Connection Rules Research Articles

Assessment of transmission network connection solutions based on HVAC for offshore wind

AbstractThe diffusion of offshore wind power plants (OWPPs), useful to fulfil renewable diffusion policy targets, implies several technological challenges for integration in power transmission network. The definition of the OWPP connection to the transmission network affects possible exploitation of the assets, involves an evaluation of active power losses and loadability and points out the need for compensation devices, in order to comply with connection rules fixed by the grid operator. This paper proposes a methodology to determine the most suitable electric connection for an OWPP based on high‐voltage alternating current (HVAC) cables, for providing indications for transmission system operators. In particular, different HVAC connection schemes for OWPPs are analysed, focusing on new 66 kV standard voltage and high‐voltage levels, providing technical insight on transmission cable optimal operation conditions. An economic analysis is carried out by proper estimation of construction costs and production forecast, accounting for active power losses and reliability impact. The most suitable connection solution depending on OWPP size and distance in Italian framework is evaluated by means of synthetic techno‐economic indicators, further analysing possible sensitivities of energy price and capacity factor.

Retinal direction of motion is reliably transmitted to visual cortex through highly selective thalamocortical connections.

Motion perception is crucial to animal survival and effective environmental interactions. In mammals, detection of movement begins in the retina. Directionally selective (DS) retinal ganglion cells were first discovered in the rabbit eye,1 and they have since been found in mouse,2,3 cat,4 and monkey.5,6 These DS retinal neurons contact a small population of neurons in the visual thalamus (dorsal lateral geniculate nucleus [LGN]) that are highly DS.7,8,9,10 The primary visual cortex (V1) also contains DS neurons, but whether directional selectivity in V1 emerges de novo11,12,13 or is inherited from DS thalamic inputs14,15,16 remains unclear. We previously found that LGN-DS neurons generate strong and focal synaptic currents in rabbit V1, similar to those generated by LGN concentric cells.17 Thus, the synaptic drive generated by LGN-DS neurons in V1 is spatially well situated to influence the firing of layer 4 (L4) simple cells, most of which show strong directional selectivity.18 However, two important questions remain: do LGN-DS neurons synaptically target DS simple cells in L4, and, if so, do they contribute to the directional preferences of these V1 DS neurons? We used spike-train cross-correlation analysis of pairs of LGN-DS and L4 simple cells to address these questions. We found that LGN-DS neurons do target L4 DS simple cells and that the targeting is highly selective, largely following a simple set of "connectivity rules." We conclude that this highly selective thalamocortical connectivity of LGN-DS neurons contributes to the sharp directional selectivity of cortical simple cells.

Importance-Induced Customer Segmentation Using Explainable Machine Learning

Abstract Customer segmentation plays a critical role in enhancing a company’s product penetration rate in the market. It enables numerous downstream applications such as customer-oriented product development and trend analysis. Previous approaches to customer segmentation have relied either on survey-based methods or data-driven approaches. However, these methods face challenges such as high human labor requirements or the generation of noisy segments. To address these challenges, this paper proposes a new methodology based on data-driven network construction and an importance-enhanced framework. The framework incorporates two techniques: (1) the utilization of a neural network model to compute feature importance values and (2) the proposal of a novel network connection rule. This framework addresses the limitation of the previous approach, sentiment-polarity-based networking, by connecting customers based on feature importance. We further validated the effectiveness of the framework using three real-world datasets and demonstrated that the proposed method outperformed the previous approach.

Theoretical framework for learning through structural plasticity.

A growing body of research indicates that structural plasticity mechanisms are crucial for learning and memory consolidation. Starting from a simple phenomenological model, we exploit a mean-field approach to develop a theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules, and noisy stimuli. More importantly, it describes the effects of stabilization, pruning, and reorganization of synaptic connections. This framework is used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in training and testing procedures as the number of training patterns and other model parameters vary. The results are then compared with those obtained through simulations with firing-rate-based neuronal network models.

Functional connectomics reveals general wiring rule in mouse visual cortex.

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas - including feedback connections - suggesting the universality of the 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting 'like-to-like' connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

Neuronal parts list and wiring diagram for a visual system

A catalogue of neuronal cell types has often been called a ‘parts list’ of the brain1, and regarded as a prerequisite for understanding brain function2,3. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven to be essential for understanding fly vision4,5. Here we analyse the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. Most new cell types contain 10 to 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi)6–10 at least double in number of types. A new serpentine medulla (Sm) interneuron family contains more types than any other. Three families of cross-neuropil types are revealed. The consistency of types is demonstrated by analysing the distances in high-dimensional feature space, and is further validated by algorithms that select small subsets of discriminative features. We use connectivity to hypothesize about the functional roles of cell types in motion, object and colour vision. Connectivity with ‘boundary types’ that straddle the optic lobe and central brain is also quantified. We showcase the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity and reduction of the connectome to a substantially simpler wiring diagram of cell types, with immediate relevance for brain function and development.

Distinct brain-wide presynaptic networks underlie the functional identity of individual cortical neurons.

Neuronal connections provide the scaffolding for neuronal function. Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behavior. Yet, the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behavior. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioral state 1-12 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell based monosynaptic input tracing, and optogenetics. We show that behavioral state-dependent neuronal activity patterns are stable over time. These are minimally affected by neuromodulatory inputs and are instead driven by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioral state-dependent activity profiles revealed characteristic patterns of anatomical input. While both behavioral state-related and unrelated neurons had a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet, neurons that tracked behavioral state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioral state-dependent activity in S1, but this activity was not externally driven. Our results revealed distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioral state.

Navigating connectivity expectations and work–life boundaries through sensemaking in global teams

ABSTRACT Connectivity expectations, the experience of being expected to be technologically connected to work during non-work hours, can pose challenges for employees working in a global environment. This study employs the sensemaking framework to examine how global team members and leaders negotiate these expectations and how they reflect in team members' work–life boundary management. The data consists of 55 in-depth interviews with employees working in nine different teams in a global organization. Team members collaboratively made sense of connectivity expectations within the team, while team leaders engaged in sensegiving to influence team members interpretations. Sensemaking was reflected in team members’ work–life boundary management as cocreated connectivity rules enabled them to disconnect from work. Team leaders’ sensegiving allowed work–life boundary adjustments, creating a supportive culture. The study makes theoretical and practical contributions to sensemaking in global work by emphasizing its communicative nature and its reflection on employees’ boundary management and well-being.

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky–Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.

Analysis of fast calcium dynamics of honey bee olfactory coding

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution. We observed a heterogeneity of response profiles and an abundance of inhibitory activities, resulting in various response latencies and stimulus-specific post-odour neural signatures. Recorded calcium signals were fed to a mushroom body (MB) model constructed implementing the fundamental features of connectivity between olfactory projection neurons, Kenyon cells (KC), and MB output neurons (MBON). The model accounts for the increase of odorant discrimination in the MB compared to the AL and reveals the recruitment of two distinct KC populations that represent odorants and their aftersmell as two separate but temporally coherent neural objects. Finally, we showed that the learning-induced modulation of KC-to-MBON synapses can explain both the variations in associative learning scores across different conditioning protocols used in bees and the bees' response latency. Thus, it provides a simple explanation of how the time contingency between the stimulus and the reward can be encoded without the need for time tracking. This study broadens our understanding of olfactory coding and learning in honey bees. It demonstrates that a model based on simple MB connectivity rules and fed with real physiological data can explain fundamental aspects of odour processing and associative learning.

Transcriptomic cell-type specificity of local cortical circuits

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

Extracting Geometry and Topology of Orange Pericarps for the Design of Bioinspired Energy Absorbing Materials.

As a result of evolution, many biological materials have developed irregular structures that lead to outstanding mechanical performances, like high stiffness-to-weight ratios and good energy absorption. However, reproducing these irregular biological structures in synthetic materials remains a complex design and fabrication challenge. Here, a bioinspired material design method is presented that characterizes the irregular structure as a network of building blocks, also known as tiles, and rules to connect them. Rather than replicating the biological structure one-to-one, synthetic materials are generated with the same distributions of tiles and connectivity rules as the biological material and it is shown that these equivalent materials have structure-to-property relationships similar to the biological ones. To demonstrate the method, the pericarp of the orange, a member of the citrus family known for its protective, energy-absorbing capabilities is studied. Polymer samples are generated and characterized under quasistatic and dynamic compression and display spatially-varying stiffness and good energy absorption, as seen in the biological materials. By quantifying which tiles and connectivity rules locally deform in response to loading, it is also determined how to spatially control the stiffness and energy absorption.

Development and Validation of the High-Voltage Direct-Current Modular Multilevel Converter (HVDC-MMC) Model for Converter Transformer Protection Studies.

The electrical protection of power networks with fault contribution from inverter-based power sources imposes new application challenges that have to be dealt with by protection engineers. This paper describes the development of a study case model of an HVDC-MMC link for testing the protection behaviour of connected converter transformers. The paper summarises the implementation and validation of the converter control as well as enhancements to provide Fault Ride-Through capability and fast fault current injection as required by the German Technical Connection Rules for HVDC. The grid code standard requires positive- and negative-sequence reactive current injection in the case of grid faults. A Doubled Decoupled Synchronous Reference Frame Phase Locked Loop (DDSRF-PLL) for Vector Current Control (VCC) is implemented. Additionally, a Fault Detection and Fault Ride-Through Reference Generator with a Current Limitation strategy is introduced. Though these techniques are well described in the literature, the DDSRF is improved for current control stability. The relationship between the parameters of the PLL and the control, as well as the behaviour of the protection system, are demonstrated. Grid faults with large voltage dips pose a significant challenge to the stability of the control system. Nevertheless, it is shown that with the developed model, it is possible to make general statements about the protection behaviour in an inverter-based environment.

Conceptual Configuration Analysis of Tetrahedral-Octahedral Heterogeneous Unit and Topological Design of Shape Morphing Mechanism

Abstract The truss static structure system has excellent large load and lightweight characteristics. It is easy to embed actuators by substituting specific members in the structure and has the potential for multidimensional space deformation. Inspired by uniform tessellation structures, this article proposes a novel shape morphing mechanism (SMM) based on the combined design of tetrahedral-octahedral heterogeneous units (TOHUs). The relationship between the motion properties and driving configuration of the tetrahedral-octahedral units is analyzed to determine their conceptual configuration based on graph theory. Then, the weighted graph, adjacency matrix, and connection rules are evaluated to synthesize the conceptual structure for the truss mechanism. The results show that this configuration can allow multidimensional continuous deformations, including span, bend, sweep, and twist. A prototype is built to verify its deformability. Finally, the stiffness performance is analyzed based on the matrix displacement method. This research provides a comprehensive design method for constructing SMM, expands the range of combinable units and connection methods, and offers theoretical guidance for the innovative design of multidimensional deformation SMM in aerospace.

RECENT ADVANCES AND APPLICATIONS OF APRIORI ALGORITHM IN EXPLORING INSIGHTS FROM HEALTHCARE DATA PATTERNS

The growing accessibility of extensive healthcare datasets has highlighted the need for solid methods for discovering significant connections and trends that can guide clinical decision-making and management of healthcare practices. The Apriori algorithm, recognized for effectively detecting common item groups and connection rules, presents a hopeful method to tackle these issues. This paper explores the latest progress and real-world uses of the Apriori algorithm in analysing patterns in healthcare data. By thoroughly examining the literature and case studies, this paper proposes the Healthcare Analytics and Insight Framework (HAIF), in which the Apriori algorithm can find meaningful connections and trends in healthcare data. The information obtained by Apriori-based analysis can help improve individualized patient care, resource management, and predictive analytics, leading to better healthcare delivery and patient outcomes. Moreover, the research investigates several challenges and opportunities associated with utilizing the Apriori algorithm for medical data analysis. It highlights the need of interpretability, adaptability, and ethical concerns in examining healthcare data, underlining the importance of conscientious data handling and security protocols. This research offers essential information about how the Apriori algorithm can reveal valuable insights from patterns in healthcare data that can help improve data-driven medical decision-making and patient-focused care delivery strategies.

Правоприменительная практика по договору технологического присоединения к электрическим сетям

The article analyzes materials of the judicial practice on issues of technological connection to electrical grids. The reviewed examples show that: a) the judicial practice is contradictory, and judgments in certain cases are issued at violation of the principle of technological connection on a once-only basis; b) the laws on technological connection need to be updated in terms of determination of the party responsible for payment for grid operator services if the technological connection procedure started by the initial owner of a connected real estate unit is completed by a new owner. Law improvement proposals are brought forward as a result of the performed study, in particular, it is suggested to include in the Technological Connection Rules a provision to place title holders of real estate units, on the territory of which the applicant’s energy receiver is located, under an obligation to throw no obstacles in the way of a grid operator performing technological connection. It is also suggested to supplement the standard technological connection agreement with a clause imposing the obligation to pay for grid operator services on the real estate owner.

When microscopy and electrophysiology meet connectomics-Steve Massey's contribution to unraveling the structure and function of the rod/cone gap junction.

Electrical synapses, formed of gap junctions, are ubiquitous components of the central nervous system (CNS) that shape neuronal circuit connectivity and dynamics. In the retina, electrical synapses can create a circuit, control the signal-to-noise ratio in individual neurons, and support the coordinated neuronal firing of ganglion cells, hence, regulating signal processing at the network, single-cell, and dendritic level. We, the authors, and Steve Massey have had a long interest in gap junctions in retinal circuits, in general, and in the network of photoreceptors, in particular. Our combined efforts, based on a wide array of techniques of molecular biology, microscopy, and electrophysiology, have provided fundamental insights into the molecular structure and properties of the rod/cone gap junction. Yet, a full understanding of how rod/cone coupling controls circuit dynamics necessitates knowing its operating range. It is well established that rod/cone coupling can be greatly reduced or eliminated by bright-light adaptation or pharmacological treatment; however, the upper end of its dynamic range has long remained elusive. This held true until Steve Massey's recent interest for connectomics led to the development of a new strategy to assess this issue. The effort proved effective in establishing, with precision, the connectivity rules between rods and cones and estimating the theoretical upper limit of rod/cone electrical coupling. Comparing electrophysiological measurements and morphological data indicates that under pharmacological manipulation, rod/cone coupling can reach the theoretical maximum of its operating range, implying that, under these conditions, all the gap junction channels present at the junctions are open. As such, channel open probability is likely the main determinant of rod/cone coupling that can change momentarily in a time-of-day- and light-dependent manner. In this article we briefly review our current knowledge of the molecular structure of the rod/cone gap junction and of the mechanisms behind its modulation, and we highlight the recent work led by Steve Massey. Steve's contribution has been critical toward asserting the modulation depth of rod/cone coupling as well as elevating the rod/cone gap junction as one of the most suitable models to examine the role of electrical synapses and their plasticity in neural processing.

Cost allocation in energy distribution networks

This paper presents a cost allocation problem arising from energy distribution and proposes cost allocation rules that depend on the distribution network and consumer demands. To determine relevant rules, we adopt a normative approach and compare two principles: (i) the connection principle and (ii) the uniformity principle. The Connection rule is proposed in accordance with (i), while the Uniform rule is developed in line with (ii). However, (i) and (ii) are incompatible. To make a trade-off between them, we propose a family of Mixed rules. Each rule is axiomatically characterized. Then, we demonstrate that the Connection rule coincides with the multi-choice Shapley value of a specific multi-choice game derived from the original problem. Moreover, the Connection rule is in the Core of this game. Similarly, we show that the Uniform rule and the Mixed rules coincide with other solution concepts from multi-choice games.

A spanning tree algorithm with adaptive pruning with low redundancy coverage rate

In reality, when UAV is used in region reconnaissance, the shape of region is usually irregular. Selecting an algorithm to find a complete coverage path with smaller coverage redundancy rate, lower path motion complexity and theoretical energy consumption has certain research significance for the complete coverage path planning (CCPP). In existing algorithms, there is always a high redundant coverage rate under irregularly shaped regions. The motion complexity and the theoretical energy consumption of the path also increase. In this paper, a kind of spanning tree with adaptive pruning (STAP) algorithm based on the grid map is proposed to solve the above problems: on the one hand, it uses a greedy growth strategy to determine the branching growth direction, and proposes an adaptive pruning method for self-optimization of the initial path; on the other hand, a kind of nearest neighbor sub-region connection rule of adjacent paths is proposed to reduce the complexity and the coverage redundancy rate. The STAP algorithm searches for all grid centers, grows, and optimizes the spanning tree by creating paths between the centers. At the end of the optimization, it creates a complete coverage path based on the distribution of the spanning tree. Experiments show that when the STAP algorithm is applied to unobstructed regions, it plans the path with the lowest redundant coverage rate, which reaches 2.6296% and 4.9402%; when it is applied to the regions include obstacles, the advantages of the algorithm are more significant: On the one hand, the path planned by STAP has the least total length, the lowest number of turning and turning back. On the other hand, the redundant coverage rate of the path is still the lowest, reaching 4.1362% and 2.7765%, respectively. The STAP is primarily suitable for situations about CCPP under grid maps formed by irregularly shaped regions, and it holds certain significance for ground reconnaissance of UAVs.

Fragility of Delay Margin Against Platoon Size in a Connected Vehicle System; Network Design and Multiple Trials

Abstract Linear stability of a car following model is investigated considering time delays due to human reaction times and communication delays. In multiple trials, we generate networks of randomly arranged legacy and autonomous vehicles, each vehicle type respecting some connectivity rules. Next, the largest amount of delay the vehicle network can tolerate without becoming unstable is computed. This delay, also known as the delay margin (DM), can become sensitive with respect to platoon size under certain connectivity rules. We demonstrate this ‘fragility’ property, and next propose a new network-design policy with which DM can be made robust against platoon size.