Resource Configuration Research Articles

State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata

Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework.

Effect of the Grain‐for‐Green Program on forest fragmentation

AbstractFragmentation profoundly alters the character and pattern of forests, influencing biodiversity in the context of global climate change. Forest area density (FAD) is currently considered the most effective method for characterizing forest fragmentation. Therefore, reasonable and accurate quantification of FAD and its relationship with forest cover changes are essential for evaluating forest fragmentation in the Loess Plateau under the implementation of the Grain‐for‐Green Program (GGP). Based on land use/cover (LULC) in 1998–2022, we integrated the Random forest (RF) algorithm and the conversion of land use and its effects at small regional extent (CLUE‐S) model to derive LULC in 2030. Subsequently, we proposed a multi‐scale window threshold to evaluate forest fragmentation by constructing nonlinear models. Furthermore, we explored the relationship between FAD and forest cover changes by spatial regression models. The results are as follows: (1) in 1998–2030, the main conversion of LULC was of the cropland to broad‐leaf forests and grassland. (2) The 43 × 43 window served as the optimal multi‐scale threshold to assess forest fragmentation. (3) The GGP slowed down forest fragmentation. (4) The degree of fragmentation in broad‐leaf forests was higher than that in coniferous forests. (5) Spatial error model (SEM) was the most appropriate for establishing the spatial relationship between forest fragmentation and forest cover changes. In conclusion, since the implementation of the GGP, forest cover changes had a significant impact on forest fragmentation and its multi‐scale threshold. This research provides a basis for the reasonable configuration of land resources in the Loess Plateau and novel methods for studying forest cover changes and fragmentation.

Online Scheduling Algorithm for Heterogeneous Distributed Machine Learning Jobs

Distributed machine learning (ML) has played a key role in todays proliferation of AI services. A typical model of distributed ML is to partition training datasets over multiple worker nodes to update model parameters in parallel, adopting a parameter server or AllReduce architecture. A fundamental problem in a distributed ML cluster is how to explore the demand elasticity of ML jobs and schedule them with different resource configurations, such that the average job completion time is minimized. To address it, we propose an online scheduling algorithm to decide the execution time window, the number and the type of concurrent workers and parameter servers for each job upon its arrival, with a goal of minimizing the weighted average completion time. Our online algorithm consists of (i) an online scheduling framework that groups unprocessed ML training jobs into a batch iteratively, and (ii) a batch scheduling algorithm that configures each ML job to maximize the total weight of scheduled jobs in the current iteration. Our online algorithm guarantees a good parameterized competitive ratio with polynomial time complexity. Extensive evaluations using real-world data demonstrate that it outperforms state-of-the-art schedulers in todays AI cloud systems.

Transnational digital entrepreneurship and enterprise effectiveness: A micro-foundational perspective

Transnational digital entrepreneurship (TDE), the establishment of digital enterprises by combining home- and host-country value creation to serve domestic and foreign customers, is increasing. In order to understand the role of entrepreneurs in transnational digital enterprise effectiveness, we investigate how the competencies of transnational digital entrepreneurs influence social interactions among stakeholders that contribute to enterprise effectiveness. We apply the micro-foundations perspective of management and entrepreneurship and semi-structured interview data from transnational entrepreneurs from six countries—Finland, Sweden, Norway, New Zealand, Australia, and the USA—and their partners in Pakistan. We find that entrepreneurs’ digital knowledge, skills, and abilities—versatile cognitive capabilities, digital managerial capabilities, and multicultural capabilities—affect social interactions through four socio-structural mechanisms—structural support, trust-building, knowledge sharing, and resource configuration—that enhance enterprise effectiveness. We discuss the theoretical and practical implications for transnational entrepreneurship, policymakers, and migrant entrepreneurs.

Deep Reinforcement Learning-Based Grant-Free NOMA Optimization for mURLLC

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic resource configuration in GF-NOMA systems is challenging due to random traffics and collisions, that are unknown at the base station (BS). Meanwhile, joint consideration of the latency and reliability requirements makes the resource configuration of GF-NOMA for mURLLC more complex. To address this problem, we develop a novel learning framework for signature-based GF-NOMA in mURLLC service taking into account the multiple access signature collision, the UE detection, as well as the data decoding procedures for the K-repetition GF and the Proactive GF schemes. The goal of our learning framework is to maximize the long-term average number of successfully served users (UEs) under the latency constraint. We first perform a real-time repetition value configuration based on a double deep Q-Network (DDQN) and then propose a Cooperative Multi-Agent learning technique based DQN (CMA-DQN) to optimize the configuration of both the repetition values and the contention-transmission unit (CTU) numbers. Our results show the superior performance of CMA-DQN over the conventional load estimation-based uplink resource configuration approach (LE-URC) in heavy traffic and demonstrate its capability in dynamically configuring in long term for mURLLC service. In addition, with our learning optimization, the Proactive scheme always outperforms the K-repetition scheme in terms of the number of successfully served UEs, especially under the high backlog traffic scenario.

Two-layer Optimization of Energy Efficiency Based on Provincial Smart Energy Service Platform

The study relies on the provincial smart energy service platform of Jiangsu Province, optimizes its accurate prediction module of energy consumption and the energy efficiency improvement module, and proposes an efficiency improvement strategy based on the two modules of the platform. The accurate prediction module adopts the data processing and feature analysis method based on STL-MIC and builds a deep learning framework based on CNN-BiLSTM. The projected predicted data is then input to the energy efficiency improvement module, which employs the method of flexible load scheduling to achieve the best configuration of demand-side resources.

A novel hierarchical structural pruning-multiscale feature fusion residual network for intelligent fault diagnosis

Residual learning is a commonly used method in the intelligent fault diagnosis (IFD) field. The existing residual networks usually have a large model volume, whose training process puts forward high requirements for both time consumption and the computing resource configuration. This paper proposes a novel hierarchical structural pruning-multiscale feature fusion residual network (HSP-MFFRN) for IFD. The multiple multi-scale feature extraction modules and feature fusion modules are designed in the proposed HSP-MFFRN to extract, fuse and compress the multi-scale features without changing the size of the convolutional filter. In addition, hierarchical structural pruning is implemented for HSP-MFFRN to delete redundant channels. Two experimental study cases are conducted to verify model diagnosis performance, including a rolling bearing IFD case and an aerostat capsule IFD case. The experimental results show that the proposed model can effectively compress the learnable parameters and model volume after hierarchical structural pruning, and can still achieve superior diagnostic performance compared with other deep learning methods, residual learning methods, and other advanced methods reported in the literature.

Risk-constrained planning of rural-area hydrogen-based microgrid considering multiscale and multi-energy storage systems

Recent advances in renewable hydrogen production and storage technologies have offered a promising path towards the carbon-neutral energy supply of rural communities. This paper presents a risk-constrained planning method for hydrogen-based multi-energy off-grid microgrids under economics and resilience considerations. A two-stage risk-constrained stochastic programming formulation is proposed, which is to optimize the energy resources configuration in the first stage, and conducts long-term economic dispatch as well as the on-emergency feasibility verification in the second stage. Sophisticated analytical models are developed to coordinate the operations of multi-timescale and multi-energy storage facilities (especially the short-term and seasonal hydrogen storage). Also, the risk constraints are imposed via sampling approximation strategy to control the risks of crucial components failures for resilience enhancement. Moreover, through the data-driven power flow linearization, our planning problem can be recasted as a mixed-integer linear program (MILP), and efficiently computed by developing a dual cutting-plane based enhanced decomposition algorithm. Numerical studies on a real-world rural energy system in Southwestern China validates the effectiveness of the proposed planning method. It has significantly reduced the levelized system costs through seasonal storage deployment and multi-energy synergy. Besides, our customized solution algorithm demonstrates a strong scalable capacity that support planning decisions under complex uncertainties.

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.

Plant clustering generates negative plant–soil feedback without changing the spatial distribution of soil fauna

Abstract. The spatial distribution of resources affects ecological processes at all levels of biological organization. However, it remains to be tested how the spatial configuration of belowground resources affects the community dynamics of soil organisms and resulting plant–soil feedbacks. We used Agrostis stolonifera plants in different spatial configurations in mesocosms to study the dispersal patterns of soil nematodes and rotifers. Plant–soil feedbacks were later assessed by re-sowing the mesocosms with Lolium perenne and Plantago lanceolata after removing all the initial A. stolonifera plants from the mesocosms. Bacterial-feeding nematodes and rotifers spread fast, whereas plant-feeding nematodes barely dispersed from the release sites. These spread patterns of nematodes and rotifers depended on the life-history traits and not on the spatial pattern of the plants. However, more clustered plants developed a higher total biomass and caused a reduced growth of the subsequent vegetation. Our results demonstrate that the mere spatial pattern of a single plant species can alter the strength of plant–soil feedbacks. This has important implications for understanding the impact of planting or replanting schemes and other changes in the spatial configuration of plants on long-term vegetation development and succession.

Correlation Properties Investigation of Signaling Sequences to Optimize the 5G NR Random Access Channel Characteristics

The article describes a random access channel optimization technique based on the choice of roots for constructing random access preambles, the detection probability of which is higher than the roots selected in the classical way according. The results of simulation modeling are presented for the entire set of preambles 839 samples long. It is shown that with an increase in the number of simultaneously transmitted preambles, the spread of possible values of the correlation coefficient increases, which directly affects the success of detection. The conclusion is made about the possibility of optimizing the characteristics of the random access channel, both in terms of the configuration of the random access channel resources and based on the rational choice of the roots of the signal-forming preambles.

Unpacking Organizational Adaptation Strategies: A Conceptual Review

This paper examines strategies that help organizations adapt to changing and complex environments. Drawing from existing research, the study explores key strategies like agile learning, organizational ambidexterity, digital transformation, and resource configuration. These strategies are crucial in helping organizations become more resilient and adaptable. The paper discusses each strategy, providing valuable insights into how organizations can effectively manage uncertainties and challenges in today’s business world. In conclusion, these strategies are highlighted as vital tools for enhancing organizational adaptability amidst ongoing changes and challenges. The paper also emphasizes the need for more practical research to understand better how these strategies impact organizational performance.

A Configurable Hierarchical Architecture for Parallel Dynamic Contingency Analysis on GPUs

Dynamic contingency analysis (DCA) for modern power systems is fundamental to help researchers and operators look ahead of potential issues, arrange operational plans, and improve system stabilities. However, since the system size and the number of contingency scenarios continue to increase, pursuing more effective computational performance faces many difficulties, such as slow algorithms and limited computing resources. This research accelerates the intensive computations of massive DCAs by implementing a two-level hierarchical computing architecture on graphical processing units (GPUs). The performance of the designed method is examined using four test systems of different sizes and compared with a CPU-based parallel approach. The results show up to 2.8x speedup using one GPU and 4.2x speedup using two GPUs, respectively. More accelerations can be observed once more GPUs are configured. It demonstrates that the proposed architecture can significantly enhance the overall computational performance of massive DCAs while maintaining a strong scaling capability under various resource configurations.

Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems.

Enumeration PCRLB-Based Power Allocation for Multitarget Tracking With Colocated MIMO Radar Systems in Clutter

An effective resource allocation strategy can maximize the remote sensing performance of radar systems, such as target detection and tracking. In this paper, two typical power allocation (PA) strategies are developed for the multi-target tracking (MTT) task in colocated MIMO (C-MIMO) radar systems with the consideration of the clutter. The multi-beam concept and the posterior PDF fusion are adopted by the C-MIMO radar system to obtain the global posterior distribution. Specifically, each radar generates multiple simultaneous beams with controllable power during each interval. To ensure that the limited system resources can be utilized effectively, the online PA scheme is implemented according to the prior knowledge predicted from the tracking cyclic recursive feedback results. The posterior Cramér-Rao lower bound (PCRLB) is derived by enumerating all possible target detection and false alarm occurrence cases, and is utilized as the tracking performance metric since it provides a more accurate lower bound on the target state estimation in clutter. Besides, to solve the computationally expensive problem of this PCRLB caused by enumeration operation, we propose a two-step approximate approach. Then, combined with the system resource configuration, two different types of resource optimization problems are designed, namely, performance maximization for a fixed power budget and direct resource minimization. These formulated PA problems are shown to be non-convex and non-linear. Therefore, we further propose a modified particle swarm optimization (MPSO) algorithm to solve these problems efficiently. Simulation results verify the superiority and effectiveness of the proposed PA strategies in terms of tracking performance in clutter.

Resource Configuration for Throughput Maximization in UAV-WPCN With Intelligent Reflecting Surface

UAV-based wireless powered communication network is a promising method of power supply for battery-free IoT devices, but the limited wireless transmission capability of the UAV constrains the coverage area and transmission throughput. This paper aims to address this issue by exploring intelligent reflecting surfaces with optimized configurations, including the number of reflective elements, transmission power, and the UAV’s altitude. The scheme design is challenging because such a throughput maximization problem is essentially a non-convex optimization problem due to the random wireless channel state and the unknown probability distribution of the objective function. By sequentially applying alternating optimization, successive convex approximation, penalty function, and difference-convex optimization, the schemes proposed in this paper can transform the original non-convex optimization problem into a convex one. Extensive evaluation proves the efficacy of the proposed scheme. The paper further compares two settings of the intelligent reflecting surface, namely dynamic phase shift and static phase shift, and provides their performance gap.

American social media on the Russia-Ukraine war: A multimodal analysis

This research focuses on political narrative about the Russia-Ukraine war in American social media. It aims to investigate the type and configuration of multimodal semiotic resources used to present the events of this war and shape their perception by the audience, primarily American; The New York Times Telegram channel coverage of the 2022 Russian invasion as a case study. What kind of semiotic resources do American social media deploy to construct the narrative about the war? What are the dominant post formats, how they deploy information about the Russia-Ukraine war and construe its perception: the concepts of the war, of Ukraine as a victim of aggression and a fighter for independence, of Russia as a terrorist-state, and of their allies and opponents? Theoretically, this study is underpinned by a cognitive-semiotic, political linguistic and multimodal approach. In political media narrative, storytelling shapes perception of war realities: it frames and reframes the readers’ construal of the world, ascribes connotations to facts and events. A multimodal narrative involves different semiotic resources—verbal, graphic, audial, and cinematographic (in video). The configuration of these heterogeneous signs in a particular Telegram channel’s post defines its influence as predominantly rational or emotional. The findings show that the following post formats are typical for The New York Times Telegram channel: image followed by text and link to another site (the most frequent); gallery (a gallery of images) and quote (a quotation with its source/author as the title) (less frequent); video and audio (rarely used). They serve to accentuate, elaborate, and extend the information in the post. I argue that Telegram posts together with users’ emoji-reactions are a specific interactive genre of a multimodal political discourse. Emojis realize both pragmatic and cognitive functions: on the one hand, they are effective instruments to measure users’ reaction; on the other, they contribute to meaning-making by evaluating and making salient the concepts described. The dominance of rational and verified information in The New York Times Telegram channel creates the true image of Russia’s genocidal war against Ukraine and provides a fair assessment of related concepts.

Compound Optimization of Territorial Spatial Structure and Layout at the City Scale from "Production-Living-Ecological" Perspectives.

Land-use optimization, as an important resource-allocation method, can be defined as the process of allocating various activities to different geographic units. How to manage and control land expansion has become an urgent issue, leading a series of problems such as environmental damage and a sharp decrease in cultivated land, leading to unfavorable phenomena such as excessive urban expansion, occupation of cultivated land and important ecological spaces, and overheating of real estate development. Based on the land-use data of Wuhan city in 2020, a coupling MOP (Multi-Objective Programming) and FLUS (Future Land-Use Simulation) model was used to examine the national spatial structure and the optimization of the spatial layout. Our results showed that (1) in terms of quantitative optimal allocation, the ecological space and urban space increased, while the agricultural space greatly decreased under the three development scenarios. (2) In the simulation of spatial layout, the urban space mainly expanded vertically in the north-south direction. In the ecological space scenario, the ecological space occupied part of the cultivated land in the northeast of the city, resulting in a high degree of landscape fragmentation, which is not conducive to large-scale agricultural management. However, under optimal comprehensive benefit, part of the fragmented ecological space in the western part of Wuhan was transformed into an agricultural space. (3) A combination of the MOP and FLUS models could effectively determine land-use structure and address spatial layout optimization problems and can project space in the future urban resource configuration mode. This finding can provide a reference for the optimization of the spatial structure and layout of similar cities.

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. One experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

REACHING THE SOCIALLY ISOLATED OLDER ADULTS IN DEMENTIA RESEARCH

Abstract Sexual and gender minority (SGM) older adults are at greater risk for dementia and social isolation compared to their heterosexual peers. Lack of an available caregiver is of major concern. Yet, most dementia interventions to date have been designed primarily for caregiver-care recipient dyads or solely caregivers. A pilot study evaluated a 9-session virtual program designed to address social isolation, physical functioning and quality of life among SGM older adults living with dementia without a caregiver. Participants in the experimental group (n = 14) showed high attendance (75%) and retention rates. Social support was associated with intervention adherence. Compared to controls, the experimental group showed a significant increase in physical functioning and quality of life and decrease in depressive symptomatology and perceived stress. To meet the needs of this population without adequate social resources, research must address the different configurations of resources to better serve socially isolated SGM older adults.