Loop Network Research Articles

Neurosteroids as a possible new horizon in the treatment of fibromyalgia

Fibromyalgia (FM) is a central chronic pain syndrome with fatigue, sleep disorders, and other symptoms. It has 1–5 % worldwide prevalence, 3:1 female-to-male ratio, and shows correlation with stress. The pathogenic mechanism is unknown, biomarkers are missing, and patient management is extremely difficult due to the lack of resolutive therapies. We developed a pathogenic model of FM based on a thalamocortical loop network that shifts from monostability to bistability for decreasing GABAergic and increasing glutamatergic transmission, leading to the appearance of a high-firing-rate steady state that represents FM altered central pain processing. Here, we propose the hypothesis that the pathogenic GABA/glutamate unbalance could be effectively counteracted by the use of neurosteroid drugs acting as positive allosteric modulators of both synaptic and extrasynaptic GABAA receptors. Our hypothesis is based on evidence suggesting the involvement of large fluctuations of gonadal neurosteroids (notably brain allopregnanolone withdrawal in perimenstrual/peripartum periods) and of adrenocortical hormones (notably cortisol rise during stress activation) in FM pathogenesis and in the regulation of central GABA/glutamate balance. Therefore, our hypothesis provides a link between FM clinical features (such as female prevalence and correlation with stress) and endocrine influences on neurotransmission, suggesting the use of neurosteroid drugs for the treatment of the disease.

Erratum for the Research Article: "Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals," by He et al.

Erratum for the Research Article: "Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals," by He et al.

Research on the minimum network loss optimization control strategy of the active distribution network based on the distributed power flow controller

With large-scale access to distributed new energy, the power flow form of the distribution network tends to be complex. The distribution loop network rate increases, resulting in serious circulation of the distribution network, which places significant pressure on the loss of the distribution network. The problem of network loss change caused by distributed new energy grid connection, coupled with the inconsistency between load fluctuation and output power change of distributed new energy, will have a significant impact on the safety, reliability, and economic operation of the distribution network. Therefore, it is urgent to have good economic and technical means of power flow control. This paper proposes a minimum network loss optimization control strategy for active distribution networks based on a distributed power flow controller. First, according to the characteristics of the distribution network load and the new energy power supply along the distribution line, the multiple sub-modules of the distributed power flow controller are arranged in sections in the distribution loop network line. The regulation effect of the distributed power flow controller on the voltage and power flow of the distribution loop network is analyzed. The loop network current equation and power loss equation after the distributed power flow controller are constructed, and the power equation with the minimum active loss of the active distribution loop network is derived. Second, the control strategy for minimum network loss of the active distribution network based on the distributed power flow controller is proposed. Finally, the simulation model of the active distribution loop network is constructed by PSCAD/EMTDC. The loss of the loop network before and after the distributed power flow controller is simulated and analyzed, and the effectiveness of the proposed strategy is verified.

Coexistence of Defect Morphologies in Three-Dimensional Active Nematics.

We establish how active stress globally affects the morphology of disclination lines of a three-dimensional active nematic liquid crystal under chaotic flow. Thanks to a defect detection algorithm based on the local nematic orientation, we show that activity selects a crossover length scale in between the size of small defect loops and that of long and tangled defect lines of fractal dimension 2. This length scale crossover is consistent with the scaling of the average separation between defects as a function of activity. Moreover, on the basis of numerical simulation in a 3D periodic geometry, we show the presence of a network of regular defect loops, contractible onto the 3-torus, always coexisting with wrapping defect lines. While the length of regular defects scales linearly with the emerging active length scale, it verifies an inverse quadratic dependence for wrapping defects. The shorter the active length scale, the more the defect lines wrap around the periodic boundaries, resulting in extremely long and buckled structures.

Topological Spectra and Entropy of Chromatin Loop Networks

Topological Spectra and Entropy of Chromatin Loop Networks

Combinatorics and topological weights of chromatin loop networks.

Polymer physics models suggest that chromatin spontaneously folds into loop networks with transcription units (TUs), such as enhancers and promoters, as anchors. Here we use combinatoric arguments to enumerate the emergent chromatin loop networks, both in the case where TUs are labeled and where they are unlabeled. We then combine these mathematical results with those of computer simulations aimed at finding the inter-TU energy required to form a target loop network. We show that different topologies are vastly different in terms of both their combinatorial weight and energy of formation. We explain the latter result qualitatively by computing the topological weight of a given network-i.e., its partition function in statistical mechanics language-in the approximation where excluded volume interactions are neglected. Our results show that networks featuring local loops are statistically more likely with respect to networks including more nonlocal contacts. We suggest our classification of loop networks, together with our estimate of the combinatorial and topological weight of each network, will be relevant to catalog three-dimensional structures of chromatin fibers around eukaryotic genes, and to estimate their relative frequency in both simulations and experiments.

Control and Protection of Medium Voltage Distribution Network Based on Unified Power Flow Controller

The integration of diverse load types, such as large-scale distributed generators and charging stations, presents considerable challenges to distribution networks. The Unified Power Flow Controller (UPFC) provides multiple benefits, including its compact dimensions, cost efficiency, seamless regulation, and swift response. When deployed in distribution networks, the UPFC facilitates the creation of a Distribution Unified Power Flow Controller (DUPFC) system. The DUPFC system enhances medium voltage distribution network regulation but also affects power quality, fault characteristics, and relay protection accuracy. To address these issues, a power flow equivalent circuit model for the medium voltage distribution loop network was developed. The operating principle and steady-state model of the UPFC were investigated, based on distribution loop network parameters and power flow distribution characteristics. A UPFC control strategy was designed to create a suitable DUPFC system for the medium voltage distribution loop network, ensuring coordination between UPFC protection and the distribution system’s protection mechanisms. This system accomplishes rapid fault isolation and automatic load transfer. Finally, a simulation model based on a typical 20 kV distribution loop network structure was selected to validate the effectiveness of the UPFC control strategy and the DUPFC protection system in MATLAB/Simulink.

Optimal dispatch of integrated electricity and heating systems considering the quality-quantity regulation of heating systems to promote renewable energy consumption

The integrated electricity and heating system (IEHS) can improve energy efficiency and promote renewable energy consumption. When quality-quantity regulation (QQR) is adopted, namely adjusting hydraulic and thermal conditions of heating system, the increase in operation flexibility is more remarkable. However, present IEHS dispatch models considering QQR overlook hydraulic characteristics of valves and variable frequency pumps resulting in inexecutable dispatch results easily and hindering IEHS's flexibility potential. Furthermore, loop networks and the access of multiple heat sources in heating systems may lead to flow reversal, but current research adopts a fixed flow direction which suppresses the potential of flow reversal to improve flexibility. In this paper, we propose an IEHS dispatch model considering QQR to promote renewable energy consumption, which considers hydraulic characteristics of valves and variable frequency pumps, and flow reversal. Specifically, we introduce two binary flow direction labels for every branch in heating system model and then the IEHS dispatch model which can deal with flow reversal is established. A sequential solution process combining linear and nonlinear optimization is formulated to overcome the non-convex feature of IEHS dispatch model. Specifically, piecewise linearization and piecewise McCormick relaxation are combined to handle complex nonlinear terms in the dispatch model. Therefore, a mixed integer linear programming model is obtained and solved, of which results are used as initial values for nonlinear optimization. Results in the case study show that operation cost is decreased by 0.97 % and renewable power consumption rate is increased from 83.31 % to 84.42 % after considering valve adjustment. Operation cost is further decreased by 6.06 % and renewable power consumption rate is increased to 94.04 % after considering flow reversal.

Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals.

The development of radiation-tolerant structural materials is an essential element for the success of advanced nuclear energy concepts. A proven strategy to increase radiation resistance is to create microstructures with a high density of internal defect sinks, such as grain boundaries (GBs). However, as GBs absorb defects, they undergo internal transformations that limit their ability to capture defects indefinitely. Here, we show that, as the sink efficiency of GBs becomes exhausted with increasing irradiation dose, networks of irradiation loops form in the vicinity of saturated or near-saturated GB, maintaining and even increasing their capacity to continue absorbing defects. The formation of these networks fundamentally changes the driving force for defect absorption at GB, from "chemical" to "elastic." Using thermally-activated dislocation dynamics simulations, we show that these networks are consistent with experimental measurements of defect densities near GB. Our results point to these networks as a natural continuation of the GB once they exhaust their internal defect absorption capacity.

Air entrapment modelling in water supply networks during pipe filling events

ABSTRACT Intermittent water supply systems are prone to air entrapments during the pipe filling phase. This work aims to analyse and discuss the numerical results obtained by applying the recently developed AirSWMM model, an extension of SWMM incorporating air phase, to a laboratory network. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapments are collected in a single loop network with a high point, for different pipe-filling conditions, system layouts and node elevations. Experimental tests have shown that the air entrapment occurred not only at the high point but also throughout the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipes. AirSWWM model with air-entrapment formation, growth and transport is tested in the pipe network, and results are compared with measurements. AirSWWM model can correctly locate large air pockets but underestimates their volume.

Efficient Resource Scheduling in Fog: A Multi-Objective Optimization Approach

Fog computing is a novel idea that extends cloud computing by offering services like processing, storage, analysis, and networking on fog devices closer to IoT devices. Numerous fog devices are required to process the ever-growing amount of data generated by IoT applications. The heterogeneous tasks from various IoT applications compete for a limited number of resources of these devices. The process of assigning this set of tasks to different available fog nodes according to QoS requirements for processing is resource scheduling. Resource scheduling aims to optimize resource utilization and performance metrics however, the dynamic nature of the Fog environment, resource-constrained, and heterogeneity in fog devices make resource scheduling a complex issue. This research presents the design and implementation of a multi-objective optimization-based resource scheduling algorithm using Modified Particle Swarm Optimization (MPSO) that addresses the application module placement and task allocation issues. This two-step MPSO-based resource scheduling model finds the optimal fog node to place each application module and assigns appropriate tasks to the most optimal fog nodes for execution. The proposed model unlocks the full potential of fog resources along with maximization of overall system performance in terms of optimization of cost, latency, energy consumption, and network usage. The simulation results indicate that using MPSO energy consumption is reduced by 53.94% and 43.58% as compared to First Come First Serve (FCFS) and Particle Swarm Optimization (PSO), respectively. The loop delay, network usage and cost using MPSO are reduced by 40.3%, 67.69% and 90.01% respectively, as compared to PSO algorithm.

A deep learning-based interactive medical image segmentation framework with sequential memory

Background and objective. Image segmentation is an essential component in medical image analysis. The case of 3D images such as MRI is particularly challenging and time consuming. Interactive or semi-automatic methods are thus highly desirable. However, existing methods do not exploit the typical sequentiality of real user interactions. This is due to the interaction memory used in these systems, which discards ordering. In contrast, we argue that the order of the user corrections should be used for training and lead to performance improvements.Methods. We contribute to solving this problem by proposing a general multi-class deep learning-based interactive framework for image segmentation, which embeds a base network in a user interaction loop with a user feedback memory. We propose to model the memory explicitly as a sequence of consecutive system states, from which the features can be learned, generally learning from the segmentation refinement process. Training is a major difficulty owing to the network's input being dependent on the previous output. We adapt the network to this loop by introducing a virtual user in the training process, modelled by dynamically simulating the iterative user feedback.Results. We evaluated our framework against existing methods on the complex task of multi-class semantic instance female pelvis MRI segmentation with 5 classes, including up to 27 tumour instances, using a segmentation dataset collected in our hospital, and on liver and pancreas CT segmentation, using public datasets. We conducted a user evaluation, involving both senior and junior medical personnel in matching and adjacent areas of expertise. We observed an annotation time reduction with 5'56” for our framework against 25' on average for classical tools. We systematically evaluated the influence of the number of clicks on the segmentation accuracy. A single interaction round our framework outperforms existing automatic systems with a comparable setup. We provide an ablation study and show that our framework outperforms existing interactive systems.Conclusions. Our framework largely outperforms existing systems in accuracy, with the largest impact on the smallest, most difficult classes, and drastically reduces the average user segmentation time with fast inference at 47.2±6.2 ms per image.

Ecosystem Energy Changes

In this paper energy in ecosystem was studied. Energy balance equations of ecosystem were derived. Most of the energy in the Earth’s system comes from just a few sources: solar energy, gravity, radioactive decay, and the rotation of the earth. Earth is constantly changing as energy flows through the system. Earth’s weather and climate are mostly driven by energy from the Sun. The Sun provides the energy that drives the water cycle on Earth. In this paper different forms of energy, specific types, using and energy conservation were studied. Environments are highly complex systems whose evolution is determined by complicated networks of positive and negative feedback loops. In this paper macroscopic and microscopic approaches to energy flow through the ecosystem have been used.

Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation

Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation

Design and modeling of an all-solid-state high power bidirectional DC switch

In order to improve the reliability of the aircraft ring network power distribution system for the future development needs of advanced multi-power and all-power aircraft, a design scheme of an all-solid-state high-power bidirectional DC switch is proposed to upgrade the aircraft power distribution system originally composed of traditional mechanical switches and some low-power solid-state power controllers to an all-solid-state aircraft power distribution system. The solid-state high-power bi-directional DC switch uses SiC MOSFETs as switching elements is modeled and simulated by Saber simulation software and uses reverse time overcurrent protection and short circuit protection design to realize high voltage control distribution, circuit protection, and fault isolation functions. The simulation verifies that the designed all-solid-state high-power bidirectional DC switch has short-circuit protection and overcurrent protection intelligent protection functions. Applying the design to the aircraft distribution loop network greatly enhances the reliability of the aircraft distribution system drives the development of aircraft multi-electricity-all-electricity technology, and also promotes the application of digital solid-state series modular switches in civil transportation and high-voltage power supply fields.

Tracing the birth of structural domains from loops during protein evolution

The structures and functions of proteins are embedded into the loop scaffolds of structural domains. Their origin and evolution remain mysterious. Here, we use a novel graph-theoretical approach to describe how modular and non-modular loop prototypes combine to form folded structures in protein domain evolution. Phylogenomic data-driven chronologies reoriented a bipartite network of loops and domains (and its projections) into ‘waterfalls’ depicting an evolving ‘elementary functionome’ (EF). Two primordial waves of functional innovation involving founder ‘p-loop’ and ‘winged-helix’ domains were accompanied by an ongoing emergence and reuse of structural and functional novelty. Metabolic pathways expanded before translation functionalities. A dual hourglass recruitment pattern transferred scale-free properties from loop to domain components of the EF network in generative cycles of hierarchical modularity. Modeling the evolutionary emergence of the oldest P-loop and winged-helix domains with AlphFold2 uncovered rapid convergence towards folded structure, suggesting that a folding vocabulary exists in loops for protein fold repurposing and design.

Factors affecting the leaching of micro and nanoplastics in the water distribution system

The role of the water distribution system (WDS) requires that it supply water of sufficient quality to households. Unregulated leaching of micro and nanoplastics from plastic pipes of the distribution system is therefore a cause for concern, particularly with the rise in research associating these plastic particles to adverse health impacts in living organisms. Within this study, four parameters (pH, free chlorine concentration, pipe material, and time) were varied in a pipe loop network to observe their effect on microplastic (MP) and nanoplastic (NP) leaching into the simulated distribution network. Results indicated an abundance of MPs/NPs in different shapes and sizes throughout the samples. Graphical trends illustrated that basic pH values contributed to a higher number of particles. Statistical analysis via analysis of variance (ANOVA) confirmed this observation and further showed interaction of chlorine dose and pH concentration (p-value = 0.000), and chlorine dose and pipe material (p-value = 0.038) was also significant to leaching. Numerically, polyethylene (PE) particles were the most abundant with a total of 15194 particles, followed by 12920 polypropylene random copolymer (PPR) particles and 12317 polyvinyl chloride (PVC) particles. It was also noticed that the number of particles decreased with time.© 2023 Elsevier Inc. All rights reserved.

Inflammation o'clock: interactions of circadian rhythms with inflammation-induced skeletal muscle atrophy.

Circadian rhythms are ∼24 h cycles evident in behaviour, physiology and metabolism. The molecular mechanism directing circadian rhythms is the circadian clock, which is composed of an interactive network of transcription-translation feedback loops. The core clock genes include Bmal1, Clock, Rev-erbα/β, Per and Cry. In addition to keeping time, the core clock regulates a daily programme of gene expression that is important for overall cell homeostasis. The circadian clock mechanism is present in all cells, including skeletal muscle fibres, and disruption of the muscle clock is associated with changes in muscle phenotype and function. Skeletal muscle atrophy is largely associated with a lower quality of life, frailty and reduced lifespan. Physiological and genetic modification of the core clock mechanism yields immune dysfunction, alters inflammatory factor expression and secretion and is associated with skeletal muscle atrophy in multiple conditions, such as ageing and cancer cachexia. Here, we summarize the possible interplay between the circadian clock modulation of immune cells, systemic inflammatory status and skeletal muscle atrophy in chronic inflammatory conditions. Although there is a clear disruption of circadian clocks in various models of atrophy, the mechanism behind such alterations remains unknown. Understanding the modulatory potential of muscle and immune circadian clocks in inflammation and skeletal muscle health is essential for the development of therapeutic strategies to protect skeletal muscle mass and function of patients with chronic inflammation.

A novel deinterleaving method for radar pulse trains using pulse descriptor word dot matrix images and cascade‐recurrent loop network

AbstractTraditional signal deinterleaving methods depend on preset parameters, use complicated steps and have limitations in handling complex electromagnetic environments where signals overlap in the space, time and frequency domains. This paper presents a novel approach using a deep segmentation network for radar signal deinterleaving. The pulse descriptor word data is transformed into a dot matrix image, which is then processed by a Cascade‐Recurrent Loop Network (CRLN) for segmentation. The CRLN consists of a Type Segmentation Network, an Amount Decision Network and a Recurrent Individual Segmentation Network. By recursively segmenting each target and determining the number of sources, the CRLN overcomes the challenges posed by the discontinuity and high overlap of individuals in the dot matrix image. Experimental results demonstrate the effectiveness of the proposed method, surpassing traditional deinterleaving methods, achieving high accuracy, low omission rate and effectively mitigates the increasing batch problem in complex scenarios. The experiments conducted on a randomly generated dataset yielded impressive results: over 90% accuracy when dealing with pulses from 15 radars of different types, over 96% accuracy when deinterleaving pulses from 4 radar individuals of a same type and over 90% accuracy when handling 5 radars of different types with 10% pulse loss.

Predictive Control of a Human–in–the–Loop Network System Considering Operator Comfort Requirements

We propose a model-predictive control (MPC)-based approach to solve a human-in-the-loop control problem for a network system lacking sensors and actuators to allow for a fully automatic operation. The humans in the loop are, therefore, essential; they travel between the network nodes to provide the remote controller with measurements and to actuate the system according to the controller’s commands. Time instant optimization MPC is utilized to compute when the measurement and actuation actions are to take place to coordinate them with the network dynamics. The time instants also minimize the burden of human operators by tracking their energy levels and scheduling the necessary breaks. Fuel consumption related to the operators’ travel is also minimized. The results in a digital twin of the Dez Main Canal illustrate that the new algorithm outperforms previous methods in terms of meeting operational objectives and taking care of human well-being, but at the cost of higher computational requirements.