Dynamic Reconfiguration Research Articles

Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder.

Thalamocortical circuitry has a substantial impact on emotion and cognition. Previous studies have demonstrated alterations in thalamocortical functional connectivity (FC), characterized by region-dependent hypo- or hyper-connectivity, among individuals with major depressive disorder (MDD). However, the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear. Hence, we analyzed dynamic FC (dFC) between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls (HCs) to investigate time-varying changes in thalamocortical FC in patients with MDD. Moreover, dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time. Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated. We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks, compared with the HCs. In addition, MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration. The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort. Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms. Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients. The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.

Optimal two-stage dispatch method of distribution network emergency resources under extreme weather disasters

In recent years, frequent extreme weather disasters have posed severe challenges to the safe and reliable power supply of distribution networks. In this paper, a resilience enhancement method is proposed for distribution networks, which establishes the full-phase analytical modeling of the distribution system defense and recovery process under extreme events, i.e., pre-event phase, degradation phase and restoration phase. Before a disaster strikes, multiple emergency resources are pre-allocated, including mobile emergency generators (MEGs) and repair crews and proactive islands are formed to improve the resistance of distribution networks to extreme weather disasters. After the disaster, real-time dispatching of MEGs and repair crews is coordinated with dynamic network reconfiguration, which considers the coupling processes among fault isolation, fault repair, and load restoration to improve the recovery ability of distribution networks. The IEEE 123-bus distribution system is used to verify the effectiveness and efficiency of the proposed method in enhancing the survivability of loads and the rapid recovery ability of distribution networks.

A multi-objective artificial hummingbird algorithm for dynamic optimal volt-var controls for high electric vehicle load penetration in a photovoltaic distribution network

Renewable energy systems (RESs) and electric vehicles (EVs) have become global necessities to meet the sustainable development goals (SDGs). Because they are intermittent and stochastic, these two technologies have caused power grid operation and regulation challenges despite their environmental benefits. A new combined dynamic optimal network reconfiguration (DONR) and capacitor bank switching (CBS) optimization technique reduces solar and multi-type load effects on the distribution network performance. Considering substantial EV load penetrations, ONR and CBS should coordinate to reduce energy loss, improve the voltage profile, and save money during 24 h operation. The artificial hummingbird algorithm (AHA) solves this difficult optimization problem for the first time. An enhanced IEEE 33-bus benchmark test system was used to simulate various instances to evaluate the computational characteristics of the AHA and compare it with other similar algorithms. The obtained results demonstrate the superiority of the combined DONR and CBS for real-time scenarios over any individual approach.

Dynamic reconfigurations of brain networks in depressive and anxiety disorders: The influence of antidepressants

Major Depressive Disorder (MDD) and anxiety disorders are highly comorbid recurrent psychiatric disorders. Reduced dynamic reconfiguration of brain regions across subnetworks may play a critical role underlying these deficits, with indications of normalization after treatment with antidepressants. This study investigated dynamic reconfigurations in controls and individuals with a current MDD and/or anxiety disorder including antidepressant users and non-users in a large sample (N = 207) of adults. We quantified the number of subnetworks a region switched to (promiscuity) as well as the total number of switches (flexibility). Average whole-brain (i.e., global) values and subnetwork-specific values were compared between diagnosis and antidepressant groups. No differences in reconfiguration dynamics were found between individuals with a current MDD (N = 49), anxiety disorder (N = 46), comorbid MDD and anxiety disorder (N = 55), or controls (N = 57). Global and sensorimotor network (SMN) promiscuity and flexibility were higher in antidepressant users (N = 49, regardless of diagnosis) compared to non-users (N = 101) and controls. Dynamic reconfigurations were considerably higher in antidepressant users relative to non-users and controls, but not significantly altered in individuals with a MDD and/or anxiety disorder. The increase in antidepressant users was apparent across the whole brain and in the SMN when investigating subnetworks. These findings help disentangle how antidepressants improve symptoms.

Dynamically tuning and reconfiguring microwave bandpass filters using optical control of switching elements

AbstractWe report a novel approach for dynamically tuning and reconfiguring microwave bandpass filters (BPFs) based on optically controlled switching elements using photoconductivity modulation in semiconductors. For a prototype demonstration, a BPF circuit featuring a second‐order design using two closely coupled split‐ring resonators embedded with multiple silicon chips (as switching elements) was designed, fabricated, and characterized. The silicon chips were optically linked to fiber‐coupled laser diodes (808 nm light) for switching/modulation, enabling dynamic tuning and reconfiguring of the BPF without any complex biasing circuits. By turning on and off the two laser diodes simultaneously, the BPF response can be dynamically reconfigured between bandpass and broadband suppression. Moreover, the attenuation level of the passband can be continuously adjusted (from 0.7 to 22 dB at the center frequency of 3.03 GHz) by varying the light intensity from 0 to 40 W/cm2. The tuning/reconfiguring 3‐dB bandwidth is estimated to be ~200 kHz. In addition, the potential and limitations of the proposed approach using photoconductivity modulation are discussed. With the strong tuning/reconfiguring capability demonstrated and the great potential for high‐frequency operation, this approach holds promise for the development of more advanced tunable filters and other adaptive circuits for next‐generation sensing, imaging, and communication systems.

A Dynamic Reconfiguration-Based Hybrid Network Design Methodology for IPT CC/CV Output

A Dynamic Reconfiguration-Based Hybrid Network Design Methodology for IPT CC/CV Output

Regulating Ru−O Bonding Interactions by Ir Doping Boosts the Acid Oxygen Evolution Performance

AbstractHighly active and stable oxygen evolution reaction (OER) catalysts are crucial for the large‐scale application of proton exchange membrane water electrolyzers. However, the dynamic reconfiguration of the catalyst surface structure and active centers is still undefined, which greatly hinders the development and application of efficient OER catalysts. Herein, we report an Ir0.3Ru0.7Ox/C catalyst with a facile low‐temperature synthesis route, which can reach 10 mA cm−2 at an overpotential of 217 mV with a Tafel slope as low as 39.4 mV dec−1, and yields a mass activity 61 times that of commercial IrO2/C at an overpotential of 300 mV. The lattice oxygen structure of RuOx is stabilized by the introduction of Ir species, thus greatly promoting the OER activity and durability. Further in situ Raman reveals that RuOx emerges as the active species at high potentials, and Ru−O bonding interactions are enhanced with Ir regulation, stabilizing the solvation of Ru at high potentials and accelerating the nucleophilic attack of water molecules, leading to the improved OER performance. This work deepens the fundamental understanding of OER and offers an effective way to advance the utilization of Ru‐based OER catalysts.

Ferroelectric FET-based context-switching FPGA enabling dynamic reconfiguration for adaptive deep learning machines.

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)-based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively.

Maximizing aged photovoltaic array power: a computer modelling study

Abstract Photovoltaic (PV) modules age with time for various reasons such as corroded joints and terminals and glass coating defects, and their ageing degrades the PV array power. With the help of the PV array numerical model, this paper explores the effects of PV module ageing on the PV array power, and the power gains and costs of rearranging and recabling aged PV modules in a PV array. The numerical PV array model is first revised to account for module ageing, rearrangement and recabling, with the relevant equations presented herein. The updated numerical model is then used to obtain the array powers for seven different PV arrays. The power results are then analysed in view of the attributes of the seven PV array examples. A guiding method to recommend recabling after rearranging aged modules is then proposed, leading to further significant power gains, while eliminating intra-row mismatches. When certain conditions are met, it was shown that recabling PV modules after rearranging them may lead to further significant power gains, reaching 57% and 98% in two considered PV array examples. Higher gains are possible in other arrays. A cost–benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic merits. In the considered examples, recabling costs can be recovered in <4 years. Compared with the powers of the aged arrays, power gains due to our proposed rearranging and recabling the PV arrays ranged between 73% and 131% in the considered examples—well over the gains reported in the literature. Moreover, the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

Dynamic reconfiguration of brain coactivation states that underlying working memory correlates with cognitive decline in clinically unimpaired older adults.

Impairments in working memory (WM) are evident in both clinically diagnosed patients with mild cognitive decline and older adults at risk, as indicated by lower scores on neuropsychological tests. Examining the WM-related neural signatures in at-risk older adults becomes essential for timely intervention. WM functioning relies on dynamic brain activities, particularly within the frontoparietal system. However, it remains unclear whether the cognitive decline would be reflected in the decreased dynamic reconfiguration of brain coactivation states during WM tasks. We enrolled 47 older adults and assessed their cognitive function using the Montreal Cognitive Assessment. The temporal dynamics of brain coactivations during a WM task were investigated through graph-based time-frame modularity analysis. Four primary recurring states emerged: two task-positive states with positive activity in the frontoparietal system (dorsal attention and central executive); two task-negative states with positive activity in the default mode network accompanied by negative activity in the frontoparietal networks. Heightened WM load was associated with increased flexibility of the frontoparietal networks, but the cognitive decline was correlated with reduced capacity for neuroplastic changes in response to increased task demands. These findings advance our understanding of aberrant brain reconfiguration linked to cognitive decline, potentially aiding early identification of at-risk individuals.

A Hardware Design Framework for Computer Vision Models Based on Reconfigurable Devices

In computer vision, the joint development of the algorithm and computing dimensions cannot be separated. Models and algorithms are constantly evolving, while hardware designs must adapt to new or updated algorithms. Reconfigurable devices are recognized as important platforms for computer vision applications because of their reconfigurability. There are two typical design approaches: customized and overlay design. However, existing work is unable to achieve both efficient performance and scalability to adapt to a wide range of models. To address both considerations, we propose a design framework based on reconfigurable devices to provide unified support for computer vision models. It provides software-programmable modules while leaving unit design space for problem-specific algorithms. Based on the proposed framework, we design a model mapping method and a hardware architecture with two processor arrays to enable dynamic and static reconfiguration, thereby relieving redesign pressure. In addition, resource consumption and efficiency can be balanced by adjusting the hyperparameter. In experiments on CNN, vision Transformer, and vision MLP models, our work’s throughput is improved by 18.8x–33.6x and 1.4x–2.0x compared to CPU and GPU. Compared to others on the same platform, accelerators based on our framework can better balance resource consumption and efficiency.

Dynamic reconfiguration of active distribution network based on improved backtracking search algorithm

This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method is validated by an IEEE 30-node simulation distributed system.

Multi-criteria Optimization of Real-time DAGs on Heterogeneous Platforms under P-EDF

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications’ slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals.

Performance analysis of winnowing dynamic reconfiguration in partially shaded solar photovoltaic system

To extract maximum power out of solar PV array when there is partial shadowing, a dynamic reconfiguration is required. Partial shade has significantly decreased output power, which lowers PV panel’s efficiency. Numerous strategies have been put out to prevent power loss reduction when partial shade occurs. Existing techniques have not been able to provide better power extraction at extremely low irradiation. The proposed winnowing technique for dynamic reconfiguration is appropriate to get rid of power loss due to mismatch under very low radiation. The suggested approach enhances the output power under partially shade condition in low-irradiation. Fifteen different shading scenarios and six reconfiguration switching patterns were investigated to confirm the proposed concept. The proposed winnowing technique increases the solar PV array output power up to 59.3% by effectively decreasing mismatch losses in the solar PV system.

Enhancing Resilience in Distribution Systems through Dynamic Reconfiguration

Enhancing Resilience in Distribution Systems through Dynamic Reconfiguration

Collaborative Reconfiguration of Supply Networks Based on GNN and ALC

With the prevalence of lean and just-in-time principles, traditional supply chains often exhibit inflexibility, leading to challenges in satisfying extensive customized orders and managing risks during disruptions. Thus, there is a need for a more flexible, resilient, and collaborative network and strategies to tackle the aforementioned challenges. In this study, we introduce a new supply network called the industry supply chain, aimed at enabling collaborative decision-making and dynamic reconfiguration. We create a graph neural network model to promptly identify sudden disturbances and devise a distributed multidisciplinary optimization model to facilitate collaborative reconfiguration. The experimental findings from an air-conditioning industry supply chain show that network reconfiguration under real-time disturbance detection reduces losses and improves operational stability.

Enhancing Approaches for Thriving in the Digital Economy

The introduction of cutting-edge digital technology has triggered a sea change in many industries, heralding a potential reconfiguration of labor dynamics, strategic approaches, and legislative frameworks. This article critically examines the dynamic nature of the digital economy, analyzing its emerging characteristics while acknowledging its persistent components. The article reveals three fundamental business techniques that constitute the foundation of its structure: modularity, open innovation, and platforms. These techniques embody the fundamental principles of organization and their significant ramifications for society. Within the context of underdeveloped countries, the digital economy brings a unique convergence of both obstacles and potential. Organizations and governmental decision-makers face the challenge of reconciling the need for innovation with the pursuit of strategic positioning. This study examines the strategic opportunities available to businesses operating in underdeveloped countries, with a particular emphasis on market dynamics and innovation. Importantly, in order to spur innovation and secure a competitive edge in the digital economy, you don't have to be able to see whole world's info. Several openings exist, especially in industrial applications, where physical systems and domain-specific knowledge are still necessary. Although key platform owners possess significant skills, such as data access and analysis, the ability to promote innovation and establish new market niches is not limited to them alone. The digital economy represents a fundamental change in the way economic activities are conducted, requiring individuals and organizations to possess the capacity to adapt, customize, and continuously innovate in order to fully use its capabilities.

Dynamic mobile X-haul reconfiguration by fast network switching for low-latency MEC service

Dynamic mobile X-haul reconfiguration by fast network switching for low-latency MEC service

A Blended Approach to Improve Reliability and Efficiency of Active EDN via Dynamic Feeder Reconfiguration, Demand Response, and VVO

A Blended Approach to Improve Reliability and Efficiency of Active EDN via Dynamic Feeder Reconfiguration, Demand Response, and VVO

SiPhyR: An End-to-End Learning-Based Optimization Framework for Dynamic Grid Reconfiguration

SiPhyR: An End-to-End Learning-Based Optimization Framework for Dynamic Grid Reconfiguration