Interconnection Pattern Research Articles

Modelling and simulation of AdDoKu based reconfiguration technique to harvest maximum power from photovoltaic array under partial shading conditions

A new AdDoKu method of photovoltaic (PV) module arrangement for total cross tied (TCT) interconnection pattern has been introduced in this paper. This arrangement is aimed at minimizing the PV array (PVA) losses caused by partial shading conditions and, thereby, increases the power output. Simulations are carried out in an extensive scale to validate the improvement in performance as compared to the TCT connection. Comparisons are also made between the newly proposed AdDoKu pattern and the SuDoKu configuration. The data obtained during this comparison place the proposed scheme at par with the SuDoKu configuration in terms of operational characteristics. The physical location of modules is rearranged without altering any electrical connections. The data obtained from the simulation tests show that the AdDoKu arrangement pattern reduces the number of local peaks in the P-V characteristics and, thereby, making the global maximum power point (GMPP) tracking algorithm much simpler and more accurate. MATLAB/SIMULINK is used for the modelling of all the PVAs considered in this work. Thus, the results establish the performance improvement of the proposed AdDoKu arrangement. Four different types of shading patterns (i.e., short wide, long wide, short narrow, and long narrow) are considered for analyzing the performance of the PVA system based on GMPP, mismatch power loss, % fill factor, % power loss, % power enhancement and energy saving with annual income generation analysis for the proposed topology. The output results shows that the AdDoKu method generates better power than TCT method by 28.76%, 30.15%, 11.66% and 0.34% in short wide, long wide, short narrow and long narrow shading pattern, respectively. Furthermore, the proposed AdDoKu method shows an improvement of 1.94% as compared to SuDoKu method under different shading conditions.

Cerebral white matter vasculature: still uncharted?

White matter vasculature plays a major role in the pathophysiology of permanent neurological deficits following a stroke or progressive cognitive alteration related to small vessel disease. Thus, knowledge of the complex vascularization and functional aspects of the deep white matter territories is paramount to comprehend clinical manifestations of brain ischaemia. This review provides a structured presentation of the existing knowledge of the vascularization of the human cerebral white matter from seminal historical studies to the current literature. First, we revisit the highlights of prenatal development of the endoparenchymal telencephalic vascular system that are crucial for the understanding of vessel organization in the adult. Second, we reveal the tangled history of debates on the existence, clinical significance and physiological role of leptomeningeal anastomoses. Then, we present how conceptions on white matter vascularization transitioned from the mixed ventriculopetal/ventriculofugal theory, in which a low-flow area was interposed in between concurrent arterial flows, to the purely ventriculopetal theory. The latter model explains variable white matter sensitivity to ischaemia by various organizations of ventriculopetal vessel terminals having different origin/length properties and interconnection patterns. Next, arteries supplying primarily the white matter are described according to their length and overall structure. Furthermore, the known distribution territories, to date, are studied in relation to primary anatomical structures of the human cerebral white matter, emphasizing the sparsity of the 'ground truth' data available in the literature. Finally, the implications for both large vessel occlusion and chronic small vessel disease are discussed, as well as the insights from neuroimaging. All things considered, we identify the need for further research on deep white matter vascularization, especially regarding the arterial supply of white matter fibre tracts.

Stochastic blockmodeling for learning the structure of optimization problems

AbstractDecomposition‐based solution algorithms for optimization problems depend on the underlying latent block structure of the problem. Methods for detecting this structure are currently lacking. In this article, we propose stochastic blockmodeling (SBM) as a systematic framework for learning the underlying block structure in generic optimization problems. SBM is a generative graph model in which nodes belong to some blocks and the interconnections among the nodes are stochastically dependent on their block affiliations. Hence, through parametric statistical inference, the interconnection patterns underlying optimization problems can be estimated. For benchmark optimization problems, we show that SBM can reveal the underlying block structure and that the estimated blocks can be used as the basis for decomposition‐based solution algorithms which can reach an optimum or bound estimates in reduced computational time. Finally, we present a general software platform for automated block structure detection and decomposition‐based solution following distributed and hierarchical optimization approaches.

Power System Zone Partitioning Based on Transmission Congestion Identification Using an Improved Spectral Clustering Algorithm

The ever-expanding power system is developed into an interconnected pattern of power grids. Zone partitioning is an essential technique for the operation and management of such an interconnected power system. Owing to the transmission capacity limitation, transmission congestion may occur with a regional influence on power system. If transmission congestion is considered when the system is decomposed into several regions, the power consumption structure can be optimized and power system planning can be more reasonable. At the same time, power resources can be properly allocated and system safety can be improved. In this paper, we propose a power system zone partitioning method where the potential congested branches are identified and the spectral clustering algorithm is improved. We transform the zone partitioning problem into a graph segmentation problem by constructing an undirected weighted graph of power system where the similarities between buses are measured by the power transfer distribution factor (PTDF) corresponding to the potential congested branches. Zone partitioning results show that the locational marginal price (LMP) in the same zone is similar, which can represent regional price signals and provide regional auxiliary decisions.

Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN

Heart weakness and restricted blood flow into the cavities can cause a range of strokes from mild to severe Heart strokes are primary caused due to the fat deposited on artery walls. The process reduces the intake of blood and internally causes a pseudo vacuum of air bubbles leading to a stroke which can be identified with high-end instrumentations. In this article, a detailed evaluation is processed with a Hybrid Optimization Algorithm (HOA). In the proposed technique, data are preprocessed using a label encoder and the missing values of the dataset are filled. Whale Optimization Algorithm (WOA) and Crow Search Algorithm(CSA) extract inter-connected patterns and learning features using a dedicated Deep Neural Networking (DNN) support. The proposed Hybrid Optimization Algorithm extracts features and the resultant values demonstrate a high accuracy range of 97.34%.

Electro-active properties of nanostructured films of cytosine and guanine nucleobases

The discovery of multifunctional properties related to electro-activity of organic systems of biomolecules is important for a variety of applications, especially for devices in the realm of biocompatible sensors and/or bioactuators. A further step towards such applications is to prepare thin films with the required properties. Here, the investigation is focused on the characterization of films of guanine and cytosine nucleobases, prepared by thermal evaporation—an industrial accessible deposition technique. The cytosine films have an orthorhombic non-centrosymmetric structure and grow in two interconnected nanostructured fractal patterns, of nearly equal proportion. Piezoresponse force microscopy images acquired at room temperature on the cytosine films display large zones with antiparallel alignment of the vertical components of the polarization vector. Guanine films have a dense nano-grained morphology. Our studies reveal electrical polarization switching effects which can be related to ferroelectricity in the films of guanine molecules. Characteristic ferroelectric polarization–electric-field hysteresis loops showing large electrical polarization are observed at low temperatures up to 200 K. Above this temperature, the guanine films have a preponderant paraelectric phase containing residual or locally induced nano-scopic ferroelectric domains, as observed by piezoresponse force microscopy at room temperature.

Alzheimer’s disease under the purview of Graph Theory Centric Genetic Networks

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.

Controlling of RuO2 Nanostructures by Deposition Temperature and Its Effect on Structural, Morphological and Electrochemical Properties

Nanostructured ruthenium oxide electrodes materials were synthesized on stainless steel substrate at 473 K–773 K temperatures with 100 K intervals using automatic spray pyrolysis technique via an aqueous route. The physical and supercapacitive properties of the deposited electrodes were characterized thoroughly. Deposited electrodes show an amorphous structural property. Electrodes deposited at various temperature shows various morphologies such as rough porous interconnected pattern like granular morphology and well-covered dense sheet-like morphology. Electrode deposited at 673 K shows superior electrochemical properties viz highest specific capacitance of 524.9 F g−1 at 5 mV s−1, specific energy 128.2 Wh kg−1, specific power 256 kW kg−1, and columbic efficiency 94% in 1 M H2SO4 electrolyte. Electrochemical impedance spectroscopy reveals the capacitive behavior of the electrode. Electrode shows 3.39Ω combined internal resistance. Overall results demonstrate that spray deposited RuO2 at 673 K can be excellent electrode material for next-generation energy storage applications.

Copper-graphene heterostructure for back-end-of-line compatible high-performance interconnects

Here, we demonstrate the fabrication of a Cu-graphene heterostructure interconnect by the direct synthesis of graphene on a Cu interconnect with an enhanced performance. Multilayer graphene films were synthesized on Cu interconnect patterns using a liquid benzene or pyridine source at 400 °C by atmospheric pressure chemical vapor deposition (APCVD). The graphene-capped Cu interconnects showed lower resistivity, higher breakdown current density, and improved reliability compared with those of pure Cu interconnects. In addition, an increase in the carrier density of graphene by doping drastically enhanced the reliability of the graphene-capped interconnect with a mean time to failure of >106 s at 100 °C under a continuous DC stress of 3 MA cm−2. Furthermore, the graphene-capped Cu heterostructure exhibited enhanced electrical properties and reliability even if it was a damascene-patterned structure, which indicates compatibility with practical applications such as next-generation interconnect materials in CMOS back-end-of-line (BEOL).

Topological analysis of fracture networks integrated with flow simulation models for equivalent fracture permeability estimation

Models that predict permeability by considering analogues of fractured media have been used to enhance the understanding of the relative contribution of fracture networks to reservoir quality. The analogue data can overcome scale limitations (sub-seismic to mm) and/or lack of consistency in subsurface datasets (e.g., seismic data, well logs, and cores). This paper presents a numerical tool (TopoScan), designed to process images of fractured systems based on topological methods to determine fracture attributes and automatically make these available for fracture modeling and numerical simulations. We use this program to carry out fracture modeling considering different topological patterns of two-dimensional synthetic fracture networks, with variable patterns of interconnection, aperture, and spatial organization. We used the finite element method for modeling the fracture system, with the incorporation of discontinuities for the representation of fractures in the medium. The models were used for numerical simulations of fluid flow (flow-based upscaling), and the results demonstrated that the influence of fractures interconnection degree (topological patterns) on the equivalent permeability of the fractured medium.

Brain network motifs are markers of loss and recovery of consciousness

Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8–13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall’s W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.

Biology at the macrosystem scale

In the midst of the winter of 2020–2021, infections and deaths from the novel coronavirus are peaking. At any given time, outbreaks are amplified in some regions more than others, with peaks and troughs shifting as viral population dynamics interact with human metapopulations, prompting waves of transmission that propagate around the globe. With each evening’s news report, the scale of biology is compactly demonstrated: a single virion, measuring one billionth of a meter (~10–9 m) in diameter, becomes a part of a pandemic that influences humans across the Earth, a little over ten million meters (107 m) in diameter. In introductory science textbooks, biology is conceptually organized within nested scales: molecules within organelles within cells, organs and tissues within organisms, populations and communities within ecosystems, and so on. But this traditional hierarchy of biological organization – where lower scales fit neatly within higher ones – is challenged in nature: for example, complex microbial ecosystems can be nested within individual organisms, or even tissues (think of the community of symbiotic microorganisms inside the gut of a caddisfly). Although such phenomena require reconciliation within the existing hierarchy, the trope persists: in every ecology and biology textbook on our bookshelves, the “scaling” figure occurs in the first chapter. While its place in concept lies somewhere between the landscape and the globe, the macrosystem scale is – so far – absent from the standard hierarchy, yet it may appear there over time, if the promise of the field is realized. The challenge of macrosystems biology is to conduct quantitative, interdisciplinary, and systems-oriented research that helps us understand interconnected patterns and processes from regional to continental scales. Indeed, macrosystems biology seeks not a single holy grail but rather a collection of grails, requiring effective collaboration across population and community ecology, biogeography, and biogeochemistry, and possibly other disciplines, all at once. It’s no real surprise that, since its emergence approximately 10 years ago and in light of its inherent complexity, the discipline has yet to reach its full potential. The papers in this Special Issue describe many of the ongoing challenges in macrosystems biology, and the development of new tools to address them. For example, Patrick et al. provide evidence that preserving biodiversity locally and regionally promotes macrosystem stability, implying that the reduced diversity affiliated with environmental homogenization can be a destabilizing force. Likewise, Ballantyne et al. describe regional-scale overestimates of terrestrial CO2 uptake as measured by eddy covariance towers, possibly resulting from bias in tower site selection, and possibly because carbon taken up in the tower footprint is transported through groundwater and released from lakes and streams, a rebalancing the towers do not detect. These are just two of multiple non-mutually exclusive possibilities, and application of tools like data assimilation may help discern whether such mismatches across biological scales are errors, insights, or, more likely, both. Other tools address the difficulty of clearly attributing cause when drivers interact and change over time, as discussed in this issue by Rollinson et al. as nonstationarity. The need to integrate data across scales is as trumpeted a truism as occurs in ecology, an imperative where novel approaches in data assimilation (like those described by Zipkin et al.) hold both promise in assessing models and making inferences, as well as challenges in minimizing bias. Tromboni et al. explore how metacoupling applies to the field of macrosystems biology, simultaneously evaluating nearby and distant connections with both ecological and socioeconomic dimensions, while LaRue et al. analyze the language of and themes in macrosystems biology to demonstrate that working across scales in self-identified macrosystem studies is actually quite similar to doing so in other ecological disciplines. In addition, Farrell et al. discuss the importance of honing skills in collaboration to tackle macroscale problems and a mismatch in the training required for macrosystem scientists and practitioners to succeed. The traditional scaling hierarchy depicts how biology is organized across space spanning at least ~16 orders of magnitude, from the cell to the planet, but fails to demonstrate how influence transmits between scales. Success in the field of macrosystems biology could help reimagine how we depict biological scaling. Global pandemics can be framed as macrosystems phenomena, where some of the controls across scales are reasonably well understood (viral population dynamics), some more speculative (the food–wildlife–disease interface), and some seemingly intractable (the intersection of human behavior, politics, and public health). The reports in this Special Issue capture the current state of macrosystems biology as dynamic and filled with potential as the discipline moves into its next phase. BRUCE A HUNGATE ECOSS, Northern Arizona University, Flagstaff, AZ JANE C MARKS ECOSS, Northern Arizona University, Flagstaff, AZ

Evaluation of low power consumption network on chip routing architecture

Network on Chip (NoC) is growing technology whereby multiprocessor state interconnect patterns are formed. NoC technology is adapted to support a variety of multiprocessor requirements. The existing designs do not support the growth requirements of user applications. Because of the complex routing connections, several problems exist about traffic congestion and Power consumption contributing to a network's low efficiency. Traffic Congestion, Power consumption, and latency are a significant concern in Network on Chip architectures because of various dynamic routing connections. The existing models do not consider all the above-mentioned factors and struggle to achieve higher performance. The previous methods do not trigger the circuits according to the traffic condition and maximum power consumption. For this, the proposed High-Speed Virtual Logic Network on Chip router architecture is utilized for controlling the traffic congestion and deadlock issues, reduce the latency by selecting the minimal interval paths. In this research work, an architecture containing a Virtual router is introduced which yields low power consumption resulting in improving the performance of a network by performing the routing in a diagonal direction along with the other directions. Also, the method selects an optimal path according to various conditions that neglect the unnecessary triggering of chips which reduces the power consumption. The proposed model considers the dynamic congestion and route available to perform routing with the least power consumption. By comparing both the architectures, VC Router outperformed 15% of low power consumption for the 8-bit system, 10% of low power consumption for the 16-bit system, and 22% of low power consumption for the 32-bit system.

Study on electrolytes variation in middle aged type 2 diabetics and hypertensive diabetics in comparison with normal individuals

Background: Chronic hypertension, cellular dehydration, features of renal failure, and lack of certain important electrolytes - all show an interconnected pattern of development during courses of Type 2 diabetes mellitus. Aim and Objective: The present study was undertaken to evaluate and compare the electrolytes changes in short-term, middle-aged Type 2 diabetics and in diabetics, also associated with primary hypertension so as to evaluate the association of diabetes and hypertension on electrolytes metabolism. Materials and Methods: In the present study, estimation of fasting blood glucose and glycated hemoglobin was done by automatic analyzer. Similarly, serum electrolytes such as calcium, sodium, potassium, chloride, magnesium, and phosphorus were estimated in different groups in the central laboratory, using auto analyzer. Results: It is evident in the present study that serum sodium, potassium, magnesium, chloride, phosphorus, and glycosylated hemoglobin showed a statistical significant variation in hypertensive diabetics compared to control and other groups. Whereas, serum creatinine is unchanged in any group. However, all these variations are within the normal expected range of the parameters, studied. Conclusion: Variation observed with all the electrolytes even in the initial stage of treatment in hypertensive, diabetics, and in hypertensive diabetics, suggesting to maintain electrolytes balance by alteration in dietary habits to prevent their deficiency/excess in these patients. Providing magnesium supplementation to these patients in the early diagnose phase may prevent further complications related to it.

Subjects Involved in Czech Insolvency Proceedings: An Assessment of Their Future Impact

Dynamic social networks follow several specific patterns during their evolution, including preferential attachment, forming of giant components, or densification. Especially preferential attachment impacts the emergence of relatively few influential high-degree nodes that attract most newly added nodes. Previous research on dynamic social networks, in general, assumes the so-called power-law degree to govern the number of nodes with a particular degree. But real social networks might display more complex behavior despite their apparent dependence on the preceding interconnection pattern. This paper contributes to the field by developing a new methodology applicable to assessing a particular node’s actual influence. A significant part of the approach represents a strategy for training and pruning of a neural network used to extract proper knowledge from the data we have at our disposal. Sensitivity analysis applied to the trained neural network further contributes to understanding other nodes’ involvement in the entire process. Supporting experiments performed so far on the data publicly available from the Czech Insolvency Register (which covers more than 300 000 insolvency proceedings) indicate promising results regarding the model’s accuracy and explainability.

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.

Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.

In situ SEM observation and interface adhesion strength analysis of a serpentine interconnect on a flexible electronic composite film

The buckling deformation of a serpentine interconnect on a flexible electronic composite film is analyzed by a scanning electron microscope miniature tensile stage in situ observation workstation. The buckle formed at the junction of the interconnect pattern under a tensile load is observed. Combined with the buckle size and a theoretical model, the interface adhesion energy and shear stress are derived. The relationship between thickness of the interconnect Cu layer and interface performance is further analyzed. Results show that within the thickness range of 25–500 nm, a 75 nm thick serpentine interconnect has the best adhesion performance on a flexible substrate (about 10.09 J m–2).

How education can help shape a new story in a post-pandemic world.

Globally, nations are grappling with massive social and economic disruptions and the disparities exposed by the COVID-19 pandemic. The world is in the middle of a wicked problem—one so complex that it is difficult to find a solution. The “Story Model” was developed as a transdisciplinary curriculum model and a collaborative problem-solving model for K to 16. The model can address wicked problems in ways that traditional problem-solving procedures cannot because it allows for messiness, multilayered complexity, and a big picture perspective. With a deeper understanding of the interdependent and interconnected patterns occurring both locally and globally, educators and students alike can contribute actively to shaping a new story that reflects their values, beliefs, and actions.

The role of interconnection patterns on synchronizability of duplex oscillatory power network

This paper firstly proposes the multiplex oscillatory power network dynamical model, and then investigates the influence of interconnection patterns on synchronous ability of duplex power network being comprised of two subnetworks. In this work, we provide analytical vital conditions for synchronous stability in a multiplex oscillatory power network. Meanwhile, the relationship between interconnection types and synchronizability of duplex oscillatory power network is investigated. Moreover, we also find a counterintuitive phenomenon of connecting high-degree nodes where the synchronization has worst performance, on a specific duplex network composed of two star networks. In addition, our results show that increasing the interlink number favors synchronizability of duplex network consisting of two ring networks with the same properties in separate layers. However, the interlink number has no obvious effect on synchronizability for oscillatory network with strong heterogeneity in separate layers. Nonetheless, synchronization has better performance for duplex network composed of two ring networks than two star networks when the inter-layer connections are one-to-one. Our results provide an innovative guide for constructing a cost-effective multiplex power network.

An Existential Analysis of Responses to the 2020 Coronavirus Outbreak

This essay examines people’s responses to the 2020 coronavirus pandemic from the perspective of existential psychology. The existential anxieties associated with the pandemic, as well as people’s responses to them, can be understood and articulated through Sartre’s concept of bad faith. Using this existential lens, we examine the ways in which people’s responses to the virus interact with long-standing societal patterns of interconnection as well as inequity, and how these processes are rooted within the cultural context of late modernity. This analysis reveals that our interconnection simultaneously constitutes the most profound challenges in our psychological response to the pandemic, as well as our greatest source of hope.