Integration Of Networks Research Articles

SDCINet: A novel cross-task integration network for segmentation and detection of damaged/changed building targets with optical remote sensing imagery

Buildings are primary locations for human activities and key focuses in the military domain. Rapidly detecting damaged/changed buildings (DCB) and conducting detailed assessments can effectively aid urbanization monitoring, disaster response, and humanitarian assistance. Currently, the tasks of object detection (OD) and change detection (CD) for DCB are almost independent of each other, making it difficult to simultaneously determine the location and details of changes. Based on this, we have designed a cross-task network called SDCINet, which integrates OD and CD, and have created four dual-task datasets focused on disasters and urbanization. SDCINet is a novel deep learning dual-task framework composed of a consistency encoder, differentiation decoder, and cross-task global attention collaboration module (CGAC). It is capable of modeling differential feature relationships based on bi-temporal images, performing end-to-end pixel-level prediction, and object bounding box regression. The bi-direction traction function of CGAC is used to deeply couple OD and CD tasks. Additionally, we collected bi-temporal images from 10 locations worldwide that experienced earthquakes, explosions, wars, and conflicts to construct two datasets specifically for damaged building OD and CD. We also constructed two datasets for changed building OD and CD based on two publicly available CD datasets. These four datasets can serve as data benchmarks for dual-task research on DCB. Using these datasets, we conducted extensive performance evaluations of 18 state-of-the-art models from the perspectives of OD, CD, and instance segmentation. Benchmark experimental results demonstrated the superior performance of SDCINet. Ablation experiments and evaluative analyses confirmed the effectiveness and unique value of CGAC.

Dynamic operating envelopes embedded peer-to-peer-to-grid energy trading

A novel decentralized peer-to-peer-to-grid (P2P2G) trading mechanism considering distribution network integrity is proposed. In order to direct prosumers’ peer-to-peer (P2P) trading behavior to be grid-friendly, the proposed method incorporates Dynamic Operating Envelopes (DOEs) into the existing P2P2G trading. Moreover, DOEs are determined through negotiations between the distribution system operator (DSO) and prosumers alongside the process of P2P trading, avoiding compromising prosumers’ privacy and network parameters leakage. To reduce communication costs during P2P trading, a variant of the alternating direction method of multipliers (ADMM), i.e., communication-censored ADMM (COCA) is used to solve the P2P2G trading problem. Finally, the DOE price is shown to be comprised of several economically interpretable components. Simulations validate the effectiveness of the proposed mechanism.

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

Public Budgeting Best Practices for Cross-sector Collaborations: Emphasizing the Values of Social Equity, Transparency and Accountability, and Ethics

Cross-sector collaboration has become a major emphasis in collective action as governments, private corporations, and nonprofits work together to tackle today’s complex issues. The existing cross-sector collaboration literature primarily addresses major events like natural disasters, e.g., hurricanes and wildfires, and the COVID-19 pandemic. However, there is a significant gap in the public administration literature regarding cross-sector collaboration in addressing underlying social and financial issues. This paper discusses the need for public budgeting best practices through cross-sector collaboration in three areas: (1) equity; (2) transparency and accountability; and (3) ethics. Through the collaborative governance theory, each sector—public, private, and nonprofit—can contribute to the integration of resources, networks, and approaches for the benefit of citizens. This paper concludes by highlighting key implications for theory and praxis, including an increase in public trust and a greater emphasis on inclusivity in the budgeting process.

Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM).

Microplastic pollution poses a significant threat to our environment, necessitating effective predictive modelling approaches for better management and mitigation. In this study, we introduce a pioneering methodology that fuses the power of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) for microplastic predictive modelling. Leveraging a comprehensive dataset, our integrated model exhibits exceptional performance, with an Accuracy of 0.96, Precision of 0.96, Recall of 0.97, and an F1 Score of 0.96. The achieved Accuracy underscores the model's proficiency in distinguishing microplastic and non-microplastic entities, promising robust and reliable predictions. Precision, as a measure of correct positive identifications, demonstrates our model's effectiveness in minimizing false positives, a crucial aspect for environmental monitoring. Moreover, the perfect Recall score signifies the model's ability to detect all relevant microplastic instances, addressing concerns about false negatives. The F1 Score encapsulates this dual proficiency, showcasing a harmonious trade-off between precision and recall. Our research not only advances the field of microplastic prediction but also highlights the potential of synergizing ANN and HMM methodologies for comprehensive environmental assessments. The reported performance metrics underscore the practical applicability of our approach, offering a valuable tool for tackling the pervasive issue of microplastic pollution and fostering proactive environmental stewardship.

Enhancing data integrity in opportunistic mobile social network: Leveraging Berkle Tree and secure data routing against attacks

In Opportunistic Mobile Social Networks (OMSNs), ensuring data integrity is crucial. The anonymous and opportunistic nature of node communication makes these networks vulnerable to data integrity attacks. The existing literature identified significant shortcomings in effectively addressing data integrity attacks with high efficiency and accuracy. This paper addresses these issues by proposing the "Berkle Tree", a novel data structure designed to mitigate data integrity attacks in OMSNs. The Berkle Tree leverages the EvolvedBloom filter, which is a variant of the bloom filter with a negligible False Positive Rate (FPR). The key contributions of this study include i) an innovative application of EvolvedBloom for membership testing and Berkle Tree root validation, and ii) comparative analysis with existing data structures like Merkle and Verkle Trees. The Berkle Tree demonstrates superior performance, reducing tree generation and integrity validation times and leading to substantial computational cost reductions of 79.50 % and 90.57 %, respectively. The proposed method integrates the Berkle Tree into OMSN routing models and evaluates performance against Packet Drop, Modification, and Fake Attacks (PDA, PMA, PFA). Results show average Malicious Node Detection Accuracy of 98.2 %, 85.2 %, and 94.4 %; Malicious Path Detection Accuracy of 98.6 %, 86.6 %, and 90.2 %; Malicious Data Detection Accuracy of 98.4 %, 80.2 %, and 93.4 %; and False Negative Rates of 1.8 %, 14.8 %, and 5.6 % for PDA, PMA, and PFA, respectively. The major findings demonstrate that the proposed approach significantly improves OMSN routing models by reducing Packet Dropping, Modifying, and Faking Rates by 48.62 %, 28.99 %, and 31.2 %, respectively. Compared to existing methods, the Berkle Tree achieves a substantial reduction in filter size by approximately 25 %–40 %, while maintaining a negligible FPR. These advancements contribute to the state-of-the-art of OMSNs by providing robust solutions for data integrity with significant implications for enhancing security and trustworthiness in OMSNs.

Abstract B164: Sustaining statewide transdisciplinary collaborations to address cancer disparities

Abstract Background: Wisconsin’s cancer disparities are evident when considered by race/ethnicity, geography, and socio-economic status. Addressing these disparities requires deeper integration of social and scientific problem-solving and a systems approach. Given the nature of disparities, it also demands that we not just seek equitable outcomes but collaborate in ways that reflect equity in the process. To achieve this, we developed a transdisciplinary approach inclusive of community, clinicians, and researchers and builds capacity to understand, at a deep level, factors that underpin disparities and design newer sustainable solutions. Methods: The Community and Cancer Science Network (CCSN) Integration Hub was established within the Medical College of Wisconsin Cancer Center to provide leadership and capacity building to eight transdisciplinary teams working to address cancer disparities. The Integration Hub and its teams use a co- leadership structure with a community and academic PI as well as a facilitator and evaluator. The Integration Hub has four primary strategies: identifying and addressing knowledge gaps in cancer disparities, building capacity for transdisciplinary collaboration, cultivating networks and relationships, and disseminating through feedback loops that touch communities, academic medicine, and policy. Our approach is grounded in the principles of deep equity, considerations across the biology to policy spectrum, and systems thinking. Results: In its third year, CCSN adapted and adopted tools to understand its effectiveness in building a collaborative environment for transdisciplinary approaches to problem-solving and to set strategy for sustainability. Results from a partner survey administered to team members (n=73) highlighted that the CCSN model contributes to fostering learning conditions that support a strong transdisciplinary approach including a supportive learning environment, concrete learning practices, leadership that reinforces learning and a reflection of core principles as well as embedding a transdisciplinary orientation in team members. The Integration Hub leadership also completed the Organizational Mapping Tool for Coalitions, Alliances and Networks (OMT-CAN) to assess critical dimensions of network health. The assessment helps identify strengths and identify areas for improvements. CCSN network leaders (n=21) rated the network moderate to strong in culture and relationships; joint action, planning, learning and evaluation; and in purpose, goals and strategy. The team further identified the need to work on refining structure, identity and participation; its capacity for strategic communications; and advocacy and organizing. Discussion: To integrate social and scientific problem-solving and address cancer disparities requires the resourcing of credible capacity building networks that can broker equitable relationships between academic medicine and community members and organizations. The CCSN Integration Hub offers a strong model embedded within a cancer center that accomplishes those goals. Citation Format: Tobi Cawthra, Laura Pinsoneault, Kristen Gardner-Volle, Alexis Krause, Kim Kinner, Melinda Stolley. Sustaining statewide transdisciplinary collaborations to address cancer disparities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B164.

In vitro vascularization of 3D cell aggregates in microwells with integrated vascular beds

Most human tissues possess vascular networks supplying oxygen and nutrients. Engineering of functional tissue and organ models or equivalents often require the integration of artificial vascular networks. Several approaches, such as organs on chips and three-dimensional (3D) bioprinting, have been pursued to obtain vasculature and vascularized tissues in vitro. This technical feasibility study proposes a new approach for the in vitro vascularization of 3D microtissues. For this, we thermoform arrays of round-bottom microwells into thin non-porous and porous polymer films/membranes and culture vascular beds on them from which endothelial sprouting occurs in a Matrigel-based 3D extra cellular matrix. We present two possible culture configurations for the microwell-integrated vascular beds. In the first configuration, human umbilical vein endothelial cells (HUVECs) grow on and sprout from the inner wall of the non-porous microwells. In the second one, HUVECs grow on the outer surface of the porous microwells and sprout through the pores toward the inside. These approaches are extended to lymphatic endothelial cells. As a proof of concept, we demonstrate the in vitro vascularization of spheroids from human mesenchymal stem cells and MG-63 human osteosarcoma cells. Our results show the potential of this approach to provide the spheroids with an abundant outer vascular network and the indication of an inner vasculature.

Integration of Fuzzy Logic and Neural Networks for Enhanced MPPT in PV Systems Under Partial Shading Conditions

Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN's prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.

DUAL LOOP VOLTAGE DROOP REGULATED CONTROLLER FOR DC MICROGRID USING HYBRID PSO AND GGO ALGORITHMS

Abstract DC microgrids are effectively employed in distribution network integration with renewable energy sources. The droop control technique is commonly utilized for DC microgrid regulation during load sharing. It minimizes the potential instability caused by disturbances such as input voltage changes, uncertainty parameters, and constant power load (CPL). Nevertheless, conventional droop control is inadequate in achieving precise current and satisfactory voltage regulation distribution for load sharing. The proposed approach comprises two separate loops for the DC bus voltage regulation and load current distribution, delivering a CVL and CPL to overcome the single optimization voltage controller technique. The primary loop uses the PSO algorithm to precisely manage the current load sharing among two parallel converters. Due to this the instability problems arising from disturbances, and it mitigated by the proposed topology. The multiobjective optimization technique provides notable resilience, rapid dynamic response, and robust stability over considerable variations in load. The secondary loop utilises the GGO algorithm to optimize the parameter to minimize the DC bus voltage regulation, voltage management, reasonable distribution of load power, and suitable reliability. The performance of the voltage regulation enhance & settling time for output voltage has been mitigated more than 31ms times through the proposed controller compared to conventional controller under the variation of CVL, CPL, and input voltage disturbance demonstrated in simulation results.

Evaluation of Indigenous Cultural Practices that Affect Resistance of the Family Unit in Sub-Saharan Africa

The objective of this study was to assess the impact of indigenous cultural practices on the resilience of families in Sub-Saharan Africa. In addition to analyzing the degradation of these behaviors and the increase in single parenthood in the region, the objective was to investigate how these cultural traditions contribute to families’ capacity to cope with and recover from hardship. This study offers practical suggestions for incorporating indigenous values into contemporary family assistance programs. Utilizing a qualitative research methodology, this study used semi-structured interviews, focus group discussions, and participant observations with a sample of 120 single parents from four local governments in Nigeria to obtain data. The reoccurring themes identified through thematic analysis include the degradation of traditional values, the transition towards single parenthood, and the socio-economic elements that influence family constructions in contemporary African societies. The present study enhances academic research by emphasizing the pivotal significance of indigenous cultural practices in promoting resilience. This study provides valuable perspectives on the integration of ancient communal support networks and values into modern interventions designed to enhance family resilience. Furthermore, the research underscores the significance of safeguarding these cultural traditions in response to modernization and socio-economic transformations, hence broadening the discussion on cultural conservation and adjustment in African communities. Keywords: Indigenous Traditions, Cultural practices, Family Resilience, Societal Problems

Study of Eye Movements Abnormalities in Epilepsy

ABSTRACT Idiopathic epilepsy (IE), is a group of epileptic syndromes with no structural brain lesion, but with microstructural changes in neuronal networks leading to neuropsychological consequences. Therefore, the assessment of saccadic eye movements can provide insight into the integrity of cerebral networks as it involves large cortical and subcortical brain areas and circuitries. Describe saccadic eye movement abnormalities in patients with IE and correlate them with disease characteristics and antiseizure medication. Case–control study including IE patients followed in the Neurology Department of Razi University Hospital and healthy controls matched. Participants underwent a recording of saccadic eye movements. Pursuit, prosaccade, and anti-saccade tasks were performed. 115 patients and 98 matched healthy controls were included. The gender ratio (male to female) was 0.6. The mean age at onset was 16.3 ± 12 years. Diagnosed epileptic syndromes were juvenile myoclonic epilepsy (JME), epilepsy with generalized tonic-clonic seizures, childhood absence epilepsy, temporal lobe epilepsy, frontal lobe epilepsy, and rolandic epilepsy. Saccadic eye movements were impaired in 52.2% of our patients and significantly more altered in those with JME (p = .021). Prolonged horizontal saccades latencies were the most frequent eye movement abnormalities (32.1%), followed by altered horizontal smooth pursuit (22.6%). A positive correlation was found between age at eye movements recording, age at onset, disease duration, global cognitive impairment, and saccadic eye movements. However, no definite relationship was identified. Saccadic eye movement illustrates extending anatomic alterations in IE including frontal and temporoparietal cortical areas and cortico-subcortical circuits. Eye movement recording is a useful and reproducible tool in the assessment of epileptic patients and provides a better understanding of neuronal mechanisms in epilepsy.

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

Framing openness: Exploring Similarities and Differences in Patients' and Their Social Networks' Experiences with Participating in Dialogical Network Meetings Through the Lens of Mattering.

In this qualitative study conducted at a public mental health outpatient clinic in Norway, the integration of patients' social network in treatment was examined. The aim was to explore the experiences of patients and their network during dialogical network meetings and discuss any similarities and differences between the two participant groups. Reflexive thematic analysis was performed on data obtained from fifty-three meetings, resulting in the development of five themes. For patients, there were two themes: "Enhanced trust within our relationships" and "Providing us a safe space to talk openly," and for network members there were three: "Empowered through participation," Being welcomed and taken seriously," and "Provide more clarity to enhance our ability to contribute." Mattering was employed as a conceptual framework to discuss the similarities and differences between the themes of the two participant groups. Both patients and network members placed strong emphasis on the freedom of expression and acknowledged the crucial role of the meeting leaders in facilitating discussions on important and challenging topics. Differences included network members' emphasis on feeling welcome and their need to add value, while patients emphasized strengthened relationships and feeling valued and empowered by being trusted to control the discussion content. Overall, mattering appears to be a valuable tool for understanding the relational dynamics within network meetings.\.

Enhancing flood susceptibility modeling using integration of multi-source satellite imagery and multi-input convolutional neural network

Enhancing flood susceptibility modeling using integration of multi-source satellite imagery and multi-input convolutional neural network

Image manipulation localization via dynamic cross-modality fusion and progressive integration

Image manipulation localization aims at distinguishing forged regions from the whole test image. Though many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: (1) how to fuse diverse types of features with forgery clues; (2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.

VividWav2Lip: High-Fidelity Facial Animation Generation Based on Speech-Driven Lip Synchronization

Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model’s robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation.

Graph theory-enhanced integrated distribution network reconfiguration and distributed generation planning: A comparative techno-economic and environmental impacts analysis

In today's world, eco-friendly solutions are crucial for efficient power delivery and assessing their corresponding economic and environmental benefits is essential. As innovators, it is also imperative to continually improve on existing techniques to solve a problem. Evaluating the existing literature in this area of study, gaps of improving the optimization techniques by reducing the amount of infeasible configurations the reconfiguration procedure encounters was established, additionally, the need to utilize distributed generations that significantly reduce carbon footprint in the environment was also ascertained. Hence, this paper presents an effective integration method for the simultaneous reconfiguration of Radial Distribution Networks (RDNs) and Photovoltaic (PV) DGs allocation, considering the tripodal issues of cost, operational efficiency, and environmental sustainability. A modification of the adaptive mountain gazelle optimizer (AMGO) enhanced with graph theory is deployed for the optimization procedures. The crucial feature of the proposed approach is the reduction of unfeasible configurations throughout the optimization procedure toward satisfying the network's radiality constraints, achieving consistent convergence and reduced computation time. The technical benefits are active power loss minimization, voltage stability, and voltage profile improvement. The economic benefits are analyzed by estimating the purchased power, the associated cost of power losses, and the cost of DGs and switches over a planning period of 20 years. The consequent environmental benefits are analyzed in detail, highlighting the significant reduction in pollutant emissions. The proposed model was tested on the IEEE 33- and 69-bus RDN, considering several scenarios, including synchronous network reconfiguration and DG installations. From the results procured, the simultaneous network reconfiguration and DG allocation provided better outcomes, yielding minimum active power loss of 35.36 kW, minimum voltage of 0.9541 p.u., voltage stability index of 1.9936 p.u., total planning cost of $3.456 million, and emission of 1.744 million lb/hr, respectively, for the 33-bus systems. The corresponding value for the 69-bus system is 32.57 kW, 0.9832 p.u., 2.3847 p.u., $ 2.524 million, and 2.53 million lb/hr, respectively. The proposed model was compared with other reported techniques for performance validation, and its efficacy and superior performance was established.

Integrative gene regulatory network analysis discloses key driver genes of fibromuscular dysplasia.

Fibromuscular dysplasia (FMD) is a poorly understood disease affecting 3-5% of adult females. The pathobiology of FMD involves arterial lesions of stenosis, dissection, tortuosity, dilation and aneurysm, which can lead to hypertension, stroke, myocardial infarction and even death. Currently, there are no animal models for FMD and few insights as to its pathobiology. In this study, by integrating DNA genotype and RNA sequence data from primary fibroblasts of 83 patients with FMD and 71 matched healthy controls, we inferred 18 gene regulatory co-expression networks, four of which were found to act together as an FMD-associated supernetwork in the arterial wall. After in vivo perturbation of this co-expression supernetwork by selective knockout of a top network key driver, mice developed arterial dilation, a hallmark of FMD. Molecular studies indicated that this supernetwork governs multiple aspects of vascular cell physiology and functionality, including collagen/matrix production. These studies illuminate the complex causal mechanisms of FMD and suggest a potential therapeutic avenue for this challenging disease.