6,400 research outputs found

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Orvosképzés 2023

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    Magnetic Material Modelling of Electrical Machines

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    The need for electromechanical energy conversion that takes place in electric motors, generators, and actuators is an important aspect associated with current development. The efficiency and effectiveness of the conversion process depends on both the design of the devices and the materials used in those devices. In this context, this book addresses important aspects of electrical machines, namely their materials, design, and optimization. It is essential for the design process of electrical machines to be carried out through extensive numerical field computations. Thus, the reprint also focuses on the accuracy of these computations, as well as the quality of the material models that are adopted. Another aspect of interest is the modeling of properties such as hysteresis, alternating and rotating losses and demagnetization. In addition, the characterization of materials and their dependence on mechanical quantities such as stresses and temperature are also considered. The reprint also addresses another aspect that needs to be considered for the development of the optimal global system in some applications, which is the case of drives that are associated with electrical machines

    Bio-inspired optimization in integrated river basin management

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    Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

    A Secure and Distributed Architecture for Vehicular Cloud and Protocols for Privacy-preserving Message Dissemination in Vehicular Ad Hoc Networks

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    Given the enormous interest in self-driving cars, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs would help solve many of the needs of drivers, law enforcement agencies, traffic management, etc. The contributions of this dissertation are summarized as follows: A Secure and Distributed Architecture for Vehicular Cloud: Ensuring security and privacy is an important issue in the vehicular cloud; if information exchanged between entities is modified by a malicious vehicle, serious consequences such as traffic congestion and accidents can occur. In addition, sensitive data could be lost, and human lives also could be in danger. Hence, messages sent by vehicles must be authenticated and securely delivered to vehicles in the appropriate regions. In this dissertation, we present a secure and distributed architecture for the vehicular cloud which uses the capabilities of vehicles to provide various services such as parking management, accident alert, traffic updates, cooperative driving, etc. Our architecture ensures the privacy of vehicles and supports secure message dissemination using the vehicular infrastructure. A Low-Overhead Message Authentication and Secure Message Dissemination Scheme for VANETs: Efficient, authenticated message dissemination in VANETs are important for the timely delivery of authentic messages to vehicles in appropriate regions in the VANET. Many of the approaches proposed in the literature use Road Side Units (RSUs) to collect events (such as accidents, weather conditions, etc.) observed by vehicles in its region, authenticate them, and disseminate them to vehicles in appropriate regions. However, as the number of messages received by RSUs increases in the network, the computation and communication overhead for RSUs related to message authentication and dissemination also increases. We address this issue and present a low-overhead message authentication and dissemination scheme in this dissertation. On-Board Hardware Implementation in VANET: Design and Experimental Evaluation: Information collected by On Board Units (OBUs) located in vehicles can help in avoiding congestion, provide useful information to drivers, etc. However, not all drivers on the roads can benefit from OBU implementation because OBU is currently not available in all car models. Therefore, in this dissertation, we designed and built a hardware implementation for OBU that allows the dissemination of messages in VANET. This OBU implementation is simple, efficient, and low-cost. In addition, we present an On-Board hardware implementation of Ad hoc On-Demand Distance Vector (AODV) routing protocol for VANETs. Privacy-preserving approach for collection and dissemination of messages in VANETs: Several existing schemes need to consider safety message collection in areas where the density of vehicles is low and roadside infrastructure is sparse. These areas could also have hazardous road conditions and may have poor connectivity. In this dissertation, we present an improved method for securely collecting and disseminating safety messages in such areas which preserves the privacy of vehicles. We propose installing fixed OBUs along the roadside of dangerous roads (i.e., roads that are likely to have more ice, accidents, etc., but have a low density of vehicles and roadside infrastructure) to help collect data about the surrounding environment. This would help vehicles to be notified about the events on such roads (such as ice, accidents, etc.).Furthermore, to enhance the privacy of vehicles, our scheme allows vehicles to change their pseudo IDs in all traffic conditions. Therefore, regardless of whether the number of vehicles is low in the RSU or Group Leader GL region, it would be hard for an attacker to know the actual number of vehicles in the RSU/GL region

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    OxPhos Defects Cause Hypermetabolism and Reduce Lifespan in Cells and in Patients With Mitochondrial Diseases

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    Patients with primary mitochondrial oxidative phosphorylation (OxPhos) defects present with fatigue and multi-system disorders, are often lean, and die prematurely, but the mechanistic basis for this clinical picture remains unclear. By integrating data from 17 cohorts of patients with mitochondrial diseases (n = 690) we find evidence that these disorders increase resting energy expenditure, a state termed hypermetabolism. We examine this phenomenon longitudinally in patient-derived fibroblasts from multiple donors. Genetically or pharmacologically disrupting OxPhos approximately doubles cellular energy expenditure. This cell-autonomous state of hypermetabolism occurs despite near-normal OxPhos coupling efficiency, excluding uncoupling as a general mechanism. Instead, hypermetabolism is associated with mitochondrial DNA instability, activation of the integrated stress response (ISR), and increased extracellular secretion of age-related cytokines and metabokines including GDF15. In parallel, OxPhos defects accelerate telomere erosion and epigenetic aging per cell division, consistent with evidence that excess energy expenditure accelerates biological aging. To explore potential mechanisms for these effects, we generate a longitudinal RNASeq and DNA methylation resource dataset, which reveals conserved, energetically demanding, genome-wide recalibrations. Taken together, these findings highlight the need to understand how OxPhos defects influence the energetic cost of living, and the link between hypermetabolism and aging in cells and patients with mitochondrial diseases

    Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning

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    Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts

    Student Success in Co-operative Education: An Analysis of Job Postings and Performance Evaluations

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    Co-operative education (co-op) programs combine coursework and work internships and have become popular worldwide. In this analysis, we use two separate co-op datasets to understand employer expectations and factors that contribute to student success. First, we analyze over 13000 unique filled job postings from work terms in 2021. We group skills using k-means analysis and frequency counting to characterize the types of co-op jobs available to students, finding that co-op students are frequently required to possess both technical skills (such as knowledge of specific tools) and soft skills (such as communication). Next, we construct two separate weighted bipartite graphs linking the groups of academic programs advertised to by employers to either the required skills or titles of each job. By using community detection to co-cluster the nodes in each graph, we determine the types of skills and roles expected by employers for students in different programs. We find significant differences in the expectations of employers for students in each program, including the importance of soft skills for arts students and the prevalence of data science and artificial intelligence skills in many academic programs. Second, using over 45000 performance evaluations collected separately for in-person (2019) and remote (2021) internship positions, we uncover the characteristics of successful co-op students. Each evaluation includes an overall performance rating and written comments and recommendations provided by the supervisor. By using logistic regression and word frequency counting to analyze supervisors’ general and recommendation comments, we find the most successful students to be excellent leaders and innovators, with remote students also being praised for their independence. Supervisors encourage remote students to be innovative and learn technological skills, while the supervisors of in-person students recommend improving oral communication and presentation abilities. By identifying the job roles and required skills expected by employers for students in different academic programs, institutions can better prepare students for appropriate jobs. By understanding the skills that contribute to student success in remote and in-person contexts, students can focus on developing the most important skills for their intended work environment. Together, these findings highlight important skills that students should acquire in their early careers
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