Simply-connected Research Articles

Ex ante transparency and corruption by networks

AbstractThis paper explains the structure of corruption networks as response to ex ante transparency, defined as visibility of authorities whose cooperation clients may need, in due course, to execute corrupt transactions. It also characterizes the optimal transparency policy given the network response, as a function of connection costs, sanctions, the corruption surplus and the detection probability (hence the anti‐corruption budget and ex post transparency). Corruption chains may emerge in equilibrium if authority is expected to be shared by multiple offices, where the office with higher solo assignment probability becomes the intermediary. Otherwise, clients penetrate the bureaucracy by inducing the star network, or contend with single connection. I show that the optimal policy always assigns one office, sometimes alone, sometimes jointly with others. It is often possible to deter corruption networks through an ex ante transparent policy that parcels out authority to multiple offices with probability 1—a common feature of many US bureaucracies. Decomposing transparency into its components reveals nuances in the transparency–corruption relationship, suggesting that ex ante transparency is instrumental except in environments in which anti‐corruption enforcement is extremely effective or extremely ineffective.

Evolution characteristics and performance evaluation of extended end-plate joints with single and double side connections under cyclic loading

Experimental studies were conducted on four extended end-plate joints subjected to cyclic loading at the column top, investigating the evolving patterns of the joints' mechanical performance. The paper provides a detailed analysis and discussion of the test joints' failure modes, ductility, stiffness degradation, and energy dissipation capacity. The Mann-Kendall (M − K) trend analysis tool was applied to the mechanical response curves, identifying key performance evolution points (evolution initiation point P and overall yield point Q). The trends in bolt forces, deformations, and strains at critical joints were effectively validated, revealing the transition of the energy system from quantitative to qualitative changes and the component's failure process from stability to instability. Additionally, based on the experimental joints' hysteresis curves and energy dissipation capacity, a theoretical hysteresis model was established to predict the joint's hysteresis curve and cumulative dissipated energy accurately. According to EC3 requirements, joints were classified as partially rigid connections. The experimental results of the initial rotational stiffness and plastic moment were further used to evaluate the calculated values in existing standards EN 1993-1-8, ANSI/AISC 358-16, and GB 51017-2017. The results indicate that extended end-plate connections possess sufficient strength, joint rotational stiffness, ductility, and energy dissipation capacity, making them suitable for seismic moment frames.

Network Analysis of the North American Skull Base Society Membership.

Background The membership of the North American Skull Base Society (NASBS) has grown considerably in recent years with diversity in subspecialty training, gender, and geography. The academic relationships and contributions of its membership have not been studied. Objectives This study aimed to (1) measure academic contributions of NASBS membership; (2) identify influential nodes of academic collaboration; (3) identify opportunities for future collaboration and mentorship. Methods Peer-reviewed publications of members of NASBS (2019 NASBS website) were identified using Scopus author name search. Network structures were constructed and analyzed using the graph-tool python library to produce a weighted coauthorship network and compute centrality measures. Visual network maps were then produced using Gephi network visualization software. Results The coauthor network contained 952 members with found publications and 4,996 connections. A total of 846 (88.9%) members were contained in a single connected giant component; 102 members were unconnected, and 64 members had a single connection. Girvan-Newman clustering identified 267 communities, where 13 contained at least 1% of the total membership each. The three largest communities contained 23.3, 8.4, and 6.9% of members. There were 111 published members identified as women; 5.4% of women were unconnected versus 11.4% of men. Average publication count for women was 32.3 (standard deviation [SD] 38.5) versus 70.5 (SD 106) for men. Average citation count for women was 543 (SD 1,012) versus 1,389 (SD 2,893) for men. Conclusion Network mapping of membership of the NASBS helps to visualize the academic activities and relationships of the NASBS and reveals areas of concentration and influence within the specialty.

A non-boundary-fitted-grid method, based on compact integrated-RBF approximations, for solving differential problems in multiply-connected domains

This paper presents a new non-boundary-fitted-grid numerical technique for solving partial differential equations (PDEs) in multi-hole domains. A multiply-connected domain is converted into a simply-connected domain of rectangular or non-rectangular shape that is discretised using a Cartesian grid. Compact radial basis function (RBF) stencils, which are constructed through integration rather than the conventional differentiation, are used to discretise the field variables. The imposition of inner boundary conditions is conducted by means of body forces that are derived from satisfying the governing equations and prescribed boundary conditions in small subregions. Salient features of the proposed method include: (i) simple pre-processing (Cartesian grid), (ii) high rates of convergence of the solution accuracy with respect to grid refinement achieved with compact integrated-RBF stencils, where both nodal function and derivative values are included in the approximations, (iii) the system matrix kept unchanged for the case of moving holes, and (iv) no interpolation between Lagrange and Euler meshes required. Several linear and nonlinear problems, including rotating-cylinder flows and buoyancy-driven flows in eccentric and concentric annuli, are simulated to verify the proposed technique.

HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.

Power consumption during forward locomotion of C. elegans: an electrical circuit simulation

Biological neuronal networks are of great interest for emerging technological approaches such as neuromorphic engineering due to their capability to efficiently process information. To understand the principles governing this energy efficiency, it is useful to investigate model organisms with small and well-characterized neuronal networks. Caenorhabditis elegans (C. elegans) is such a model organism and perfectly suited for this purpose, because its neuronal network consists of only 302 neurons whose interconnections are known. In this work, we design an ideal electrical circuit modeling this neuronal network in combination with the muscles it controls. We simulate this circuit by a run-time efficient wave digital algorithm. This allows us to investigate the energy consumption of the network occurring during locomotion of C. elegans and hence deduce potential design principles from an energy efficiency point of view. Simulation results verify that a locomotion is indeed generated. We conclude from the corresponding energy consumption rates that a small number of neurons in contrast to a high number of interconnections is favorable for consuming only little energy. This underlines the importance of interneurons. Moreover, we find that gap junctions are a more energy-efficient connection type than synapses, and inhibitory synapses consume more energy than excitatory ones. However, the energetically cheapest connection types are not the most frequent ones in C. elegans’ neuronal network. Therefore, a potential design principle of the network could be a balance between low energy costs and a certain functionality.Graphical abstractEnergy consumption rates during forward locomotion of C. elegans. a Rates for the ion channels of all neurons, and b average rates for ion channels of a single, active neuron. c Comparison of average rates with respect to the number of active sensory, motor, and interneurons. d Rates for all gap junctions and synapses, and e rates for all synapses of a specific neurotransmitter type. f Average rates for a single synaptic or gap junctions connection vs the total number of connections present for the type of connection (i.e. ACh-synapse, GABA-synapse, Glu-synapse, gap junction).

Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

Optimal Sizing of a Battery-Supported Electric Vehicle Charging Hub with a Limited-Capacity Grid Connection

The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.

Multipattern graph convolutional network-based autism spectrum disorder identification.

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.

Evaluation of Inner Products of Implicitly Defined Finite Element Functions on Multiply Connected Planar Mesh Cells

Evaluation of Inner Products of Implicitly Defined Finite Element Functions on Multiply Connected Planar Mesh Cells

Beam-on-Foundation modelling of dowel-type single fastener connections in cross laminated timber

In this study, a numerical model that aimed to predict the strength and stiffness, of a single laterally loaded mechanical fastener in steel-to-cross laminated timber (CLT) connections using a Beam-on-Foundation (BoF) model, is presented. The BoF model was used to predict the ductile failure modes of steel-to-CLT connections and considered the layered structure of CLT. When compared to the European Yield Model (EYM), the BoF model was found to be advantageous as it not only predicted the strength but also the stiffness of the connections. A comparison of the slip modulus and strength from BoF model simulations of experimental tests carried out on steel-to-CLT connections showed good agreement with the experimental results. Using the BoF model, the influence of; (i) density, (ii) deck layer orientation, (iii) embedment behaviour, (iv) dowel diameter, (v) steel plate thickness, and (vi) embedment length, on steel-to-CLT connections was investigated in a parameter study. The results presented herein highlight the benefit of the BoF model that considers CLT layer specific embedment behaviour in determining the shear capacity and stiffness of steel-to-CLT connections.

Взаимосвязь психических процессов с технической подготовленностью у баскетболисток 14–15 лет

For effective competitive activity in youth basketball, it is necessary to take into account both external and internal factors, since they influence the quality of technical actions in situations of confrontation with an opponent. Features of feeling and perception, as well as the speed of processing information coming from the outside, are closely related to the issues of players finding optimal solutions, which means that the coach needs to properly influence them. The athlete’s readiness for professional activity at the level of mental processes (switching attention, error correction, perception of information, etc.) becomes increasingly important and determines the relevance of this study. The research aims to reveal correlations between the technical preparedness and mental processes of basketball players aged 14–15 in the pre-competition period of training. The research objective is to determine the level of interrelations between technical preparedness and the nature of the course of mental processes of basketball players aged 14–15 based on a correlation analysis of data using the nonparametric Spearman criterion. The level of technical readiness was determined by the indicator of the shuttle run completion time, dribbling for 20 meters, figure-eight dribbling for 20 meters, dribbling and shooting, execution time and accuracy of hitting shots from medium and long distances, and free throws. Mental processes were assessed according to the data of the type of information perception (visual, kinesthetic, audial), motor response rates, estimates of time intervals, memory capacity, switching, and attention selectivity. The work was carried out in October 2020 and involved 16 female basketball players aged 14–15. The results of the correlation analysis allowed us to establish the presence of triple, double, and single connections between mental processes and indicators of technical preparedness. The largest number of connections was revealed in the following indicators: attention switching, individual minute, number of throws in motion, and long throw. The established direct and feedback links are described at high and medium levels of significance. The presence of numerous links between the indicators of technical preparedness and mental processes associated with the perception and processing of information at high and medium levels indicates the need for targeted training of basketball players aged 14–15 to teach the ability to manage sensory-perceptual processes, distinctive sensitivity, modality of information perception, operational thinking, and attention.

Surveillance Camera Using Wi-Fi Connection

This research paper aims to design an energy-efficient surveillance camera utilizing the ESP32-CAM module, incorporating Wi-Fi connectivity, and using 3D printed custom case made from ABS plastic. The central goal is to determine the most effective video streaming protocol for transmitting real-time data to a web-based interface that manages the camera. The camera prototype is developed through a technical feasibility study and prototype construction methods, with a primary focus on assessing power consumption across various protocols including HTTP, RTP, RTSP, and WebSocket. The findings indicate that WebSocket emerges as one of the most optimal and power-efficient protocols for streaming video from the ESP32-CAM web server to a client device. This superiority arises from WebSocket's browser-based approach, enabling full-duplex communication through a single TCP connection, thereby facilitating real-time interaction between the client and server.

Розробка цифрового інтерфейсу для керування імпульсним блоком живлення

Most switching power supplies are not designed to change their output voltage. However, situations arise where a different voltage from the power source is needed. The purpose of this article is to develop a practical interface scheme for digitally controlling the output voltage of a switching power supply with an analog feedback loop. The article presents the results of the research of isolated flyback switch-mode power supply (SMPS) circuits, determines the method of controlling the output voltage of the SMPS, and proposes a digital interface for controlling the output voltage of the power supply. The emphasis was on using readily available, inexpensive components with minimal modification to the existing design. The study revealed limitations in existing switching power supply designs regarding effective output voltage control. Modifying the internal reference source is often not an available option. Therefore, influencing the feedback circuit emerges as a simple and viable control method. All power supply feedback loops include a voltage divider measuring the output voltage of switching power supply. This research demonstrates that controlling output voltage can be achieved by injecting or draining current within or from output voltage divider of power supply. Draining current increases the output voltage, while injecting decreases it. The additional resistor only requires a single connection point within the existing circuit. The proposed method provides a linear dependence of the output voltage increase on the value of the injected/drained current. It is shown that the digital interface scheme depends on the number of discrete levels of the required output voltage. For a small number of levels, field-effect transistors controlled by a positional or binary code can be used to control the power supply, and with a significant number of levels, it is advisable to use ready-made digital-to-analog converters.

A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks

This article presents the development of a estimation model using an artificial neural network to estimate the traffic factor parameter, which reflects changes in travel time for a single transport connection. The modeling of traffic is highly challenging due to the complex and non-linear nature of road systems, the interactions between diverse road users, and the ripple effects of congestion. The research problem addressed is the need to consider a wide range of factors that contribute to congestion, as identified in the literature. To address this challenge, a universal Feedforward Neural Network (FNN) was designed, specifically the Multilayer Perceptron (MLP) 38–10-1 model. The input layer neurons of the MLP receive a vector of input values representing various factors such as road types, technical properties, time of day, days of the week, incidents, weather conditions, and population density. The model was trained using a dataset consisting of 41,945 records of fully completed input variables, extracted from a larger dataset of 300,000 travel time measurements collected from a real transport network mapped on a macro scale (Mazovia, Poland). During the research, the model achieved a satisfactory level of Mean Absolute Percentage Error (MAPE) for the test set (9.12%) and a Normalized Root Mean Squared Error (NRMSE) below 0.02, indicating good estimation performance. Furthermore, this work proposes specialized models with the structure of MLP 34–10-1 for individual types of roads, which also demonstrated satisfactory estimation abilities. These models have practical applications in traffic management and planning. Overall, this research addresses the research problem of modeling factors for predicting travel time changes using an artificial neural network approach. The developed models provide accurate estimations and offer potential applications in transportation systems.

Maximizing the Second Robin Eigenvalue of Simply Connected Curved Membranes

Maximizing the Second Robin Eigenvalue of Simply Connected Curved Membranes

From single to multiple habitat connectivity: The key role of composite ecological networks for amphibian conservation and habitat restoration

Many graph-based studies consider just one habitat type (breeding habitat) and one ecological process (dispersal) when measuring connectivity. However, in the course of their life cycle, some species use different habitats and are particularly sensitive to their spatial arrangement. We propose a new graph modeling approach that considers heterogeneous habitats and movements when assessing connectivity. Our hypothesis is that a multiple habitat approach is more relevant than a single habitat approach for studying the relationships between connectivity and composite habitat species occurrence. We constructed a bipartite graph with separate categories of nodes for aquatic and terrestrial habitats and an inter-habitat link set. Different connectivity values were measured from the graph depending on the ecological process under study. We then compared our models against field observation data for amphibian communities and tested whether multiple habitat connectivity models provide better predictions. Multiple habitat connectivity was as effective as single habitat connectivity, and often more so, in explaining the presence, abundance, and species richness of amphibian communities. Application to habitat restoration revealed that connectivity gains vary greatly depending on the ecological process, emphasizing the importance of links connecting habitats of different types. Multiple habitat graphs appear promising for incorporating habitat and movement heterogeneity into ecological network modeling. We encourage further theoretical and empirical work on multiple habitat connectivity to consolidate the results and evaluate its full potential, especially for global change issues.

Functional connectivity of cognition-related brain networks in adults with fetal alcohol syndrome

BackgroundFetal alcohol syndrome (FAS) can result in cognitive dysfunction. Cognitive functions affected are subserved by few functional brain networks. Functional connectivity (FC) in these networks can be assessed with resting-state functional MRI (rs-fMRI). Alterations of FC have been reported in children and adolescents prenatally exposed to alcohol. Previous reports varied substantially regarding the exact nature of findings. The purpose of this study was to assess FC of cognition-related networks in young adults with FAS.MethodsCross-sectional rs-fMRI study in participants with FAS (n = 39, age: 20.9 ± 3.4 years) and healthy participants without prenatal alcohol exposure (n = 44, age: 22.2 ± 3.4 years). FC was calculated as correlation between cortical regions in ten cognition-related sub-networks. Subsequent modelling of overall FC was based on linear models comparing FC between FAS and controls. Results were subjected to a hierarchical statistical testing approach, first determining whether there is any alteration of FC in FAS in the full cognitive connectome, subsequently resolving these findings to the level of either FC within each network or between networks based on the Higher Criticism (HC) approach for detecting rare and weak effects in high-dimensional data. Finally, group differences in single connections were assessed using conventional multiple-comparison correction. In an additional exploratory analysis, dynamic FC states were assessed.ResultsComparing FAS participants with controls, we observed altered FC of cognition-related brain regions globally, within 7 out of 10 networks, and between networks employing the HC statistic. This was most obvious in attention-related network components. Findings also spanned across subcomponents of the fronto-parietal control and default mode networks. None of the single FC alterations within these networks yielded statistical significance in the conventional high-resolution analysis. The exploratory time-resolved FC analysis did not show significant group differences of dynamic FC states.ConclusionsFC in cognition-related networks was altered in adults with FAS. Effects were widely distributed across networks, potentially reflecting the diversity of cognitive deficits in FAS. However, no altered single connections could be determined in the most detailed analysis level. Findings were pronounced in networks in line with attentional deficits previously reported.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

AbstractMillimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.