Enhancing Performance and Health Outcomes with Optimal Exercise and Nutrition Programming

Exercise and nutrition, when used as a mode to improve health outcomes is well-researched and accepted by researchers and clinicians, alike. Numerous health organizations have developed general recommendations such as physical activity and exercise to inform the public how to improve health outcomes. More often than not, these guidelines are vague and do not suggest how to achieve optimal health via exercise and nutrition. These guidelines also fail to consider physiological and psychological variability for patients and individuals aiming to follow such guidelines. For example, current recommendations include exercise intensities based on low, moderate, and vigorous activity and many people may not understand the physiological cost of such exercise intensities. Presently, accessible consumer-grade technology allows for accurate measurements of relative heart rate, exercise time, distance, and estimated caloric expenditure which is presumed easy for any person to understand. Therefore, creating guidelines that target specific and measurable variables, such as relative heart rate may be more advantageous for individualized health optimization.

Evaluating the Impacts of Optimization Horizon on the Shared Autonomous Vehicle Reservation Request System

With the development of technology, shared autonomous vehicles may become one of the main traffic modes in the future. Especially, shared autonomous vehicle reservation system, commuting, and other trips with fixed departure time mostly submit their travel requests in advance. Therefore, it is important to reasonably match shared autonomous vehicles and reservation demands. In this paper, reservation requests are divided into short-term and long-term requests by inputting requests in a more realistic way. An integer linear programming model considering operator scheduling cost and system service level is established. A detailed scheme considering rolling horizon continuity and ridesharing is used to improve the dispatching result. Based on traffic data in Delft, the Netherlands, 164 scenarios are tested in which the parking cost, fuel cost, ridesharing effect, service level, and network size are analyzed. The results show that a better relocation and ridesharing matching scheme can be obtained when the rolling horizon is small, while the overall effect is better when the rolling horizon is large. Moreover, the buffer time, distance, and travel time limit for vehicle relocation should be selected according to the request quantity and the calculation time requirement. The result can provide a suggestion for the dispatching of shared autonomous vehicle reservation system with ridesharing.

Quantification method influences training load change in high school cross-country runners across a competitive season

Context: Running programs traditionally monitor external loads (e.g., time, distance). There has been a recent movement to encompass a more comprehensive approach to also monitor training loads that account for internal loads (e.g., intensity, measured as session rating of perceived exertion [sRPE]). The combination of an external and internal load accounts for the potential interaction between these loads. While differences in weekly change in training loads have been reported between external loads and the combination of an external and internal load during 2- and 4-week training cycles, there are no reports whether these differences are apparent during an entire cross-country season in high school runners. Objective: To compare change in training loads, as measured by external loads and combinations of an external and internal load, in high school runners during an interscholastic cross-country season. Design: Case-series. Setting: Community-based with daily online surveys. Participants: Twenty-four high school cross-country runners (female=14, male=10, age=15.9±1.1 years, running experience=9.9±3.2 years). Main Outcome Measure(s): Week-to-week percent change in training load when measured by external loads (time, distance) and the combination of an external and internal load (timeRPE, distanceRPE). Results: Overall, the average weekly change was 7.1% greater for distanceRPE compared to distance (p=.04, d=0.18). When decreasing weekly running duration, the average weekly change was 5.2% greater for distanceRPE compared to timeRPE (p=.03, d=0.24). When maintaining or increasing weekly running duration, the average weekly change was 10–15% greater when an external load was combined with an internal load compared to external loads alone, but these differences were non- significant (p=.11–.22, d=0.19–0.34). Conclusions: Our results demonstrate that progression in training load may be underestimated when relying solely on external loads. The interaction between internal loads (sRPE) and external loads (distance or time) appears to provide a different measure of training stresses experienced by runners than external loads alone.

Effect of commuting on the risk of COVID-19 and COVID-19-induced anxiety in Japan, December 2020

Background To prevent the spread of coronavirus disease (COVID-19), it is important to avoid 3Cs (closed spaces, crowded places, and close-contact settings). However, the risk of contact with an unspecified number of people is inevitable while commuting to and from work. In this study, we investigated the relationship between commuting, and the risk of COVID-19 and COVID-19-induced anxiety. Methods An internet-based questionnaire survey was conducted to obtain a dataset from 27,036 respondents. One-way commuting time was evaluated using a five-case method. The commuting distance was estimated using zip codes of the home and workplace. Logistic regression analysis was performed with the following outcomes: COVID-19 risk, close contact, infection anxiety, and infection anxiety due to commuting. Commuting distance and commuting time were analyzed separately in the model. We excluded participants with incalculable commuting distance, commuting distance exceeding 300 km, commuting distance of 0 km, or who telecommuted at least once a week. Results The total number of participants included in the analysis was 14,038. The adjusted odds ratios (aORs) of using public transportation for severe acute respiratory syndrome coronavirus 2 infection were 4.17 (95% confidence interval [CI]: 2.51–6.93) (commuting time) and 5.18 (95% CI: 3.06–8.78) (commuting distance). The aOR of COVID-19 diagnosis decreased significantly with increasing commuting distance. The aORs of using public transportation to infection anxiety were 1.44 (95% CI: 1.31–1.59) (commuting time) and 1.45 (95% CI: 1.32–1.60) (commuting distance). The longer the commuting time, the more the aOR increased. Conclusions COVID-19 risk, close contact, and infection anxiety were all associated with the use of public transportation during commuting. Both commuting distance and time were associated with infection anxiety due to commuting, and the strength of the association increased with increase in commuting time distance. Since transportation by commuting is associated with COVID-19 risk and anxiety, we recommend the use of telecommuting and other means of work.

A Real-Time Distance Measurement System for a Digital Twin Using Mixed Reality Goggles

This paper presents a new system architecture for controlling industrial devices using Mixed Reality (MR) applications and a new method based upon them for measuring the distance between real and virtual points. The research has been carried out using a physical robot and its Digital Twin (DT). The possibility of controlling them using gestures recognized by Mixed Reality goggles has been presented. The extension of the robot’s environment with a 3D model capable of following its movements and positions was also analyzed. The system was supervised by an industrial Programmable Logic Controller (PLC) serving as an end point for the data sent by the goggles and controlling the movements of the real robot by activating the corresponding control. The results of the preliminary measurements presented here concerned the responsiveness of the system and showing the influence of system parameters in the accuracy of distance estimation between measured points.

Meta-Heuristic Algorithm based on Ant Colony Optimization Algorithm and Project Scheduling Problem (PSP) for the Traveling Salesman Problem

The main target of Traveling Salesman Problem (TSP) is to construct the path with the lowest time between different cities, visiting every one once. The Scheduling Project Ant Colony Optimization (SPANCO) Algorithm proposes a way to solve TSP problems adding three aspects: time, cost effort and scope, where the scope is the number of cities, the effort is calculated multiplying time, distance and delivering weight factors and dividing by the sum of them and optimizing the best way to visit the cities graph.

How analysis of mobile app reviews problematises linguistic approaches to internet troll detection

State-sponsored internet trolls repeat themselves in a unique way. They have a small number of messages to convey but they have to do it multiple times. Understandably, they are afraid of being repetitive because that will inevitably lead to their identification as trolls. Hence, their only possible strategy is to keep diluting their target message with ever-changing filler words. That is exactly what makes them so susceptible to automatic detection. One serious challenge to this promising approach is posed by the fact that the same troll-like effect may arise as a result of collaborative repatterning that is not indicative of any malevolent practices in online communication. The current study addresses this issue by analysing more than 180,000 app reviews written in English and Russian and verifying the obtained results in the experimental setting where participants were asked to describe the same picture in two experimental conditions. The main finding of the study is that both observational and experimental samples became less troll-like as the time distance between their elements increased. Their ‘troll coefficient’ calculated as the ratio of the proportion of repeated content words among all content words to the proportion of repeated content word pairs among all content word pairs was found to be a function of time distance between separate individual contributions. These findings definitely render the task of developing efficient linguistic algorithms for internet troll detection more complicated. However, the problem can be alleviated by our ability to predict what the value of the troll coefficient of a certain group of texts would be if it depended solely on these texts’ creation time.

A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.