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Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput., CC BY 4.0 DEEDCorresponding author at: Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. E-mail address: masood.fathi@his.se (M. Fathi).This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency via the ACCURATE 4.0 (grant agreement No. 20200181) and PREFER projects, respectively., ACCURATE 4.0, PREFER

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput., CC BY 4.0 DEEDCorresponding author at: Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. E-mail address: masood.fathi@his.se (M. Fathi).This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency via the ACCURATE 4.0 (grant agreement No. 20200181) and PREFER projects, respectively., ACCURATE 4.0, PREFER

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

In mammalian systems, no cell acts in isolation, but rather coordinates to achieve higher-order function. Such cell behaviors are complex and influenced by context. To comprehensively understand them, we must understand how molecular interactions affect cell phenotypes, and analogously, how cell interactions affect higher-order phenotypes. I begin by examining the role of resource allocation in cellular decision-making processes. I underscore the significance of resource constraints and context in shaping cellular phenotypes and enabling population-level behaviors. My research then pivots to a detailed investigation into a rare systemic inflammatory disorder, Disabling Pansclerotic Morphea (DPM). I report on the discovery of novel variants in the STAT4 gene that are linked to DPM. Leveraging these insights, we propose a successful therapeutic approach using the JAK inhibitor, ruxolitinib, thereby demonstrating the importance of a context-informed genetic understanding in disease management. The JAK-STAT pathway is a key signaling pathway mediating immune cell communication. Thus, I shift the focus of my research to intercellular communication. My novel unsupervised method, Tensor-cell2cell, deciphers complex cell-cell communication patterns across multiple contexts (e.g., time points, disease severities, or spatial contexts). Given the generalizability of this approach to use other communication methods’ outputs as its input, I then introduce a protocol integrating two computational tools, LIANA and Tensor-cell2cell. LIANA is similarly generalizable in that it provides a centralized resource to run many methods, thus providing a natural preceding step to Tensor-cell2cell. The protocol enhances robustness and flexibility in identifying cell-cell communication programs across multiple samples. Finally, I present humanME, a computational tool for generating and analyzing human ME-Models from input metabolic models. This approach refines the prediction accuracy of growt

This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.

We consider the general problem of online convex optimization with time-varying budget constraints in the presence of predictions for the next cost and constraint functions, that arises in a plethora of network resource management problems. A novel saddle-point algorithm is designed by combining a Follow-The-Regularized-Leader iteration with prediction-adaptive dynamic steps. The algorithm achieves $\c O(T^{(3-\beta)/4})$ regret and $\c O(T^{(1+\beta)/2})$ constraint violation bounds that are tunable via parameter $\beta\!\in\![1/2,1)$ and have constant factors that shrink with the predictions quality, achieving eventually $\c O(1)$ regret for perfect predictions. Our work extends the seminal FTRL framework for this new OCO setting and outperforms the respective state-of-the-art greedy-based solutions which naturally cannot benefit from predictions, without imposing conditions on the (unknown) quality of predictions, the cost functions or the geometry of constraints, beyond convexity., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Embedded Systems

Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes. Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning. Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023. Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects. Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery.

As the cloud native landscape flourishes, microservices emerge as a central pillar for contemporary software development, enabling agility, resilience, and scalability in modern computing environments. While these modular services promise opportunities, particularly in the transformative ecosystem of 5G and beyond, they also introduce a myriad of complexities. Notably, the migration from hardware-centric to software-defined environments, culminating in Virtual Network Functions (VNF), has facilitated dynamic deployments across cloud data centers. In this transition, VNFs are often deployed within cloud native environments as independent services, mirroring the microservices model. However, the advantage of flexibility in cloud native systems is shadowed by bottlenecks in computational resource allocation, sub-optimal service chain placements, and the perpetual quest for performance enhancement. Addressing these concerns is not just pivotal but indispensable for harnessing the true potential of microservice chains. In this thesis, the inherent challenges presented by cloud native microservice chains are addressed through the development and application of various tools and methodologies. The NFV-Inspector is introduced as a foundational tool, employing a systematic approach to profile and analyze Virtual Network Functions, subsequently extracting essential system KPIs essential for further modeling. Subsequent research introduced a Machine Learning (ML) based SLA-Aware resource recommendation system for cloud native functions. This system leveraged regression modeling techniques to correlate key performance metrics. Following this, PerfSim is proposed as a performance simulation tool designed specifically for cloud native computing environments, aiming to improve the accuracy of microservice chain simulations. Further research is conducted on Service Function Chain (SFC) Placement, emphasizing the equilibrium between cost-efficiency and latency optimization. The thes, In the dynamic cloud native landscape, microservices stand out as pivotal for modern software development, enhancing agility, resilience, and scalability. These services, crucial in the transformative 5G era, introduce complexities such as resource allocation, service chain placement, and performance optimization challenges. This thesis delves into these challenges, emphasizing the development and application of tools and methodologies specific to microservice chains. Key contributions include the NFV-Inspector, which, while focusing on Virtual Network Functions, is instrumental in profiling and analyzing microservices, extracting vital KPIs for advanced modeling. Further, a Machine Learning-based SLA-Aware system is introduced for resource recommendation in cloud-native functions, utilizing regression modeling to link performance metrics. PerfSim, another simulation framework, is proposed for simulating microservice chains in cloud environments. The thesis also explores Service Function Chain (SFC) placement, aiming to balance cost-efficiency with latency optimization. The thesis concludes by integrating Deep Learning (DL) for service chain optimization, employing both Graph Attention Networks (GAT) and Deep Q-Learning (DQN), showcasing the potentials of DL in SFC optimization.

As the cloud native landscape flourishes, microservices emerge as a central pillar for contemporary software development, enabling agility, resilience, and scalability in modern computing environments. While these modular services promise opportunities, particularly in the transformative ecosystem of 5G and beyond, they also introduce a myriad of complexities. Notably, the migration from hardware-centric to software-defined environments, culminating in Virtual Network Functions (VNF), has facilitated dynamic deployments across cloud data centers. In this transition, VNFs are often deployed within cloud native environments as independent services, mirroring the microservices model. However, the advantage of flexibility in cloud native systems is shadowed by bottlenecks in computational resource allocation, sub-optimal service chain placements, and the perpetual quest for performance enhancement. Addressing these concerns is not just pivotal but indispensable for harnessing the true potential of microservice chains. In this thesis, the inherent challenges presented by cloud native microservice chains are addressed through the development and application of various tools and methodologies. The NFV-Inspector is introduced as a foundational tool, employing a systematic approach to profile and analyze Virtual Network Functions, subsequently extracting essential system KPIs essential for further modeling. Subsequent research introduced a Machine Learning (ML) based SLA-Aware resource recommendation system for cloud native functions. This system leveraged regression modeling techniques to correlate key performance metrics. Following this, PerfSim is proposed as a performance simulation tool designed specifically for cloud native computing environments, aiming to improve the accuracy of microservice chain simulations. Further research is conducted on Service Function Chain (SFC) Placement, emphasizing the equilibrium between cost-efficiency and latency optimization. The thes, In the dynamic cloud native landscape, microservices stand out as pivotal for modern software development, enhancing agility, resilience, and scalability. These services, crucial in the transformative 5G era, introduce complexities such as resource allocation, service chain placement, and performance optimization challenges. This thesis delves into these challenges, emphasizing the development and application of tools and methodologies specific to microservice chains. Key contributions include the NFV-Inspector, which, while focusing on Virtual Network Functions, is instrumental in profiling and analyzing microservices, extracting vital KPIs for advanced modeling. Further, a Machine Learning-based SLA-Aware system is introduced for resource recommendation in cloud-native functions, utilizing regression modeling to link performance metrics. PerfSim, another simulation framework, is proposed for simulating microservice chains in cloud environments. The thesis also explores Service Function Chain (SFC) placement, aiming to balance cost-efficiency with latency optimization. The thesis concludes by integrating Deep Learning (DL) for service chain optimization, employing both Graph Attention Networks (GAT) and Deep Q-Learning (DQN), showcasing the potentials of DL in SFC optimization.

The focus of strategy research has long revolved around strategy formulation rather thanstrategy implementation, despite the evidence indicating that intended strategies are rarelyachieved. Project portfolio management, PPM, assumes a crucial role in enabling strategyimplementation and can be regarded as a representation of the organization's actual pursuedstrategy. Existing research on PPM has predominantly centered around portfolios in the contextof new product development, NPD, and research and development, R&D. However, there hasbeen relatively less exploration of PPM within internal development departments, warrantingfurther investigation. To contribute to the understanding on strategy implementation throughPPM and its conditional factors, this qualitative case study expands the existing research bystudying the PPM process within an internal development department. The study wasconducted at the Business Improvement department, which oversees improvement projects forthe service branch of EnergyComp, a company specializing in the development of complexenergy solutions. Using an abductive research approach, a literature review was conducted inparallel with data collection and analysis. The empirical data was mainly collected throughsemi-structured interviews at the company, but also through meetings and companydocumentation. The results of the study show that PPM actions connected to projects, portfolio and resourceallocation are undertaken to effectively implement the organization's strategy within theinternal development department. Common to all areas is the importance of accurate andavailable information that effects the decisions connected to strategy implementation. On aproject level, Insufficient information poses challenges in accurately assessing project success,resulting in measurements that fail to cover all strategic objectives. In the context of theportfolio, the absence of project information and uncertainties can lead to a misalignmentbetween th

Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

The advanced developments in information technology have enabled resource exchanges among different transportation service providers (TSPs). One can offer its mobility resources (e.g., vehicles, charging facilities, parking spaces, etc.) to the other (even its competitors) to gain rewards. They then compete for mobility service users by setting the service prices simultaneously. There are three strategies that TSPs might choose: (i) resource seller-only strategy, (ii) service provider-only strategy, and (iii) multichannel strategy, in which a TSP simultaneously sells ride resources and maintains its own transport service. We study the optimal strategies of resource exchange and pricing for two TSPs using a sequential-move game. In the first stage, we develop a Nash bargaining game to characterize the interactive resourcemoney-exchange process. The mobility resource price is modeled as a two-part tariff in terms of the exchange quantities. In the second stage, we develop a duopoly competition game to characterize the pricing strategy of the two TSPs. The utility of end users is affected by TSP's prices and supply-demand congestion effects. By doing so, we can derive the conditions under which TSPs will adopt the above three strategies. Analytical results show that a "win-win"outcome can be achieved when the unit price decreases in the number of exchange mobility resources. We also find that regardless of how the service prices are set, as the units of exchange resources increase, the demand for resource sellers will decrease and the demand for resource buyers will increase. © 2023 IEEE.

Integrated sensing and communications (ISAC) has been considered as a key technology for future wireless networks. Orthogonal time frequency space (OTFS) modulation is considered as an effective technology to achieve ISAC in high-mobility scenarios due to its ability to combat high delay and Doppler shifts. However, although the performance analysis of OTFS-based ISAC system has been investigated, resource allocation and joint optimization for communication and sensing are still in its fancy. In this paper, we consider resource allocation in OTFS-based ISAC systems. We first introduce the system model and formulate the resource allocation problems. Then, several related works are discussed in detail. Next, an OTFS-based multiple-user multiple-input multiple-output (MIMO) ISAC system model is investigated, where the base station is considered as an ISAC transmitter and sends the message to different vehicles while simultaneously estimating their relative range and velocity via output feedback, respectively. We then formulate the sensing and communication trade-off optimization problem considering the delay Doppler resource block (DDRB) allocation and provide some sample results. Finally, we show several future directions for resource allocation in OTFS-based ISAC systems. © 2023 IEEE.

Climate change is rapidly altering weather patterns, resulting in shifts in climatic zones. The survival of trees in specific locations depends on their functional traits. Local populations exhibit trait adaptations that ensure their survival and accomplishment of growth and reproduction processes during the growing season. Studying these traits offers valuable insights into species responses to present and future environmental conditions, aiding the implementation of measures to ensure forest resilience and productivity. This study investigates the variability in functional traits among five black spruce ( Picea mariana (Mill.) B.S.P.) provenances originating from a latitudinal gradient along the boreal forest, and planted in a common garden in Quebec, Canada. We examined differences in bud phenology, growth performance, lifetime first reproduction, and the impact of a late-frost event on tree growth and phenological adjustments. The findings revealed that trees from northern sites exhibit earlier budbreak, lower growth increments, and reach reproductive maturity earlier than those from southern sites. Late-frost damage affected growth performance, but no phenological adjustment was observed in the successive year. Local adaptation in the functional traits may lead to maladaptation of black spruce under future climate conditions or serve as a potent evolutionary force promoting rapid adaptation under changing environmental conditions.

Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

One of the emerging computing paradigms, mobile edge computing (MEC, also known as fog computing), has been developed to reduce both energy consumption and computation latency for computation-extensive IoT applications. Further, thanks to advantages brought by non-orthogonal multiple access (NOMA) in increasing the capacity of multiple-access channels (MAC), and by service caching in alleviating the burden of responding to repeated computation requests, this paper considers the joint design of communication, computation, and caching for multi-user MEC systems. Aiming for minimizing the weighted-sum energy consumption of communication and computation, given a finite set of computation services, we jointly optimize the NOMA transmission, the computation resources, and the Boolean-variable modeled cache placement, subject to the computation and caching capacity of the edge server as well as the computation latency constraints. To solve the formulated mixed-integer non-convex problem, first, given the cache placement, we solve the non-differentiable convex problem by Lagrangian dual method leveraging a semi-closed form of NOMA transmission power, followed by a one-dimension search for the optimal common task offloading time. Next, an optimal branch-and-bound (BnB) based caching strategy is proposed. Meanwhile, we also provide a heuristic suboptimal cache placement design to reduce computational complexity. Finally, numerical results show the striking performance of the proposed joint optimization of NOMA-based task offloading and service caching compared to the greedy cache placement and other benchmarks without either NOMA-based task offloading or service caching. © 2023 IEEE.

Integrated sensing and communication (ISAC) is a promising technique to provide sensing services in future wireless networks. Numerous existing works have adopted a monostatic radar architecture to realize ISAC, i.e., employing the same base station (BS) to transmit the ISAC signal and receive the echo. Yet, the concurrent information transmission causes unavoidable self-interference (SI) to the radar echo at the BS. To overcome this difficulty, we propose a coordinated cellular network-supported multistatic radar architecture to implement ISAC, which allows us to spatially separate the ISAC signal transmission and radar echo reception, intrinsically circumventing the problem of SI. To this end, we jointly optimize the transmit and receive beamforming policy to minimize the sensing beam pattern mismatch error subject to ISAC quality-of-service requirements. The resulting non-convex optimization problem is tackled by an alternating optimization-based suboptimal algorithm. Simulation results showed that the proposed scheme outperforms the two baseline schemes adopting conventional designs. © 2023 IEEE.

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users' Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network's state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms. © 2014 IEEE.

With the proliferation of mobile devices (e.g., vehicles and smartphones), rich media content services from massive users lead to high network resource consumption and energy usage. How to effectively allocate heterogeneous network resources and achieve green content delivery is a major challenge to be addressed in urgency, especially when vehicular users are involved and the spatiotemporal distribution of the content requests can change drastically. In this paper, we propose a new deep reinforcement learning (DRL)-aided task offloading and resource allocation scheme, named TORA-DRL, to minimize power consumption in cloud-edge cooperation environments, where in-network caching and request aggregation are incorporated to reduce replicated transmissions of network contents. Based on the history of content requests and available network resources in the system, TORA-DRL jointly optimizes the decisions about task offloading, as well as computing, caching and communication resource allocation, adapting to the changes in network states and user requirements. Simulation results demonstrate that the proposed TORA-DRL solution converges fast and has much higher energy efficiency than the existing popular cloud-edge cooperation schemes under different network environments. IEEE

Recently, many exciting usage scenarios and groundbreaking technologies for sixth generation (6G) networks have drawn more and more attention. The revolution of 6G mainly lies in ubiquitous intelligence, which promotes the development of edge intelligence (EI) by running artificial intelligence (AI) algorithms at the network edge. By embedding training capabilities across the network nodes, federated learning (FL) can achieve high security and alleviate network traffic congestion, which provides a promising way to realize the ubiquitous EI. While traditional FL usually relies static terrestrial base stations (BSs) for the global model aggregation, unmanned aerial vehicles (UAVs) could effectively supplement the terrestrial BSs because of their high maneuverability, thereby building the air-ground integrated FL (AGIFL). Nevertheless, how to effectively deploy the UAV and allocate resources to boost the learning performance and achieve high energy efficiency in the AGIFL remains largely unexplored. In this paper, we study how to jointly optimize the UAV location and resource allocation to minimize the incurred cost in terms of two objectives: i) the minimization of terrestrial users' energy consumption; ii) the minimization of tradeoff between energy consumption and training latency. The formulated non-convex problems are efficiently solved by alternating optimization techniques based on successive convex approximation (SCA) approaches after appropriate problem decomposition. Extensive simulation results show that our proposed algorithms can reduce more cost than three benchmarks while guaranteeing the learning accuracy. Furthermore, we construct a real-world AGIFL system, implement the proposed algorithms in the system, and carry out field experiments to verify the superiority of our algorithms. © 2017 IEEE.

The objective of this work was to identify and propose integrated solutions for the main problems related to team management in an engineering project consulting organization with a matrix structure. A case study was developed in an organization that included document analysis and participatory observation of three projects developed with shared teams. In addition, a questionnaire was applied to part of the members of the engineering and projects department. The perception of those directly involved in project development on some aspects of team management was observed: functioning of the matrix structure, allocation of resources, management and functioning of teams, training and lessons learned. It was found that the organization has characteristics of the three types of matrix structures: weak, moderate and strong. Several team management problems in these structures were observed: conflict over resources, double subordination, low engagement, difficulties in allocation and team development. The main integrated solutions were related to leveling knowledge about the structure and its functioning, managing the engagement of those involved, increasing integration between project and functional teams, providing feedback and recording lessons learned in projects, in addition to indicating the server profile for members of the project management team., O objetivo deste artigo foi propor soluções integradas para os principais problemas relacionados à gestão de equipes em uma organização de consultoria de projetos de engenharia com estrutura matricial. Para tanto, foi desenvolvido um estudo de caso em uma organização que contou com análise documental e observação participativa de três projetos desenvolvidos com equipes compartilhadas. Além disso, foi aplicado um questionário em parte dos membros do departamento de engenharia e projetos. Observou-se a percepção dos envolvidos diretos no desenvolvimento de projetos sobre alguns aspectos da gestão de equipes: funcionamento da estrutura matricial, alocação de recursos, gestão e funcionamento de equipes, treinamento e lições aprendidas. Também foi constatado que a organização possui características dos três tipos de estruturas matriciais: fraca, moderada e forte. Diversos problemas de gestão de equipes nessas estruturas foram observados: conflito por recursos, dupla subordinação, baixo engajamento, dificuldades de alocação e de desenvolvimento de equipes. As principais soluções integradas relacionaram-se ao nivelamento de conhecimento sobre a estrutura e seu funcionamento, gestão do engajamento dos envolvidos, aumento da integração entre equipes de projeto e funcionais, realização de feedbacks e registro de lições aprendidas nos projetos, além da indicação de perfil servidor para os membros da equipe de gestão de projeto.

Purpose: As corporate sustainability is systemic, it cannot be achieved without effective involvement of suppliers. This study aims to examine the drivers of supplier companies’ resource allocation to a sustainability issue that affects customer companies and society at large. Design/methodology/approach: Supplier companies’ resource allocation for a sustainability issue is explained from variables at the levels of the institutional, supply chain and internal environments of a supplier company. The framework is tested with a moderated regression model on 102 supplier companies in animal-based supply chains, focussing on their resource allocation for farm animal welfare. Findings: The findings show that supply chain factors have the strongest influence on suppliers’ resource allocation, including a strong effect of investment specificity and a U-shaped effect of chain integration. Also, significant effects from institutional variables, namely, the pressure on consumer companies, and an inverted U-shaped effect of sustainability competition are found. The innovativeness, referring to the internal environment of supplier companies, appears as another important factor for the allocation of resources to animal welfare, as a sustainability issue. Research limitations/implications: The results have implications for consumer market companies to deal with sustainability issues that require involvement of their suppliers, for supplier companies to increase their competitive positions and strengthen their relationships within the supply chain, and for policymakers seeking solutions for sustainability issues in the market domain. Originality/value: While existing literature focusses mostly on the corporate sustainability of highly visible and large consumer companies, to the best of the authors’ knowledge, this study is the first to examine the drivers of supplier companies’ resource allocation for a sustainability issue, namely, animal welfare. It provides insights on what dri

Mathematical modeling is a promising tool for better understanding of cellular processes. In recent years, the development of coarse-grained models has gained attraction since these simple models are able to capture and describe a broad range of growth conditions. Coarse-grained models often comprise only two cellular components, a low molecular component as representative for central metabolism and energy generation and a macromolecular component, representing the entire proteome. A framework is presented that presents a strict mass conservative model for bacterial growth during a biotechnological production process. After providing interesting properties for the steady-state solution, applications are presented 1) for a production process of an amino acid and 2) production of a metabolite from central metabolism.

Agent-based scheduling refers to applying Multi-Agent System (MAS) to model the scheduling environment, where intelligent agents cooperate to determine the task scheduling, so as to achieve scheduling objectives autonomously. To date, agent-based scheduling approaches have been developed for multiple domains, such as cloud computing, manufacturing systems, and smart grids. However, with the applications of the Internet of Things (IoT) and advanced communication technologies, real-world scheduling approaches are challenging to achieve satisfactory performances due to four challenges, which are (1) the expansion of the scheduling scale, (2) the involvement of uncertainties during the scheduling, (3) multiple real-world constraints, and (4) system resilience and robustness.

Precision medicine can put clinicians in a position where they must act more as resource allocators than their traditional role as patient advocates. In the allocation of transplantable organs and tissues, the use of eplet matching will enhance precision medicine but, in doing so, generate a tension with the present reliance on rule of rescue and justice-based factors for allocations. Matching donor and recipient human leukocyte antigens (HLA) is shown to benefit virtually all types of solid organ transplants yet, until recently, HLA-matching has not been practical and was shown to contribute to ethnic/racial disparities in organ allocation. Recent advances using eplets from the HLA molecule has renewed the promise of such matching for predicting patient outcomes. The rule of rescue in organ allocation reflects a combination of ethical, policy, and legal imperatives. However, the rule of rescue can impede the allocation strategies adopted by professional medical associations and the optimal use of scarce transplant resources. While eplet-matching seeks to improve outcomes, it may potentially frustrate current ethics-motivated initiatives, established patient-practitioner relationships, and functional conventions in the allocation of medical resources such as organ and tissue transplants. Eplet-matching allocation schemes need to be carefully and collaboratively designed with clear, fair and equitable guidelines that complement functional conventions and maintain public trust., La médecine de précision peut placer les cliniciens dans une position où ils doivent agir davantage comme des répartiteurs de ressources que dans leur rôle traditionnel de défenseurs des patients. Dans l’attribution d’organes et de tissus transplantables, l’utilisation de l’appariement eplet renforcera la médecine de précision mais, ce faisant, créera une tension avec la dépendance actuelle au devoir d’assistance et des facteurs fondés sur la justice pour l’attribution des ressources. L’appariement des antigènes leucocytaires humains (HLA) du donneur et du receveur est bénéfique pour pratiquement tous les types de greffes d’organes solides. Pourtant, jusqu’à récemment, l’appariement HLA n’était pas pratique et il a été démontré qu’il contribuait aux disparités ethniques/raciales dans l’attribution des organes. Des avancées récentes utilisant des eplets de la molécule HLA ont renouvelé la promesse d’un tel appariement pour prédire les résultats pour les patients. Le devoir d’assistance dans l’attribution d’organes reflète une combinaison d’impératifs éthiques, politiques et juridiques. Cependant, le devoir d’assistance peut entraver les stratégies d’attribution adoptées par les associations médicales professionnelles et l’utilisation optimale des ressources limitées en matière de transplantation. Alors que l’appariement d’eplet cherche à améliorer les résultats, il peut potentiellement contrecarrer les initiatives actuelles motivées par l’éthique, les relations établies entre patients et praticiens et les conventions fonctionnelles dans l’attribution des ressources médicales telles que les transplantations d’organes et de tissus. Les systèmes d’attribution de l’eplet doivent être conçus avec soin et en collaboration, avec des lignes directrices claires, justes et équitables qui complètent les conventions fonctionnelles et maintiennent la confiance du public.

The allocation of critical care resources and triaging patients garnered a great deal of attention during the COVID-19 pandemic, but there is a paucity of guidance regarding the ethical aspects of resource allocation and patient prioritization in ‘normal’ circumstances for Canadian healthcare systems. One context where allocation and prioritization decisions are required are surgical waitlists, which have been globally exacerbated due to the COVID-19 pandemic. In this paper, we detail the process used to develop an ethics framework to support prioritization for elective surgery at The Hospital for Sick Children, Toronto, a tertiary pediatric hospital. Our goal was to provide guidance for the more value-laden aspects of prioritization, particularly when clinical urgency alone is insufficient to dictate priority. With this goal in mind, we worked to capture familial, relational, and equity considerations. As part of our institution’s concerted efforts to ethically and effectively address our surgical backlog, an ethics working group was formed comprising clinicians from surgery, anesthesiology, intensive care, a hospital bioethicist, a parent advisor, and an academic bioethics researcher. A reflective equilibrium process was used to develop an ethics framework. To this end, the same methodology was used to create a support for patient prioritization that identifies clinically and morally relevant factors for prioritization among medically similar surgical cases, with a substantive goal being to identify and redress health inequities in surgical prioritization, inasmuch as this is possible. While further steps are needed to validate several aspects of the framework, our research suggests that an ethics framework grounded in the practical realities of hospital operations provides consistency, transparency, and needed support for decisions that are often left to individual clinicians, as well as an opportunity to reflect upon the presence of health inequities in all doma, L’allocation des ressources en soins intensifs et le triage des patients ont fait l’objet d’une grande attention pendant la pandémie de COVID-19, mais il y a peu de conseils concernant les aspects éthiques de l’allocation des ressources et de la priorisation des patients dans des circonstances "normales" pour les systèmes de soins de santé canadiens. Les listes d’attente chirurgicales, qui ont été globalement exacerbées par la pandémie de COVID-19, sont l’un des contextes dans lesquels des décisions d’allocation et de priorisation sont nécessaires. Dans cet article, nous détaillons le processus utilisé pour développer un cadre éthique afin de soutenir la priorisation des opérations chirurgicales non urgentes à l’Hôpital pour enfants malades de Toronto, un hôpital pédiatrique tertiaire. Notre objectif était de fournir des conseils pour les aspects les plus valorisants de l’établissement des priorités, en particulier lorsque l’urgence clinique n’est pas suffisante pour dicter la priorité à elle seule. Dans cette optique, nous nous sommes efforcés de prendre en compte les aspects familiaux, relationnels et d’équité. Dans le cadre des efforts concertés de notre institution pour traiter de manière éthique et efficace notre retard en matière de chirurgie, un groupe de travail sur l’éthique a été formé, composé de cliniciens de la chirurgie, de l’anesthésie, des soins intensifs, d’un bioéthicien de l’hôpital, d’un conseiller parental et d’un chercheur en bioéthique de l’université. Un processus d’équilibre réflexif a été utilisé pour développer un cadre éthique. À cette fin, la même méthodologie a été utilisée pour créer un support pour la priorisation des patients qui identifie les facteurs cliniquement et moralement pertinents pour la priorisation parmi les cas chirurgicaux médicalement similaires, avec un objectif substantiel étant d’identifier et de corriger les inégalités en matière de santé dans la priorisation chirurgicale, dans la mesure où cela est possible. Bien

Machine learning (ML) has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training data in communication networks, especially in physical and MAC layer, are extremely limited; 2). The high-dynamic wireless environment and fast changing transmission schemes in communication networks make offline training impractical; 3). ML tools are treated as black boxes, which lack of explainability. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, where real-time online learning-based symbol detection algorithms are designed for MIMO-OFDM and massive MIMO-OFDM systems by utilizing reservoir computing, extreme learning machine, multi-mode reservoir computing, and StructNet; 2) Channel estimation, where residual learning-based offline method is introduced for WiFi-OFDM systems, and a StructNet-based online method is devised for MIMO-OFDM systems; 3) Radio resource management, where reinforcement learning-based schemes are designed for dynamic spectrum access, as well as ORAN intelligent network slicing management. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks.

Addressing fairness among users and risk mitigation in the context of resource allocation in transportation systems under uncertainties poses a crucial challenge yet to be satisfactorily resolved. This dissertation attempts to address this challenge, focusing on achieving a balance between system-wide efficiency and individual fairness in stochastic transportation resource allocation problems. To study complicated fair and risk-averse resource allocation problems - from public transit to urban air mobility and multi-stage infrastructure maintenance - we develop three models: DrFRAM, FairUAM, and FCMDP. Each of these models, despite being proven NP-hard even in a simplistic case, inspires us to develop efficient solution algorithms. We derive mixed-integer linear programming (MILP) formulations for these models, leveraging the unique properties of each model and linearizing non-linear terms. Additionally, we strengthen these models with valid inequalities. To efficiently solve these models, we design exact algorithms and approximation algorithms capable of obtaining near-optimal solutions. We numerically validate the effectiveness of our proposed models and demonstrate their capability to be applied to real-world case studies to adeptly address the uncertainties and risks arising from transportation systems. This dissertation provides a foundational platform for future inquiries of risk-averse resource allocation strategies under uncertainties for more efficient, equitable, and resilient decision-making. Our adaptable framework can address a variety of transportation-related challenges and can be extended beyond the transportation domain to tackle resource allocation problems in a broader setting.

The yeast Saccharomyces cerevisiae is a widely-used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimizing biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates, and standardized sample preparation procedures ensured reproducibility and accuracy. In addition, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimization of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared with the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step toward better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engine, BT/Industriele Microbiologie, BT/Environmental Biotechnology

Climate change poses a significant global challenge and the aviation industry, a major contributor to greenhouse gas emissions, must address its environmental impact. As the demand for air travel continues to rise, transitioning to sustainable forms of aviation becomes crucial. This paper focuses on the challenges and opportunities associated with implementing full electric aircraft at regional airports, using Rotterdam the Hague Airport (RTHA) asa case study. The research aims to determine the optimal infrastructure sizing of RTHA and provide insights into the integration of electric aviation. A novel mixed integer linear programming (MILP) model is proposed to optimize the charging schedule and determine the required number of charging stations and electricity grid capacity. The study considers different scenarios of electric aviation uptake, aircraft types, charger types, and spatial aspects of charging locations. The findings offer comprehensive recommendations for regional airport infrastructure planning and address uncertainties through sensitivity analysis.

Western Carolina University (WCU) enrolls students from many different backgrounds and demographic categories. While access to the institution has expanded over the years, equity gaps still exist relative to retention and graduation outcomes for students in different demographic groups. Several offices at the University offer programing and support for specific marginalized student populations. These offices are generally small, and do not have the data access or necessary expertise to effectively utilize data in their operations. The concept of this initiative is that success for marginalized student populations at WCU is tied to the success of these offices that support them. The success of these supporting offices is then tied to their ability to access and use data to seek funding, plan programming and offerings, and communicate with a wide variety of stakeholders (such as administrators, current and prospective students, and alumni). The initiative centers on the development of quantitative and qualitative reports and tools that utilize close-to-practice data, and then training/consultation about how stakeholders can use these data for funding requests, departmental planning, and stakeholder communication. The work took place with Intercultural Affairs and focused on their intensive program offering, Project C.A.R.E. (an acronym for Culturally Aligned Retention Enhancement). This program seeks to provide a welcoming transition and support community for new students, particularly African American and Latinx students, as they enter WCU, a predominantly white institution (PWI). Improvement Science was used as a framework for the project, incorporating techniques for problem identification, causal analysis, development and selection of change idea, and iterative and collaborative execution of these intervention steps, with pre-defined formative and summative data collection and assessment activities throughout the project timeline. Post-initiative participant inter

Non-governmental organizations (NGOs) comprise the backbone of civilization. Whether intended or not, governments fail to improve social situations, financial standings, and spirituality comprehensively. NGOs attempt to cover these areas. From Angola to Zambia, countless NGOs exist around the world; however, many fail due to a misplaced focus internally on the annual donations received rather than externally on the disenfranchised. Additionally, even if NGOs manage to stay afloat, they will need help procuring funds and then distributing those resources with the most effective impact for the impoverished. The honors thesis proposed will attempt to determine more effective impact measurements for NGOs, examining the context of varying financial obtainment strategies, resource allocation decisions, qualitative and quantitative evaluation mechanisms, and critical accountability metrics.

Here I theorize about a common challenge that research on technology and organizing has not yet considered: how organizations preserve results given the challenges of managing increasingly heterogeneous technological portfolios. I do so via a study of how a top-tier hospital allocated scarce resources across two surgical robots. After acquiring its second robot, the hospital divided resources between the older and newer robots to build its surgical capacity: it allocated the best available infrastructure to the new robot, and it prioritized assigning inexperienced talent to the new technology to facilitate use and skill development. The hospital then adjusted its resources to build on initial successes, committing both the best available maintenance and more-complex surgical cases to the newer robot. These dynamics inadvertently degraded the older robot, making it increasingly difficult to use. In response, more-experienced surgeons and staff made do with the degrading system: they developed and mastered workarounds, and they developed a venting cycle with management. Their actions reduced concerns about the older technology and stabilized the situation for the hospital, such that for years this portfolio resourcing process facilitated satisfactory outcomes on organizational goals such as growth, new capability, and patient care. But by shunting scarce resources away from the older technology, this process also stressed the experienced talent (even as it built their resilience) and limited exploration of changes that could benefit the hospital.

The COVID-19 pandemic demonstrated the potential for healthcare systems to lack capacity to meet demand for critical care in times of crisis. Recent research suggests a rise in the utilization of intensive care unit (ICU) resources and the potential misallocation of resources to patients without clinical need or hope of benefit. Several authorities have published resource allocation policies to guide healthcare systems, commonly relying on measures of illness severity to determine the priority by which ICU resources would be allocated. This raises concerns about the properties of and the potential for allocation criteria to exacerbate racial disparities in clinical outcomes. Policymakers intend that such policies would maximize the number of lives saved by prioritizing provision of critical care services to patients most likely to benefit. This dissertation examines the interrater reliability of the University of California’s Scarce Resource Allocation Policy (SRAP) in determining the allocation priority of a cohort of consecutively admitted ICU patients at the University of California, Los Angeles (UCLA) Health System. Use of the SRAP had relatively poor reliability in determining allocation priority as laid out within the policy itself. A microsimulation model examined the likely impact of allocation decisions and likelihood of allocation of resources under four defined scenarios of resource constraint, for the outcome of ICU mortality. Mortality differed significantly across tested constraint levels compared to the case of no constraint (i.e., current capacity). Mortality was greater in subgroups with lower priority for constrained ICU resources. A mediation analysis examined if observed differences in mortality risk among racial groups are related to the use of SOFA scores or the selected comorbid conditions. Results suggest that these policy criteria do not mediate the effect of race on mortality. Understanding the projected outcomes related to use of these pol

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results., Part of ISBN 9781665477161QC 20230821

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs' channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs' channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs' decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms., QC 20230824

We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. We develop a game theoretic model of the interaction between rate adaptive applications and a load balancing operator under a function-oriented pay-as-you-go pricing model. We show that under perfect information, the strategic interaction between the applications can be formulated as a generalized Nash equilibrium problem, and use variational inequality theory to prove that the game admits an equilibrium. For the case of imperfect information, we propose an online learning algorithm for applications to maximize their utility through rate adaptation and resource reservation. We show that the proposed algorithm can converge to equilibria and achieves zero regret asymptotically, and our simulation results show that the algorithm achieves good system performance at equilibrium, ensures fast convergence, and enables applications to meet their latency constraints., QC 20230523

The ever-expanding volume of data generated by network devices such as smartphones, personal computers, and sensors has significantly contributed to the remarkable advancements in artificial intelligence (AI) and machine learning (ML) algorithms. However, effectively processing and learning from this extensive data usually requires substantial computational capabilities centralized in a server. Moreover, concerns regarding data privacy arise when collecting training data from distributed network devices. To address these challenges, collaborative ML with decentralized data has emerged as a promising solution for large-scale machine learning across distributed devices, driven by the parallel computing and learning trends. Collaborative and distributed ML can be broadly classified into two types: server-based and fully decentralized, based on whether the model aggregation is coordinated by a parameter server or performed in a decentralized manner through peer-to-peer communication. In cases where communication between devices occurs over wireless links, which are inherently imperfect, unreliable, and resource-constrained, how can we design communication protocols to achieve the best learning performance? This thesis investigates decentralized learning using decentralized stochastic gradient descent, an established algorithm for decentralized ML, in a novel setting with imperfect and constrained communication. "Imperfect" implies that communication can fail and "constrained" implies that communication resources are limited. The communication across a link between two devices is modeled as a binary event with either success or failure, depending on if multiple neighbouring devices are transmitting information. To compensate for communication failures, every communication round can have multiple communication slots, which are limited and must be carefully allocated over the learning process. The quality of communication is quantified by introducing normalized throughput

As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability) and edge (energy efficient, low latencies). Despite its promises, the continuum has only been studied in silos of various computing models, thus lacking strong end-to-end theoretical and engineering foundations for computing and resource management across the continuum. Consequently, devel-opers resort to ad hoc approaches to reason about performance and resource utilization of workloads in the continuum. In this work, we conduct a first-of-its-kind systematic study of various computing models, identify salient properties, and make a case to unify them under a compute continuum reference architecture. This architecture provides an end-to-end analysis framework for developers to reason about resource management, workload distribution, and performance analysis. We demonstrate the utility of the reference architecture by analyzing two popular continuum workloads, deep learning and industrial IoT. We have developed an accompanying deployment and benchmarking framework and first-order analytical model for quantitative reasoning of continuum workloads. The framework is open-sourced and available at https://github.com/atlarge-research/continuum.

The ever-expanding volume of data generated by network devices such as smartphones, personal computers, and sensors has significantly contributed to the remarkable advancements in artificial intelligence (AI) and machine learning (ML) algorithms. However, effectively processing and learning from this extensive data usually requires substantial computational capabilities centralized in a server. Moreover, concerns regarding data privacy arise when collecting training data from distributed network devices. To address these challenges, collaborative ML with decentralized data has emerged as a promising solution for large-scale machine learning across distributed devices, driven by the parallel computing and learning trends. Collaborative and distributed ML can be broadly classified into two types: server-based and fully decentralized, based on whether the model aggregation is coordinated by a parameter server or performed in a decentralized manner through peer-to-peer communication. In cases where communication between devices occurs over wireless links, which are inherently imperfect, unreliable, and resource-constrained, how can we design communication protocols to achieve the best learning performance? This thesis investigates decentralized learning using decentralized stochastic gradient descent, an established algorithm for decentralized ML, in a novel setting with imperfect and constrained communication. "Imperfect" implies that communication can fail and "constrained" implies that communication resources are limited. The communication across a link between two devices is modeled as a binary event with either success or failure, depending on if multiple neighbouring devices are transmitting information. To compensate for communication failures, every communication round can have multiple communication slots, which are limited and must be carefully allocated over the learning process. The quality of communication is quantified by introducing normalized throughput

When dealing with latency-critical applications in Kubernetes, a common strategy is to over-allocate resources to ensure the application can meet its latency guarantees during traffic surges. However, this practice often leads to resource underutilizationas the application will not fully utilize its reserved resources. The Kubernetes scheduler cannot initiate new workloads on the node because of perceived full resource utilization. This study explored the utility of two existing methods, Container Runtime Interface (CRI-RM), which we configured to use the 'balloon policy' and the vertical Pod Autoscaler (VPA) in addressing resource underutilization problems on single node Kubernetes clusters while maintaining latency-grantees of certain pods. Utilizing tc-sim, a network traffic simulator, we deployed four latency-critical and two non-latency-critical pods, all subjected to overallocation. Our finding reveals that VPA was ineffectivein detecting and addressing the underutilization of resources because of its slow response in adjusting requests inside the pods. Moreover, it worsened the underutilization issues of the node. Our configuration of the 'balloon policy' failed to detect theover-allocation issues and further led to performance degradation in the simulator, potentially due to the overhead introduced by CRI-RM. These results underscore the intricacy of over-allocation challenges in latency-critical applications, emphasizing the need for proposed-designed solutions that enable quick and dynamic exchange of resources between pods.

Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

In nectivorous pollinators, timing and pattern of allocation of consumed nectar affects fitness traits and foraging behavior. Differences in male and female behaviors can influence these allocation strategies. These physiological patterns are not well studied in Lepidoptera, despite the fact that they are important pollinators. In this study we investigate crop-emptying rate and nectar allocation in Manduca sexta (Sphingidae), and how sex and activity influence these physiological patterns. After a single feeding event, moths were dissected at fixed time intervals to measure crop volume and analyze sugar allocation to flight muscle and fat body. Then we compared sedentary and flown moths to test how activity may alter these patterns. Sedentary males and females emptied their crops six hours after a feeding event. Both males and females preferentially allocated these consumed sugars to fat body over flight muscle. Moths began to allocate to the fat body during crop-emptying and retained these nutrients long-term (four and a half days after a feeding event). Males allocated consumed sugar to flight muscles sooner and retained these allocated nutrients in the flight muscle longer than females did. Flight instigated increased crop-emptying in females, but had no effect on males. Flight did not significantly affect allocation to flight muscle or fat body in either sex. This study showed that there are inherent differences in male and female nectar sugar allocation strategies, but that male and female differences in crop-emptying rate are context dependent. These differences in physiology may be linked to distinct ways males and females maximize their own fitness.

Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloud-network integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B&B-CCRA and WF-CCRA, based on the Branch & Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B&CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.

Energy Efficiency (EE) has become an essential metric in Device-to-Device (D2D) communication underlaying Unmanned Aerial Vehicles (UAVs) Among the several technologies that provide significant energy, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve EE. However, it is a challenging task to study the EE under nonlinear energy harvesting (EH) due to the limited sensitivity and the composition of the nonlinear circuit. Moreover, when D2D users transmit information using the EH from UAVs, interferences to cellular users occur and deteriorate the throughput. To tackle these problems, we leverage concepts from artificial intelligence (AI) to optimize EE of UAV-assisted D2D communication. Specifically, multi-agent deep reinforcement learning was proposed to jointly maximize throughput and EE, where the reward function is defined in terms of the introduced goal. Simulation results verify the supremacy of proposed approach over traditional algorithms.

Crop breeding has been successful in increasing crop grain yield (GY; reproductive biomass) largely through reduced vegetative size, increased reproductive effort (RE = reproductive biomass/total biomass) and increased water-use efficiency (WUE) in grain production. Flowering time is an important life history trait that signifies the switch from vegetative to reproductive growth. The relationship between GY and time from sowing to flowering (Tsf) is unclear. We fit the relationships between GY and RE vs. Tsf to the logistic model using data from 18 spring wheat genotypes grown under simulated rainfed conditions. Tsf accounted for water use before and after flowering, root length density, total leaf area, and the time from flowering to harvest. Early flowering meant decreased water use before flowering and increased water use afterward. Soil water remaining at harvest was positively correlated with yield. Early flowering genotypes have a higher WUE of grain production, but there was no significant difference in the WUE of total biomass production. The relationship between grain yield and Tsf is described as a unimodal curve, as is the relationship between RE and Tsf. Higher yields and a higher RE have been achieved through earlier flowering, and both RE and Tsf reached their optimal values for maximizing GY. Crop breeding is unlikely to achieve further increases in GY through this route in the future. The results suggest that breeding does not improve biomass’s water-use efficiency, but causes changes in biomass allocation strategy, and this could be a new direction for genetically improving grain yield.

Purpose: Business case (BC) analyses are performed in many different business fields, to create a report on the feasibility and competitive advantage of an intervention within an existing organisation to secure commitment from management to invest. However, most BC research papers on decisions regarding internal funding are either based on anecdotal insights, on analyses of standards from practice, or focused on very specific BC calculations for a certain project, investment or field. A clear BC process method is missing. Design/methodology/approach: This paper aims to describe the results of a systematic literature review of 52 BC papers that report on further conceptualisation of what a BC process should behold. Findings: Synthesis of the findings has led to a BC definition and composition of a 20 step BC process method. In addition, 29 relevant theories are identified to tackle the main challenges of BC analyses in future studies to make them more effective. This supports further theoretical development of academic BC research and provides a tool for BC processes in practice. Originality/value: Although there is substantial scientific research on BCs, there was not much theoretical development nor a general stepwise method to perform the most optimal BC analysis.

224 p., ill., 1 CHAPTER 1 – STUDY CONTEXT .13 -- 1.1 THE EVOLUTION OF PALLIATIVE CARE IN BELGIUM 13 -- 1.2 THE NEED FOR A CATEGORISATION TOOL 15 -- 1.3 OBJECTIVES AND RESEARCH QUESTIONS 17 -- 1.4 METHODS 17 -- 2 CHAPTER 2 - BELGIAN LANDSCAPE .18 -- 2.1 DEFINITIONS 18 -- 2.2 SEVERAL COMPETENT AUTHORITIES 19 -- 2.3 OVERVIEW OF PALLIATIVE PATIENTS FINANCIAL BENEFITS 22 -- 2.3.1 In-hospital palliative care units 24 -- 2.3.2 Palliative care for patients at home .26 -- 2.3.3 Palliative care for patients in nursing homes or other residential facilities .35 -- 2.3.4 Day centres for palliative care and other facilities 35 -- 2.3.5 Role of the private funding 35 -- 2.3.6 Support for informal carers: palliative leave 36 -- 2.3.7 The specific situation of paediatric palliative patients 36 -- 2.4 KEY MESSAGES – ORGANISATION OF PALLIATIVE CARE IN BELGIUM .36 -- 3 CHAPTER 3 - CURRENT PALLIATIVE CARE TOOLS DEVELOPED IN BELGIUM 37 -- 3.1 PALLIATIVE CARE INDICATOR TOOL .37 -- 3.1.1 PICT-1 38 -- 3.1.2 PICT 2 42 -- 3.2 BELRAI PALLIATIVE CARE .45 -- 3.2.1 The InterRAI suite of instruments as assessment instrument 45 -- 3.2.2 The implementation of the InterRAI suite of instruments in Belgium (BelRAI) .48 -- 3.2.3 The BelRAI palliative care 49 -- 3.3 KEY MESSAGES 51 -- 4 CHAPTER 4 - OVERVIEW OF ASSESSMENT TOOLS TO DETERMINE THE LEVEL OF PALLIATIVE CARE INTENSITY AND COMPLEXITY OF PATIENT NEEDS 51 -- 4.1 INTRODUCTION .51 -- 4.1.1 Background .51 -- 4.1.2 Aim 52 -- 4.2 METHODS 52 -- 4.3 RESULTS 54 -- 4.3.1 Palliative care needs 54 -- 4.3.2 Overview of assessment tools 61 -- 4.4 COMPARISON OF ASSESSED NEEDS DOMAINS AND SCORING SYSTEMS OF THE IDENTIFIED TOOLS .81 -- 4.4.1 Needs domains assessed in the instruments 81 -- 4.4.2 Scoring system of the instruments 83 -- 4.5 PSYCHOMETRIC PROPERTIES OF TOOLS .84 -- 4.5.1 Psychometric properties of identified tools .84 -- 4.5.2 Validity and reliability of assessment tools included in palliative care complexity assessment tools .84 -- 4.6 DISCUSSION 85 -- 5 CHAPTER 5