Interconnection Pattern Research Articles

Intrinsic steady-state pattern of mouse cardiac electrophysiology: analysis using a characterized quantitative electrocardiogram strategy

To explore the intrinsic steady-state electrophysiological properties of mouse heart under physiological conditions by high-resolution quantitative analysis. Twenty-two young adult C57BL/6 mice with a 1:1 male-to-female ratio were used. The limbs of the mice were fixed without anesthesia, and electrocardiographic waveforms, including characteristic P-waves, R-waves, and ST-waves, were recorded using a sensitive 12-lead electrophysiological recorder (ECGsqa) under spontaneous breathing. LabScribe software was used to extract and quantify high-resolution time course and amplitude parameters within a single cardiac cycle from the V3 precordial lead. Pearson correlation test combined with simple linear regression was used to generate a scatter plot of ECG parameter fitting. The common and unique correlation parameters were separately identified by joint associations for profiling the quantitative association network. ECGsqa analysis identified and quantified 14 characteristic ECG parameters, 28.6% of which showed statistical differences between the groups. Compared to male mice, female mice exhibited higher amplitudes and velocities of R and ST waves. Among the 51 association pairs identified in primary association analysis, 47.1% were positively correlated, including shared (29.2%), male-specific (29.2%), and female-specific (41.7%) association groups. Second-order clustering of the association pairs revealed that the amplituderate association pairs of each waveform voltage in both male and female mouse hearts were strongly correlated. The male mice showed an atrioventricular interconnection pattern, while the female mice showed a unique atrial conduction system quality dependence. The distribution network characteristics of the association groups showed that sex-specific and common correlation sets formed a certain series pattern. We discovered a novel intrinsic correlation network of cardiac electrophysiological traits in male and female mice, which reveals the key internal quantitative characteristics and gender difference of both atrial and ventricular conduction systems.

Unveiling the mass-loading effect on the electrochemical performance of Mn3O4 thin film electrodes: A combined computational and experimental study

Abstract The remarkable storage performance of manganese oxide (Mn3O4) makes it an appealing option for use as electrodes in electrochemical capacitors. However, the storage kinetics were significantly influenced by the mass loading of the electrode. Herein, we have inspected the dependency of mass loading on the storage performance of the spray pyrolyzed Mn3O4 thin film electrodes along with the correlation of structural and morphological characteristics. X-ray diffraction and Raman spectroscopic studies proven the formation of spinel Mn3O4 with a tetragonal structure. Morphological analysis revealed that all films exhibited fibrous structures with interconnected patterns at higher mass loadings. Moreover, the surface roughness and wettability of the electrode surface were influenced by variations in mass loading. Notably, thin-film electrode with a mass loading of 0.4 mg/cm2 exhibited the highest specific capacitance value of 168 F/g at 5 mV/s in a three-electrode system. Further, electrochemical impedance spectroscopic studies showed that there were noticeable changes in the capacitive behaviour of the electrode with respect to variations in mass loading. Moreover, the Dunn approach was employed to differentiate the underlying storage mechanism of the Mn3O4 electrode. Additionally, first-principles Density Functional Theory (DFT) studies were carried out in connection with the experimental study to comprehend the structure and electronic band structure of Mn3O4. This study underscores the critical importance of mass loading for enhancing the storage performance of Mn3O4 thin-film electrodes.

Analysis and identification of gas-liquid two-phase flow pattern based on multivariate multi-scale dispersion entropy and interconnected dispersion pattern complex network

Gas-liquid two-phase flow is polymorphic and unstable. The investigation of the flow behavior of two-phase flow is vital for the application of gas-liquid transport in ocean engineering areas. We propose an analytical framework that combines multivariate multi-scale dispersion entropy (mvMDE) and interconnected dispersion pattern complex networks (IDPCN) for flow patterns. The resistance sensor array is used to capture the two-phase flow fluctuation signals in the riser, and the dimensionally reduced time series are nonlinearly mapped into a multivariate symbolic sequence. The mvMDE of the multivariate symbolic sequence is computed to measure the complexity of the flow pattern signals at the level of amplitude variation. In addition, a new multis-cale complex network based on the symbolic sequence is proposed to study the pseudo spatial coupling behavior of flow patterns. The evolution of the flow pattern from bubble to churn flow is quantitatively characterized using the mean value of the complex network indices in the optimal scale range. Finally, we construct the joint plane of mvMDE and two network indices to complete flow pattern identification. The research results show that the framework can effectively fuse multi-channel flow pattern information at different time scales to reveal the evolution mechanism of gas-liquid two-phase flow.

Network analysis to prioritize issues for intervention to improve the health-related quality of life of people with HIV in Spain.

The objective is to assess the interconnectedness of a network of health-related quality of life (HRQoL) variables among people with HIV (PHIV) to identify key areas for which clinical interventions could improve HRQoL for this population. Between 2021 and 2023, we carried out a cross-sectional study within the Spanish CoRIS cohort. We conducted a weighted and undirected network analysis, which examines complex patterns of relationships and interconnections between variables, to assess a network of eight HRQoL dimensions from the validated Clinic Screening Tool for HIV (CST-HIV): anticipated stigma, psychological distress, sexuality, social support, material deprivation, sleep and fatigue, cognitive problems and physical symptoms. A total of 347 participants, predominantly male (93.1%), currently working (79.0%), self-reported homosexual (72.6%) and college-educated (53.9%), were included in the study. Psychological distress showed the highest centrality in the network, indicating its strong connections with sleep and fatigue, cognitive problems and social support within the HRQoL network. Psychological distress, sleep and fatigue, cognitive issues and social support were identified as key factors in an HRQoL network, indicating that interventions focused on these areas could significantly enhance overall well-being.

A hybrid spatiotemporal distribution forecast methodology for IES vulnerabilities under uncertain and imprecise space-air-ground monitoring data scenarios

The weak spots in an integrated energy system that may jeopardize the overall reliability call for timely and efficient Inspection and Maintenance (I&M). One core step is the reasonable allocation and deployment of limited I&M personnel or apparatus to the regions or periods with higher event risks, which requires a pinpoint spatiotemporal distribution forecast of future vulnerabilities. This paper presents a hybrid forecast methodology, the Saliency-Rough Fuzzy Utility Pattern recognition ensemble, in light of space-air-ground multi-source-heterogeneous input data. A parallel learning architecture is established and identifies the critical components with higher yields to enhance efficiency. Accordingly, more reasonable quantitative and qualitative evaluations can be carried out concurrently. Potential imprecise and uncertain data scenes are handled in quantitative assessments, both the failure hazard path sets and survival function likelihood boxes are incorporated in the designed relative path-Fussell Vesely Saliency (rp-FVS) model; and in qualitative analyses, the underlying perilous components can be distinguished via a combination of the variable precision-rough model. The rp-FVS-based fuzzy inference logic configures all membership functions identically according to components’ impacts. These two parts are integrated into the rough-fuzzy Utility Measure to discover concealed component-vulnerability interconnection patterns. Finally, an empirical case study is conducted for validation.

Targeting the Manifestations of Subclinical and Overt Hypothyroidism Within the Hippocampus.

The past decade has witnessed a surge of articles describing the neurocognitive sequelae and associated structural and functional brain abnormalities of patients with overt hypothyroidism (OH) and subclinical hypothyroidism (SCH). Findings show effects primarily within the frontal lobes with usually worse outcomes for OH than SCH. Several recent studies have also indicated hypothyroid patients may have smaller hippocampi, a key structure for memory. The JCEM paper by Zhang and colleagues applies 2 novel approaches for analyzing hippocampal structure and function. One uses an automated processing tool that segments the hippocampus into distinct subregions, and the other performs connectivity analysis to assess the relationships between specific hippocampal subregions and cortical areas. Relatively large samples of OH and SCH patients and healthy controls received a test of global cognitive functioning and underwent structural and functional magnetic resonance imaging. Results showed hypothyroid groups scored significantly below controls on the memory scale and also had smaller hippocampal volumes in selective subregions. Effects were stronger for SCH than OH groups, who also showed different patterns of interconnectivity between hippocampal subregions and specific frontal lobe areas. To make sense of these findings, I explored the rodent and human literatures on thyroid hormone's role in hippocampal functioning and on hippocampal subfields and their purported functions and interconnections. Because current results suggest SCH may represent a distinct clinical entity with unique brain manifestations, I hypothesized 2 explanations for these findings, one involving transporter defects in the brain barriers and the other, differential neurodegeneration of the blood-brain barrier vascular unit.

Exploring the interplay of corporate and ecosystem change

PurposeCorporate changes not only impact the firms involved but also have consequences for their ecosystems. However, the existing literature on ecosystem change is limited. This paper describes and discusses the interconnected patterns between corporate and ecosystem change, shedding light on the various forms these changes take.Design/methodology/approachThe empirical part of the paper is based on the case study of two previously merged organizations' separation.FindingsThe paper reveals intensifying and dissipating change patterns, illustrating the linkages between rapid and gradual corporate and ecosystem changes within and across various ecosystem spheres. Three spheres are conceptualized: sphere of control, sphere of interdependency and sphere of negotiation, each indicating a separate change pattern.Originality/valueThe contribution of this paper lies in its discussion on interconnected corporate and ecosystem changes, offering valuable insights for situating corporate change within the ecosystem and establishing a vocabulary for ecosystem change. Moreover, through the empirical study of a corporate divorce, the paper enhances our understanding of this specific form of change.

Implementation and testing of a KNS topology in an InfiniBand cluster

The InfiniBand (IB) interconnection technology is widely used in the networks of modern supercomputers and data centers. Among other advantages, the IB-based network devices allow for building multiple network topologies, and the IB control software (subnet manager) supports several routing engines suitable for the most common topologies. However, the implementation of some novel topologies in IB-based networks may be difficult if suitable routing algorithms are not supported, or if the IB switch or NIC architectures are not directly applicable for that topology. This work describes the implementation of the network topology known as KNS in a real HPC cluster using an IB network. As far as we know, this is the first implementation of this topology in an IB-based system. In more detail, we have implemented the KNS routing algorithm in the OpenSM software distribution of the subnet manager, and we have adapted the available IB-based switches to the particular structure of this topology. We have evaluated the correctness of our implementation through experiments in the real cluster, using well-known benchmarks. The obtained results, which match the expected performance for the KNS topology, show that this topology can be implemented in IB-based clusters as an alternative to other interconnection patterns.

Mild liver dysfunction in Klinefelter syndrome is associated with abdominal obesity and elevated lipids but not testosterone treatment

ContextKlinefelter syndrome (KS) is associated with hypergonadotropic hypogonadism, which contributes to characteristic phenotypical manifestations including metabolic alterations. Extensive research has demonstrated important associations between androgens and liver function.ObjectivesInvestigation of the association between metabolic parameters, sex hormones and liver function in males with KS, both treated (T-KS) and untreated (U-KS) and healthy control males.MethodsA total of 65 KS males were recruited, of which 32 received testosterone replacement therapy (TRT). Also, 69 healthy controls were recruited. We used alanine aminotransferase (ALAT), alkaline phosphatase and PP (prothrombin-proconvertin time ratio) as the main liver markers. Multivariable regression was performed within the three groups. All statistics were calculated using STATA. Principal component analysis was utilized to demonstrate the interconnected patterns among all measured biomarkers, and to elucidate how the different groups were linked to these patterns.ResultsHigher levels of main liver markers were observed in U-KS compared to controls, with no significant differences between U-KS and T-KS. T-KS had lower abdominal fat, total cholesterol, and LDL cholesterol than U-KS. Using multivariable models, variation in ALAT in U-KS was explained by HOMA2%S; in T-KS by BMI and SHBG; and in controls by hip circumference and estradiol. We found no multivariable models explaining variation in PP in U-KS; in T-KS, PP was explained by BMI and LDL cholesterol, and in controls by total cholesterol. Using principal component analysis U-KS was positively associated to D1 (an obese profile, which also included ALAT) and controls negatively associated with D1 (non-obese profile).ConclusionKS males have mild liver dysfunction reflected by a significant increase in the main liver markers and decrease in albumin. The presented data underscore a primary role of metabolic conditions including obesity, insulin resistance and unfavourable lipid profile, in the elevated liver function markers seen in males with KS. Whether TRT can improve liver function in KS warrants further studies. Our findings, highlight that an evaluation of the liver function should be part of the clinical care in males with KS.

Enhancing core–periphery robustness of networks against link-based attacks with imprecise information

Core-Periphery structure, as a critical mesoscale structure, is commonly found in diverse real-world networks, in which nodes are endogenously categorized as core or peripheral nodes by the underlying interconnection patterns. It plays an important role in sustaining the intrinsic order and functional behavior of networked systems. However, despite the study on inherent core–periphery vulnerabilities to node removals, little is known on the core–periphery robustness when networks are suffering from the attacks that happen on links between nodes, especially for the more practical situations where attackers have limited ability to obtain precise network information. In this paper, a novel index is proposed for measuring the capacity of the core–periphery structure to resist link-based attacks. By introducing an attack precision parameter, we establish a unified evaluation framework for link-based attacks with imprecise information, which divides attack behaviors into localized attacks and non-localized attacks, and treats the usual random attacks and targeted attacks as the two special situations of our non-localized case. Several enhancing algorithms guided by our index with local search strategy are exquisitely devised, and experimental results are provided to demonstrate the efficacy of our framework and algorithms that remarkably improve the core–periphery robustness against link-based attacks with imprecise information.

The emergence and clinical significance of artificial intelligence–enhanced electrocardiography

The integration of artificial intelligence (AI) with electrocardiography (ECG), a technology known as AI-ECG, represents a transformative leap in the field of cardiovascular medicine. This innovative approach has significantly advanced the capabilities of ECG, traditionally used for diagnosing heart diseases. AI-ECG excels in detecting subtle changes and interconnected patterns in cardiac waveforms, offering a level of precision and sensitivity that was previously unattainable with conventional methods. The scope of AI-ECG extends beyond the realm of heart diseases. It has shown remarkable potential in predicting and identifying the impacts of noncardiac conditions on heart health, thereby broadening the diagnostic capabilities of ECG. This is especially valuable given the complex nature of cardiovascular diseases and their interactions with other health conditions. Despite its groundbreaking potential, AI-ECG faces several challenges. One of the primary concerns is the "black box" nature of AI algorithms, which can make the decision-making process opaque and difficult to interpret. This poses a challenge in medical settings where understanding the rationale behind a diagnosis is crucial. Additionally, the effectiveness of AI-ECG is dependent on the quality and diversity of the datasets used to train the algorithms. Limited or biased datasets can lead to inaccuracies and diminish the reliability of the technology. However, the benefits of AI-ECG are significant. It enables faster, more accurate diagnoses and has the potential to greatly enhance the efficiency of cardiovascular care. As research and technology continue to evolve, AI-ECG is poised to become an indispensable tool in the diagnosis and management of heart diseases.

Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity

The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric “heterogeneity” to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.

Metasurface absorber based single junction thin film solar cell exceeding 30% efficiency.

In this article, we report, as per our knowledge, for the first time, a thin film single junction solar cell with a metasurface absorber layer directly incorporated. We have used an interconnected dual inverted split ring resonator pattern in the InAsP absorber layer. The structure eliminated patterns of conventional metals, such as silver, aluminum, and gold, from the active layer, a common drawback in conventional solar absorbers, hindering their direct integration into solar cells. Optical simulation results show a peak ideal short circuit current density of 76.23mA/cm2 for the meta-absorber structure under solar illumination. This current is the highest among previously reported absorbers based on Group IV materials and III-V compounds, overcoming the low solar absorption of such metasurfaces. The final proposed solar cell structure combines this meta-absorber layer with traditional efficiency enhancement methods namely anti-reflecting coating, textured back reflector, and transparent top electrode. This novel single junction structure shows a solar absorption efficiency of 97.86% and a power conversion efficiency of 30.87%, the highest for III-V solar cells. Our device proves the ability of metasurface absorber layers to produce high-efficiency solar cells and is expected to pave the way for integrating novel meta-devices into state-of-the-art photovoltaic devices, aiding the global transition towards clean energy sources.

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age.

Aim: To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. Introduction: FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development. Methods: In this article, we introduce a novel approach to evaluate multiple overlapping FCPs by utilizing FNC matrices. We computed FNC matrices from the large-scale Adolescent Brain Cognitive Development data using fully automated spatially constrained independent component analysis (ICA). We next evaluated changes in these patterns for a 2-year period using a second-level ICA on the FNC change maps. Results: Our proposed approach reveals several highly structured (modular) FCPs and significant results including strong brain FC between visual and sensorimotor domains that increase with age. We also find several FCPs that are associated with longitudinal changes of psychiatric problems, cognition, and age in the developing brain. Interestingly, FCP cross-covariation, reflecting coupling between maximally independent FCPs, also shows significant differences between upper and lower quartile loadings for longitudinal changes in age, psychiatric problems, and cognition scores, as well as baseline age in the developing brain. FCP patterns and results were also found to be highly reliable based on analysis of data collected in a separate scan session. Conclusion: In sum, our results show evidence of consistent multivariate patterns of functional change in emerging adolescents and the proposed approach provides a useful and general tool to evaluate covarying patterns of whole-brain functional changes in longitudinal data. Impact statement In this article, we introduce a novel approach utilizing functional network connectivity (FNC) matrices to estimate multiple overlapping brain functional change patterns (FCPs). The findings demonstrate several well-structured FCPs that exhibit significant changes for a 2-year period, particularly in the functional connectivity between the visual and sensorimotor domains. In addition, we discover several FCPs that are associated with psychopathology, cognition, and age. Finally, our proposed approach for studying age-related FCPs represents a pioneering method that provides a valuable tool for assessing interconnected patterns of whole-brain functional changes in longitudinal data and may be useful to study change over time with applicability to many other areas, including the study of longitudinal changes within diagnostic groups, treatment effects, aging effects, and more.

Research advances in the application of metabolomics in exercise science.

Exercise training can lead to changes in the metabolic composition of an athlete's blood, the magnitude of which depends largely on the intensity and duration of exercise. A variety of behavioral, biochemical, hormonal, and immunological biomarkers are commonly used to assess an athlete's physical condition during exercise training. However, traditional invasive muscle biopsy testing methods are unable to comprehensively detect physiological differences and metabolic changes in the body. Metabolomics technology is a high-throughput, highly sensitive technique that provides a comprehensive assessment of changes in small molecule metabolites (molecular weight <1,500Da) in the body. By measuring the overall metabolic characteristics of biological samples, we can study the changes of endogenous metabolites in an organism or cell at a certain moment in time, and investigate the interconnection and dynamic patterns between metabolites and physiological changes, thus further understanding the interactions between genes and the environment, and providing possibilities for biomarker discovery, precise training and nutritional programming of athletes. This paper summaries the progress of research on the application of exercise metabolomics in sports science, and looks forward to the future development of exercise metabolomics, with a view to providing new approaches and perspectives for improving human performance, promoting exercise against chronic diseases, and advancing sports science research.

At the intersections: operationalizing intersectional thematic analysis in HIV prevention.

This study aims to provide theoretical and methodological tools to assist in producing thematic analyses guided by an intersectional approach in empirically-based qualitative health studies. It argues that combining an intersectional perspective with thematic analysis can update the latter-which is quite popular in qualitative health investigations-regarding meaningful discussions about multiple and interconnected patterns of privilege and oppression that operate structurally and institutionally, producing experiences of relative disadvantage in individuals according to their gender, race/ethnicity, class, sexuality, generation, among other positions. Based on an article that analyzed qualitative empirical data from a longitudinal demonstrative study on pre-exposure prophylaxis for HIV (PrEP) in adolescents and young people aged 15 to 19 years in two Brazilian capitals, this study discusses the limitations, challenges, and potentialities of the theoretical and methodological efforts undertaken by those authors. Additionally, this research that offers a proposal for operationalizing thematic analysis with intersectional sensitivity. It observed that triangulating techniques can enhance thematic analysis with intersectional sensitivity to produce qualitative data. Adopting an a priori intersectional proposal, starting from the research design phase, construction, and application of data production instruments with intersectional intentionality, enables the recognition of the relations between social markers in analytical categories. However, the absence of an intersectional theoretical-methodological perspective to conceive research and produce data fails to render intersectionality as a methodological tool unfeasible, although it may limit result analysis and discussion. Such limitations can be addressed by proposing intersectional assumptions and comparing the results with literature related to the theme and object of study.

Community-based Empowerment: Unveiling patron-client Patterns in Voluntary Trader Partnerships for Enhanced Production in Desa Kolot, Cikalong Tasikmalaya

Enhancing production activities in Desa Kolot, Cikalong Tasikmalaya is achieved through mutually beneficial partnerships among traders, exemplifying a community-driven empowerment model. This approach is introduced as a response to counter the negative perceptions associated with interventionist empowerment strategies implemented by head of village. The objective of this research is to uncover the interconnected pattern involving traders with ample capital collaborating with those lacking financial resources. Employing a qualitative methodology with a phenomenological approach, the data analysis utilizes the patron-client theory. The study identifies the effectiveness of an empowerment model that optimizes the local potential of traditional snack production, deeply rooted in the community’s traditions. The empowerment mechanism is executed through voluntary connections established between major traders providing capital and smaller traders contributing culinary products. Small traders, as beneficiaries, get provision of materials, production tools, and skill enhancements offered by larger traders. Similarly, major traders benefit by gaining access to a market for the product of culinary, thereby expanding their customer base. These personal, voluntary, and tradition-bound relationships reflect dyadic connections characterized by an imbalance, potentially leading to domination and exploitation.

Tail risk spillover effects in commodity markets: A comparative study of crisis periods

This research aims to investigate the propagation of extreme downside risk, commonly referred to as tail risk, within commodity markets using an innovative CAViaR-based connectivity model. We also evaluate the influence of various crises, including the global financial crisis, the shale oil revolution, the COVID-19 pandemic, and the Russia-Ukraine conflict, on the dynamic relationships among seventeen different commodity markets. Our findings reveal a diverse pattern of interconnections among these markets during distinct crisis periods. Surprisingly, we observe that the nature of these interconnections is remarkably similar during geopolitical and health crises. Notably, the spillover effects between different commodity categories are more pronounced during the COVID-19 pandemic and the Russia-Ukraine conflict than during the global financial crisis and the shale oil revolution. However, it is important to note that the total risk spillovers are more substantial during the global financial crisis. Furthermore, our analysis delves into the unique characteristics of each market, revealing that precious metals can function as a safe haven for both energy and industrial metals during times of economic turbulence.

Performance Analysis of Novel Solar PV Array Configurations with Reduced Tie Interconnection to Extract Maximum Power under Partial Shading

The non-uniform irradiation pattern causes the partial shading effects and decreases the performance of the solar Photovoltaic (PV) array. The effect of uneven shading condition depends on its pattern and area. The PV array’s performance is determined by the cross ties that are connected within the array. This research proposes eight different PV array tie interconnection configurations to obtain the maximum power output under uneven insolation conditions. Experimental analysis is done in the PV array consists of 16 numbers of 10 W PV panels which has a total capacity of 160 W. The practical results reveal that the losses due to partial shading are influenced by the shade pattern, shade area, and the placement of the cross ties. Overall, among the conventional configurations, the hybrid bridge link total cross tied interconnection pattern produced the best results, followed by the hybrid series parallel total cross tied and bridge link patterns. In the proposed configurations, the plus tie configuration outperformed the others in maximum of the partial shading scenarios. In general, the PLT configuration demonstrated an average power efficiency increase of 19.17%, 8.37%, and 8.28% compared to the series parallel, hybrid series parallel total cross-tied, bridge link, and bridge link total cross-tied configurations, respectively. The effectiveness of the proposed configurations is validated by the practical verifications.

Performance Enhancements and Modeling of Photovoltaic Panel Configurations During Partial Shading Conditions

The growing focus on solar energy has led to an expansion of large solar energy projects in the world. Though the most widely adopted Total Cross Tie (TCT) interconnection patterns will reduce the loss of mismatch, the appearance of shades in large-scale photovoltaic arrays drastically decreases the output power as well as several peaks of power in the P-V characteristics. To overcome these problems, the PV panels are organised using either electrical or physical reconfiguration methods. In this respect, it has been established that physical relocation methods are both cost-effective and efficient to disperse the shadow. In this context fits this work, his goal is to study the magic square view (MSV) for the physical rearrangement of the PV module in a TCT scheme. The simulation results reveal the effectiveness of the MSV method efficiently scattering the shade over the whole photovoltaic array. For validation, four types of Partial Shading Conditions (PSCs) patterns are considered and are then compared with the TCT and the recently proved Competence Square (CS) techniques: Short and Wide (SW), Long and Wide (LW), Long and Narrow (LN), and Short and Narrow (SN) shading patterns. Overall, MSV method is important in achieving improved output power enhancement of PV array under shaded conditions. A very clear improvement is obtained in the Long and Wide partial shading pattern.