Flow Constraints Research Articles

Identification and validation of ferroptosis-related hub genes and immune infiltration in liver ischemia-reperfusion injury

Ischemia–reperfusion injury (IRI) is a cumulation of pathophysiological processes that involves cell and organelle damage upon blood flow constraint and subsequent restoration. However, studies on overall immune infiltration and ferroptosis in liver ischemia-reperfusion injury (LIRI) are limited. This study explored immune cell infiltration and ferroptosis in LIRI using bioinformatics and experimental validation. The GSE151648 dataset, including 40 matched pairs of pre- and post- transplant liver samples was downloaded for bioinformatic analysis. Eleven hub genes were identified by overlapping differentially expressed genes (DEGs), iron genes, and genes identified through weighted gene co-expression network analysis (WGCNA). Subsequently, the pathway enrichment, transcription factor-target, microRNA-mRNA and protein-protein interaction networks were investigated. The diagnostic model was established by logistic regression, which was validated in the GSE23649 and GSE100155 datasets and verified using cytological experiments. Moreover, several drugs targeting these genes were found in DrugBank, providing a more effective treatment for LIRI. In addition, the expression of 11 hub genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in liver transplantation samples and animal models. The expression of the 11 hub genes increased in LIRI compared with the control. Five genes were significantly enriched in six biological process terms, six genes showed high enrichment for LIRI-related signaling pathways. There were 56 relevant transcriptional factors and two central modules in the protein-protein interaction network. Further immune infiltration analysis indicated that immune cells including neutrophils and natural killer cells were differentially accumulated in the pre- and post-transplant groups, and this was accompanied by changes in immune-related factors. Finally, 10 targeted drugs were screened. Through bioinformatics and further experimental verification, we identified hub genes related to ferroptosis that could be used as potential targets to alleviate LIRI.

Enhanced integrated energy system planning through unified model coupling multiple energy and carbon emission flows

Integrated energy system plays a crucial role in achieving low carbon emissions while supplying multiple loads. As the energy Internet continues to evolve, integrated energy system, acting as a cell-level prosumer, exhibits intricate coupling characteristics between multiple energy flows and carbon flows. This paper presents a flexible planning method based on a unified energy-carbon coupled operation model. It can realize blank-paper planning under multiple energy-carbon flow constraints, that is, jointly optimize equipment configuration and capacity size from scratch. The proposed method combines clustering algorithms and Latin hypercube sampling to generate the operational scenarios for renewable resources and energy loads, enhancing the representativeness and reliability of planning solutions. The results demonstrate that the proposed method can effectively meet the requirements of electricity, cooling and heat loads under different seasonal scenarios. Furthermore, it elucidates the corresponding carbon emission distribution across distinct energy flows of the planned system. Compared with the conventional fixed equipment connected planning method, the more flexible blank-paper planning method can achieve annual economic cost savings of about 7.5 % and reduce annual carbon emissions by 6.7 %.

Nanofluids' thermal assessment: Active and passive control approach

The nanofluids are decomposition of nano-sized metallic particles with base materials maintaining peak thermal properties. Owing to enhanced thermal features, various applications of nanofluids are observed in enhancing energy resources and cooling processes. The objective of current work is exploring the thermal impact of nanofluid associated to the oblique stagnation point flow. The thermal interpretation of nanofluid is subject to active and passive control approach. The heat trnasfer analysis is identified by using convective thermal flow constraints. The Buongiorno nanofluid model is adopted, endorsing the Brownian and thermophoresis consequences. The flow problem is first simplfied intodimensionless form. The numerical computations are performed by using famous shooting scheme with justifiable accuracy. A comparative change in the thermal phenomenon given the passive and active frameworks is presented. It is exmained that heat transfer reduces for stretching ratio parameter. The concentration profile reduces for angle of incidence for both active and passive control approach.

Analytical approximations of laminar flow states above a curved wall heated from below

An analytical framework is developed for estimating wall-bounded plane laminar flow states above curved walls subject to external shear and heating. The framework is applicable to arbitrary lower-wall geometries with arbitrary temperature prescriptions. The study relies on perturbation expansions of flow states with respect to the inverse of the dimensionless channel wavelength. Exact closed-form solutions are obtained for systems of perturbation equations up to the third order, which have the freedom to allow for any flow constraint. As an application of the analytical framework, we examine flow states above two specific instances of wall geometries with long channel wavelengths. The approximated flow states shed insights into morphologies of laminar convection states at low shear rates (i.e. low Reynolds numbers).

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Distributed optimal scheduling for virtual power plant with high penetration of renewable energy

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

Managing power balance and reserve feasibility in the AC unit commitment problem

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment problems for the day-ahead market and develops decomposition algorithms capable of obtaining high-quality solutions at industry-relevant scales. The results illustrate that a simple algorithm that only seeks to satisfy unit commitment, reserve, and AC power balance constraints can obtain surprisingly high-quality solutions to this AC unit commitment problem. However, a naive strategy that prioritizes reserve feasibility leads to AC infeasibility, motivating the need to design heuristics that can effectively balance reserve and AC feasibility. Finally, this work explores a parallel decomposition strategy that allows the proposed algorithm to obtain feasible solutions on large cases within the two hour time limit required by typical day-ahead market operations.

A techno-economic framework for optimizing multi-area power dispatch in microgrids with tie-line constraints

Fuel-fired power plants are the primary electricity sources in Saudi Arabia. This investigation aims to reduce the overall costs and emissions of electricity production from thermal units operated by the Shuqaiq Water and Electricity Company. It also considers practical thermoelectric constraints, such as power losses, ramp-rate constraints, and environmental optimization functions, to align with the sustainability goals of Saudi Vision 2030. Optimizing electric energy delivery is crucial within an energy control center. The focus of this work is to develop an advanced computational technique that reduces reliance on fossil fuels and minimizes the production cost of electricity, while also considering environmental emissions as an optimization function. An examination was conducted through two distinct practical case studies with power demand of 850 MW—one involving renewable sources and the other without—to assess the effectiveness of the hybrid crow search and JAYA optimization algorithm in optimizing performance. This system includes a multi-area setup with limitations on the tie-line power flow constraints. Additionally, the study also involved RETScreen modelling to assess the practical financial, emission, and benchmark analysis of a proposed integrated solar plant. This analyses offers a thorough assessment of the facility’s performance and reliability in practical situations. This study serves as a motivating factor for energy operators, stakeholders, governmental organizations, and grid administrators to invest resources in environmentally-friendly initiatives. The simulation results showed that the proposed approach successfully reduced the electricity cost by 2.29% and 3.70% in hybrid energy networks while effectively handling the system constraints. In addition, the analysis revealed that the optimization algorithm effectively decreased toxic emissions from power plants while also supporting the sustainability objectives of United Nations SDG-7 and Saudi Vision 2030 through the successful integration of renewable sources.

Low dissipative configuration in flow networks subject to constraints

The homothetic relationships for the design of dendritic networks are examined in terms of minimal size under constant flow resistance, and minimum flow resistance under constant size. Based on a comprehensive methodology, we offer a general approach for the homothety ratios of diameters and lengths that apply to different flow regimes and size constraints. In addition, scaling laws for diameters-lengths, sizes-diameters, and resistances-lengths are provided. Since the dendritic trees designed based on size homothety ratios have prefractal characteristics, a methodology for determining prefractal dimensions in terms of fluid flow and size constraint characteristics is also offered. Among the findings, we show that the homothety ratios are the same regardless of whether the functions we selected are used as the constraint or the cost function. The approaches presented and literature data were compared, and a significant degree of agreement was found.The findings presented here not only serve as a tool for the design of microfluidic chip devices but also deepen our understanding of natural networks such as the ones that support the life of mammals.

Exploring the Temporal Consistency of Arbitrary Style Transfer: A Channelwise Perspective.

Arbitrary image stylization by neural networks has become a popular topic, and video stylization is attracting more attention as an extension of image stylization. However, when image stylization methods are applied to videos, unsatisfactory results that suffer from severe flickering effects appear. In this article, we conducted a detailed and comprehensive analysis of the cause of such flickering effects. Systematic comparisons among typical neural style transfer approaches show that the feature migration modules for state-of-the-art (SOTA) learning systems are ill-conditioned and could lead to a channelwise misalignment between the input content representations and the generated frames. Unlike traditional methods that relieve the misalignment via additional optical flow constraints or regularization modules, we focus on keeping the temporal consistency by aligning each output frame with the input frame. To this end, we propose a simple yet efficient multichannel correlation network (MCCNet), to ensure that output frames are directly aligned with inputs in the hidden feature space while maintaining the desired style patterns. An inner channel similarity loss is adopted to eliminate side effects caused by the absence of nonlinear operations such as softmax for strict alignment. Furthermore, to improve the performance of MCCNet under complex light conditions, we introduce an illumination loss during training. Qualitative and quantitative evaluations demonstrate that MCCNet performs well in arbitrary video and image style transfer tasks. Code is available at https://github.com/kongxiuxiu/MCCNetV2.

Reliability Evaluation of Distribution Networks Considering Natural Gas Network Integration

Abstract With the transformation of energy structure, the distribution network in the form of multi-energy interconnection is gradually emphasized. The access of gas turbines to the distribution network puts high requirements on the reliability assessment of the distribution network counting and natural gas network access. Models of distribution network components and natural gas network equipment are established. Based on the traditional sequential MC reliability assessment, the calculation of reliability indexes for the distribution network with gas turbine access is accomplished by identifying the islanded areas under fault conditions and judging the gas turbine output state, taking into account the energy flow constraints. The results of the calculations show that the reliability of the distribution network with natural gas network access is improved, and the reliability of the load nodes near the gas turbine is improved significantly.

Mouse brain elastography changes with sleep/wake cycles, aging, and Alzheimer's disease

Understanding the physiological processes in aging and how neurodegenerative disorders affect cognitive function is a high priority for advancing human health. One specific area of recently enabled research is the in vivo biomechanical state of the brain. This study utilized reverberant optical coherence elastography, a high-resolution elasticity imaging method, to investigate stiffness changes during the sleep/wake cycle, aging, and Alzheimer's disease in murine models. Four-dimensional scans of 44 wildtype mice, 13 mice with deletion of aquaporin-4 water channel, and 12 mice with Alzheimer-related pathology (APP/PS1) demonstrated that (1) cortical tissue became softer (on the order of a 10% decrease in shear wave speed) when young wildtype mice transitioned from wake to anesthetized, yet this effect was lost in aging and with mice overexpressing amyloid-β or lacking the water channel AQP4. (2) Cortical stiffness increased with age in all mice lines, but wildtype mice exhibited the most prominent changes as a function of aging. The study provides novel insight into the brain's biomechanics, the constraints of fluid flow, and how the state of brain activity affects basic properties of cortical tissues.

An algorithm to generate all d-lower boundary points for a stochastic flow network using dynamic flow constraints

Several practical systems, including manufacturing and computer networks, can be modeled as stochastic flow networks (SFNs). In order to learn the performance of the SFNs, network reliability is defined as the probability that a demand d can successfully be transmitted through an SFN. Nevertheless, calculating network reliability belongs to an NP-hard problem. For further probability evaluation (network reliability), existing algorithms based on minimal paths (MPs) are developed to systematically obtain all lower boundary points for d termed d-LBPs. When facing more complex SFNs, our perpetual motivation lies in enhancing the efficiency of generating all d-LBPs. Based on the dominant property of capacity constraints over flow constraints, this study develops a series of techniques to dynamically adjust the flow constraints to generate available (remaining) capacity state vectors. All the generated solutions would directly satisfy the capacity constraints, ignoring the flow constraints. Besides, the exact d-LBPs are calculated using element-wise addition and cycle check constraints. An algorithm is collected with an illustrative example. Furthermore, numerical experiments, including a practical network, are conducted to show at least two times faster for the d-LBP generation using the proposed algorithm compared with the newest algorithm.

Two-tier synergistic optimization of integrated energy systems based on comprehensive self-adaptive operation strategy

Integrated Energy Systems (IES) play a pivotal role in achieving carbon peak and carbon neutrality goals. However, previous research in IES operation has mainly focused on energy flow constraints, neglecting the impacts of renewable energy output, energy prices, carbon emission, and storage status. To bridge this gap, this paper proposes a Comprehensive Self-Adaptive (CSA) operation strategy as a key component of our two-tier synergistic optimization method, optimizing an IES integrated with Concentrated Photovoltaic/Thermal systems. In the upper-tier capacity configuration, the proposed method considers the full life-cycle carbon emissions of equipment. In the lower-tier operational strategy optimization, the CSA operation strategy automatically adjusts energy generation, utilization, and storage by comprehensively evaluating hourly operating costs, hourly carbon emissions, and hourly storage benefits. Compared to traditional operation strategies and other adaptive methods, the newly developed strategy consistently demonstrates superior performance. When contrasted with separate energy systems, IES operating under the CSA operation strategy markedly stands out, achieving a cost savings rate of 25.87 %, a CO2 reduction rate of 61.54 %, and an electricity matching ratio of 91.24 %. Additionally, the paper explores the performance of the CSA operation strategy across various regions, confirming its feasibility and generalizability under diverse load and irradiance conditions.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

State inference for low-observable distribution system based on graph convolutional network

Distribution network state inference refers to the process of calculating the state variables of each node by using measurement data and network models in the operation of the distribution system. However, the uneven measurement layout and insufficient measurement accuracy in the distribution network have brought great challenges to the state inference of the distribution network. This paper proposes a low-observable distribution network state inference method based on a graph convolution network (GCN), which uses sparse measurement data to infer missing measurement information. Firstly, the observability of the distribution network is analyzed by the numerical probability analysis method. Secondly, the GCN is employed to extract feature information from measurement data and integrate these features. The state inference model of the distribution network based on the GCN is established. Subsequently, power flow constraints of the distribution network are incorporated into the GCN training process to enhance the precision of the generated data. Ultimately, the efficacy of the proposed method is validated using the IEEE 33-node distribution system.

Applications of Nano-biofuel cells for Reiner-Philippoff nanoparticles with higher order slip effects.

Owing to advanced thermal features and stable properties, scientists have presented many novel applications of nanomaterials in the energy sectors, heat control devices, cooling phenomenon and many biomedical applications. The suspension between nanomaterials with microorganisms is important in biotechnology and food sciences. With such motivations, the aim of current research is to examine the bioconvective thermal phenomenon due to Reiner-Philippoff nanofluid under the consideration of multiple slip effects. The assessment of heat transfer is further predicted with temperature dependent thermal conductivity. The radiative phenomenon and chemical reaction is also incorporated. The stretched surface with permeability of porous space is assumed to be source of flow. With defined flow constraints, the mathematical model is developed. For solution methodology, the numerical simulations are worked out via shooting technique. The physical aspects of parameters are discussed. It is claimed that suggested results claim applications in the petroleum sciences, thermal systems, heat transfer devices etc. It has been claimed that the velocity profile increases due to Bingham parameter and Philippoff constant. Lower heat and mass transfer impact is observed due to Philippoff parameter.

Optimal Prosumer Operation with Consideration for Bounded Rationality in Peer-to-Peer Energy Trading Systems

With the large-scale development of distributed energy on the demand side, the trend of “supply exceeding demand” has gradually become prominent, and regional peer-to-peer (P2P) energy trading has become an important measure to improve the local consumption of distributed energy. However, most existing studies usually assume that prosumers behave entirely rationally with the goal of maximum benefit, which has been proved to deviate from the observed actual behaviors. Aiming at the optimal energy of prosumers participating in the P2P market, a prospect theory-based two-stage stochastic optimization model considering the bounded rationality was proposed to accurately simulate the decision-making behavior. Then, a benefit maximization model for the energy trading service provider (ETSP) was constructed considering the power flow constraint to ensure the safe operation of the system. Finally, an improved R-ADMM algorithm considering timeout was proposed to solve the above model and improve the convergence speed. The effectiveness of the proposed model and algorithm was verified via simulation.

Performance evaluation criteria based on the first and second laws of thermodynamics

Numerical simulations were conducted to investigate the correlation between assessments based on the total entropy generation rate criterion and the thermal resistance criterion in the context of evaluating heat transfer performance. This study focused on a parallel plate channel heat sink with the lower wall subjected to a constant heat flux, under various flow constraints. The flow was assumed to be laminar and fully developed. The energy equation was discretized using the control volume-based power law scheme, and the resulting algebraic equations were solved by the line-by-line method. Optimal channel heights which lead to minimize irreversibility and temperature difference between heated wall and inlet fluid temperatures were determined by both the total entropy generation rate and thermal resistance criteria under four distinct flow constraints: identical mass flow rate, identical inlet velocity, identical pumping power, and identical pressure drop. The investigation revealed a lack of significant correlation between the optimal channel heights derived from assessments based on the total entropy generation rate and thermal resistance. Consequently, the utilization of the total entropy generation rate criterion for evaluating heat transfer performance, based in the second law of thermodynamics, instead of the thermal resistance criterion, based in the first law of thermodynamics, was deemed inappropriate. The total entropy generation rate criterion was based on the minimization of the irreversibility and served as an indicator of heat transfer quality. As a recommendation, it is suggested that results obtained from both the total entropy generation rate and thermal resistance criteria for assessing heat transfer performance be presented separately for each specific application.

Joint facial action unit recognition and self-supervised optical flow estimation

Facial action unit (AU) recognition and optical flow estimation are two highly correlated tasks, since optical flow can provide motion information of facial muscles to facilitate AU recognition. However, most existing AU recognition methods handle the two tasks independently by offline extracting optical flow as auxiliary information or directly ignoring the use of optical flow. In this paper, we propose a novel end-to-end joint framework of AU recognition and optical flow estimation, in which the two tasks contribute to each other. Moreover, due to the lack of optical flow annotations in AU datasets, we propose to estimate optical flow in a self-supervised manner. To regularize the self-supervised estimation of optical flow, we propose an identical mapping constraint for the optical flow guided image warping process, in which the estimated optical flow between two same images is required to not change the image during warping. Experiments demonstrate that our framework (i) outperforms most of the state-of-the-art AU recognition methods on the challenging BP4D and GFT benchmarks, and (ii) also achieves competitive self-supervised optical flow estimation performance.