Discrete Resource Research Articles

A theory building critical realist evaluation of an integrated cognitive-behavioural fluency enhancing stuttering treatment for school-age children. Part 1: Development of a preliminary program theory from expert speech-language pathologist data.

PurposeThis study initiated a program of research that aims to develop a program theory underlying integrated cognitive-behavioural fluency enhancing stuttering treatments for school-age children. This research asks, what in the treatment program works (or does not work), for whom, in what contexts, and why. MethodsUsing a critical realist evaluation approach, seven speech-language pathologists (SLPs) with extensive experience in treating children who stutter were asked about barriers and facilitators of optimal treatment outcomes within the context of the Comprehensive Stuttering Program - School-aged Children (CSP-SC). From these data discrete resource mechanisms, contexts, within child reasoning mechanisms, and outcomes were derived and a preliminary program theory was proposed. ResultsFacilitating and impeding child physiology, treatment and SLP resource mechanisms, family and school contexts, and within-child mechanisms were identified. Facilitating mechanisms included motivation, personality/psychological characteristics, understanding and trust of the treatment process, experience of speaking with less effort, and self-efficacy. Impeding mechanisms included reduced motivation, impeding personality/psychological characteristics, lack of buy-in, and, for some children, a prohibitive cost of effort in using learned strategies. ConclusionA preliminary program theory was hypothesized which will be further developed in future analysis of data obtained from children and parents who participated in the CSP-SC at the same centre from which the SLPs came. Subsequent research with new cohorts of SLPs, children, and parents from other treatment programs and centres will be needed to establish the generalizability of the program theory generated in this program of research

Milkweed and floral resource availability for monarch butterflies (Danaus plexippus) in the United States

The global decline of pollinators, particularly insects, underscores the importance of enhanced monitoring of their populations and habitats. However, monitoring some pollinator habitat is challenging due to widespread species distributions and shifts in habitat requirements through seasons and life stages. The monarch butterfly (Danaus plexippus), a migratory insect pollinator that breeds widely throughout North America, presents a unique case study for testing a sampling framework to overcome these challenges. Monarchs exhibit discrete resource needs across life stages (e.g., larval requirement for milkweed, adult requirement for floral nectar), utilizing many land use types across their extensive geographic range during breeding and migration seasons. The Integrated Monarch Monitoring Program (IMMP) uses a standardized protocol with a generalized random tessellation stratified (GRTS) sampling design to gather spatially balanced and ecologically representative information on monarch habitats within the United States. The IMMP is applicable to various land use types and habitats used by breeding monarchs and may be extended to sites outside of the GRTS design to collect data on non-random sites of interest, such as legacy or conservation sites. Additionally, the IMMP’s modular design and publicly available training allows for broad participation, including involvement from community scientists. Here, we summarize habitat metrics (milkweed and floral resources) across 1,233 sites covering much of the monarch’s breeding range. We examine variation in milkweed density and floral resource availability on probabilistic (random) and non-probabilistic (convenience) samples and among land use types (site types). Additionally, we assess resource availability within core geographies for monarch breeding and migration, specifically within the U.S. Fish and Wildlife Service’s Monarch Conservation Units (western, northern, and southern United States). Milkweed density, floral frequency, and floral richness were higher on non-random sites and in the North region. Among site types, milkweed density was highest on Rights-of-Way and Unclassified Grassland, while floral frequency was lowest on Rights-of-Way. The IMMP represents the first field-based habitat monitoring program of this scale for monarchs, yielding a robust dataset on monarchs and their habitats across their breeding range and offering a framework for surveying the habitat of insect species with diverse habitat requirements or widespread distributions.

When Optimization Meets AI: An Intelligent Approach for Network Disintegration with Discrete Resource Allocation

Network disintegration is a fundamental issue in the field of complex networks, with its core in identifying critical nodes or sets and removing them to weaken network functionality. The research on this problem has significant strategic value and has increasingly attracted attention, including in controlling the spread of diseases and dismantling terrorist organizations. In this paper, we focus on the problem of network disintegration with discrete entity resources from the attack view, that is, optimizing resource allocation to maximize the effect of network disintegration. Specifically, we model the network disintegration problem with limited entity resources as a nonlinear optimization problem and prove its NP-hardness. Then, we design a method based on deep reinforcement learning (DRL), Net-Cracker, which transforms the two-stage entity resource and network node selection task into a single-stage object selection problem. Extensive experiments demonstrate that compared with the benchmark algorithm, Net-Cracker can improve the solution quality by about 8∼62%, while enabling a 30-to-160-fold speed up. Net-Cracker also exhibits strong generalization ability and can find better results in a near real-time manner even when the network scale is much larger than that in training data.

Network power and mental health policy in post-war Liberia.

This article traces the influence of network power on mental health policy in Liberia, a low-income, post-conflict West African country. Based on key informant interviews, focus group discussions and document analysis, the work uses an inductive approach to uncover how a network of civil society groups, government officials, diasporans and international NGOs shaped the passage, implementation and revision of the country's 2009 and 2016 mental health policies. With relations rooted in ties of information, expertise, resources, commitment and personal connections, the network coalesced around a key agent, the Carter Center, which connected members and guided initiatives. Network power was evident when these actors channelled expertise, shared narratives of post-war trauma and mental health as a human right, and financial resources to influence policy. Feedback loops appeared as policy implementation created new associations of mental health clinicians and service users, research entities and training institutes. These beneficiaries offered the network information from lived experiences, while also pressing their own interests in subsequent policy revisions. As the network expanded over time, some network members gained greater autonomy from the key agent. Network power outcomes included the creation of government mental health institutions, workforce development, increased public awareness, civil society mobilization and a line for mental health in the government budget, though concerns about network overstretch and key agent commitment emerged over time. The Liberian case illustrates how networks need not be inimical to development, and how network power may facilitate action on stigmatized, unpopular issues in contexts with low state capacity. A focus on network power in health shows how power can operate not only through discrete resources such as funding but also through the totality of assets that network linkages make possible.

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

This paper addresses a novel multi-skill resource-constrained project scheduling problem with flexible resource profiles (F-MSRCPSP), in which the resource allocation of each activity consists of a certain number of discrete resources and is allowed to be adjusted over its duration. The F-MSRCPSP aims, therefore, to determine the flexible resource profile of each activity to minimize the make-span and total cost simultaneously. Then, a hybrid multi-objective fruit fly optimization algorithm is proposed to handle the concerned problem. In the proposed algorithm, two flexible parallel and serial schedule generation schemes are introduced, aiming to schedule activities and adjust allocated resource combinations. Additionally, two heuristic strategies are proposed to effectively select suitable resource combinations for activities. Moreover, a series of operators has been developed, including the rejoining operator, empirical re-arrangement operator, and empirical re-selection operator. These operators aim to accelerate the convergence speed and enhance the exploration of the proposed algorithm. Finally, the orthogonal test is used to select the optimal parameter combination, and comparative experiments based on tests with different scales are conducted, along with a t-test. The experimental results demonstrate that MOFOA-HS is effective in solving the F-MSRCPSP.

Providing scalable single‐operating‐system NUMA abstraction of physically discrete resources

AbstractWith an explosive increase of data produced annually, researchers have been attempting to develop solutions for systems that can effectively handle large amounts of data. Single‐operating‐system (OS) non‐uniform memory access (NUMA) abstraction technology is an important technology that ensures the compatibility of single‐node programming interfaces across multiple nodes owing to its higher cost efficiency compared with scale‐up systems. However, existing technologies have not been successful in optimizing user performance. In this paper, we introduce a single‐OS NUMA abstraction technology that ensures full compatibility with the existing OS while improving the performance at both hypervisor and guest levels. Benchmark results show that the proposed technique can improve performance by up to 4.74 on average in terms of execution time compared with the existing state‐of‐the‐art open‐source technology.

Group Incentive-Compatible Allocation of Discrete Resources When Ownership is Partitioned

Group Incentive-Compatible Allocation of Discrete Resources When Ownership is Partitioned

Coaxing corporations: Enriching the conceptualization of governments as strategic actors

AbstractResearch SummaryLittle is known about how governments secure discrete resources from global corporations over which they have limited direct control. Utilizing declassified archival sources, we examine how the UK government influenced Moody's and Standard & Poor's to provide the highest possible credit ratings in 1978, despite the UK receiving an International Monetary Fund bailout 2 years earlier. We develop a process model to explain how democratic government officials employ distinctive processes to enable and facilitate a nonmarket approach of corporate coaxing to influence corporations' decision making. We thereby enrich the concept of governments as a strategic actors by illuminating how officials act to secure resources when in a position of dependence.Managerial SummaryWe sought to understand how governments attempt to influence corporations' decision making when they have limited direct control over these corporations. We examined the historical case of the UK government seeking to influence Moody's and Standard and Poor's. In this case, we identified the distinctive strategy of corporate coaxing to explain how government officials navigate the distinctive constraints, and leverage the unique strengths, of their democratic state, to exert influence on private and global corporations. Our findings show how governments can be more active stakeholders in corporate activity than commonly assumed, as their subtle influence can extend beyond state policies or regulations.

Resource interaction in smallholder farms is linked to farm sustainability: evidence from Indian Sundarbans

Efficient resource utilization in small-scale farms is crucial to achieving farm sustainability through endogenous mechanisms. However, the precise mechanisms to integrate farm resources to achieve farm sustainability are not very clear yet. By capturing the interaction among farm resources as a network phenomenon, we aimed to identify the discrete resource interactions (RIs) associated with higher farm sustainability in different farm types of Indian Sundarbans. First, we assessed the sustainability of 140 integrated farms using a synthesized assessment framework. Then, we considered four network motifs, namely linkage (a one-way link between two resources), reciprocal linkage (a two-way link between two resources), triad (three resources having closed interconnectedness), and the presence of a farm resource at the core of a network. Using RI network data of 140 farms and employing a graph theoretic approach we identified discrete network motifs (i.e., resource interaction) associated with highly sustainable farms in different farm types. We found a predominance of rice, vegetables and pond-based integration and identified 32 linkages, 11 reciprocal linkages, 21 triads, and three resources at the network core that occurred and co-occurred on highly sustainable farms, and thus critical to achieving farm sustainability. Further, multivariate analyses established that the properties of RI networks could explain farm sustainability significantly. We anticipate that sustainability in small-scale farms can be achieved by strategically designing new RIs on the farm. However, there may be limitations to such achievement depending on the nature of RI and the type of farm.

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

Modern power systems necessitate active management of distribution networks by Distribution System Operators (DSOs). A DSO can harness the flexibility capabilities of the nodes towards ensuring the safe operation of the distribution system, while optimizing a certain system objective. The objective-optimizing dispatch can be obtained by solving the Optimal Power Flow (OPF) problem. However, selfish nodes may deviate from the dispatch instruction by calculating a profitable deviation. Thus, an implementable solution needs to be not only optimal, but also an equilibrium, i.e., a point from which no node is willing to deviate. In this paper, we propose the adoption of correlated equilibrium as a solution concept that is generally more efficient than Nash and more relevant to this setting where the DSO acts as a coordinating entity. We formulate the problem of finding an efficient correlated equilibrium for distribution networks with discrete resources. The problem’s complexity is managed by exploiting the graphical structure and sparse connectivity of distribution networks, drawing on the methodology of graphical games. Simulations showcase that the proposed approach achieves an equilibrium with near-optimal efficiency, in contrast to the standard OPF approach which is shown to be unstable when each node selfishly optimizes its own objective.

Partial recall: Implications for the discrete slot limit of working memory capacity.

The nature of working memory capacity (WMC) has been a highly contested topic among cognitive scientists. Some advocate for the discrete nature of this construct, fixed to a set number of independent slots, each capable of storing a single chunk of bound information. Others advocate for a continuous limit, guided by a pool of immediately available resources spent across the to-be-remembered items. To understand the nature of WMC, it was first essential to separate capacity from other factors, such as performance consistency, which may impact overall WM performance. Recent work by Schor et al., (2020, Psychonomic Bulletin & Review, 27[5], 1006-1013) has provided a method for separating these constructs within a single visual array task. The present study used this statistical model to extract partial information, defined as accurate recall of a correct color, but not location, at a rate greater than expected through guessing. The successful memory of this information would demonstrate that capacity does not rely on the existence of empty slots, which discrete slot model advocates argue, are necessary for successful storage and recall of items. The present study found that participants were able to successfully recall partial information at a rate significantly greater than chance, but not beyond the individual working memory capacity limit. These findings help provide additional support for the discrete resource slot model, while simultaneously casting doubt on its strong object slot model alternative.

Optimal discrete resource allocation on metapopulation networks for suppressing spatial spread of epidemic

Suppressing an epidemic among regions within limited medical resources has attracted widespread attention but remains challenges. Thereby, within a fixed budget, a reasonable strategy for resource deployment inevitably plays a significant role on constraining the epidemic. In this paper, a metapopulation network model is constructed to simulate the spatial evolution of the epidemic coupled with the discrete resource descriptions, which are mapped to the parameters with binary scopes. Besides, to obtain the optimal resource deployment, we adopt the Binary Particle Swarm Optimization (BPSO) algorithm to search the optimal solution. Experimental results show that BPSO can achieve effective discrete resource deployment, and its solution is significantly better than random allocation of resource. When the budget is low, the vaccine resources are allocated preferentially, with few curative resources allocated. With the budget increasing, all the patches are almost filled with the vaccine resources, and the curative resources are gradually allocated. In addition, under limited budget, the resources are allocated into patches by a scattered manner, and the network with intensive community structure or not has few impacts on resource allocation, which further implies that resources cannot be over concentrated in contiguous areas.

A fractal-theory-based multi-agent model of the cyber physical production system for customized products

The manufacturing of customized products relies on precise control and close collaboration between distributed production processes. However, discrete manufacturing units cannot achieve consistently efficient collaboration owing to varying grades of smart manufacturing capabilities. Traditional cyber physical production systems have already achieved service- and data-driven resource allocation optimization, but the difficulty of discrete resource collaboration management remains. To address such an issue, this paper proposes a low-complexity and flexible factory operation model, the smart fractal factory (SFF). First, we propose the model definition of the cyber–physical fractal (CPF) based on fractal theory, whose information composition and evolution mechanism are used to formally describe the multi-scale self-similarity of structural characteristics and the operation mode of the smart manufacturing system. The core of the CPF is essential information element (EIEs) and typical information flow (TIFs), which are critical to upgrading manufacturing capabilities intelligently. Second, considering the realizability of the CPF pattern, the attribute configuration of EIEs and the operation mechanism of TIFs are established. Among them, self-organizing, self-adaptation, and self-learning TIFs are utilized for the construction of resource collaboration relationships, the evaluation and optimization of dynamic collaboration, and the enhancement of autonomous analysis or decision-making capabilities. Then, by leveraging agent modeling technology, we present a CPF implementation method to encapsulate distributed resources intelligently and homogeneously into smart manufacturing units that can autonomously optimize and collaborate. Thus, the multilayered architecture of traditional factories in the industrial Internet environment is transformed into an intelligent collaboration architecture based on CPF clusters in the SFF system. Finally, the usability and effectiveness of SFF were verified in a real manufacturing case of a customized product.

Robust Deep Reinforcement Learning Scheduling via Weight Anchoring

Questions remain on the robustness of data-driven learning methods when crossing the gap from simulation to reality. We utilize weight anchoring, a method known from continual learning, to cultivate and fixate desired behavior in Neural Networks. Weight anchoring may be used to find a solution to a learning problem that is nearby the solution of another learning problem. Thereby, learning can be carried out in optimal environments without neglecting or unlearning desired behavior. We demonstrate this approach on the example of learning mixed QoS-efficient discrete resource scheduling with infrequent priority messages. Results show that this method provides performance comparable to the state of the art of augmenting a simulation environment, alongside significantly increased robustness and steerability.

Outside options in neutral allocation of discrete resources

Serial dictatorships have emerged as the canonical simple mechanisms in the literature on the allocation of indivisible goods without transfers. They are the only neutral and group-strategy-proof mechanisms in environments in which agents have no outside options and hence no individual rationality constraints (Svensson in Soc Choice Welfare 16:557–567, 1999). Accounting for outside options and individual rationality constraints, our main result constructs the class of group-strategy-proof, neutral, and non-wasteful mechanisms. These mechanisms are also Pareto efficient and we call them binary serial dictatorships. The abundance of the outside option—anybody who wants can opt out to get it—is crucial for our result.

Fairness constraint efficiency optimization for multiresource allocation in a cluster system serving internet of things

SummaryMassive, diverse, and high‐frequency Internet of Things (IoT) applications pose challenges to the operation of cluster systems that serve it. Fair and efficient multidimensional resource allocation is of great significance to the sustainable operation of these systems. However, most of the existing cluster multiresource allocation optimization researches focus too much on the fairness of resource allocation and ignore the efficiency. The unbalanced use of multidimensional system resources reduces the effective utilization of system resources, which seriously affects the service quality of IoT applications. In this paper, we define the multiresource fair and efficient sharing optimization as a fairness‐constrained efficiency optimization problem, which is from dynamics, discrete resources, and heterogeneous perspectives according to the characteristics of cluster system in practical. Moreover, we present a dynamic efficiency‐aware multiresource fair allocation algorithm, DEF, which can improve the ability of the cluster system to serve diverse IoT applications. In the algorithm, large jobs schedule to the servers that expect the least remaining resources. Simulations performed using Google cluster‐usage traces show that DEF can improve system resource utilization and guarantee the fairness of sharing among users.

Combined Communication and Computing Resource Scheduling in Sliced 5G Multi-Access Edge Computing Systems

The fifth generation (5G) cellular networks aim to deliver data with low/ultra-low latency to users and support diverse services which require two main types of resources: communication and computing resources. These motivated the development of the Multi-access Edge Computing (MEC) paradigm because it can execute tasks’ computation closer to users to reduce latency. To satisfy quality of services (QoS) requirements of different applications, both communication and computing resources in 5G MEC systems can be hard partitioned or soft partitioned among multiple network slices where each slice supports one or more services. A typical soft partition approach is using scheduling. Furthermore, packets in the multi-resource system can either be scheduled individually for each resource using a discrete resource scheduling approach or scheduled collectively only once by combined resource scheduling. In this paper, we propose a combined resource scheduler, called Extended Weighted Fair Queueing with Latency Constraint (EWFQ/LC), to schedule packets among network slices subject to system fairness and their latency constraints. By exploiting the virtual finish time feature inherent in weight fair queueing, EWFQ/LC schedules packets according to the fairness when their latency constraints can be met while according to these latency constraints when they are possibly violated. Based on simulations of realistic heterogeneous traffic scenarios, we show that EWFQ/LC reduces latency by as much as 73% compared to scheduling each resource discretely and maintains latency satisfaction ratio above 90%. More importantly, EWFQ/LC is able to accomplish these improvements with greater fairness in resource consumption especially under heavy traffic conditions, i.e., traffic with stricter latency constraints do not excessively over-consume resources of other traffic classes.

MAGIKS: Fair Multi-Resource Allocation Game Induced by Kalai-Smorodinsky Bargaining Solution

We study the problem of fair and efficient mechanism design for allocating multiple resources in multiple servers among a set of users with Leontief utilities. This problem is motivated by a mobile edge computing environment where each mobile user cannot establish a wireless connection simultaneously to multiple edge servers. Each user is a selfish utility maximizing agent that chooses a single server for its job execution. When a server is shared by multiple users, a resource allocation rule decides the utility that each user must receive. Our goal is to design a mechanism that always admits a Nash Equilibrium (NE), i.e., a state where no user has incentive to change its server, that (1) can be reached in polynomial time and (2) provides fair and efficient resource allocation. We propose the Multi-resource Allocation Game Induced by Kalai-Smorodinsky bargaining solution (MAGIKS) and prove that under discrete resource demands it finds an NE in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}({\textrm {poly}(n)})$ </tex-math></inline-formula> moves for any fixed server configuration, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of users. Furthermore, MAGIKS satisfies envy-freeness, sharing incentive, and Pareto optimality on each server. Regarding fairness among users in different servers, we prove that MAGIKS satisfies 2-approximate envy-freeness and maximin share guarantee. Moreover, we show that 2-approximate envy-freeness is the best that any mechanism that satisfies local Pareto optimality can achieve at its NEs.

Utilization of Deep Reinforcement Learning for Discrete Resource Allocation Problem in Project Management – a Simulation Experiment

This paper tests the applicability of deep reinforcement learning (DRL) algorithms to simulated problems of constrained discrete and online resource allocation in project management. DRL is an extensively researched method in various domains, although no similar case study was found when writing this paper. The hypothesis was that a carefully tuned RL agent could outperform an optimisation-based solution. The RL agents: VPG, AC, and PPO, were compared against a classic constrained optimisation algorithm in trials: “easy”/”moderate”/”hard” (70/50/30% average project success rate). Each trial consisted of 500 independent, stochastic simulations. The significance of the differences was checked using a Welch ANOVA on significance level alpha = 0.01, followed by post hoc comparisons for false-discovery control. The experiment revealed that the PPO agent performed significantly better in moderate and hard simulations than the optimisation approach and other RL methods.

Research on the Application of Computer Cloud Computing Technology in Laboratory Management Based on Docker Platform

The balance of experimental resources needs the help of distributed resource scheduling algorithm to integrate the discrete resources logically, and plan the scheduling and application uniformly, so as to ameliorate the utilization efficiency of resources, which provides a broad space for the application of Docker tech. Based on this, this paper first analyses the concept and connotation of Docker container platform, then studies the construction of laboratory cloud platform based on Docker platform, and finally gives the utilization effect of laboratory management cloud platform based on Docker tech.