Modified natural cycle allows a window of 7 days for frozen embryo transfer planning.

RESEARCH QUESTION: Should ovulation be triggered in a modified natural cycle (mNC) with recombinant human chorionic gonadotrophin (rHCG) as soon as a mean follicle diameter of 17 mm is visible, or is more flexible planning possible? DESIGN: This multicentre, retrospective, observational study of 3087 single frozen blastocyst transfers in mNC was carried out between January 2020 and September 2022. The inclusion criteria included endometrial thickness ≥7 mm and serum progesterone <1.5 ng/ml. The main outcome was ongoing pregnancy rate. Secondary end-points were pregnancy rate, implantation rate, clinical pregnancy rate and miscarriage rate. The mean follicle size at triggering was stratified into three groups (13.0-15.9, 16.0-18.9 and 19.0-22 mm). RESULTS: The baseline characteristics between the groups did not vary significantly for age, body mass index and the donor's age for egg donation. No differences were found in pregnancy rate (64.5%, 60.2% and 57.4%; P = 0.19), clinical pregnancy rate (60.5%, 52.8% and 50.6%; P = 0.10), implantation rate (62.10%, 52.9% and 51.0%; P = 0.05) or miscarriage rate (15.0%, 22.2%; and 25.0%; P = 0.11). Although ongoing pregnancy rate (54.9%, 46.8% and 43.1%; P = 0.02) varied significantly in the univariable analysis, it was no longer significant after adjustment for the use of preimplantation genetic testing for aneuploidies and egg donation. CONCLUSIONS: The findings showed rHCG could be flexibly administered with a mean follicle size between 13 and 22 mm as long as adequate endometrial characteristics are met, and serum progesterone is <1.5 ng/ml. Considering the follicular growth rate of 1-1.5 mm/day, this approach could allow a flexibility for FET scheduling of 6-7 days, simplifying mNC FET planning in clinical practice.

Planning cost-effective operational forest inventories.

We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables better scheduling decisions but also implies higher costs. We focus on the outer stage, which we formulate as a maximization of the posterior value of the inventory decision under a budget constraint. The posterior value depends on the solution to the inner stage problem and its computation is analytically intractable, featuring an NP-hard binary optimization problem within a high-dimensional integral. In particular, the binary optimization problem is a special case of a generalized quadratic assignment problem. We present a practical method that solves the outer stage problem with an approximation which combines Monte Carlo sampling with a greedy, randomized method for the binary optimization problem. We derive inventory decisions for a dataset of 100 Swedish forest tracts across a range of inventory budgets and estimate the value of the information to be obtained.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.

PURPOSE: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. METHODS: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). RESULTS: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). CONCLUSION: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm.

An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine's (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network.

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

Early morning sport scheduling is associated with poorer subjective sleep characteristics in British student-athletes.

This study presents the sleep characteristics of British student-athletes and examines the relationships between sport scheduling and time demands on sleep outcomes. Student-athletes (n = 157, 51% male) completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and the Sleep Hygiene Index (SHI). Self-reported sleep characteristics on weekdays and weekends, weekly frequencies of early morning and late evening sport sessions, and academic-related and sport-related time demands were also collected. Questionnaires revealed a high prevalence of undesired sleep characteristics including poor sleep quality (global PSQI >5 in 49.0%) and low sleep durations on weekdays (25% reporting <7 h). Paired t-tests revealed significant differences in bedtime, waketime, sleep duration, and sleep onset latency between weekdays and weekends (all p < 0.01). Hierarchical regression analyses indicated that early morning sport frequency was a significant predictor of PSQI (ß = 0.30) and SHI (ß = 0.24) global scores, weekday waketimes (ß = -0.17), and weekday sleep durations (ß = -0.25; all p < 0.05) in models adjusted for participant characteristics. Late evening sport frequency, and academic-related and sport-related time demands, were not significant predictors of any sleep outcome. Adjusting sport scheduling to avoid early start times could provide a means to improve sleep outcomes and may improve sporting performance and academic attainment.

A community-based volunteer service to reduce COVID-19 vaccination inequities in New York City.

OBJECTIVES: We describe our experiences and challenges as community volunteers in assisting individuals in scheduling initial COVID-19 vaccine appointments and highlight disparities and barriers in vaccine access in New York City (NYC). METHODS: Priority for assistance was given to individuals who were eligible for vaccination in NYC and New York State with the following barriers: technological, language, medical, physical and undocumented immigrants. Volunteers in NYC performed outreach and created program to assist in scheduling appointments. RESULTS: In sum, 2101 requests were received to schedule COVID-19 vaccine appointments from 28 February to 30 April 2021. Vaccinations were successfully scheduled for 1935 (92%) individuals. Challenges in this project included limited community outreach, language barriers, transportation difficulties and safety concerns travelling to vaccination sites. Spanish (40.5%) and Chinese (35.6%) were the primary languages spoken by appointment requesters. Most requests came from residents of Queens (40%) and Brooklyn (27.2%). CONCLUSIONS: The older population, public-facing workers, non-English speakers, undocumented immigrants and the medically complicated population experienced challenges in vaccine appointment access. In-person services and early website access in languages in addition to English may have reduced barriers in appointment navigation. While volunteers faced numerous obstacles when assisting individuals in scheduling vaccine appointments, most found the work fulfilling and rewarding.

Real-time management of intra-hospital patient transport requests.

This paper addresses the management of patients' transportation requests within a hospital, a very challenging problem where requests must be scheduled among the available porters so that patients arrive at their destination timely and the resources invested in patient transport are kept as low as possible. Transportation requests arrive during the day in an unpredictable manner, so they need to be scheduled in real-time. To ensure that the requests are scheduled in the best possible manner, one should also reconsider the decisions made on pending requests that have not yet been completed, a process that will be referred to as rescheduling. This paper proposes several policies to trigger and execute the rescheduling of pending requests and three approaches (a mathematical formulation, a constructive heuristic, and a local search heuristic) to solve each rescheduling problem. A simulation tool is proposed to assess the performance of the rescheduling strategies and the proposed scheduling methods to tackle instances inspired by a real mid-size hospital. Compared to a heuristic that mimics the way requests are currently handled in our partner hospital, the best combination of scheduling method and rescheduling strategy produces an average 5.7 minutes reduction in response time and a 13% reduction in the percentage of late requests. Furthermore, since the total distance walked by porters is substantially reduced, our experiments demonstrate that it is possible to reduce the number of porters - and therefore the operating costs - without reducing the current level of service.

Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach.

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.

Criteria-based outpatient scheduling at a nephrology clinic: prospective evaluation of patient pre-assessment and its corresponding adaptive scheduling strategy.

BACKGROUND: Outpatient Clinics (OCs) are under pressure because of increasing patient volumes and provider shortages. At the same time, many patients with chronic diseases receive routine follow-up consultations that are not always necessary. These patients block access to care for patients that are in actual need for care. Pre-assessing patient charts has shown to reduce unnecessary outpatient visits. However, the resulting late cancellations due to the pre-assessment, challenge efficient alignment of capacity with actual patient demand, leading to either empty slots or overtime. This study aims to develop a method to analyse the effect of pre-assessing patients before inviting them to the OC. This involves 1) to select who should come and 2) to optimize the impact of pre-assessment on the schedule and efficient use of OC staff. METHODS: This prospective mixed-methods evaluation study consists of 1) an expert meeting to determine a pre-assessment strategy; 2) a retrospective cohort study to review the impact of this strategy (12 months of a Dutch nephrology OC); 3) mathematical optimization to develop an optimal criteria-based scheduling strategy; and 4) a computer simulation to evaluate the developed strategy. Primary outcomes are the staff idle time and staff overtime. Secondary outcomes evaluate the number of weekly offered appointments. RESULTS: The expert group reached consensus about the pre-assessment criteria. 875 (18%) of the realized appointments in 2022 did not meet the OC visit pre-assessment criteria. In the best performing scheduling strategy, 94 slots (87% of the available capacity) should be scheduled on a weekly basis. For this schedule, 26.8% of the OC weeks will experience idle time ( µ =2.51, σ =1.44 appointment slots), and 21% of the OC weeks will experience overtime ( µ =2.26, σ =1.65 appointment slots) due to the variation in patient appointment requests. Using the pre-assessment strategy combined with the best performing scheduling strategy under full capacity (108 slots), up to 20% increase in patient demand can be handled with equal operational performance. CONCLUSIONS: This evaluation study allows OC managers to virtually test operational impact of pre-assessment strategies on the capacity of their OC, and shows the potential of increasing efficient use of scarce healthcare capacity. TRIAL REGISTRATION: Not applicable.

Development of service standards and manpower calculation criteria for hospital clinical pharmacies in South Korea: a survey-based study.

BACKGROUND: After the revision of the Korean Pharmaceutical Affairs Act, the certification of specialized pharmacists is scheduled to be legally recognized in 2023. Considering that the specialized pharmacist certification was developed based on the working model of hospital clinical pharmacists, it is necessary to establish standards for clinical pharmacists in hospitals and to calculate appropriate manpower. Through this study, we aim to establish practical standards for clinical pharmacists and propose a method for calculating staffing levels based on an investigation of actual workloads. METHODS: This survey-based study consisted of two phases. In the first phase, a literature review was conducted to establish standards for clinical pharmacy services, and tasks in relevant literature were classified to identify clinical pharmacy service tasks that are applicable to the practice of Korean hospitals. Additionally, a preliminary survey was conducted to investigate the essential tasks. In the second phase of the investigation, a multicenter survey was conducted targeting pharmacists in facilities with more than 1,000 beds to explore their perceptions and actual workloads related to tasks. RESULTS: According to the standards for clinical pharmacists in Korea, clinical pharmacy services consist of a total of 23 tasks, of which 16 have been identified as essential tasks. Essential tasks accounted for 93% of the total tasks in clinical pharmacy services. The average full-time equivalent (FTE) through workload calculation was 2.5 ± 1.9 for each field, while the FTE allocated to actual practice was 2.1 ± 1.6. The distribution of each type of clinical pharmacy service was as follows: 77% for medication therapy management, 13% for medication education, 8% for multidisciplinary team activities, and 3% for medication use evaluation. CONCLUSION: This study identified essential tasks common to clinical pharmacy services across different healthcare institutions. However, the FTE of clinical pharmacists in actual practice was insufficient compared to the required amount. In order to establish and expand clinical pharmacy services in a hospital, it is necessary to ensure an adequate workforce for essential tasks.

Wait Times for Scheduling Appointments for Prevention of Macrovascular and Microvascular Complications of Diabetes: Cross-Sectional Descriptive Study.

BACKGROUND: Diabetes is a chronic disease that requires lifelong management and care, affecting around 422 million people worldwide and roughly 37 million in the United States. Patients newly diagnosed with diabetes must work with health care providers to formulate a management plan, including lifestyle modifications and regular office visits, to improve metabolic control, prevent or delay complications, optimize quality of life, and promote well-being. OBJECTIVE: Our aim is to investigate one component of system-wide access to timely health care for people with diabetes in New York City (NYC), namely the length of time for someone with newly diagnosed diabetes to obtain an appointment with 3 diabetes care specialists: a cardiologist, an endocrinologist, and an ophthalmologist, respectively. METHODS: We contacted the offices of 3 different kinds of specialists: cardiologists, endocrinologists, and ophthalmologists, by telephone, for this descriptive cross-sectional study, to determine the number of days required to schedule an appointment for a new patient with diabetes. The sampling frame included all specialists affiliated with any private or public hospital in NYC. The number of days to obtain an appointment with each specialist was documented, along with "time on hold" when attempting to schedule an appointment and the presence of online booking capabilities. RESULTS: Of the 1639 unique physicians affiliated with (private and public) hospitals in the 3 subspecialties, 1032 (cardiologists, endocrinologists, and ophthalmologists) were in active practice and did not require a referral. The mean wait time for scheduling an appointment was 36 (SD 36.4; IQR 12-51.5) days for cardiologists; 82 (SD 47; IQR 56-101) days for endocrinologists; and 50.4 (SD 56; IQR 10-72) days for ophthalmologists. The median wait time was 27 days for cardiologists, 72 days for endocrinologists, and 30 days for ophthalmologists. The mean time on hold while attempting to schedule an appointment with these specialists was 2.6 (SD 5.5) minutes for cardiologists, 5.4 (SD 4.3) minutes for endocrinologists, and 3.2 (SD 4.8) minutes for ophthalmologists, respectively. Over 46% (158/341) of cardiologists enabled patients to schedule an appointment on the web, and over 55% (128/228) of endocrinologists enabled patients to schedule an appointment on the web. In contrast, only approximately 25% (117/463) of ophthalmologists offered web-based appointment scheduling options. CONCLUSIONS: The results indicate considerable variation in wait times between and within the 3 specialties examined for a new patient in NYC. Given the paucity of research on wait times for newly diagnosed people with diabetes to obtain an appointment with different specialists, this study provides preliminary estimates that can serve as an initial reference. Additional research is needed to document the extent to which wait times are associated with complications and the demographic and socio-economic characteristics of people served by different providers.

An Electronic Health Record-Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study.

BACKGROUND: In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician's schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations. OBJECTIVE: We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled. METHODS: In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer. RESULTS: From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million. CONCLUSIONS: Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.

Research on the water-ground multimodal transport emergency scheduling model and decision-making method considering the actual road network inundation situation.

As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.

Calibration and validation of solar radiation-based equations to estimate crop evapotranspiration in a semi-arid climate.

The Abtew and Jensen-Haise solar radiation-based equations were used to estimate evapotranspiration, considering the limited climatic data in many locations. Both equations were proven to successfully predict the potential evapotranspiration (ETO) compared with the standard Penman-Monteith (PM) method in two Mediterranean countries. Calibration of the constant coefficient k of the Abtew equation showed substantial differences compared to recommended values (1.22 vs. 0.53), with the highest values observed during September (1.46). Validation of ETO measurements using calibrated Abtew equation against the PM method indicated a high correlation coefficient (r2 = 0.97, RMSE = 0.61). Further, evapotranspiration requirements, using the calibrated empirical equation, were calculated for olives (449 mm) and citrus (807 mm) showing a good agreement with recommended values for dry climate regions. Therefore, the tested equations could be safely used to predict frequencies and doses of irrigation in semi-arid climates, considering limited climatic data availability.

Unravelling the ideal roster: A cross-sectional study of nurse shift preferences using multivariate analysis: Unravelling the ideal roster: An analysis of nurse shift preferences.

AIM: Our study aims to explore nurses' shift preferences in relation to their personal characteristics and examine how these preferences align with the rosters imposed in Belgian healthcare settings. Additionally, the study seeks to identify patterns in shift preferences across different days of the week and investigate the existence of distinct groups of nurses with similar preferences, further examining the link between these groups and their personal characteristics. DESIGN: Cross-sectional. METHODS: Questionnaires were distributed to 778 nurses across 11 general hospitals in Belgium, collecting data on demographics, chronotype, shift preferences, and roster alignment. Statistical analyses included logistic regression, principal component analysis, and k-means clustering. RESULTS: Age and chronotype significantly influence nurses' shift preferences. Preferences were consistent across the days within the week. The study revealed two groups of preferences: 'early birds' (preferring morning/day shifts) and 'night owls' (preferring evening/night shifts). Night owls were often neutral or evening-type chronotypes and had a higher alignment between imposed and ideal rosters. CONCLUSIONS: This study reinforces the importance of considering individual differences in nurses' shift preferences, linked to age and chronotype, and advocates for the adoption of flexible, personalized rostering systems. IMPLICATIONS: Personalized scheduling has the potential to improve workforce management, suggesting that healthcare administrators should consider individual preferences in rostering to mitigate the challenges of nurse understaffing. IMPACT: Tackles the pressing problem of nurse understaffing. Proposes that tailored rosters based on individual preferences could improve work conditions for nurses. Relevant to policymakers aiming to enhance nursing workforce management. REPORTING METHOD: STROBE Statement (for cross-sectional studies). PATIENT OR PUBLIC CONTRIBUTION: None.

Cohort scheduling of freshman exercise physiology majors improves social integration and perceptions of faculty but not academic performance.

Cohort scheduling intentionally places students in the same sections of several classes (e.g., biology, algebra, and writing) with a consistent peer group and is typically done for small groups (<30 students) to enable better interaction among students. The goal of this study was to compare cohort scheduling to traditional scheduling methods among freshmen in a physiology-related program. Outcomes included retention to the university and major, semester grades, and institutional integration and perceived group cohesion. Incoming freshmen (n = 209) were randomized into control (n = 43; scheduled with traditional methods) and intervention (n = 166; coenrolled in first-year seminar course, biology, and medical terminology) groups. Outcomes were collected via surveys or requested from the university registrar. There was no significant difference in the likelihood of retention to the university or major and no differences between groups in pass/fail rates for the first-year seminar or biology courses. At the end of the semester, there were no differences between groups in Perceived Cohesion for Small Groups (P = 0.102) or the Institutional Integration Scale (P = 0.357). However, the intervention group scored higher on the Institutional Integration Scale's subscales related to social integration and faculty. Cohort scheduling did not impact retention to the university or major but improved secondary outcomes related to retention, specifically social integration and student perceptions of faculty.NEW & NOTEWORTHY Compared with traditional scheduling methods, cohort scheduling freshman in physiology programs does not improve retention but improves students' social integration and perceptions of faculty.

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.