Scheduling Practices Research Articles

A Qualitative Study of the Work Systems and Culture Around End-of-Day Intraoperative Anesthesia Handoffs in a Tertiary Care Center.

Intraoperative handoffs have been implicated as a contributing factor in many perioperative adverse events. Despite conflicting data around their impact on perioperative outcomes, they remain a vulnerable point in the perioperative system with significant attention focused on improving them. This study aimed to understand the processes in place surrounding the point of information transfer in intraoperative handoffs. We used semistructured interviews with anesthesia clinicians to understand the processes and systems surrounding intraoperative handoffs. Interview data were coded deductively using the Systems Engineering Initiative for Patient Safety model as a framework, with subthemes developed inductively. Clinicians do a significant amount of work before and after the point of information transfer to ensure a smooth handoff and safe patient care. Despite not having standardization of handoffs, most clinicians have a typical handoff organization and largely agree on content that should be included. However, there is variability based on clinician and patient characteristics, including clinician discipline and patient acuity. These handoffs are additionally impacted by the overall culture in the operating room, including the teamwork and hierarchies present among the surgical and anesthesia teams. Finally, the broader operating room logistics, including scheduling practices for surgical cases and anesthesia teams, impact the quality of intraoperative handoffs and the ability of clinicians to prepare for these handoffs. Handoffs involve processes beyond the point of information transfer and are embedded in the systems and culture of the operating rooms. These considerations are important when seeking to improve the quality of intraoperative handoffs.

Scheduling choice method for flexible job shop problems using a fuzzy decision maker

The Flexible Job Shop Scheduling Problem (FJSP) emerges as a challenging extension of the classic Job Shop, presenting NP-Hard characteristics. In the FJSP, a set of jobs, comprising multiple operations, must be allocated to a predefined set of machines, each with its respective execution times. The distribution of operations across machines significantly impacts scheduling efficiency, which can be evaluated and optimized based on various performance criteria and objectives. To address this complex problem, a multi-objective optimization algorithm is employed, focusing on three main performance criteria: completion time of all operations (Makespan), load assigned to the most heavily utilized machine, and the sum of loads across all machines. An FJSP algorithm based on the Artificial Bee Colony (ABC) metaheuristic (FJSP. ABC) generates a Pareto set comprising non-dominated and dominated solutions. These solutions represent optimal or near-optimal production schedules and offer diverse representations, such as Gantt charts. However, the decision-making process for selecting the best production schedule from the Pareto set demands additional considerations. To this end, we propose adopting a decision-making (DM) algorithm based on Fuzzy TOPSIS (Technique for Order of Preference by Similarity to the Ideal Solution in a Fuzzy environment). The DM Fuzzy TOPSIS algorithm accommodates the inclusion of variables not covered by the FJSP algorithm and aids decision-makers in identifying the most suitable production schedule based on the specific requirements of the production system. These additional variables may include maximizing or minimizing machine idleness, balancing the load of operations on machines, etc. Experimental results demonstrate that the application of the proposed algorithm yields values close to the expected outcomes for the analyzed variables proposed. The DM Fuzzy TOPSIS algorithm proves to be a valuable tool for supporting decision-making in production systems, assisting in the selection of the best production schedule among the optimal or near-optimal solutions obtained from the Pareto set. By integrating multi-objective optimization and decision-making techniques, this research contributes to more efficient and informed production scheduling practices, ultimately enhancing overall system performance.

A Case Study of Drug Dependency (NSAIDs) in Shirshool (Headache) Healed Through Integrative Medicine

Shira is the Uttamang i.e., most vital part of our body. Ayurveda is limited to pain and discomfort in Shira rather to brain disorders as such. Shirashool (headache) is the most common and frequently seen in day to day life due to the Stress, Vegdharan (to control forcefully natural functions like urine, faeces, cough or sneeze etc,) Ratrijagran (late night activity), Upvas (excessive control on appetite) Shoke (depressive thoughts), excessive work habits- the major root cause is excessive movement of Vata or overactive/imbalanced Vata Dosha. Jihva (tongue) examination is an important part of Ashtavidh pariksha mentioned in the text book of Yogratnakar. A tongue examination is typically conducted in conjunction with a thorough patient history. The vital organs of the body leave the imprint on the tongue surface- specific patterns or spots or colour. Tongue has its own language; it needs continuous practice. Examination of the tongue in Ayurveda is a easy valuable diagnostic tool that provides valuable and stored information about an individual's health and well-being or disease. Allopathic medicine like (NSAIDs) are most commonly used in headache as pain relieve medicine which has a lot of side effects; while holistic approach of Ayurveda is found to be very good results. Integrative therapy involves medicines, dietary schedule and yogic practices, shows sufficient significant changes in a patient. Mild recurrence which was seen in post follow up period was managed effectively through the yogic practices and proper guidance of counselling sessions on every Wednesday.

The transition tightrope: Optimizing post-discharge care for acute leukemia patients.

323 Background: Patients with acute leukemia who are discharged after a hospitalization are at high risk of complications and increased risk of readmissions. We reviewed discharge plans at an academic cancer center and found a gap of inconsistently scheduled follow-up appointments after discharge, and we sought to improve the transition of care for this population. Methods: Utilizing A3 methodology, we focused on ensuring acute leukemia patients had a follow-up visit in the clinic or infusion treatment area within 5 days of discharge, which was the timeline supported by literature review. Our improvement team performed a Gemba walk in November 2021 to observe discharge planning and outpatient follow-up appointment scheduling processes, and we mapped root causes of poor care transitions on a fishbone diagram. Many of the coordinated steps required for a seamless discharge and acceptance into outpatient care were not consistently performed. We utilized a pareto chart to stratify root causes and narrowed in on 3 key drivers: 1) standard process for discharge communication between in- & out-patient teams; 2) access to schedulers to secure appointments; and 3) escalation pathways when no appointment slots were available. We introduced 3 interventions: a direct phone line for the inpatient team to contact the infusion area schedulers for post-discharge appointments, an escalation pathway when schedules appeared fully booked, and a discharge navigator flowsheet in our electronic medical record (EMR) to anticipate discharge needs and facilitate standardized, efficient communication. Results: As a result of our interventions, the percentage of patients with an outpatient follow-up visit within 5 days increased from 61% to 94% between January and May 2022. Streamlining communication pathways (e.g., an automated EMR message to schedulers and a direct phone line for urgent escalation) led to initial improvements in timely follow-up visits. The EMR discharge navigator facilitated upstream identification of post-discharge needs and directed communication to the appropriate infusion area and clinic pools to schedule appointments. The discharge navigator continues to be used in the hematology service; one year after its launch, the EMR tool is utilized in 38% of all hematology discharges. Conclusions: Timely outpatient follow-up to address acute symptoms is a key method to decrease readmission risk, but inconsistent scheduling practices presented barriers at our cancer center. Instituting standardized scheduling protocols in the EMR and escalation pathways enabled nearly all our patients with acute leukemia to secure outpatient visits within 5 days of discharge. We are building on these initial efforts to disseminate and sustain utilization of the tool for all hematology and oncology patients in our center.

Equitable anesthesiologist scheduling under demand uncertainty using multiobjective programming

This work addresses the practical anesthesiologist scheduling (AS) problem motivated by the needs of an academic anesthesiology department. The AS problem requires the department to plan and deploy providers to adequately meet clinical demand and institutional protocols of various clinical units over a planning horizon of up to several weeks. A data‐driven two‐step AS framework is developed by exploiting the historical demand data of anesthesia cases. The first step is a shift design which obtains the optimal shifts considering clinical demand under uncertainty using conditional value‐at‐risk constraints, and the second step is provider assignments that generate the schedule considering optimal and equitable workload distribution and provider availability using multiobjective mixed‐integer programming models. Moreover, the AS framework incorporates the provider specialties, and clinical and lifestyle preferences and aligns with the existing scheduling practices. An ɛ‐constraint solution method is applied for multiobjective optimization, and an iterative solution method is developed to improve solution quality for workload equity in clinical applications. Computational experiments are performed to evaluate the performance of three alternative forms of the workload equity objective function, and the results show that the minimization of the sum of the absolute deviations of provider workloads best balances solution runtime and quality. In the concerned academic anesthesiology department, two clinical problems, the budget and hiring planning and the monthly scheduling, are addressed via the application of the proposed AS framework. For budget and hiring, decision‐makers can make trade‐offs based on their preference using the nondominated frontiers obtained via the ɛ‐constraint method. For monthly scheduling, the iterative solution method can accommodate preassigned shifts capturing institutional requirements while improving workload equity. The workload variance has been substantially reduced from 2.92 to 1.39 after the implementation based on the historical schedule data. The provider schedule satisfaction is improved from 3.13/5 to 3.44/5, and at least 82% of scheduling burden on department leaders is relieved. The developed AS framework is generic and can be extended to the scheduling of other types of care providers, including nurses and residents.

Overwork Impacts on Low-Wage Workers: Insights from the Food Manufacturing Sector in Oregon and Washington

Unstable scheduling refers to such employer practices as last-minute schedule changes, lack of advance notice, requiring employees to be on-call, split shifts, “clopening,” and variable hours and shift times. Evidence has shown that such practices can lead to underwork, or involuntary part-time hours, particularly for service-sector workers. But another, less-explored dimension of unstable scheduling practices—and the focus of this analysis—is overwork. Overwork stems from practices that can limit the ability of workers to get adequate rest and can heighten the bodily and emotional hazards that workers experience. We identify the following forms of unstable scheduling practices that constitute overwork: • Long work days of over eight hours/day • Long work weeks of over 40 hours/week and/or over five days/week • Long stretches of over seven days without days off • Lack of sufficient breaks during shifts These multiple dimensions of unstable scheduling are sometimes layered together. For instance, workers can experience both optional and mandatory overtime, which can extend both their work day and/or their work week. When workers are assigned mandatory overtime, they face risk of penalty if they refuse to work these extended hours, including potential loss of employment. In addition, workers may experience combinations of overwork, underwork, or other unstable scheduling practices within one job, or as they move between multiple jobs to cobble together full-time, year-round employment, especially if working in an industry with high turnover and widespread seasonality. These combinations compound the strain that workers experience.

Sequential clinical scheduling with stochastic patient re-entrance: Case of Mohs Micrographic Surgery

We study the problem of appointment scheduling for outpatient clinics with stochastic patient re-entrance. It is motivated by the scheduling problem for Mohs Micrographic Surgery, which is a popular technique for the excision of skin cancers. Re-entrance occurs when a patient repeats upstream processes during the single-day outpatient appointment, usually after a medical test. In some surgical procedures, the number of re-entrances for each patient is unknown until medical test results become available, causing long patient waiting time and clinic overtime. To address these challenges, we develop a stochastic slot model, SMART, that captures the key characteristics of the appointment processes and stochastic re-entrance, utilizing a delay before possible re-entrance. We then design a sequential scheduling algorithm that balances patient waiting, clinic overtime and patient throughput while considering stochastic complications such as no-shows, processing time variability, and the number of re-entrances per patient. We establish several theoretical properties of the algorithm, including the optimality of the resulting schedules for the sequential scheduling setting. We apply SMART to appointment scheduling for Mohs clinics, where patients may have same-day re-entrance due to repetitive skin excisions. We compare SMART schedules to others that ignore re-entrance and to current scheduling practices via simulation studies under a more realistic setting. Numerical experiments show that SMART schedules dominate other schedules, and ignoring re-entrance can cost huge loss in efficiency. Furthermore, SMART schedules exhibit a special ‘stair-stepping’ pattern for the number of appointments, with most patients assigned to the early slots and many empty slots at the end.

Employment status and the on-demand economy: a natural experiment on reclassification

Abstract This article uses data from a natural experiment to address one of the most contentious issues in the on-demand platform economy—whether gig work is compatible with standard employment. We analyze a US-based package delivery platform that shifted a subset of its workers from independent contractors to employees, thereby creating a natural experiment that allowed us to exploit variation over time and across locations. We examine the impact of employment status on work scheduling practices, hours of work and the firm’s ability to match workers’ scheduled hours with the amount of time they were actively engaged in parcel delivery. We find that after the transition to employment, flexibility with respect to how work schedules were determined was maintained, and drivers’ total hours of work increased. We also find that the switch to employee status increased the firm’s ability to match scheduled and actual working time, indicating greater operational efficiency. We conclude, contrary to claims commonly made by platform firms, that employment status can coexist with the platform model.

New scheduling strategies and production culture in public service television in the digital era: The case of DR and TV 2 in Denmark

This article addresses the development of new scheduling practices within public service media (PSM) in the Nordic, focusing on DR and TV 2 in Denmark. These practices relate to in-house broadcaster-video-on-demand-services (BVoDs). Our findings show, first, that television’s time-structured identity and liveness are re-configured for a mainstream audience as opposed to the segmented audiences approach of the earlier multi-channel era. Second, we find that the public service identity and the benefits of being PSM natives are harnessed to help the survivability of PSM companies in the digital era.

Associations between parental precarious work schedules and child behavior problems among low‐income families

AbstractObjectiveThis study examined associations between parental precarious work schedules and child behavior problems among a sample of families with low incomes receiving child‐care subsidies and tested three hypothesized mediators of these associations: work–family conflict, economic insecurity, and child‐care instability.BackgroundAs “just‐in‐time,” or on‐call, scheduling practices become more prevalent among low‐paid workers, working parents must balance family demands with precarious work schedules characterized by instability, unpredictability, and lack of control. Precarious work schedules may threaten child well‐being by increasing parents' work–family conflict and stress, economic insecurity, and child‐care instability. Yet, few studies have been able to empirically test these relationships.MethodThis study uses data from a survey of child‐care subsidy recipients to test the associations between five dimensions of parental precarious work schedules—variable work hours and shifts, limited advance notice, unexpected schedule changes, and lack of schedule control—and child externalizing behavior problems via work–care conflict, economic insecurity, and child‐care instability. Analyses use Ordinary Least Squares (OLS) regression and decomposition methods and control for a host of child, parental, and household characteristics.ResultsVariable shifts were indirectly associated with more parent‐reported child behavior problems via work–care conflict, whereas unexpected schedule changes were indirectly associated with more behavior problems via both work–care conflict and material hardship.ConclusionThese findings add to a growing evidence‐base on the incongruence between precarious employer‐driven scheduling practices and the needs of families with young children.

A Review of Current Scheduling and Design Practices in the Powder River Basin

A Review of Current Scheduling and Design Practices in the Powder River Basin

Optimizing Weekend Schedules in Home Health Care: The Essential Care on Weekends for Personal Support Quality Improvement Project

The availability of personal support workers (PSWs) is a limiting factor for home care system capacity, as this workforce provides 70% to 80% paid care in this sector. Without sufficient support to live at home, many seniors and people with disabilities experience poorer outcomes and require care in more expensive and less preferred institutional long-term care or hospital settings. Insufficient PSW availability is limiting access to necessary care in the community. Capacity challenges are particularly pronounced on weekends. The Essential Care on Weekends (ECoW) program was co-developed as one solution to adapt current PSW scheduling practices to increase the number of clients with high-intensity care needs who can be served within the constraints of PSW availability. ECoW focused on increasing weekend capacity and care consistency, particularly for clients with the highest care needs, through prioritizing essential care and moving less time sensitive tasks to weekdays. ECoW was operationalized through 4 activities: communication and engagement, clinical care plan review, geographic review of PSW schedules and the creation of the ECoW schedule. Implementation of ECoW demonstrated success in increasing access to and consistency of care for clients with the highest care needs: weekend capacity increased, access to care improved for clients requiring daily or near-daily care and missed care rates decreased both on weekends and weekdays. This strategy represents a change in scheduling practices that organizations can use to provide consistent service to a growing number of clients with high-intensity care needs in the context of increasingly limited health human resource capacity.

Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics

Most outpatient clinics apply deterministic block scheduling policies to patient visits even though patients utilize varying amounts of time, leaving patients, operations managers, and clinicians frustrated because patients and physicians are kept waiting. This paper offers a decision-making model for schedulers so that the service time needed for a specific patient can be predicted to allow outpatient clinics to schedule more effectively. We employed an analytical approach, with a data driven methodology consisting of two phases. In phase one, machine learning algorithms are used to predict service time for outpatient clinics servicing patients with various characteristics. This study supports the understanding of factors that impact service time. A large dataset from an outpatient clinic is obtained and used in the analyses. Four dominant data mining models are developed to predict service time, and their performances are compared: neural networks (NNs), generalized linear model (GLM), linear regression (LR), and support vector regression (SVM). The NN models performed the best. The reason for visiting the doctor and patient type are identified as the primary characteristics to aid in predicting patient service time. We compare the proposed NN models with commonly used scheduling policies in practice in the second phase via simulation modeling and analysis. This paper contributes to the literature in four ways. First, we obtained a large dataset and extracted quality data to test the prediction accuracy of multiple models to determine which one improves scheduling the best. Second, patient characteristics are identified through machine learning modeling and sensitivity analysis to understand which ones are most important for service time prediction accuracy. Third, we analyzed the performance of standard scheduling policies used in clinics. Lastly, we provide clinical policy implications and recommendations that will provide insights and support appointment scheduling decisions.

A Binary Integer Programming Model for Personnel Scheduling: A Case Study at Fast-Food Restaurant in Johor

This study is about analysing the current practice of the personnel scheduling at a well-known fast-food restaurant, as the post-pandemic situation slightly change their operating system. Therefore, the personnel scheduling needs to be modified according to the real-time situation using the binary integer programming (BIP) model. Based on the observation made, the problem of the study is to modify the working schedule with a proper break time for each staff to give them a fair working time, keep the workstations occupied during their operating hours and to maintain the restaurant as a healthy workplace. Therefore, after a background study on the topic and data collection made from a fast-food restaurant done, a one-day schedule is prepared to determine the number of staffs working in a day and scheduled the staff’s break period. A new schedule is obtained that meets all constraint’s requirements by solving the model using Microsoft Excel Solver. The new schedule minimized the number of staffs working in a day to 11 staff as compared to 12 staff in the current schedule. In conclusion, the proposed mathematical model can serve a fast and accurate solution for minimizing the personnel size and helps each staff to have a reasonable working hour with sufficient break time.

PO-03-147 EVALUATION OF A PEDIATRIC ELECTROPHYSIOLOGY CASE COMPLEXITY PREDICTION MODEL TO OPTIMIZE PROCEDURAL SCHEDULING

While operational efficiency has been evaluated in adult interventional and surgical specialties, limited data exist regarding the prediction of case complexity in the pediatric and congenital electrophysiology (EP) laboratory. To identify pre-procedural variables that predict prolonged case durations among pediatric and adult congenital patients presenting for diagnostic EP study and catheter ablation. A retrospective cross-sectional study was performed of all EP studies performed at Boston Children’s Hospital between 1/1/2015-1/1/2020 including pediatric and adult congenital heart disease patients of all ages. The primary outcome was case duration ≥180 minutes. Univariate and multivariate analyses were performed for the primary outcome using logistic regression, with a sub-analysis repeated for pediatric patients <20 years of age. During the 5-year period, 1025 patients underwent EP study and 292 (28.5%) had a case duration ≥180 minutes. Factors associated with the primary outcome on multivariate analysis were: age ≥20 years at EP study (OR 1.95, 95% CI 1.24-3.07, p 0.004), arrhythmia diagnosis of premature ventricular contractions/ventricular tachycardia (PVC/VT, OR 3.70, 95% CI 2.10-6.55, p <0.001), Fontan (OR 3.74, 95% CI 1.69-8.30, p=0.001) or Mustard/Senning physiology (OR 4.13, 95% CI 1.48-11.60, p=0.007), and the presence of non-cardiac comorbidities (OR 2.47, 95% CI 1.29-4.73, p 0.006). Restricting the analysis to pediatric patients <20 years old, factors associated with the primary outcome on multivariate analysis were an arrhythmia diagnosis of PVC/VT (OR 3.04, 95% CI 1.64-5.63, p <0.001), Fontan physiology (OR 3.13, 95% CI 1.10-8.92, p 0.033), non-cardiac comorbidities (OR 4.32, 95% CI 2.07-9.01, p <0.001), and prior ablation (OR 1.58, 95% CI 1.03-2.41, p 0.035). Intra-atrial reentrant tachycardia was not associated with prolonged case duration in either multivariate model. A weighted risk score using factors identified on multivariate analyses correctly classified procedures with prolonged case duration with reasonable accuracy (c-statistic 0.70). Age-dependent anatomic, electrophysiologic, and comorbid factors are associated with prolonged case duration in the pediatric and congenital EP laboratory. Applying a simple risk score using pre-procedural variables can discriminate cases with and without prolonged duration, and may aid in efficient procedural scheduling practices.

A cross-sectional analysis of work schedule notice and depressive symptoms in the United States

The implementation of last-minute work scheduling practices, including fluctuations in work hours, shift cancellations, and short notice, reflects a new norm in employment in the United States. This study aimed to investigate whether work schedule notice of ≤2 weeks was associated with high depressive symptoms. We used data from the 2019 cycle of the National Longitudinal Survey of Youth 1997 (N = 4963 adults aged 37–42 years). Using adjusted gender-stratified modified Poisson models, we tested the association between schedule notice (≤2 weeks, >2 weeks, consistent scheduling) and high depressive symptoms. Presence of high depressive symptoms was assessed using the 7-item Center for Epidemiologic Studies Depression (CES-D) Short-Form scale and defined as CES-D-SF ≥8. Respondents reporting >2 weeks schedule notice (versus ≤2 weeks) were disproportionately non-Hispanic Black or Hispanic and resided in the South and/or in a rural area. High depressive symptoms were 39% more prevalent among women with schedule notice of ≤2 weeks compared to those with >2 weeks notice (Prevalence Ratio [PR]: 1.39, 95% Confidence Interval (CI): 1.07, 1.80). We did not observe an association among men (PR: 1.06, 95% CI: 0.75, 1.50). Schedule notice of ≤2 weeks was associated with a greater burden of high depressive symptoms among US women. Policies to reduce precarious work scheduling practices should be further evaluated for their impacts on mental health.

MACHINE LEARNING PREDICTION AND OPTIMIZATION IMPROVES ELECTIVE ORTHOPAEDIC SURGERY SCHEDULING

Total knee and hip arthroplasty (TKA and THA) are the most commonly performed surgical procedures, the costs of which constitute a significant healthcare burden. Improving access to care for THA/TKA requires better efficiency. It is hypothesized that this may be possible through a two-stage approach that utilizes prediction of surgical time to enable optimization of operating room (OR) schedules.Data from 499,432 elective unilateral arthroplasty procedures, including 302,490 TKAs, and 196,942 THAs, performed from 2014-2019 was extracted from the American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database. A deep multilayer perceptron model was trained to predict duration of surgery (DOS) based on pre-operative clinical and biochemical patient factors. A two-stage approach, utilizing predicted DOS from a held out “test” dataset, was utilized to inform the daily OR schedule. The objective function of the optimization was the total OR utilization, with a penalty for overtime. The scheduling problem and constraints were simulated based on a high-volume elective arthroplasty centre in Canada. This approach was compared to current patient scheduling based on mean procedure DOS. Approaches were compared by performing 1000 simulated OR schedules.The predict then optimize approach achieved an 18% increase in OR utilization over the mean regressor. The two-stage approach reduced overtime by 25-minutes per OR day, however it created a 7-minute increase in underutilization. Better objective value was seen in 85.1% of the simulations.With deep learning prediction and mathematical optimization of patient scheduling it is possible to improve overall OR utilization compared to typical scheduling practices. Maximizing utilization of existing healthcare resources can, in limited resource environments, improve patient's access to arthritis care by increasing patient throughput, reducing surgical wait times and in the immediate future, help clear the backlog associated with the COVID-19 pandemic.

The Impact of using some Practice Scheduling Methods on Learning of motor Building for a number of Football and athletics skills for the first Intermediate grade students

The study aimed at expounding the impacts of a number of practice scheduling methods that were used in the educational programs of three experimental groups and the control group that used the followed style in the school in learning the motor building and comparing the results of the post-test between the three experimental groups that used some of the practice method and the control group that used the style followed in the school in learning the motor building of students of the first intermediate grade in a number of football and athletics skills . The researchers used the experimental method appropriate to the nature of the study, the research community was (232) students of the first intermediate class in Al-Shumookh Intermediate School for boys distributed on )4(classes as for the sample of the study it was taken on the intentional approach and it was distributed on three experimental group and one is a controlling group and it was taken randomly by choosing (15) students for each group which means that the number was (60) students. The researcher adopted the design that he called called “Designing the equivalent group with the pre & post-test” and the adopted a design that is called “Designing the stable equivalent group with the post-test observation also they have done a questionnaire from ,personal interviews ,scientific observation tests ,measurements ,and a form for redressing the operant building. A party is done between the groups of the research in a number of variables (age, height mass, mental abilities (attention, intelligence) and some physical and motor factors that affect motor building learning of a number of team and individual game). The educational program of the school which is (The style used in the school) and (A guide for the teacher of the physical education for the intermediate school) are applied on the control group, Also the special educational programs were applied with the styles of the scheduling practice on the experimental groups and these programs has (72) educational unit with (18) educational unit for each group they are done twice a week in )45(minutes. The researchers conclude that the educational programs concerning the styles of the scheduling practices (the distributed, the intensive & the random) and the special program with the control group (the followed style in the school) has affected positively in learning the mobile building for a number of football and athletics skills.

Impacts of Different Irrigation Schedules and Nutrients Management Practices on Economics of Wheat

A field investigation was conducted during two kharif seasons of 2020-21 and 2021-2022 at Agriculture Research Farm, Rabindranath Tagore University, Raisen, Madhya Pradesh, India, to evaluate the effect of different irrigation scheduling and nutrient management practices on yield and economics of wheat crop in split-plot design with three replications. The irrigation level N0 - No irrigation, N1- One Irrigation at CRI (crown root inition) stage, N2- One Irrigation at CRI &amp; one irrigation before flowering were arranged in main plots whereas nutrient management treatments of N0 – Control, N1-100% RDF + FYM @ 2.5 t/ha. + Azotobacter, N2 - 100% RDF + Vermicompost @ 1.5 t/ha + PSB, N3 - 50% RDF+ FYM@5.0 t/ha, N4 -50% RDF+ Vermicompost@2.5 t/ha were used in the sub plots. The results shown that one Irrigation at CRI &amp; one irrigation before flowering along with application of 50% RDF+ FYM@5.0 t/ha found to be best in the terms of grain and straw yield 28.70 and 41.88 q ha-1 in 2021 and 29.52 q ha-1, 42.13 q ha-1 in 2022. Minimum yield and cost benefit ratio recorded under control plot. The study suggests economically combinations of currently used irrigation scheduling and nutrient management practices that may help farmer to obtaining optimum yield.

Factors Influencing Scheduling Activities of Construction Projects

While construction projects continue to grow rapidly every year, the industry still faces challenges in the form of underperformance as the result of inefficient project management. This is reflected in low productivity and cost and time overruns for construction projects. Better planning and scheduling are important in order to increase the success of construction projects. This study aimed to investigate factors influencing scheduling activities of construction projects in Indonesia. A systematic literature review was performed, leading to the identification of twenty factors influencing scheduling activities at the planning stage and twelve factors influencing scheduling activities at the implementation stage. Questionnaire surveys were distributed to obtain the primary data in this study. Following data collection, factor analysis was conducted to discover comprehensive underlying concepts influencing scheduling activities. Six groups of factors influencing scheduling activities at the planning stage and three groups influencing scheduling activities at the implementation stage were identified. These findings were synthesized to develop a framework. Therefore, this study contributes by identifying key factors influencing scheduling activities in both planning and implementation stages through factor analysis within a context-specific environment and presenting the scheduling-dispute framework as a practical tool for managing delay-related disputes that may arise due to these factors. Construction professionals can take advantage of this study to implement better scheduling practices and minimize construction disputes; such improvements ultimately lead to project success.