magnaporthe oryzae), panicle-neck blast (Magnaporthe oryzae), sheath blight (Rhizoctonia solani), and false smut (Ustilaginoidea virens). Our findings showed that the RandomForestRegressor demonstrated the highest performance in simulating disease severity, with mean absolute errors (MAE) below 0.5 % and root mean square errors (RMSE) below 2.5 %. The RLR model showed a distinct advantage over the other four widely used CD models based on five evaluation metrics. Additionally, the paired RandomForestRegressor+RLR model achieved the highest F1-score, ranging from 0.7 to 0.8, in predicting disease occurrence, outperforming 29 other ML+CD model pairs. Furthermore, the RandomForestRegressor+RLR model predicted onset dates with fewer than 6 error days and accuracies ranging from 74 % to 87 %. Combining ML with CD models not only shows robust generalization across diverse environmental conditions but also proves highly effective for large-scale disease risk forecasting in rice farming regions. The adaptability of ML techniques, when sufficient training data are available, particularly enhances decision support systems aimed at optimizing rice disease management practices for growers across various regions. Our hybrid model thus presents a compelling advancement in the precision agriculture domain, with significant implications for improving disease management strategies for crops beyond rice through timely intervention. This approach can contribute to safeguarding global food security by reducing crop losses due to disease damage.

Accuracy of continuous glucose monitoring in the hospital setting: an observational study.

Continuous glucose monitoring (CGM) improves glycaemic outcomes in the outpatient setting; however, there are limited data regarding CGM accuracy in hospital. We conducted a prospective, observational study comparing CGM data from blinded Dexcom G6 Pro sensors with reference point of care and laboratory glucose measurements during participants' hospitalisations. Key accuracy metrics included the proportion of CGM values within ±20% of reference glucose values >5.6 mmol/l or within ±1.1 mmol/l of reference glucose values ≤5.6 mmol/l (%20/20), the mean and median absolute relative difference between CGM and reference value (MARD and median ARD, respectively) and Clarke error grid analysis (CEGA). A retrospective calibration scheme was used to determine whether calibration improved sensor accuracy. Multivariable regression models and subgroup analyses were used to determine the impact of clinical characteristics on accuracy assessments. A total of 326 adults hospitalised on 19 medical or surgical non-intensive care hospital floors were enrolled, providing 6648 matched glucose pairs. The %20/20 was 59.5%, the MARD was 19.2% and the median ARD was 16.8%. CEGA showed that 98.2% of values were in zone A (clinically accurate) and zone B (benign). Subgroups with lower accuracy metrics included those with severe anaemia, renal dysfunction and oedema. Application of a once-daily morning calibration schedule improved accuracy (MARD 11.4%). The CGM accuracy when used in hospital may be lower than that reported in the outpatient setting, but this may be improved with appropriate patient selection and daily calibration. Further research is needed to understand the role of CGM in inpatient settings.

A high-performance dynamic scheduling for sparse matrix-based applications on heterogeneous CPU–GPU environment

A high-performance dynamic scheduling for sparse matrix-based applications on heterogeneous CPU–GPU environment

Chemical Desiccation in the Preharvest of Cowpea: A Study of How the Time of Application Interferes in the Enzymatic and Physiological Aspects of Seedlings from Desiccated Plants.

Chemical desiccation in the preharvest of grains and seeds is commonly used in production fields. Using herbicides for this purpose is a viable alternative to reduce beans' exposure to adverse crop conditions. Our objectives were to evaluate (1) the efficacy of herbicides for accelerated defoliation of cowpea, (2) the impact of herbicide application on antioxidant enzyme activity and protein and amino acid contents in seeds, and (3) the effects of different herbicide application schedules on the physiological aspects of seeds. In the first experiment, in addition to the control treatment (without herbicides), seven herbicides and two mixtures were applied at night: diquat, flumioxazin, diquat + flumioxazin, glufosinate ammonium, saflufenacil, carfentrazone, diquat + carfentrazone, atrazine, and glyphosate. Diquat and its mixtures showed greater efficacy in anticipating the harvest. Flumioxazin and diquat alone reduced amino acid content by 61.72 and 51.44%, respectively. The same trend was observed for total soluble proteins. The activity of antioxidant enzymes (CAT, POD, PPO) increased, indicating oxidative stress caused by diquat and flumioxazin. In the second experiment, we tested three application times (6 a.m., 12 p.m., 6 p.m.) with diquat, diquat + flumioxazin, and diquat + carfentrazone. The lowest damage to chlorophyll a was at 6 a.m.; other times reduced photosynthetic pigments and increased carotenoid content. Total soluble sugars decreased by 27.74% with nocturnal application of diquat + flumioxazin. Our data indicate that herbicide use for desiccation affects seed quality. These findings highlight the need for selecting appropriate herbicides and application times. Future research should explore long-term impacts on crop yield and quality.

One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial-Temporal Dependency.

Traffic prediction is a keystone for building smart cities in the new era and has found wide applications in traffic scheduling and management, environment policy making, public safety, and so on. Instead of creating a traffic predictor for each city, this article focuses on designing a unified network model that could be directly applied for traffic prediction in any city, by learning the essential spatial-temporal dependencies, i.e., the mutual relationship between traffic and the corresponding fine-grained road network. To achieve this goal, this article proposes a joint knowledge- and data-driven mechanism that novelly divides dependencies into three kinds of correlations, i.e., road segment, intra-intersection, and inter-intersection correlation, which capture the microcosmic, middle, and macroscopic dependencies between traffic and the road network, respectively. Specifically, we first construct traffic datasets that could cover all road segments from real-world trajectory datasets, which makes it possible to model the whole road network as a graph, with the help of fine-grained road topology. Then, we propose meta road segment learner, connection-aware spatial-temporal graph convolutional network (GCN), and multiscale residual networks for capturing the microcosmic, middle, and macroscopic dependencies, respectively. Our experiments on three real-world datasets demonstrate that our proposed method could: 1) achieve better prediction accuracy compared with several approaches and 2) capture the mutual relationship between traffic and the fine-grained road network since our model trained only using data from the source city achieves good performance when it is directly applied for traffic prediction in the target city, without any fine-tuning. The codes will be made publicly available.