Data Scheduling Research Articles

Vaccination of premature newborns

Premature newborns are most vulnerable to the severe infectious diseases. The timeliness of vaccination in this group of children is extremely important. Historically, however, vaccination coverage for premature newborns has been significantly reduced due to unjustified contraindications. This is due to the fear of developing post-vaccination complications and the opinion that the immune response to vaccines in preterm newborns is reduced. In addition, in our country, there are no approved federal clinical guidelines for the vaccination of premature newborns, which determines the lack of a unified approach among medical workers and raises doubts among parents.The article presents a review of Russian and foreign literature data, highlighting the state of the problem of timeliness and completeness of vaccination coverage of premature newborns. Features of the immunity of a child born prematurely and the possibility of an adequate response to vaccine antigens in various degrees of prematurity. According to the list of the National Immunization Schedule, scientific and practical data on the safety and efficacy of vaccines registered in Russia, the benefits of complying with approved schedule and the positive non-specific effects of individual vaccines are given. Data on the specific prevention of RS-virus infection are presented. It has been shown that conditions that can develop after vaccination and are characteristic of prematurity are not directly related to the vaccine and that premature newborns is able to develop a sufficient immune response. Accordingly, children born prematurely should be immunized in accordance with the passport age with the stabilization of the child’s condition and adequate weight gain.

What Is the Connection? Understanding Shared Micromobility Links to Rail Public Transit Systems in Major California Cities

As shared micromobility (bikes and scooters) has proliferated throughout urban areas, there has been growing interest in how it facilitates connections with rail transit systems. This study explores the magnitude of interactions between shared micromobility and rail public transit systems using shared micromobility trip data and rail transit schedule data. We evaluate over one million trips from October 2019 to February 2020 in four California cities (San Francisco, Los Angeles, Sacramento, and San Jose) and develop criteria to identify trips connecting to rail transit. These include spatial and temporal rules, such as whether a trip starts/terminates close to public transit stations and whether a trip takes place when transit systems are operating. The criteria are examined via sensitivity analyses. The results indicate the degree of interaction between rail public transit and shared micromobility varies across cities and systems (i.e., docked/dockless). Most connections take place in the downtown or around public transit hubs. About 5–20% of all shared micromobility trips are identified as accessing or egressing from rail transit. These connecting trips exhibit commute-driven patterns and greater measured velocities. We conclude by examining the applicability of incorporating schedule information into the identification process of shared micromobility trips connecting to rail transit systems.

Designing a Posyandu Scheduling System using a Data-base to Improve Patient Service [Perancangan Sistem Penjadwalan Posyandu Menggunakan Basis Data Untuk Meningkatkan Layanan Pasien

Posyandu is a form of community-based health effort (UKBM) that is menaged and organized frm, by, and with the community in implementing healh development in order to empower the community and provide convenience to the obtaining basis heal/social service to accelerate the reduction in maternal and infant mortality. The Posyandu Scheduling System has a very important role to support the services of this public health program. Over time, problems arise regarding the system, in terms of preparing service schedules, still using the manual method, namely aliases, which are still handwritten to the recorder the Posyandu schedule in Kebonagung Village. This research is to design a website-based scheduling system that is useful for making it easier for cadres to disable and process Posyandu scheduling data in order to improve patient service. This system design uses the Hypertext Preprocessor (PHP) programming language, with a MySQL database, and uses Black Box testing to find out if the program is running as expected.

PreScheck Team Study: prehabilitation clinic as an effective patient management tool in elective cardiac surgery.

The main purpose of the study was to assess the impact of preoperative interdisciplinary assessment by the PreScheck Team on optimization of the final selection for elective cardiac surgery. This is a single-centre prospective observational study. The examined population consisted of 933 adult patients planned for cardiac surgery. After the exclusion of urgent operations, the study group consisted of 288 patients planned for elective cardiac surgery within 3 months from 1.01.2023 with PreScheck assessment (PreScheck Team group 2) and a control group of 311 patients scheduled for elective cardiac surgery between 1.03.2022 and 30.06.2022 (4 months), without preoperative interdiscipli-nary assessment (No PreScheck Team group 2). Fifty-two patients (18.06%) from the study group were finally excluded from the surgery on the scheduled date. In 46 patients (88.46%) the temporary or permanent exclusion from surgery was a result of PreScheck Team assessment. In the control group 42 patients (13.5%) did not undergo surgery on the scheduled date. Twenty-seven of those patients (8.97%) were permanently excluded from cardiac surgery after admission to the hospital and required additional tests before the final clinical decision, with total hospitalization time of 146 days. Pre Surgery Check (PreScheck) Team is an original concept that combines classical preoperative assessment and an outpatient prehabilitation clinic. The approach we are proposing here should be a complementary stage in the process of selection for elective cardiac surgery, in addition to the Heart Team recommendation. This two-step decision-making enables real individual risk assessment, selection of the most suitable intervention and better use of medical resources.

Deep Learning-based Network Security Protection for Scheduling Data in Power Plant Systems

Abstract Scheduling data of power plant systems plays a pivotal role in grid security. In this paper, the intrusion detection model IDP-TSW is constructed by using deep learning technology, feature extraction of raw traffic data based on density peak clustering algorithm and control variable method, and the final classification is realized by softmax. After completing the intrusion detection, for the network malicious intrusion situation further proposed the security protection strategy selection model HMS-BAG based on the Bayesian attack graph, formalized the description of the protection strategy selection problem, and proposed the optimal security protection strategy selection algorithm based on PSO. In the network intrusion detection performance experiments, the performance of the IDP-TSW model proposed in this paper outperforms the Bi-LSTM and CNN+Bi-LSTM models in terms of accuracy, recall, and F1 value. The F1 values for determining normal and abnormal data are 96.57% and 95.75%, respectively, and the precision and recall are also higher than 94%. Detecting Generic and Reconnaissance attacks is more than 90% accurate, but Dos attacks and others are relatively absent. In the network security defense performance experiment, the proposed HMS-BAG model achieved a defense success rate of 94.2% and a defense gain of 170.68.

Does use of GP and specialist services vary across areas and according to individual socioeconomic position? A multilevel analysis using linked data in Australia

ObjectiveTimely access to primary care and supporting specialist care relative to need is essential for health equity. However, use of services can vary according to an individual’s socioeconomic circumstances or...

An Electronic and Web-Based Authentication, Identification, and Logging Management System

The need for participants’ performance assessments in academia and industry has been a growing concern. It has attendance, among other metrics, is a key factor in engendering a holistic approach to decision-making. For institutions or organizations where managing people is an important yet challenging task, attendance tracking and management could be employed to improve this seemingly time-consuming process while keeping an accurate attendance record. The manual/quasi-analog approach of taking attendance in some institutions could be unreliable and inefficient, leading to inaccurate computation of attendance rates and data loss. This work, therefore, proposes a system that employs embedded technology and a biometric/ web-based application to enhance attendance management. The hardware encompasses the integration of an ESP8266 NodeMCU and the biometric AS608 fingerprint sensor interfaced with the database for which the front-end and back-end integration was through the React framework. The system uses a web application that displays the attendance results and scheduled course data from its database, provides feedback to lecturers on which student missed classes and calculates a student’s average attendance for the semester. By implementing this system, the accuracy of student attendance is expected to experience an appreciable improvement, eliminate proxy signing, provide a more secure alternative for attendance recording, curtail tardiness to classes, and ultimately improve student productivity on execution.

Autonomous millimeter scale high throughput battery research system

The high-throughput Auto-MISCHBARES platform streamlines reliable autonomous experimentation across laboratory devices through scheduling, quality control, live feedback, and real-time data management, including measurement, validation and analysis.

Research on real-time scheduling optimization technology of power system based on deep learning

Abstract In the context of the increasingly severe world climate form, how to rationally arrange and dispatch energy has become an urgent need. This paper proposes a deep learning-based power system scheduling model based on the concept of perfect scheduling, using GRU to learn scheduling data. A different training set is constructed to train the model according to the load characteristics at different moments, and the model is updated in real time based on the data at the current moment. The analysis of the algorithms reveals that the scheduling error rate of this model ranges from −-3% to 2%, and the average RMSE of the scheduling scheme is 2.72, placing it in close proximity to the optimal scheduling strategy. Due to a 6.5% reduction in scheduling cost compared to the average cost of the two analyzed algorithms, the average time reduction is 76.3%. The scheduling optimization model proposed in this paper exhibits excellent performance.

Comprehensive Analysis of Key Technologies for Smart Grid

Smart grid tech, using advanced comm info systems to enhance power system efficiency, reliability, sustainability, transforms energy sector, supporting global energy transition sustainable achievement. This article explores smart grid dispatch system research for improved power system reliability, security, and economics. It begins by outlining key research areas: real-time monitoring and data acquisition collect operational data via sensors, forming the basis for scheduling; forecasting and optimal scheduling, leveraging historical and real-time data to predict load demand and enhance efficiency; safety and fault monitoring, promptly identifying and mitigating risks; new energy access and management improving reliability and economics; smart grid scheduling system development integrating and managing distributed energy resources. These areas are pivotal for modernizing power systems. The research in this dissertation aims to elucidate how smart grid tech drives innovation in power systems, meeting future demands while advancing sustainable energy goals. Beyond the power industry, these technologies have profound societal and environmental implications, laying a robust foundation for a sustainable, clean energy future.

Oral polio vaccine stockpile modeling: insights from recent experience

ABSTRACTBackgroundAchieving polio eradication requires ensuring the delivery of sufficient supplies of the right vaccines to the right places at the right times. Despite large global markets, decades of use, and large quantity purchases of polio vaccines by national immunization programs and the Global Polio Eradication Initiative (GPEI), forecasting demand for the oral poliovirus vaccine (OPV) stockpile remains challenging.Research design and methodsWe review OPV stockpile experience compared to pre-2016 expectations, actual demand, and changes in GPEI policies related to the procurement and use of type 2 OPV vaccines. We use available population and immunization schedule data to explore polio vaccine market segmentation, and its role in polio vaccine demand forecasting.ResultsWe find that substantial challenges remain in forecasting polio vaccine needs, mainly due to (1) deviations in implementation of plans that formed the basis for earlier forecasts, (2) lack of alignment of tactics/objectives among GPEI partners and other key stakeholders, (3) financing, and (4) uncertainty about development and licensure timelines for new polio vaccines and their field performance characteristics.ConclusionsMismatches between supply and demand over time have led to negative consequences associated with both oversupply and undersupply, as well as excess costs and potentially preventable cases.

Intelligent scheduling and optimization of microenergy grid: the application and development of computer technology

Abstract For the intelligent management of micro-energy grid, the limitations of traditional scheduling optimization methods have begun to be highlighted, and computer technology has become a new generation of power system support means. This paper proposes a state estimation method based on smart grid measurement technology, and for the characteristics of micro-energy grid anomaly data, adopts the sampling value detection anti-anomaly data method based on amplitude comparison to estimate the grid dynamic process. Based on the grid state estimation dataset, a smart grid scheduling strategy based on cloud computing is constructed. The conditional value-at-risk of the penalty function of generation cost, grid cost, and motion cost is used as the objective function of the dispatch modeling, and the solution method for the optimization of the value-at-risk model is provided. Finally, an example has been built for this dispatch model to access a simulated micro-energy grid system that contains multiple nodes. The results show that the error between the voltage amplitude obtained from the dynamic state estimation of the micro-energy grid using the method of sampled-value detection against anomalous data and the real value is no more than 0.5%, and the data scheduling error rate of the cloud computing does not fluctuate significantly when dealing with a large number of scheduling data tasks, all of which are controlled to be below 0.0015. The scheduling strategy based on cloud computing has a certain degree of generalization when dealing with random and variable source and load scenarios, which can effectively improve the level of power grid business applications and complete the corresponding intelligent scheduling.

AUTOMATING THE PROCESS OF PREPARING THE SCHOOL TIMETABLE

This paper presents a development that optimizes the process of educational scheduling by creating algorithms that automatically distribute the load of the educational process and take into account all the preferences of teachers. One of the currently popular platforms for deploying these developments is the google tables platform for creating google tables and creating code based on google app script. The peculiarity of the system implemented with the help of this platform is the ease of use and minimization of resources spent on software storage, which greatly simplifies interaction with the system.
 Purpose – create a tool for drawing up a study schedule, interacting with a database created in Google Tables, interacting with participants of an educational institution online, which saves personal time of teachers and employees of the dean’s office.
 Method or methodology of the work: the article discusses a way to optimize the creation of a weekly schedule, taking into account the convenience of the teacher in the distribution of pairs and taking into account the load of the educational process by creating algorithms based on Google app script. For implementation, online tables are used that are accessible to all teachers and employees of the dean’s office, updated in real time, Tables created in Google and the google app script programming language.
 Result: a unique tool has been developed that implements the functions of adding, storing, interacting and round-the-clock access to training schedule data.
 Scope of application of the results: the curriculum, stored in online tables and updated by the dean’s office staff, should be used for monitoring by each teacher, keeping track of their time of couples in educational institutions.

Analyzing global passenger flows based on choice modeling in the air transportation system

Airline passenger decisions determine operation of airlines and aircraft. Therefore, global air transport models need to consider these decisions. Thereby, we develop a discrete choice model for passenger decisions describing global passenger flows based on different parameters. The model distinguishes between passenger itineraries defined by attributes like ticket price, travel time and number of transfers. Additional attributes are added gradually. It is estimated using schedule and passenger demand data. Hence, based on topological structures of the air transport system, global passenger flows and elasticities can be identified. Additionally, we provide quantified results describing the elasticity of several types of attributes, the values of travel time, the quality of travel and compare them for different regions, itineraries and travel types.

CLINIC MANAGEMENT SYSTEM

This study explores the implementation and benefits of a Clinic Management System (CMS) in healthcare settings. It focuses on the objectives of the CMS, including eliminating errors in form writing, establishing standardized processes and data formats, and generating reports for doctor scheduling and patient disease data storage. The study discusses the challenges faced by clinics in traditional manual-based systems, such as errors in form writing, lack of standardized processes, and the absence of robust reporting mechanisms. It highlights the significance of transitioning to a digital platform for minimizing errors in form writing and emphasizes the importance of intelligent form validation and auto-correction features.

The practice of youth participation in local government elections. Preliminary evidence from Mwanza city, Tanzania

Worldwide it has been reported the challenge of the decline of youth political participation in democratic elections. Hence the study aims to investigate the practice of local government elections among youth in Mwanza City Tanzania East Africa. A snowball sampling technique was applied to select 123 youths as a sample in study areas. Quantitative data was collected by using an interview schedule and qualitative data were collected by using Focus Group Discussions and key informant interviews. Quantitative data was analyzed by using descriptive statistics such as frequency counts and qualitative data was analyzed by using content analysis. The study findings showed that youth competence, especially knowledge of technical know-how and financial resources, affected the practice of youth participation in local government elections. The study concludes that the possession of financial resources and knowledge by youth are important practices in local government elections Therefore, the study recommends policymakers and political parties provide training on elections regulations and financial resources to support youth practices in local government elections.

Markov decision process based value chain calculation of water distribution network scheduling

Precise value of scheduling decisions forms the cornerstone of water distribution network (WDN) scheduling optimization, which aims at conserving energy and enhancing network operational efficiency. This article proposes a computational methodology for evaluating the value chain of scheduling decisions in WDN. The scheduling process is modeled as a Markov decision process with immediate reward function, action and state space. Due to the periodicity of water supply and sequential nature of scheduling, the calculation quantifies cumulative value of scheduling decisions by incorporating state transition probability with expected value. The effectiveness and applicability of the proposed evaluation method are demonstrated using scheduling data from a real world WDN. The method provides rational values on scheduling period and strategies, offering practical feedback for scheduling decisions.

PP147 What Does Real World Evidence (RWE) Offer Health Technology Assessment (HTA) Procedures In Australia?

IntroductionMedical device health technology assessment (HTA) in Australia is largely coordinated by the Medical Services Advisory Committee (MSAC). Its remit to improve the public’s health by deciding where to allocate public healthcare funding, can be enhanced by considering real world evidence (RWE). Existing data sources have limitations that can be addressed through RWE, including coverage of Australian patient populations who may not meet trial eligibility criteria, and long-term follow-up through data linkage and datasets. We partnered with a university to explore what information could be gained from an analysis of linked administrative patient data, with a view to addressing current evidence gaps and/or limitations. The findings can be used as a source of local data to define patient populations, estimate actual costs of care, and enable more comprehensive economic modeling to inform medical device HTA.MethodsThe University-developed New South Wales Cardiovascular Cohort dataset, comprising person-level longitudinal NSW administrative data for all patients admitted to hospital with a cardiovascular diagnosis from 2001 onwards, linked to national Medicare Benefits Schedule and Pharmaceutical Benefits Scheme claims data, was interrogated.ResultsWorking with RWE is resource intensive in terms of time and costs. The potential of these data was revealed as the research progressed. It was possible to continually refine the data analyzed and reported,as well as expand the data requested. Varied expertise is required to accurately analyze the administrative datasets, particularly clinical classification skills and expertise in methods for causal inference using observational data. Findings from this study will enable the refinement of information for MSAC submissions, including identifying the most relevant patient population and reporting comprehensive costs, beyond an admitted hospital setting. The data will enhance engagement with clinicians and refine messaging, for example regarding patient risk factors.ConclusionsRWE enhances Australian HTA applications. Local data, extended periods of time and insights not apparent from a focus on admitted hospital episodes can be revealed. Data can be refined during the process for specificity and applicability.

Optimal electric vehicle scheduling in unbalanced distribution system by spatial and temporal data mining and bi‐level particle swarm optimisation

AbstractThis research proposes a new comprehensive methodology aimed at optimally managing electric vehicle (EV) charging in an unbalanced distribution system with consideration of grid capacity and voltage quality constraints as well as the stochastic driving and charging behaviour of EV users. A data mining algorithm is designed to generate suitable spatial and temporal EV and charger input data for EV scheduling that is decomposed into two subproblems. The lower‐level subproblem identifies the maximum load at each node and time and the upper‐level subproblem allocates charging slots for each EV being charged. Both subproblems are solved by developed particle swarm optimisation (PSO) algorithms. The effectiveness and robustness of the algorithms have been thoroughly validated by conducting rigorous tests on a modified IEEE 37‐bus distribution system with different EV penetration scenarios. The test results have confirmed the algorithms' performance in accommodating the increasing load associated with EV charging and successfully improving the system performance by maximising the system load factor, minimising load unbalance, and reducing the system power loss. All operational constraints on node voltages, and distribution transformer and feeder capacities of the existing power distribution infrastructure are fully respected while maintaining high user satisfaction represented by the average state of charge (SoC) of all EVs.

Perancangan Sistem Informasi Pembelajaran Online (E-Learning) Berbasis Web

Education is a basic human need that constantly transforms and updates in line with developments in all aspects of life. Changes and improvements in the world of education encompass various components, including teacher competence, educational quality, curriculum, infrastructure, educational management, as well as innovative teaching methods and strategies. The limitation of time in school often becomes the cause of restricted material delivered by teachers. Additionally, when teachers are unable to attend classes, it results in difficulties for students in accessing materials for learning. This system involves various inputs, such as student data, teacher data, class data, subject data, teaching schedule data, lesson material data, assignment data, student registration data, and assignment answer data. The outputs of this system include reports on student data, teacher data, classes, subjects, teaching schedules, lesson materials, assignment grades, and assignment grade data. This e-learning application is developed using the PHP programming language and utilizes the MySQL database in this Final Project.