2,368 research outputs found

    Combined Nutrition and Exercise Interventions in Community Groups

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    Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Current issues of the management of socio-economic systems in terms of globalization challenges

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    The authors of the scientific monograph have come to the conclusion that the management of socio-economic systems in the terms of global challenges requires the use of mechanisms to ensure security, optimise the use of resource potential, increase competitiveness, and provide state support to economic entities. Basic research focuses on assessment of economic entities in the terms of global challenges, analysis of the financial system, migration flows, logistics and product exports, territorial development. The research results have been implemented in the different decision-making models in the context of global challenges, strategic planning, financial and food security, education management, information technology and innovation. The results of the study can be used in the developing of directions, programmes and strategies for sustainable development of economic entities and regions, increasing the competitiveness of products and services, decision-making at the level of ministries and agencies that regulate the processes of managing socio-economic systems. The results can also be used by students and young scientists in the educational process and conducting scientific research on the management of socio-economic systems in the terms of global challenges

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    On a Vehicle Routing Problem with Customer Costs and Multi Depots

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    The Vehicle Routing Problem with Customer Costs (short VRPCC) was developed for railway maintenance scheduling. In detail, corrective maintenance jobs for unexpected occurring failures are planned to a short time horizon. These jobs are geographically distributed in the railway net. Furthermore, dependent on the severity of the failure, it can be necessary to reduce the top speed on the track section in order to avoid safety risks or a too fast deterioration. For fatal failures, it can even be necessary to close the track section. The resulting limitations on railway service lead to penalty costs for the maintenance operator. These must be paid until the track is repaired and the restrictions are removed. By scheduling the maintenance tasks, these penalty costs can be reduced by proceeding corresponding maintenance tasks earlier. However, this may in return lead to increased costs for moving the maintenance machines and crews. For this scheduling problem, the VRPCC was developed. With it, for each maintenance vehicle and crew, a route is defined that describes the order to proceed maintenance tasks. Two kinds of costs are considered: Firstly, travel costs for machinery and crew; and secondly, penalty costs for an unsafe track condition that have to be paid for each day from failure detection to maintenance completion. To model the penalties, the novel customer costs are defined. In detail, for each maintenance activity a customer cost coefficient is given which incur for each day between failure detection and failure repair. The objective function of this problem is defined by the sum of travel costs and time-dependent customer costs. With it, the priority of customers can be taken into account without losing the sight on travel costs. This new vehicle routing problem was introduced in this thesis by a non-linear partition and permutation model. In this model, a feasible solution is defined by a partition of the job set into subsets that represent the allocation of jobs to vehicles and a permutation for each subset that represent the order of processing the jobs. Then, the start times of the jobs were calculated based on the order given by the permutations. It was taken into account that work can only be done in eight hour shifts during the night. Based on the start times, the customer cost value of each job is computed which equals to the paid penalty costs. Then, the costs of a schedule are calculated via the sum of travel costs and customer costs. To solve the VRPCC by a commercial linear programming solver, different formulations of the VRPCC as mixed-integer linear program were developed. In doing so, the start times became decision variables. It turned out that including customer costs led to problems harder to solve than vehicle routing problems where only travel costs are minimized. Further, in the thesis several construction heuristics for the VRPCC were designed and investigated. Also two local search algorithms, first and best improvement, were applied. The computational experiments showed that the solutions generated by the local search algorithm were much better than the solutions of the construction heuristics. The main part of this thesis was to design a Branch-and-Bound algorithm for the VRPCC. For this purpose, new lower bounds for the customer cost part of the objective function were formulated. The computational experiments showed that a lower bound computed from the LP relaxation of a specific bin packing problem had the best trade-off between computational effort and bound quality. For the travel cost part of the objective function, several known lower bounds from the TSP were compared. To design a Branch-and-Bound algorithm, beside efficient lower bound, also suitable branching strategies are necessary to split the problem space into smaller subspaces. In this thesis two branching strategies were developed which are based on the non-linear partition and permutation model to take advantage from the problem structure. To be more precise, new branches are generated by appending or including a job to an uncompleted schedule. Consequently, the start times can be computed directly from the so far planned jobs and more tight lower bounds can be computed for the so far unplanned jobs. By means of computational experiments, the developed Branch-and-Bound algorithms were compared with the classical approach, which means solving a mixed-integer linear program of the VRPCC by a commercial solver. The results showed that both Branch-and-Bound algorithms solved the small instances faster than the classical approach

    Data ethics : building trust : how digital technologies can serve humanity

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    Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

    Amplifying Marginal Voices of the Global Movement for Deeper Learning: A Case Study of a Rural K-12 Mission School in Cambodia

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    Several paradigms have been developed to define what constitutes deeper learning, how to foster it, and what desired outcomes or competencies can result from it. Much of the literature, however, has been based on studies in economically developed Western countries. There has been little, if any, that is based on developing country settings where culture and context can account for differences in the manner of promoting deeper learning. This qualitative case study explored the experiences of learners in the Mudita Mission School (MMS; pseudonym), a K-12 school in a rural part of northern Cambodia, and investigated how deeper learning was enacted, valued, and fostered there. It also examined challenges and opportunities for promoting deeper learning faced by the school. This study sought to contribute to the global movements for deeper learning by highlighting voices from marginalized communities, thus expanding the conceptual frameworks which have been exclusive of experiences of students and educators in impoverished country contexts. This study also sought to contribute to the literature that informs Cambodian educational reform. Study findings suggest that fostering eco-humanistic value-systems and respect for Khmer culture scaffold arcs of deeper learning in the MMS, and that several innovative pedagogical practices uncommon to many rural schools in Cambodia were transforming the educational experiences of students there. Based on the findings, the author proposes a theory of Epistemologies of Deeper Learning to complement frameworks in the literature

    Performance Modeling of Vehicular Clouds Under Different Service Strategies

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    The amount of data being generated at the edge of the Internet is rapidly rising as a result of the Internet of Things (IoT). Vehicles themselves are contributing enormously to data generation with their advanced sensor systems. This data contains contextual information; it's temporal and needs to be processed in real-time to be of any value. Transferring this data to the cloud is not feasible due to high cost and latency. This has led to the introduction of edge computing for processing of data close to the source. However, edge servers may not have the computing capacity to process all the data. Future vehicles will have significant computing power, which may be underutilized, and they may have a stake in the processing of the data. This led to the introduction of a new computing paradigm called vehicular cloud (VC), which consists of interconnected vehicles that can share resources and communicate with each other. The VCs may process the data by themselves or in cooperation with edge servers. Performance modeling of VCs is important, as it will help to determine whether it can provide adequate service to users. It will enable determining appropriate service strategies and the type of jobs that may be served by the VC such that Quality of service (QoS) requirements are met. Job completion time and throughput of VCs are important performance metrics. However, performance modeling of VCs is difficult because of the volatility of resources. As vehicles join and leave the VC, available resources vary in time. Performance evaluation results in the literature are lacking, and available results mostly pertain to stationary VCs formed from parked vehicles. This thesis proposes novel stochastic models for the performance evaluation of vehicular cloud systems that take into account resource volatility, composition of jobs from multiple tasks that can execute concurrently under different service strategies. First, we developed a stochastic model to analyze the job completion time in a VC system deployed on a highway with service interruption. Next, we developed a model to analyze the job completion time in a VC system with a service interruption avoidance strategy. This strategy aims to prevent disruptions in task service by only assigning tasks to vehicles that can complete the tasks’ execution before they leave the VC. In addition to analyzing job completion time, we evaluated the computing capacity of VC systems with a service interruption avoidance strategy, determining the number of jobs a VC system can complete during its lifetime. Finally, we studied the computing capacity of a robotaxi fleet, analyzing the average number of tasks that a robotaxi fleet can serve to completion during a cycle. By developing these models, conducting various analyses, and comparing the numerical results of the analyses to extensive Monte Carlo simulation results, we gained insights into job completion time, computing capacity, and overall performance of VC systems deployed in different contexts

    Hindsight Learning for MDPs with Exogenous Inputs

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    Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class of data-efficient algorithms for them termed Hindsight Learning (HL). Our HL algorithms achieve data efficiency by leveraging a key insight: having samples of the exogenous variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We compare HL against classic baselines in the multi-secretary and airline revenue management problems. We also scale our algorithms to a business-critical cloud resource management problem -- allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider. We find that HL algorithms outperform domain-specific heuristics, as well as state-of-the-art reinforcement learning methods.Comment: 53 pages, 6 figure
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