Big Data Analytics for Spatio-Temporal Service Orders Demand Forecasting in Electric Distribution Utilities

This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil.

Implementation challenges from a prospective, interventional biopsy-based study of Environmental Enteropathy in rural Pakistan

The Study of Environmental Enteropathy (EE) and Malnutrition in Pakistan (SEEM) is a community intervention trial designed to understand the pathophysiology of EE and its associated clinical phenotypes in children less than two years of age. Undernutrition is the underlying cause of three million deaths worldwide annually, accounting for 45% of all deaths among children under five years of age. Linear growth stunting affects a further 165 million, especially in low- and middle-income countries. In Pakistan, an estimated one in three children are underweight and nearly half are stunted. This manuscript summarizes the challenges faced by the field team and the solutions employed for achieving our research objectives at the rural Matiari field site. Our study design involved a longitudinal collection of field data and biological samples which required constant engagement of field staff with the participating families. Challenges faced in SEEM included: 1) quality assurance parameters of anthropometry data; 2) 24-hour food recall data collection; 3) clinical management of sick enrolled children; 4) obtaining informed consent for upper gastrointestinal (UGI)endoscopy (an invasive procedure); and 5) biological sample collection (blood, urine, stool) from enrolled children. We enrolled 350 malnourished children(Weight for Height Z (WHZ) score <-2) and 50 well-nourished children(WHZ score ≥ 0 and height-for-age Z (HAZ) score ≥ -1) with more than 95% success rate of collecting blood, urine and stool specimens. We collected 72% microbiome stool samples within 30 minutes of the child passing stool. We conducted a UGI biopsy in 63 children. Solutions to these challenges included good project governance, the establishment of credibility, and the development of a longitudinal bidirectional relationship embedded in trust and effective communication with the caretakers of the enrolled children. Continuous monitoring, regular training of staff, and effective teamwork were key factors that led to the successful execution of our study.

Optimized Allocation of Resources for Intelligent Construction of Training Venues for Track and Field Teams

Because of the problems of low allocation efficiency and accuracy in the traditional resource allocation model for the intelligent construction of track and field team training venues, this paper proposes a new intelligent construction resource allocation model for track and field team training venues. A distributed data collection method is adopted to select the index data for the intelligent construction of the track and field team training venues to optimize the allocation of resources and the mined resource allocation index through extracting the optimized index characteristics. The goal of the optimization problem is transformed into a single-objective solving problem. The suggested approach is based on the two-level programming concept. The simulation experimental results show higher efficiency and better allocation effect of the proposed scheme compared to other state-of-the-art approaches. Our evaluation and observations suggest that the accuracy of the intelligent construction of track and field team training venues is between 90% and 99% which is higher than two other benchmarked models. Furthermore, the proposed model can quickly reach to an optimal decision.

Staffing and Capacity Planning for SARS-CoV-2 Monoclonal Antibody Infusion Facilities: A Performance Estimation Calculator based on Discrete-Event Simulations

Objective: The COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions. Materials and Methods: Using real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions. Results: 162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥ 2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels. Discussion: Physical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff. Conclusion: Simulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

The Team Handball Game-Based Performance Test Is Better than the Yo-Yo Intermittent Recovery Test to Measure Match-Related Activities in Female Adult Top-Elite Field Team Handball Players

In team handball, suitable tests determining the match-related physical performance are essential for the planning of optimal physical training regimens. Thus, the aims of the present study were (a) to determine the relationships between the physical and physiological test results from a team handball game-based performance test (GBPT), the Yo-Yo intermittent recovery test, level 1 (Yo-Yo IR1 test) and a separate linear 30-m single sprint performance test (SSPT) in female adult top-elite field team handball players, in order to establish the significance (validity) of tests for measuring relevant elements for team handball match-play; and (b) to compare and evaluate the results from the aforementioned tests for the same players in relation to the different playing positions. Twenty-three female adult top-elite field team handball players from the Danish Premier Female Team Handball League performed the GBPT, the Yo-Yo IR1 test and the 30-m SSPT test on separate days. As main findings, significant correlations between the GBPT and the Yo-Yo IR1 test in about 1/3 of the variables were found, indicating that the Yo-Yo IR1 test is reflecting most of the locomotive match activities in female adult top-elite team handball. However, the Yo-Yo IR1 test results were not correlated to the GBPT in any of the match-related activities in the team handball GBPT that included technical playing actions such as tackles, passes, jumping and shooting during specialized movements in offence and defence. Overall, the results revealed that the GBPT is better than the Yo-Yo IR1 test to evaluate female adult top-elite field team handball players’ ability to perform physical match-related activities including both locomotive and technical playing actions executed as during competitive match-play. Similar to the Yo-Yo IR1 test, the 30-m SSPT was not correlated to any of the team handball GBPT activities, which included technical playing actions. These data suggest that the SSPT only to a certain extent can measure the individual sprint capacity of elite team handball players. In addition, unexpectedly no significant differences between the various playing positions were found neither for the GBPT, the SSPT nor the Yo-Yo IR1 test. However, several effects sizes indicated that the lack of positional differences primarily was due to the relatively small sample size in each playing position and the composition of the specific group of players. In conclusion, this study clearly indicated that team handball specific physical performance, as measured by the GBPT, and general physical performance, as measured by the Yo-Yo IR1 test and the 30-m SSPT, are different components. This must be taken in consideration when using physical test results for the planning of optimal physical training regimens in elite team handball.

Robust linear quadratic mean field social control: A direct approach

This paper investigates a robust linear quadratic mean field team control problem. The model involves a global uncertainty drift which is common for a large number of weakly-coupled interactive agents. All agents treat the uncertainty as an adversarial agent to obtain a "worst case'' disturbance. The direct approach is applied to solve the robust social control problem, where the state weight is allowed to be indefinite. Using variational analysis, we first obtain a set of forward-backward stochastic differential equations (FBSDEs) and the centralized controls which contain the population state average. Then the decentralized feedback-type controls are designed by mean field heuristics. Finally, the relevant asymptotically social optimality is further proved under proper conditions.

Training Package for Emergency Medical Teams Deployed to Disaster Stricken Areas: Has ‘TEAMS’ Achieved its Goals?

In spite of their good intentions, Emergency Medical Teams (EMTs) were relatively disorganized for many years. To enhance the efficient provision of EMT’s field team work, the Training for Emergency Medical Teams and European Medical Corps (TEAMS) project was established. The purpose of this study was to assess the effectiveness and quality of the TEAMS training package in 2 pilot training programs in Germany and Turkey. A total of 19 German and 29 Turkish participants completed the TEAMS training package. Participants were asked to complete a set of questionnaires designed to assess self-efficacy, team work, and quality of training. The results suggest an improvement for both teams’ self-efficacy and team work. The self-efficacy scale improved from 3.912 (± 0.655 SD) prior to training to 4.580 (± 0.369 SD) after training (out of 5). Team work improved from 3.085 (± 0.591 SD) to 3.556 (± 0.339 SD) (out of 4). The overall mean score of the quality of the training scale was 4.443 (± 0.671 SD) (out of 5). In conclusion, The TEAMS Training Package for Emergency Medical Teams has been demonstrated to be effective in promoting EMT team work capacities, and it is considered by its users to be a useful and appropriate tool for addressing their perceived needs.

How to Prepare Your Team for an Emergency

ABSTRACTAny field team, regardless of the professionalism of the leadership, will eventually experience a critical event. Some events will result in a subtle degradation of the team's work; others will cause emergent threats to the safety of the team. A team's sustained performance during a field season depends partly on such chance events and partly with the team's ability to plan for and respond to the dynamic environment of the field. The duty of a field leader is to conduct clear-eyed conversations and ensure that solid preparations are laid for both the group as a whole and the individual team members. Some of these plans need to be manifested by material preparations, some of which require months of forethought. This article walks readers through a two-hour exercise, giving them frameworks from business continuity and military field doctrines to understand risk. Readers will conduct a SWOT analysis, define emergencies within their organizations, and then apply risk management practices of qualitative risk assessment and all-hazards planning to develop planning priorities. By the end, readers will have built specific action plans for improving field season readiness.

Hot Spot Mapping and Size Estimation of Illicit Drug Users in Iran: A Pilot Study

Background: Mapping and size estimation of people who use (PWUD) or inject drugs (PWID) are important issues for control of drug use. The aim of this pilot study is to mapping and estimate the number of PWUD and PWID in Iran.Methods: We used the hotspot mapping and size estimation method to estimate the number of illicit drug users in four cities of Iran from December 2017 to January 2019. We identified hotspots by local knowledge of key informants. For size estimation in each hotspot, three estimates were made during hotspot visit: a) KAP-KI (Key Affected Population Key Informant) estimate (drug users met in the hotspot), b) Non-KAP KI estimate (e.g. taxi drivers, shopkeepers nearby the hotspot); c) estimation made by field observers. Locations of hotspots were recorded by the field team and ArcGIS software was used for mapping. Results: A total number of 335 hot spots were identified in four cities of them, 70 (20.9%) were in Ahvaz, 90 (26.9%) in Sari, 67 (20%) in Yazd and 108(32.2%) in district 2 of Tehran metropolitan. We identified nine high-density areas for PWUDs of them three were in Ahvaz, one in the south of Sari, two in district 2 of Tehran and three in Yazd. The total numbers of 1524 key informants were interviewed by the field team in 335 identified hot spots. Based on the median estimated number of PWUD, Ahvaz had the highest estimated number of PWUD (1242, range, 606-1285), followed by Sari (788, range, 373-1044), district 2 of Tehran (684, range, 642-728) and Yazd (123, range, 76-316). The median estimated number of PWID indicated that Ahvaz had the highest estimated number of PWID (843, range, 703-887), followed by Sari (346, range, 307-666), Yazd (228, range, 221-471) and Tehran (18, range, 18-34). Conclusions: Findings highlight the importance of geographical targeting of illicit drug users to provide harm reduction programs with a broader reach in Iran.