Network Imbalance Research Articles

Optimal control for district heating systems based on imbalance analysis of pipe networks

In district heating (DH) systems, the transmission and distribution process occupy large amount of energy consumption. Hydraulic and thermal imbalance in pipe networks often lead to uneven heating, affecting user comfort and resulting in energy losses. To solve this problem, this study investigates the root causes of hydraulic and thermal imbalances in DH systems. Key factors have been identified such as initial flow rates, initial loads, and positions in the network of end-users. And the relationship between hydraulic and thermal imbalances have been analyzed. Building on this analysis, we further propose a novel strategy for differential pressure set-point adjustment. This strategy is developed based on a quantitative understanding of the flow rate changes and positions of end-users. It aims to rectify imbalances efficiently, thus enhancing the overall performance of the network. The effectiveness of the proposed strategy is validated through comparative analysis with the conventional constant differential pressure (CCDP) control strategy and an exist variable differential pressure (VDP) control strategy. Results demonstrate significant improvements in network performance, including enhanced stability and reduced energy losses. The proposed strategy reduces the average pressure difference in the main pipe by up to 10.9 %, improves the supply and return temperature difference by 10.9 %, and maintains valve openings above 70 % during peak periods. Additionally, it achieves 23.5 % energy savings in transmission and distribution, highlighting its potential to enhance system performance and efficiency. In addition, this strategy is more in line with the actual situation of end-user demand change for valve adjustment.

Targeting excitatory:inhibitory network imbalance in Alzheimer's disease.

This scientific commentary refers to ‘Seizures exacerbate excitatory: inhibitory imbalance in Alzheimer’s disease and 5XFAD mice’ by Barbour et al. (https://doi.org/10.1093/brain/awae126).

Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features.

Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals.

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

An Appraisal of the Challenges and Limitations of Developing African Countries in Negotiations with Multinational Entities and Developed Nations: Proposed Interventions

The paper takes a look at the limitations and challenges that negotiators of developing African countries encounter with multinational entities and developed nations. It identifies that negotiators of multinational and advanced economies appear to be very competitive and confrontational as against negotiators of developing African negotiators, who appear cooperative and collaborative, a soft-styled approach to negotiations, these disparities arise from power, network and resource imbalance. Some of the challenges encountered by African negotiators include aggressive demands, unilateralism and setting self-serving agendas, negative impact of demographic variables as well as intercultural challenges. Others include inadequate cultural intelligence, negotiating process fatigue, political interest and inadequate competent and experienced human resource and financial limitations among others. The Doha declaration points to the fact that weak nations such as African countries and partners, even though may appear as weaker actors can attain a successful negotiated outcome, when in strategic partnership and networks with identified mutual interest and benefits. The paper recommends the need for the establishment of a networked governance strategy, a more formidable one to the TRIPs network, which delivered a BATNA. Within the state and regional levels, African countries must consciously endeavor to provide their domestic institutions such as Foreign Ministries, Trade and Industry Ministries, national negotiation agencies, with the requisite capacities and expertise needed to be very effective and competitive. Good political well of governments is central in avoiding conflict of interest and corruption at the expense of the State for a BATNA. This would enable them to be better prepared with the requisite confidence, knowledge, and skills to negotiate for a better outcome with multinational corporations and nations.

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder.

Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients. Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls. Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks. Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

Потоки інформації в мережі з обмеженнями кількості потоків, дозволених на кожному вузлі

This paper explores a classic problem in transport network research: the analysis of networks with specified total traffic values for each node. We employ linear algebraic methods to derive a comprehensive set of solutions, ensuring statistical reliability and enabling robust analysis of the results. A mathematical model is presented for determining solution sets in fully connected, loop-free networks with three and four nodes. Based on this model, we developed software to calculate the statistical distribution of entropy values within the network. Furthermore, we investigate the statistical properties of information flow entropy for networks with and without constraints that permit uniform flow distribution. This characteristic holds practical significance for analyzing network dynamics and predicting flow redistribution processes from initial unbalanced states, which inherently proceed towards higher entropy. The findings presented in this paper hold additional practical implications for network imbalance detection and adaptability to diverse network topologies. The results can serve as a foundation for algorithms designed to quantify the degree of network imbalance induced by substantial external influences that do not significantly alter the overall network load. This capability proves particularly valuable in identifying covert DDoS (Distributed Denial of Service) attacks that aim to reduce network bandwidth by supplanting legitimate traffic. While the proposed method has been demonstrated on fully connected networks, it demonstrates potential for adaptation to networks with a wide range of topological structures. This includes networks with partial connectivity or loosely connected networks, which constitute a significant proportion of real-world networks. The significance of the method is further amplified by advancements in cloud computing technologies, which offer substantial computational power and enable the accumulation of extensive statistics regarding information flow distributions across networks of diverse purposes. Such advancements create opportunities for the integration of the developed network analysis technique with machine learning and artificial intelligence technologies, fostering enhanced automation, scalability, and adaptability.

Spatial-temporal knowledge distillation for lightweight network traffic anomaly detection

Deep learning-based network traffic anomaly detection methods have been the mainstream approaches to enhancing the accuracy performance of Network Intrusion Detection Systems (NIDSs). However, there are several problems that remain to be addressed in practical scenarios. First, the memory and computing power of intelligent terminals restrict the deployment of computationally intensive deep learning methods. Second, the depth and width of representations are of central significance for the accuracy of detection, at the cost of memory consumption and computational complexity. Third, the long tail effect spawned by the category imbalance of network traffic is prevalent in real-world fine-grained anomaly detection tasks. Therefore, we propose a Spatial-Temporal Knowledge Distillation (STKD) algorithm framework for lightweight network traffic anomaly detection to tackle the challenges. Integrating multi-scale One-Dimensional Convolutional Neural Network (1D CNN) and Long Short-Term Memory Network (LSTM), and adopting identity mapping, we propose a Multi-Scale Spatial-Temporal Residual Network (MSSTRNet) as the teacher model for deep spatial-temporal feature extraction of network traffic. Based on Knowledge Distillation (KD), we compress MSSTRNet to the lightweight student model named LENet which is suitable for deployment. Introducing Focal Loss (FL) instead of Cross Entropy (CE) Loss into the KD process, we attempt to alleviate the long tail effect in the fine-grained anomaly detection tasks. Experiments demonstrate the superiority of our proposed methods on accuracy performance, memory consumption and computation complexity.

A Novel Refined Regulation Method with Modified Genetic Commutation Algorithm to Reduce Three-Phase Imbalanced Ratio in Low-Voltage Distribution Networks

The three-phase imbalance in low-voltage distribution networks (LVDNs) seriously threatens the security and stability of the power system. At present, a standard solution is automatic phase commutation, but this method has limitations because it does not address the branch imbalance and premature convergence or instability of the commutation algorithm. Therefore, this paper proposes a novel refined regulation commutation system, combined with a modified optimized commutation algorithm, and designs a model and simulation for feasibility verification. The refined regulatory model incorporates branch control units into the traditional commutation system. This effectively disperses the main controller’s functions to each branch and collaborates with intelligent fusion terminals for precise adjustment. The commutation algorithm designed in this paper, combined with the above model, adopts strategies such as symbol encoding, cubic chaotic mapping, and adaptive adjustment based on traditional genetic algorithms. In addition, in order to verify the effectiveness of the proposed method, this paper establishes a mathematical model with the minimum three-phase imbalance and commutation frequency as objectives and establishes a simulation model. The results of the simulation demonstrate that this method can successfully lower the three-phase imbalance of the low-voltage distribution network. It leads to a decrease of the main circuit’s three-phase load imbalance rate from 27% to 6% and reduces each branch line’s three-phase imbalance ratio to below 10%. After applying the method proposed in this paper, the main and branches circuit three-phase imbalance are both lower than the limit ratio of the LVDNs, which can improve the quality and safety of electricity consumption. Additionally, the results also prove that the commutation algorithm under this method has faster convergence speed, better application effect, and better stability, which has promotion and application value.

An automated defect detection method for optimizing industrial quality inspection

Detecting defects in industrial-quality inspection is an important task, but defect detection remains challenging due to limited size, various types of defects, and imbalanced samples in images. In this paper, we propose a novel automatic defect detection network that efficiently detects defects. We introduce the Automated Sample Assignment (SCID) algorithm, which automatically divides the distribution of positive and negative samples based on similarity scores using the Gaussian mixture model. The Automated Sample Assignment (SCID) algorithm solves the problem of sample imbalance in defect detection networks caused by manually setting the intersection over the union (IOU) threshold and assigning positive and negative labels to samples. Then, instead of the pooling method used by most backbone networks, we use a loss fusion pool named LE-Pool to retain more detailed information. Finally, we use the soft weighted attention mechanism of the detection head (S-head) to identify noise information. Experimental results on multiple defect detection datasets show that the proposed method is superior to other detection methods in terms of accuracy.

Evaluating the Connectivity and Imbalance Contribution of New Sections towards Highway Network: A Complex Network Perspective

The evaluation of the impacts of new sections on the highway network is an essential aspect of the feasibility study. Existing studies predominantly concentrated on engineering-oriented feasibility assessments, often overlooking their potential effects on parallel sections and the overall network. In this research, we present an evaluation model for new sections based on complex networks, focusing on the connectivity and imbalance of transportation networks. This model serves as a supplementary approach for enhancing the feasibility analysis of new highway projects. The model comprises three distinct modules, namely, complex network, eigenvalue, and evaluation. Therein, the complex network provides diverse attributes for sections with the dynamic edge weights. Moreover, probability betweenness centrality and volume betweenness centrality have been presented as an eigenvalue of sections based on the multilayer complex network. Furthermore, the connectivity evaluation based on the eigenvalue and the imbalance evaluation based on the entropy and Gini coefficient are conducted. Through the case study, the results of the model demonstrate the connectivity and imbalance contribution of new sections and provide a novel perspective for the feasibility study.

Flexible load control of new energy based on improved genetic algorithm

In the medium and low voltage distribution network, the load form of users is complex and changeable. There are a large number of single-phase and two-phase loads connected to the distribution network, resulting in a three-phase unbalanced operation of the distribution network. With the development of the new energy, the high proportion of distributed new energy will further aggravate the three-phase imbalance of the distribution network. Therefore, this paper proposes a coordinated optimization framework of droop parameters based on the multi-converter droop control, which takes the minimum loss of the distribution network as the optimization objective, and optimizes the reference point and the slope of the VSC droop hierarchically. A small-signal stability optimization dispatching method for the VSC droop slope in the DC distribution network is proposed. By adding small-signal stability constraints to the slope optimization model, the optimal slope command and slope stability region which can ensure the small-signal stable operation of the system are obtained. Experiments show that the optimization model of the VSC small-signal stability slope can make the droop control instruction significantly improve the small-signal stability of the system to adapt to the intra-day source load power fluctuations with a small economic cost. The slope stability region pre-optimization model can provide a reliable stability slope upper limit for the slope optimization problem based on ensuring the system operation economy. The research in this paper can make full use of the flexible control ability of power electronic equipment, and then suppress the three-phase imbalance, which is of great significance to improve the security and economy of the distribution system operation.

Dynamic reconfiguration of three‐phase imbalanced distribution networks considering soft open points

AbstractThe characteristics of three‐phase imbalances are present in medium‐ and low‐voltage distribution networks. Integrating single‐phase distributed generation (DG) exacerbates network imbalances, resulting in increased power losses and potential safety hazards. To address these issues, a dynamic reconfiguration strategy (DNR) for three‐phase imbalanced distribution networks, considering soft open points (SOP), has been proposed. The objective is to alleviate the three‐phase imbalance and minimize the operational costs of the distribution network. Within the reconfiguration strategy, the constraints of DG current imbalance in practical system operations are considered. A three‐phase imbalanced DNR model that simultaneously considers the constraints of the SOP and DG current imbalances is introduced. This model aims to optimize the operation of all devices, including the SOP, to address the overall imbalance of the distribution network. This enables the authors to transform the non‐linear model into a mixed‐integer linear programming (MILP) model, significantly improving the solution efficiency. To validate the proposed strategy, simulations of a modified IEEE 34‐node distribution system and an actual 78‐node distribution system were conducted. The results demonstrate that this strategy offers significant economic benefits and ensures the security of the distribution network.

Construction of a plasmid-free l-leucine overproducing Escherichia coli strain through reprogramming of the metabolic flux

Backgroundl-Leucine is a high-value amino acid with promising applications in the medicine and feed industries. However, the complex metabolic network and intracellular redox imbalance in fermentative microbes limit their efficient biosynthesis of l-leucine.ResultsIn this study, we applied rational metabolic engineering and a dynamic regulation strategy to construct a plasmid-free, non-auxotrophic Escherichia coli strain that overproduces l-leucine. First, the l-leucine biosynthesis pathway was strengthened through multi-step rational metabolic engineering. Then, a cooperative cofactor utilization strategy was designed to ensure redox balance for l-leucine production. Finally, to further improve the l-leucine yield, a toggle switch for dynamically controlling sucAB expression was applied to accurately regulate the tricarboxylic acid cycle and the carbon flux toward l-leucine biosynthesis. Strain LEU27 produced up to 55 g/L of l-leucine, with a yield of 0.23 g/g glucose.ConclusionsThe combination of strategies can be applied to the development of microbial platforms that produce l-leucine and its derivatives.

Management of three-phase electrical loads using phase balancing optimization technique to reduce voltage imbalance in distribution networks

Management of three-phase electrical loads using phase balancing optimization technique to reduce voltage imbalance in distribution networks

Optimization Method of Energy Storage Configuration for Distribution Network with High Proportion of Photovoltaic Based on Source–Load Imbalance

After a high proportion of photovoltaic is connected to the distribution network, it will bring some problems, such as an unbalanced source and load and voltage exceeding the limit. In order to solve them, this paper proposes an optimization method of energy storage configuration for a high-proportion photovoltaic distribution network considering source–load imbalance clustering. Taking the minimum total voltage deviation, the minimum total power loss and the minimum total operating cost as the objective function, and considering various constraints such as power balance constraints and energy storage operation constraints, a mathematical model for energy storage configuration optimization is established. Firstly, the source–load imbalance of the distribution network with a high proportion of photovoltaic is defined. Therefore, according to the 24 h photovoltaic and load data, the 24 h source–load imbalance can be obtained, and the optimal k value can be determined by the elbow rule, so that 24 h a day can be clustered into k periods by the k-means algorithm. Then, the fuzzy comprehensive evaluation method is used to determine the weight factors of each objective function in each period, and three scenes are determined according to the different amount of energy storage. Then, the hybrid particle swarm optimization algorithm proposed in this paper is used to solve the model, and the minimum objective function value, optimal position and optimal capacity of each energy storage grid in each scene are obtained. Finally, it is applied to an example of IEEE33. In the results, the total voltage deviation is increased by more than 10%, the total power loss is increased by more than 8% and the total operating cost is increased by more than 12%, which verifies the effectiveness of the proposed model.

Cytokine network imbalance in children with B-cell acute lymphoblastic leukemia at diagnosis

Immune imbalance has been proved to be involved in the pathogenesis of hematologic neoplasm. However, little research has been reported altered cytokine network in childhood B-cell acute lymphoblastic leukemia (B-ALL) at diagnosis. Our study aimed to evaluate the cytokine network in peripheral blood of newly diagnosed pediatric patients with B-ALL. Serum levels of interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor (TNF), interferon (IFN)-γ, and IL-17A in 45 children with B-ALL and 37 healthy control children were measured by cytometric bead array, while the level of transforming growth factor-β1 (TGF-β1) in the serum was measured by enzyme-linked immunosorbent assay. Patients showed a significant increase in IL-6 (p < 0.001), IL-10 (p < 0.001), IFN-γ (p = 0.023) and a significant reduction in TGF-β1 (p = 0.001). The levels of IL-2, IL-4, TNF and IL-17A were similar in the two groups. Higher concentrations of pro-inflammatory cytokines were associated with febrile in patients without apparent infection by using unsupervised machine learning algorithms. In conclusion, our results indicated a critical role for aberrant cytokine expression profiles in the progression of childhood B-ALL. Distinct cytokine subgroups with different clinical features and immune response have been identified in patients with B-ALL at the time of diagnosis.

Relationship between carotid intima-media thickness (cIMT) and dual-system imbalance in tobacco dependence: An rs-fMRI research.

According to the classic cognitive behavioral theory proposes, dysfunctional goal-directed and habit control systems are considered central to the pathogenesis of dependent behavior and impair recovery from addictions. The functional connectivity (FC) of the brain circuits for goal-directed or habitual behavior has not been clearly reported in tobacco-dependent groups. Smoking is one of the factors in the formation of atherosclerosis. Studies have shown that the thickness of carotid intima-media (cIMT) is associated with attention-executive-psychomotor functioning. Therefore, we hypothesized whether cIMT in tobacco-dependent individuals is associated with changes in the FC of the dual-system network. A total of 29 male tobacco-dependent subjects (tobacco-dependent group) (mean age: 64.20 years, standard deviation [SD]: 4.81 years) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Exactly 28 male nonsmokers (control group) (mean age: 61.95 years, SD: 5.52 years) were also recruited to undergo rs-fMRI. We used the dorsolateral striatum (putamen) and dorsomedial striatum (caudate) as regions of interest for whole-brain resting-state connectivity to construct habitual and goal-directed brain networks, respectively. In addition, all participants were evaluated by carotid artery ultrasound to obtain the cIMT values. Then, we compared the dual-system brain networks between the tobacco dependence and control groups and the relationship between cIMT and imbalance of dual-system brain networks in tobacco dependence. The results showed a reduction in the connection between the caudate and precuneus and an increased connection between the putamen and prefrontal cortex; and supplementary motor area. The bilateral connectivity between the caudate and inferior frontal gyrus showed a significant negative correlation with the cIMT, and no positive correlation was observed with cIMT in the brain region that connects to the caudate. However, for the putamen, increased connectivity with the inferior temporal and medial frontal gyri was strongly associated with a high cIMT. The results indicate that the formation of tobacco dependence behavior is related to changes in the dual-system brain network. Carotid sclerosis is associated with the weakening of the goal-directed network and enhancement of the habit network in tobacco dependence. This finding suggests that tobacco dependence behavior and clinical vascular diseases are related to changes in brain functional networks.

Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks

Although many real-world applications, such as disease prediction, and fault detection suffer from class imbalance, most existing graph-based classification methods ignore the skewness of the distribution of classes; therefore, tend to be biased towards the majority class(es). Conventional methods typically tackle this problem through the assignment of weights to each one of the class samples based on a function of their loss, which can lead to over-fitting on outliers. In this paper, we propose a meta-learning algorithm, named Meta-GCN, for adaptively learning the example weights by simultaneously minimizing the unbiased meta-data set loss and optimizing the model weights through the use of a small unbiased meta-data set. Through experiments, we have shown that Meta-GCN outperforms state-of-the-art frameworks and other baselines in terms of accuracy, the area under the receiver operating characteristic (AUC-ROC) curve, and macro F1 Score for classification tasks on two different datasets.

Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia.

The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition. A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores. Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC). Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.