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The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations. [ABSTRACT FROM AUTHOR]

Embedded systems, particularly when integrated into the Internet of Things (IoT) landscape, are critical for projects requiring robust, energy-efficient interfaces to collect real-time data from the environment. As these systems become complex, the need for dynamic reconfiguration, improved availability, and stability becomes increasingly important. This paper presents the design of a framework architecture that supports dynamic reconfiguration and "on-the-fly" code execution in IoT-enabled embedded systems, including a virtual machine capable of hot reloads, ensuring system availability even during configuration updates. A "hardware-in-the-loop" workflow manages communication between the embedded components, while low-level coding constraints are accessible through an additional abstraction layer, with examples such as MicroPython or Lua. The study results demonstrate the VM's ability to handle serialization and deserialization with minimal impact on system performance, even under high workloads, with serialization having a median time of 160 microseconds and deserialization having a median of 964 microseconds. Both processes were fast and resource-efficient under normal conditions, supporting real-time updates with occasional outliers, suggesting room for optimization and also highlighting the advantages of VM-based firmware update methods, which outperform traditional approaches like Serial and OTA (Over-the-Air, the ability to update or configure firmware, software, or devices via wireless connection) updates by achieving lower latency and greater consistency. With these promising results, however, challenges like occasional deserialization time outliers and the need for optimization in memory management and network protocols remain for future work. This study also provides a comparative analysis of currently available commercial solutions, highlighting their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

With the unprecedented explosive growing amount of global data, the development of computing chips, which encounter bottlenecks such as power wall and memory wall, cannot satisfy the demanding requirement. This work proposes a software-defined process-near-memory (SDPNM) computing architecture implemented using 3D hybrid bonding integration. The software-defined chip architecture, featuring spatial computations and dynamic reconfiguration, innovates in a top-down manner to achieve high energy efficiency while maintaining flexibility after fabrication. The process-near-memory integration further advances the SDNPM chip in a bottom-up way to reduce the energy consumption of data movement while improving the bandwidth. Utilizing a relatively mature fabrication and bonding process can result in feasible solutions for both data-intensive and compute-intensive applications including digital signal processing and artificial intelligence. The logic die is fabricated in the SMIC 40 nm process and the DRAM die is fabricated in the PSMC 25 nm process. The hybrid bonding is implemented by XMC. The experimental results show that the energy efficiency of the proposed SDPNM chip is 33.1× better than the state-of-the-art FPGA ranging from 8.2× to 104.1×. [ABSTRACT FROM AUTHOR]

Abstract To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm’s feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.94% and the minimum node voltage is elevated from 0.9273 p.u to 0.9447 p.u after the reconstruction with LDEDBO. The power dissipation of the IEEE-69 node bus is reduced by 36.45%, and the minimum node voltage is increased from 0.9224 p.u to 0.9481 p.u. The LDEDBO enhances both the pace of convergence and the precision of the optimization model, leading to a superior solution for the switching combination.

Abstract Continuous development of photonic crystals (PCs) over the last 30 years has carved out many new scientific frontiers. However, creating tunable PCs that enable flexible control of geometric configurations remains a challenge. Here we present a scheme to produce a tunable plasma photonic crystal (PPC) ‘kaleidoscope’ with rich diversity of structural configurations in dielectric barrier discharge. Multi-freedom control of the PPCs, including the symmetry, dielectric constant, crystal orientation, lattice constant, topological state, and structures of scattering elements, has been realized. Four types of lattice reconfigurations are demonstrated, including transitions from periodic to periodic, disordered to ordered, non-topological to topological, and striped to honeycomb Moiré lattices. Furthermore, alterations in photonic band structures corresponding to the reconstruction of various PPCs have been investigated. Our system presents a promising platform for generating a PPC ‘kaleidoscope’, offering benefits such as reduced equipment requirements, low cost, rapid response, and enhanced flexibility. This development opens up new opportunities for both fundamental and applied research. Graphical Abstract

To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm's feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.94% and the minimum node voltage is elevated from 0.9273 p.u to 0.9447 p.u after the reconstruction with LDEDBO. The power dissipation of the IEEE-69 node bus is reduced by 36.45%, and the minimum node voltage is increased from 0.9224 p.u to 0.9481 p.u. The LDEDBO enhances both the pace of convergence and the precision of the optimization model, leading to a superior solution for the switching combination. [ABSTRACT FROM AUTHOR]

This paper investigates on dynamic topology reconfiguration of distribution networks to enhance PV hosting capacity. Firstly, a dynamic topology reconfiguration model of distribution networks considering N-1 security constraints is proposed to accommodate intermittent PV generation, and thus high penetration of renewable resources can be ensured to satisfy operational requirements under both normal and fault conditions with load transfer of feeders. Then, a hosting capacity enhancement strategy is presented to decrease risks of voltage and line overloading with reactive power flexibility of energy storage inverters, and is implemented to enhance the maximum hosting capacity of distributed renewable energy sources. Finally, simulation results have validated the effectiveness of the proposed method for PV hosting capacity enhancement of distribution networks. [ABSTRACT FROM AUTHOR]

Interfacing intelligent responsive molecules with biocompatible nanofibers is a promising strategy for developing overheated self‐managing photothermal dressings for convenient and cost‐effective clinical applications. Here, an anisotropic staggered cellulose nanocage dressing with overheated self‐managing properties is constructed by temperature‐responsive flexible triblock copolymer Pluronic® F‐127 interpenetrating with smart cellulose nanofiber network skeleton, which interfaced with white light (635 nm) responsive protoporphyrin IX, and NIR‐I (808 nm) responsive dopamine, NIR‐II (980 nm) responsive upconversion nanoparticles. Ingeniously, the dressing can release 1O2 and produce heat energy under the stimulation by continuous natural light and NIR‐I laser during the anti‐inflammation phase, respectively, and further promote the formation of new capillaries by producing NO during the epithelial formation stage under NIR‐II laser. Interestingly, the dressing, which resembled water droplets, can penetrate and adapt to irregular wounds via sol–gel transformation at 32 °C, while also facilitating intelligent management of the self‐cooling regulation via the reconstruction of its hydrogen bond network driven by overheating temperatures (>50 °C). The proposed method overcomes the shortcoming of requiring auxiliary strategies for temperature monitoring, providing new insights for guiding the design of practical dressings. [ABSTRACT FROM AUTHOR]

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. We propose ADIONA, a dynamic segmented bus interconnect to address design scalability while reducing energy and latency of spike traffic. ADIONA consists of parallel bus lanes arranged in a ladder-shaped structure that allows any tile to connect to another, offers multiple routing options for communication links, and provides a high level of customization for different mapping scenarios and use cases. Each lane in the ladder bus is partitioned into segments using lightweight bufferless switches. Based on compile-time communication information, these switches can be dynamically reconfigured at runtime to execute the target application. Our dynamic segmented bus interconnect substantially enhances hardware utilization, improves fault tolerance, and offers adaptability to execute different applications on a single hardware platform. We evaluate ADIONA using three synthetic and three realistic machine learning workloads on a cycle-accurate neuromorphic simulator. Our results show that ADIONA reduces energy consumption by $2.1\times $ , latency by $40\times $ , and interconnect area by $2\times $ , compared to a state-of-the-art interconnect for neuromorphic systems. [ABSTRACT FROM AUTHOR]

Distribution network reconfiguration (DNR) plays a vital role in enhancing network sustainability by optimizing its topology. This process achieves key objectives such as reducing power losses, improving voltage profiles, balancing loads, and increasing network reliability, aligning with sustainability metrics. Depending on the goals and equipment available, reconfiguration may be applied for short-term or long-term durations. Long-term or static reconfiguration suits both conventional switches and traditional as well as modern networks. In modern networks equipped with remote-control switches, however, reconfiguration can be implemented rapidly to meet specific operational objectives. This study provides a comprehensive review of recent advancements in network reconfiguration, categorizing methods into four groups: heuristic, metaheuristic, conventional, and modern approaches. Each category is broadly defined and compared, with applications discussed for both static and dynamic reconfiguration. Dynamic reconfiguration is highlighted as a key area for future exploration in smart and modern distribution networks. This article serves as a resource for engineers and researchers, helping them select the most suitable method based on network equipment and performance goals. [ABSTRACT FROM AUTHOR]

Continuous development of photonic crystals (PCs) over the last 30 years has carved out many new scientific frontiers. However, creating tunable PCs that enable flexible control of geometric configurations remains a challenge. Here we present a scheme to produce a tunable plasma photonic crystal (PPC) 'kaleidoscope' with rich diversity of structural configurations in dielectric barrier discharge. Multi-freedom control of the PPCs, including the symmetry, dielectric constant, crystal orientation, lattice constant, topological state, and structures of scattering elements, has been realized. Four types of lattice reconfigurations are demonstrated, including transitions from periodic to periodic, disordered to ordered, non-topological to topological, and striped to honeycomb Moiré lattices. Furthermore, alterations in photonic band structures corresponding to the reconstruction of various PPCs have been investigated. Our system presents a promising platform for generating a PPC 'kaleidoscope', offering benefits such as reduced equipment requirements, low cost, rapid response, and enhanced flexibility. This development opens up new opportunities for both fundamental and applied research. [ABSTRACT FROM AUTHOR]

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system's security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators. [ABSTRACT FROM AUTHOR]

Abstract Episodic memory is essential for forming and retaining personal experiences, representing a fundamental aspect of human cognition. Traditional studies of episodic memory have typically used static analysis methods, viewing the brain as an unchanging entity and overlooking its dynamic properties over time. In this study, we utilized dynamic functional connectivity analysis on fMRI data from healthy adults performing an episodic memory task. We quantified integration and recruitment metrics and examined their correlation with memory performance using Pearson correlation. During encoding, integration across the entire brain, especially within the frontoparietal subnetwork, was significantly correlated with memory performance. During retrieval, recruitment becomes significantly associated with memory performance in visual subnetwork, somatomotor subnetwork, and ventral attention subnetwork. At the nodal level, a significant negative correlation was observed between memory scores and integration of the anterior cingulate gyrus, precentral gyrus, and inferior frontal gyrus within the frontoparietal network during encoding task. During retrieval task, a significant negative correlation was found between memory scores and recruitment in the left progranular cortex and right transverse gyral ventral, whereas positive correlations were seen in the right posterior inferior temporal, left middle temporal, right frontal operculum, and left operculum nodes. Moreover, the dynamic reconfiguration of the functional network was predictive of predict memory performance, as demonstrated by a significant correlation between actual and predicted memory scores. These findings advance our understanding network mechanisms underlying memory processes and developing intervention approaches for memory-related disorders as they shed light on critical factors involved in cognitive processes and provide a deeper understanding of the underlying mechanisms driving cognitive function.

Episodic memory is essential for forming and retaining personal experiences, representing a fundamental aspect of human cognition. Traditional studies of episodic memory have typically used static analysis methods, viewing the brain as an unchanging entity and overlooking its dynamic properties over time. In this study, we utilized dynamic functional connectivity analysis on fMRI data from healthy adults performing an episodic memory task. We quantified integration and recruitment metrics and examined their correlation with memory performance using Pearson correlation. During encoding, integration across the entire brain, especially within the frontoparietal subnetwork, was significantly correlated with memory performance. During retrieval, recruitment becomes significantly associated with memory performance in visual subnetwork, somatomotor subnetwork, and ventral attention subnetwork. At the nodal level, a significant negative correlation was observed between memory scores and integration of the anterior cingulate gyrus, precentral gyrus, and inferior frontal gyrus within the frontoparietal network during encoding task. During retrieval task, a significant negative correlation was found between memory scores and recruitment in the left progranular cortex and right transverse gyral ventral, whereas positive correlations were seen in the right posterior inferior temporal, left middle temporal, right frontal operculum, and left operculum nodes. Moreover, the dynamic reconfiguration of the functional network was predictive of predict memory performance, as demonstrated by a significant correlation between actual and predicted memory scores. These findings advance our understanding network mechanisms underlying memory processes and developing intervention approaches for memory-related disorders as they shed light on critical factors involved in cognitive processes and provide a deeper understanding of the underlying mechanisms driving cognitive function. [ABSTRACT FROM AUTHOR]

Background: Multilayer networks have been used to identify abnormal dynamic reconfiguration in bipolar disorder (BD). However, these studies ignore the differences in information interactions between adjacent layers when constructing multilayer networks, and the analysis of dynamic reconfiguration is not comprehensive enough; Methods: Resting-state functional magnetic resonance imaging data were collected from 46 BD patients and 54 normal controls. A multilayer temporal network was constructed for each subject, and inter-layer coupling of different nodes was considered using network similarity. The promiscuity, recruitment, and integration coefficients were calculated to quantify the different dynamic reconfigurations between the two groups; Results: The global inter-layer coupling, recruitment, and integration coefficients were significantly lower in BD patients. These results were further observed in the attention network and the limbic/paralimbic and subcortical network, reflecting reduced temporal stability, intra- and inter-subnetwork communication abilities in BD patients. The whole-brain promiscuity was increased in BD patients. The same results were observed in the somatosensory/motor and auditory network, reflecting more functional interactions; Conclusions: This study discovered abnormal dynamic interactions of BD from the perspective of dynamic reconfiguration, which can help to understand the pathological mechanisms of BD. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have been mainly powerful and important artificial intelligence techniques, which are exploited in various computer vision applications, such as facial point detection (FPD), owing their versatility and high performance. The role of DCNNs in this area is pivotal due to their ability to learn hierarchical features, which are essential for recognizing complex patterns. On the other hand, DCNNs have considerable computational complexity due to their topology nature. An FPGA-based SoC design is needed for CNN acceleration due to the rapid development, energy efficiency, low latency and high reconfigurability of FPGAs. Despite their performance benefits, FPGA-based SoCs also come with several limitations like complexity of design. This paper proposes a dynamic partial reconfiguration (DPR) and hybrid architecture for DCNN accelerators. We propose (i) a GPU-based software implementation for DCNN-based FPD, (ii) a CNN-based acceleration and optimization method using the high-level synthesis technique, (iii) a DPR-based hybrid architecture to improve the performance of the suggested approach. To validate our design, four scenarios are put forward. The experimental results prove that the proposed work achieves a better performance in terms of reconfigurability and execution time, hardware cost, and power consumption. [ABSTRACT FROM AUTHOR]

The snow-shaped distribution network (SDN) is a cable distribution network composed of eight (or six) 10 kV feeders from four (or three) substations in a regular connection. Compared with the traditional 10 kV distribution network, SDN can support a wide range of load transfer among six or eight feeders. Aiming at the problem of load spatial-temporal unbalanced condition caused by the integration of distributed generators (DGs) and different load types in different feeders, this paper proposes a dynamic reconfiguration strategy for load balancing in SDN considering DGs and energy storage system (ESS). Firstly, the basic structure of SDN is analyzed and the power flow model for its dynamic reconfiguration is developed. Secondly, the dynamic reconfiguration optimization model for load balancing in SDN considering DGs and ESS is proposed to utilize the load transfer capability to mitigate the load unbalanced condition and reduce active power loss. Thirdly, the original nonconvex model is converted into a mixed-integer second-order cone programming (MISOCP) model by applying the second-order cone relaxation and the big-M method, which is solved by CPLEX solver. Finally, the effectiveness of the proposed model and method are verified by an actual case in Tianjin and IEEE 33-node system. The analysis results show that the proposed method can significantly alleviate the load unbalanced spatial-temporal distribution and improve the economic efficiency by regulating the operation of SDN including ESS optimization and dynamic reconfiguration. [ABSTRACT FROM AUTHOR]

This paper investigates on dynamic topology reconfiguration of distribution networks to enhance PV hosting capacity. Firstly, a dynamic topology reconfiguration model of distribution networks considering N-1 security constraints is proposed to accommodate intermittent PV generation, and thus high penetration of renewable resources can be ensured to satisfy operational requirements under both normal and fault conditions with load transfer of feeders. Then, a hosting capacity enhancement strategy is presented to decrease risks of voltage and line overloading with reactive power flexibility of energy storage inverters, and is implemented to enhance the maximum hosting capacity of distributed renewable energy sources. Finally, simulation results have validated the effectiveness of the proposed method for PV hosting capacity enhancement of distribution networks.

The combat domain of modern warfare is becoming increasingly multidimensional. It is important to evaluate the resilience of the air-ground cooperative network for defending against attack threats and recovery performance. First, a resilience analysis model was proposed to effectively analyze and evaluate the resilience of the air-ground cooperative network. Then, considering the available resources, three dynamic reconfiguration strategies were given from the global perspective to help the air-ground cooperative network quickly recover performance and enhance combat capabilities. Finally, a typical 50-node network was taken as an example to prove the effectiveness and feasibility of the proposed model. The proposed method can provide scientific guidance for improving the air-ground cooperative network combat capabilities.

Embedded systems, particularly when integrated into the Internet of Things (IoT) landscape, are critical for projects requiring robust, energy-efficient interfaces to collect real-time data from the environment. As these systems become complex, the need for dynamic reconfiguration, improved availability, and stability becomes increasingly important. This paper presents the design of a framework architecture that supports dynamic reconfiguration and “on-the-fly” code execution in IoT-enabled embedded systems, including a virtual machine capable of hot reloads, ensuring system availability even during configuration updates. A “hardware-in-the-loop” workflow manages communication between the embedded components, while low-level coding constraints are accessible through an additional abstraction layer, with examples such as MicroPython or Lua. The study results demonstrate the VM’s ability to handle serialization and deserialization with minimal impact on system performance, even under high workloads, with serialization having a median time of 160 microseconds and deserialization having a median of 964 microseconds. Both processes were fast and resource-efficient under normal conditions, supporting real-time updates with occasional outliers, suggesting room for optimization and also highlighting the advantages of VM-based firmware update methods, which outperform traditional approaches like Serial and OTA (Over-the-Air, the ability to update or configure firmware, software, or devices via wireless connection) updates by achieving lower latency and greater consistency. With these promising results, however, challenges like occasional deserialization time outliers and the need for optimization in memory management and network protocols remain for future work. This study also provides a comparative analysis of currently available commercial solutions, highlighting their strengths and weaknesses.

Modern real-time systems started leveraging runtime reconfigurable architectures to reduce size, weight, and power while ensuring predictability. However, the time to reconfigure a platform (referred to as reconfiguration delay) is non-negligible and subject to fluctuations caused by conflicts in accessing shared components during the reconfiguration process. Different from existing solutions that impact either system resources or system performance, in this letter, we propose an approach, together with its supporting tools, allowing to characterize the reconfiguration delay and to be able to capture and produce evidence of the actual impact of the resource contention. In addition, we report the experiments of the proposed approach in a representative configuration on a Xilinx Zynq Ultrascale+ MPSoC; results show that conflicts can impact the reconfiguration delay at most by 230%. [ABSTRACT FROM AUTHOR]

The combat domain of modern warfare is becoming increasingly multidimensional. It is important to evaluate the resilience of the air-ground cooperative network for defending against attack threats and recovery performance. First, a resilience analysis model was proposed to effectively analyze and evaluate the resilience of the air-ground cooperative network. Then, considering the available resources, three dynamic reconfiguration strategies were given from the global perspective to help the air-ground cooperative network quickly recover performance and enhance combat capabilities. Finally, a typical 50-node network was taken as an example to prove the effectiveness and feasibility of the proposed model. The proposed method can provide scientific guidance for improving the air-ground cooperative network combat capabilities. [ABSTRACT FROM AUTHOR]

With the rapid development of new energy generation technology, the thermoelectric generation technology (TEG) can make good use of the waste heat generated in new energy generation. However, the change of temperature distribution will worsen the output characteristics and reduce the power generation efficiency of the TEG system. In this paper, a TEG array reconfiguration method based on the artificial bee colony (ABC) algorithm is proposed. In three different temperature distributions, ABC is used for dynamic reconfiguration of symmetric 9×9 and unsymmetric 10×15 TEG arrays. Three meta-heuristic algorithms, the genetic algorithm, the particle swarm optimization algorithm, and the bald eagle search are compared with the proposed method, and the temperature distribution of the TEG array reconfiguration by ABC is given. The results show that ABC can improve the output power of the TEG array, and the output power-voltage curves tend to show a single peak value. In addition, real-time hardware-in-the-loop (HIL) experiment based on the RTLAB platform is undertaken to verify the implementation feasibility.

Reconfiguration of a distribution network is one of the main approaches to control and enhance distribution network indices, such as voltage profile and power losses. Distribution network operators perform reconfiguration for long-term or short-term periods based on network equipment and intended objectives. Long-term or static reconfiguration is suitable for traditional and modern networks with conventional switches. On the other hand, modern distribution networks that are equipped with one or more remote control switches can perform reconfigurations within short-term periods, to maximize predefined objectives. This paper presents a comprehensive review of recent literature on network reconfiguration. Reconfiguration methodologies are classified into five groups: classical methods, heuristic methods, metaheuristic methods, hybrid methods, and methods based on machine learning. The paper provides a general definition and comparison of the categories and discusses their application in dynamic and static reconfiguration. The paper introduces dynamic reconfiguration as the future challenges in smart and modern distribution networks and for the first time categorizes various methodologies in dynamic reconfiguration. The paper serves as a guide to assist engineers and researchers in selecting the most suitable methodology based on their system equipment and objectives.

In the field of power electronics research, the expansion of the number of phases and voltage levels in power converters may pose several challenges for central processors. In the space vector pulse width modulation (SVPWM) method, the microprocessor must simultaneously calculate the multi-vector switching and the corresponding switching time based on complex computational algorithms. The complexity of the algorithm flowchart tends to increase significantly as the number of phases and voltage levels increase. This problem can be easily solved in decentralized controlled power converters. The decentralized control structure and method allow cells to divide themselves into tasks based on limited information exchange with neighboring cells. This study presents a decentralized control topology for a multilevel multiphase power converter (MMPC) using the SVPWM method. Depending on the number of active cells, each cell exchanges information with its neighbors, determining the exact switching vector and switching time. Using this approach, the requirements for computing power and processing speed of the cells are significantly reduced. Digital signal processing (DSP) TMS320F28379D is used as a controller of cells to verify the proposed structure and method in addition to simulation results using on MATLAB/Simulink software.

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

The charging and discharging process of battery energy storage (BES) directly affects its cycle aging. Therefore, considering the cycle aging cost and demand response of BES, this paper proposes a dynamic reconfiguration method of AC/DC hybrid distribution network. By sampling the operation area of charging and discharging power, the BES model in the non-ideal state is reconstructed into a linear model in the convex envelope set of sampling points. On this basis, the cycle aging cost models based on the cumulative throughput and discharge depth are established respectively, and the AC/DC hybrid distribution network reconfiguration model with the minimum comprehensive cost is constructed, and the mixed integer second-order cone programming method is used to solve it. The effectiveness and economy of the proposed reconfiguration method for AC/DC hybrid distribution network are verified by simulation on a modified 33-bus and 70-bus system.

The snow-shaped distribution network (SDN) is a cable distribution network composed of eight (or six) 10 kV feeders from four (or three) substations in a regular connection. Compared with the traditional 10 kV distribution network, SDN can support a wide range of load transfer among six or eight feeders. Aiming at the problem of load spatial-temporal unbalanced condition caused by the integration of distributed generators (DGs) and different load types in different feeders, this paper proposes a dynamic reconfiguration strategy for load balancing in SDN considering DGs and energy storage system (ESS). Firstly, the basic structure of SDN is analyzed and the power flow model for its dynamic reconfiguration is developed. Secondly, the dynamic reconfiguration optimization model for load balancing in SDN considering DGs and ESS is proposed to utilize the load transfer capability to mitigate the load unbalanced condition and reduce active power loss. Thirdly, the original non-convex model is converted into a mixed-integer second-order cone programming (MISOCP) model by applying the second-order cone relaxation and the big-M method, which is solved by CPLEX solver. Finally, the effectiveness of the proposed model and method are verified by an actual case in Tianjin and IEEE 33-node system. The analysis results show that the proposed method can significantly alleviate the load unbalanced spatial-temporal distribution and improve the economic efficiency by regulating the operation of SDN including ESS optimization and dynamic reconfiguration.

Aiming at the problem of distribution network reconfiguration with wind-solar-load uncertainty, a distributed generation and load output model was established. A multi-objective function was constructed with system operating cost and voltage offset. Firstly, an improved particle swarm optimization and K-means( IPSO-Kmeans) clustering algorithm was proposed to divide the typical daily load curve. Then, the improved Harris Hawk optimization algorithm ( IHHO ) was applied to the distribution network reconfiguration for optimization calculation. In order to improve the Harris hawk optimization ( HHO ) algorithm, the population distribution was uneven, the optimal solution space range cannot be completely searched, and it was easy to fall into local convergence. The good point set was introduced to generate population initialization to improve the uniformity of population search space. The explorer position update formula in the sparrow search algorithm was combined with the position update formula in the HHO algorithm exploration stage. This improved the global search ability of the algorithm. The Cauchy-Gaussian mutation perturbation strategy was used to jump out of the local optimal solution. Finally, the simulation results of IEEE33 node distribution network system show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Chiral artificial nanosystems constructed from inorganic nanoparticles brought about a paradigm shift in chiral sensing, enantioselective catalysis, chiral polarization engineering, and chiral emission due to their strong light–matter interaction and their precisely engineered properties. Especially bottom‐up approaches hold great potential for the inexpensive, scalable production of plasmonic, excitonic, and upconversion chiral materials. This perspective first reviews the state‐of‐the‐art fabrication schemes: chiral molecule‐induced chirality, chiral nanoparticle growth, and assembly of chiral superstructures from achiral building blocks. Next, the emerging field of dynamically tunable chiroptical materials, promising for adaptive thin‐film optics, display technologies, and information encoding, is explored. Here, the strength of chiral assemblies based on their rich tuning mechanisms and their ability for geometrical reconfiguration is highlighted. Finally, promising future directions and opportunities in the field of active chiroptical materials are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) arrays are often affected by partial shading (PS), which can significantly reduce their power output. Dynamic reconfiguration is a promising technique for mitigating the negative effects of PS by adjusting the electrical connections of the PV modules in real-time. This paper introduces a hierarchical-based switching block scheme for the dynamic reconfiguration of PV arrays under PS conditions. With the aim of mitigating the negative impact of PS on PV arrays, the proposed system employs a low complexity and easily scalable architecture, making it well-suited for practical applications. Comparative assessments against conventional configurations such as bridge-linked (BL), total-cross-tied (TCT), and series–parallel (SP) reveal superior energy harvesting efficiency for the proposed system under various shading conditions. The hierarchical switching block architecture, featuring multiple levels of switching blocks, enables efficient and flexible reconfiguration of the PV array, even in the presence of complex shading patterns. Through extensive simulations, the system consistently outperforms conventional configurations by adapting effectively to changing shading patterns and optimizing the PV array's output. The proposed switching block (SB) reconfiguration technique significantly outperforms existing methods like TCT, Sudoku, dynamic proposals, and Magic Square in terms of both power generation (up to 42.52% increase) and efficiency (up to 42.13% improvement) under diverse partial shading conditions. The proposed hierarchical-based switching block scheme thus presents a promising solution for enhancing the dynamic reconfiguration of PV arrays under PS conditions, offering a balance between low complexity, scalability, and superior energy harvesting efficiency for practical applications in the realm of solar energy. [ABSTRACT FROM AUTHOR]

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We propose a multi-threaded system that supports reconfigurable hardware accelerators in an embedded computing platform, integrating real-time operating system and CPU-FPGA heterogeneity. Our system focuses on efficient hardware resource management in image signal processing applications, where computational complexities vary in real-time based on data characteristics. Specifically, we implement a high-performance JPEG decoder with reconfigurable 2D IDCT hardware accelerators, allowing dynamic reconfiguration of up to four accelerators synchronized with software threads. The hardware accelerators are used as hardware threads to compute the 2D-IDCT, which is a computationally intensive task in JPEG decoding. The hardware multi-threaded system integrates software and hardware components, providing seamless interaction between them through the OS services. Our results demonstrate significant performance improvements ranging from approximately 11.00 to 173.96 times when processing 1920 × 1080 compressed images by dynamically adjusting the number of hardware accelerators to provide flexibility and efficient resource utilization. Additionally, we observe that higher resolutions and compression ratios lead to greater performance improvements, leveraging the architectural features of the 2D-IDCT hardware accelerator. [ABSTRACT FROM AUTHOR]

Social relations can effectively alleviate the data sparsity problem in recommendation, but how to make effective use of social relations is a difficulty. However, the existing social recommendation models have two deficiencies. First, these models assume that social relations are applicable to various interaction scenarios, which does not match the reality. Second, it is believed that close friends in social space also have similar interests in interactive space and then indiscriminately adopt friends' opinions. To solve the above problems, this paper proposes a recommendation model based on generative adversarial network and social reconstruction (SRGAN). We propose a new adversarial framework to learn interactive data distribution. On the one hand, the generator selects friends who are similar to the user's personal preferences and considers the influence of friends on users from multiple angles to get their opinions. On the other hand, friends' opinions and users' personal preferences are distinguished by the discriminator. Then, the social reconstruction module is introduced to reconstruct the social network and constantly optimize the social relations of users, so that the social neighborhood can assist the recommendation effectively. Finally, the validity of our model is verified by experimental comparison with multiple social recommendation models on four datasets.