Connection Probability Research Articles

Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)

To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corridors</i> , that link the aerodromes, thus avoiding the use of complex routing strategies such as those of modern-day helicopters and alleviating the burden on the ground transportation system. For safety, a UAM aircraft must use air-to-ground communications to report flight plan, off-nominal events, and real-time movement to ground base stations (GBSs). A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a suitable spatial model is proposed. For the considered setup, assuming that any given aircraft communicates with the closest GBS, the distribution of the distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ratio (SIR)-based connectivity probability is determined to capture the connectivity performance of the UAM aircraft-to-ground communication network. Then, leveraging these connectivity results, a wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network is proposed to tackle the challenging problem of turbulence prediction during UAM operations. For this AFL scheme, a staleness-aware global aggregation scheme is introduced to expedite the convergence to the optimal turbulence prediction model used by UAM aircraft. Simulation results validate the theoretical derivations for the UAM wireless connectivity. The results also demonstrate that the proposed AFL framework converges to the optimal turbulence prediction model faster than the synchronous federated learning baselines and a staleness-free AFL approach. Furthermore, the results characterize the performance of wireless connectivity and convergence of the aircraft’s turbulence model under different parameter settings, offering useful UAM design guidelines.

Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy

Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy

Signal-Vehicle Coordination Control Modeling and Roadside Unit Deployment Evaluation under V2I Communication Environment

Signal-vehicle coordinated control holds substantial promise for enhancing urban transportation efficiency. However, its development faces notable challenges: (1) most existing studies have been conducted based on the assumption of perfect communication conditions. This assumption overlooks the significant impact of vehicle-to-infrastructure (V2I) communication quality on control performance, which leads to poor applicability in practice. (2) The evaluation of roadside unit (RSU) deployment for optimizing signal-vehicle control has not been well studied. Hence, the modeling of signal-vehicle coordination control and RSU deployment evaluation under V2I environment are studied in this paper. First, we introduce a communication model that characterizes the imperfections in communication between RSUs and connected vehicles (CVs). Second, we propose a model for signal-vehicle coordination control within this connected environment. This model integrates strategies from both signal control optimization and the speed optimization of CV platoons. Finally, to assess the impact of the RSU deployment parameters on the performance of signal-vehicle coordination control, we introduce a systematic evaluation method. The reduction in vehicle delays is introduced as the evaluation indicator for control performance. Six other indicators—the number of vehicles in the RSU communication domain, connectivity probability between the CV and RSU, number of vehicles whose speeds are successfully optimized, number of speed adjustments, green extension time, and overlap rate of the communication domains of multiple RSUs—are introduced as the observation indicators. The simulation experiments verify the effectiveness of the proposed model in implementing signal-vehicle coordination control under imperfect communication and environments in low-traffic, medium-traffic, and high-traffic scenarios. Furthermore, these experiments show the quantitative impact of RSU deployment parameters (communication distance, command transmission cycle, installation position, and number of RSUs) on control performance.

Conformal covariance of connection probabilities and fields in 2D critical percolation

AbstractFitting percolation into the conformal field theory framework requires showing that connection probabilities have a conformally invariant scaling limit. For critical site percolation on the triangular lattice, we prove that the probability that n vertices belong to the same open cluster has a well‐defined scaling limit for every . Moreover, the limiting functions transform covariantly under Möbius transformations of the plane as well as under local conformal maps, that is, they behave like correlation functions of primary operators in conformal field theory. In particular, they are invariant under translations, rotations and inversions, and for any . This implies that and , for some constants C2 and C3.We also define a site‐diluted spin model whose n‐point correlation functions Cn can be expressed in terms of percolation connection probabilities and, as a consequence, have a well‐defined scaling limit with the same properties as the functions . In particular, . We prove that the magnetization field associated with this spin model has a well‐defined scaling limit in an appropriate space of distributions. The limiting field transforms covariantly under Möbius transformations with exponent (scaling dimension) 5/48. A heuristic analysis of the four‐point function of the magnetization field suggests the presence of an additional conformal field of scaling dimension 5/4, which counts the number of percolation four‐arm events and can be identified with the so‐called “four‐leg operator” of conformal field theory.

US and European Stock Markets in Response to Exogenous Shocks: Cross-Regional Analysis of Dynamic Networks During the COVID-19 Pandemic

This study aims to analyse the impact of force majeure events or exogenous shocks, characterized by a high level of uncertainty, on the US and European stock market behaviour through cross-regional and inter-industrial comparisons. Based on the sample of 832 largest stock-listed companies publicly traded in both regions, the authors investigate whether specific regional and industrial differences in the stock market reaction to exogenous shocks—as exemplified by the ongoing COVID-19 pandemic—and their recovery paths to be determined. The novelty of the research conducted by the authors is underpinned by the application of a dynamic network analysis method—supported by the exponential random graph modelling—for a data sample of publicly traded companies, encompassing major players and industrial leaders in both regions. Furthermore, the methods are applied quarterly for an identical set of industries in cross-market comparison, within a time span of periods before, during and beyond the exogenous shock. The results reveal significant differences in the reaction of US and European stock markets to exogenous shocks, despite globally integrated financial markets. The connectivity of the largest listed companies on the US market is higher than the European ones. Thus, publicly traded companies in the United States are more likely to be closely connected to their industry peers. As demonstrated by a cross-market comparison of the European and US stock market network configuration, the US network reveals higher probabilities of intra-industrial connections of its market participants. In contrast, the European stock market exhibits fewer connections and a less dense network of market participants in times of uncertainty or exogenous shocks. Except for the European financial industry, which behaves similarly to its US counterpart, the overall interconnectedness of European companies is weaker within the uncertainty-related timespan. The latest could be partially explained by numerous national regulations being implemented at a country level, which are partly deviating from the EU single-market policies. In contrast to a relatively homogeneous setting of the US market, a low level of interconnectedness of publicly traded companies in Europe could be further argued by the application of non-harmonized industrial policies, with the latest being expanded during the ongoing crisis in selected European locations. This difference could also be explained by the absence of a mutual strategy for alleviating the effects of the COVID-19 pandemic in Europe. The obtained results could be valuable for academics, conducting similar thematic research; for portfolio investors, and policymakers in forecasting, reacting and assessing stock market behaviour overall—and at the level of industrial sectors in particular—in response to events characterized as force majeure, exogenous shocks or periods of uncertainty.

Reciprocal Connections between Parvalbumin-Expressing Cells and Adjacent Pyramidal Cells Are Regulated by Clustered Protocadherin γ.

Functional neural circuits in the cerebral cortex are established through specific neural connections between excitatory and various inhibitory cell types. However, the molecular mechanisms underlying synaptic partner recognition remain unclear. In this study, we examined the impact of clustered protocadherin-γ (cPcdhγ) gene deletion in parvalbumin-positive (PV+) cells on intralaminar and translaminar neural circuits formed between PV+ and pyramidal (Pyr) cells in the primary visual cortex (V1) of male and female mice. First, we used whole-cell recordings and laser-scan photostimulation with caged glutamate to map excitatory inputs from layer 2/3 to layer 6. We found that cPcdhγ-deficient PV+ cells in layer 2/3 received normal translaminar inputs from Pyr cells through layers 2/3-6. Second, to further elucidate the effect on PV+-Pyr microcircuits within intralaminar layer 2/3, we conducted multiple whole-cell recordings. While the overall connection probability of PV+-Pyr cells remained largely unchanged, the connectivity of PV+-Pyr was significantly different between control and PV+-specific cPcdhγ-conditional knock-out (PV-cKO) mice. In control mice, the number of reciprocally connected PV+ cells was significantly higher than PV+ cells connected one way to Pyr cells, a difference that was not significant in PV-cKO mice. Interestingly, the proportion of highly reciprocally connected PV+ cells to Pyr cells with large unitary IPSC (uIPSC) amplitudes was reduced in PV-cKO mice. Conversely, the proportion of middle reciprocally connected PV+ cells to Pyr cells with large uIPSC amplitudes increased compared with control mice. This study demonstrated that cPcdhγ in PV+ cells modulates their reciprocity with Pyr cells in the cortex.

Joint Optimization Scheme of User Association and Channel Allocation in 6G HetNets

The sixth-generation (6G) wireless cellular network integrates several wireless bands and modes with the objectives of improving quality of service (QoS) and increasing network connectivity. The 6G environment includes asymmetrical heterogeneous networks (HetNets) with the intention of making effective use of the available frequencies. However, selecting a suitable gNB and a communication channel that works for users in the network is an enormous challenge in 6G HetNets. This paper investigates a joint user association (UA) and channel allocation (CA) problem in two-tier HetNets by considering the downlink scenario to improve QoS. Our study presents an innovative scheme for user association and channel allocation, wherein the user can be connected to either the macro base station (MBS) or a possible small base station (SBS) in a direct or relay-assisted link. Furthermore, the proposed scheme identifies the optimal channel to be allocated to each user so that the overall network QoS can be maximized. A symmetric matching game-based user association is proposed to find the optimal association for users. Moreover, a modified auction game is applied to allocate the optimal channel by considering the quota of each gNB. Regarding connection probability, throughput, energy efficiency (EE), and spectrum efficiency (SE), the simulation results show that the proposed approach performs well over the state-of-the-art techniques.

Research on the Dynamic Mechanism of Technological Innovation Diffusion in Enterprise Communities Based on a Predation Diffusion Model

In order to study the dynamic mechanism of the impact of technological innovation diffusion on enterprise population networks, a corresponding relationship between enterprise population networks and predatory models was established based on a predatory model. Without considering the impact of technological innovation diffusion, the stability of the enterprise population network was analyzed, and the results showed that it has the characteristic of local asymptotic stability at a positive equilibrium point. Considering the influence of technological innovation diffusion, the stability of the enterprise population network becomes complex, and its stability at the positive equilibrium point is also affected by the eigenvalue of the Laplacian matrix and technological innovation diffusion coefficient. The simulation experimental results indicate that in addition to the influence of technological innovation diffusion coefficient, the connection probability density of enterprise population networks has an important impact on stability. Only when the connection probability density is very small can the enterprise population network resist the impact of technological innovation diffusion and maintain stability.

Connectivity probability evaluation of a large-scale highway bridge network using network decomposition

The existing NP-hard problem makes it difficult to evaluate the connectivity probability of large-scale networks, causing great barriers to evaluating and ensuring network safety. In this study, the connectivity probability for a highway bridge network composed of 1772 bridges is evaluated using network decomposition. First, the multilevel k-way graph partition is employed recursively to decompose the network into several approximately equally-sized subnetworks and minimum edge-cuts in series. Then, the decomposed network connectivity probability could be achieved in two steps: subnet and simplified network evaluations. In the subnet evaluation step, the subnet states with and without edge-cuts are judged respectively. Different from the existing connectivity binary definition as connected or disconnected, three states are redefined using adjacent matrices for each subnet: subnet with and without edge-cuts both connected (CCS), both disconnected (DDS), subnet with edge-cuts connected while without edge-cuts disconnected (DCS). For DDS would inevitably lead to the disconnection of the bridge network, such state wouldn't be enumerated in the following step for efficiency, while the CCS and DCS would be further represented by the terminal nodes. In the simplified network evaluation step, the highway bridge network connectivity can be thoroughly represented by treating terminal nodes and edge-cuts as a serial simplified network whose connectivity probability could be calculated by analyzing the limited states. The connectivity probability evaluation shows high efficiency and accuracy in the investigated bridge network.

A fuzzy structure processing mechanism for graph grammar

Abstract A strict graph-matching mechanism brings normativeness to graph grammar but leads to graph grammar insufficiency when processing fuzzy grammatical structures. To address this issue, the current paper proposes an improved formal framework for graph grammar that enables it to effectively specify the ambiguity of graph models while maintaining normativeness and intuition. First, the improved framework defines the connection probability for edges and classifies the edges based on the connection probability, which is used as the quantitative and qualitative description of the graph grammar structure’s ambiguity. Second, the concepts of credibility threshold, credible subgraphs and candidate subgraphs are defined, and the constraints on the redex are adjusted to increase the fault tolerance of the graph-matching process. Finally, the grammatical operation is redesigned, with a matching weight defined for each redex based on the connection probability and the credibility threshold, thereby providing a theoretical basis and practical guidance for the selection of multiple redexes.

Digital rock modeling to quantify scale dependence of petrophysical measurements in burrowed reservoir rocks: An example using Thalassinoides

Recent evaluations of burrow connectivity have shown that burrow morphology, abundance, and size impact the sampling scheme required for faithfully representing permeability in the laboratory. Quantifying these controls to design appropriate sampling is essential. This study illustrates a new approach using multipoint statistics modeling and applies it to Thalassinoides, to simulate a range of burrow abundances and sizes, and then interrogates the outcome to develop a statistical model that allows sampling strategies to be designed. Our workflow started with digital models that were known to have open or permeable Thalassinoides that connected one side of the model volume to another, (i.e., high permeability). We cut the model volumes into 540 digital samples with a range of burrow abundance, burrow size and sample cross section. The Thalassinoides showed no cross-sample connectivity in 173 (32%) samples; the remaining 367 (68%) showed cross-sample Thalassinoides connectivity. The data on burrow abundance, burrow size, and sample dimensions (as independent variables) and the result of samples representing Thalassinoides connectivity or not (as a dependent variable), was analyzed using logistic regression modeling. An important outcome from the logistic regression model is an equation that provides a probability of Thalassinoides connectivity across samples, given burrow abundance, burrow size, and sample dimension inputs. This equation has practical applications in determining the minimum sample cross-sections and sample lengths needed for samples to adequately represent the permeability. As examples, for samples with cross sections similar to core plugs and full diameter core, given various burrow percentages and sizes, only 37% yield burrow connectivity across any and all sample lengths. For the rest, our data on the largest connected burrow length allows for calculation of the probabilities of samples of typical lengths being representative of the burrow connectivity. Thus, researchers can assess the likelihood of their samples representing Thalassinoides connectivity given a range of burrow abundance, burrow size, sample cross section and sample length. The spreadsheet to make these calculations and lookup tables are provided as digital accessory files. The example using Thalassinoides provides proof-of-concept of a methodology that should be broadly applicable to any given burrow morphology. An algorithm and lookup table that can be modeled for each given burrow morphology with an appropriate training image.

Intrinsic neural diversity quenches the dynamic volatility of neural networks

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.

Modeling and Performance Analysis of Flying Mesh Network

Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β -Ginibre point process ( β -GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β -GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β , UAV intensity ρ , and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh.

Target layer state estimation in multilayer complex dynamical networks using functional observability

In large-scale complex dynamical networks, it is significant to estimate the states of target nodes with only a part of measured nodes. Meanwhile, multilayer complex dynamical networks exist widely in society and engineering. Therefore, it has important theoretic meaning and practical value to study the state estimation of target nodes in multilayer complex dynamical networks with limited node measurements. In this paper, with the measurable state information of a portion of nodes in one layer in the multilayer complex dynamical network, the state estimation of target nodes in other layers is studied. First, we build the model of the multilayer complex dynamical network which includes some target nodes and sensor nodes. Second, auxiliary nodes are selected by using the maximum matching principle in graph theory to construct the augmented node set. Third, we discuss the relationship between the minimum number of auxiliary nodes and interlayer connection probability in the multilayer complex dynamical network. Forth, an appropriate functional state observer is designed with limited number of measured nodes according to a typical model-based algorithm. Finally, numerical simulations are given to demonstrate the accuracy of the proposed method. The proposed method can achieve the accurate estimation with less placement of observers and fewer computational costs in the multilayer complex dynamical network.

Influences of urban stormwater management ponds on wetlandscape connectivity

Wetlands have been degraded and destroyed in many human-dominated landscapes worldwide, threatening vital ecosystem services. Landscape connectivity is critical for wetland biota, but is often reduced among remaining wetlands. With urban development, stormwater management (SWM) ponds are installed to hold runoff and associated contaminants. Unfortunately, SWM ponds do not adequately replace the ecosystem services and functions provided by a connected wetlandscape. We use a graph theory approach to analyze landscape connectivity of wetlands and stormwater management ponds in seven municipalities in southern Ontario, Canada. We analyze changes in connectivity through time, alongside potential effects of SWM pond creation on connectivity. We calculate the number of links (NL) and components (NC) at the landscape-level (i.e. connectivity within a municipality), and the probability of connection and two of its components (i.e. dPCflux and dPCconnector) at the habitat-level (i.e. contributions of individual wetlands and SWM ponds to landscape connectivity). Our results suggest that landscape connectivity has decreased with wetland loss, while SWM pond construction has improved connectivity. Wetlands appear to be more connected over the landscape, while SWM ponds might act as stepping-stones for species moving between wetlands. Our results point towards the need to protect and restore wetlands to safeguard critical ecosystem services. Stormwater ponds may not provide habitat of equivalent quality to wetlands; however, their strategic placement within urban areas could improve wetlandscape connectivity.

An Efficient IoT-based Ubiquitous Networking Service for Smart Cities Using Machine Learning Based Regression Algorithm

In recent days, the smart city project has been emerging concept all over the world. In this process, the proper communication between the sensors and the smart devices, and identification of optimal path between sensors and mutation sensors in large geographical area is very difficult. The main objective has been considered to overcome the drawbacks as mentioned above. The proposed algorithm is efficient to provide integrated communication of IoT-based ubiquitous networking (UBN) devices to improve in large geographically distributed area. The data storage capacity and accuracy of sensors and smart devices are enhanced using the proposed algorithm. The communication latency and data pre-processing of IoT-based UBN nodes deployed in smart cities are reduced. The proposed algorithm also analyses the performance of IoT-based UBN nodes by considering geographical testbeds that represent a smart city scenario. The analysis and comparison are carried out by considering the heuristic parameters. The proposed algorithm will also optimize the communication latency and data pre-processing time by analyzing various sensitivity levels by considering the heuristic parameters in different probability of nodes in smart cities. The proposed IoT-based UBN computing devices improve the objective function due to proper integrated communication between the sensors using a machine learning based regression algorithm. The proposed algorithm also identifies the probability sensitivity of optimal path between smart devices in a smart city thereby enhancing the connectivity of mutated sensor nodes. The proposed algorithm also enhances the probability of smart device connectivity to improve the accuracy, flexibility and large geographical coverage using machine learning based regression algorithm.

Performance Analysis of Multi-Hop Flying Mesh Network Using Directional Antenna Based on β-GPP

Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial distribution of the UAVs since it does not restrict the minimum distance between them. The spatial deployment of FlyMesh varies depending on the environment. Considering the relevance and practicality, we modeled the multi-hop FlyMesh using the β-Ginibre point process (β-GPP) and equipped each UAV with a directional antenna. Under the condition of the decode-and-forward protocol, we derived the connection probability and ergodic capacity of a multi-hop FlyMesh utilizing the Laplace transform of interference. Then, we calculated an approximate expression for the interference Laplace transform based on the diagonal approximation and further obtained the coverage probability. Finally, the numerical simulation results verified the correctness of the theoretical derivation, indicating that it is possible to optimize the system’s performance based on the expressions derived in this paper.

TS-CAGR:Traffic sensitive connectivity-aware geocast routing protocol in internet of vehicles

High vehicular mobility, frequent path link disruption and disconnection as well as random distribution of vehicular nodes make the establishment of path link for data dissemination highly challenging and the existing ad hoc routing protocols ineffective. The presence of traffic lights in metropolitan environments has a significant impact on connectivity because changes to traffic signals can induce variations in the geographical distribution of nodes. The designing of connectivity-aware geocast routing protocol using multi-hop communicational pattern, which is sensitive to traffic signal and traffic noise is a significant and contemporary issue in urban Internet of Vehicles (IoV). In this context, this paper proposes a Traffic Sensitive Connectivity-Aware Geocast Routing (TS-CAGR) protocol to minimize the impact of a traffic signals on link connectivity among vehicles along a path. First, a system connectivity model focusing on segmentation of a road into platoon and cell formation due to traffic light is presented. Secondly, connectivity model is presented to compute and analysis the connectivity probability using both platoon and cell formation. Thirdly, the routing algorithm is proposed comprising of an adaptive path selection mechanism to find suitable path segments based on the On-the-Fly Segment Density (OFSD) approach. Next Hop Vehicle (NHV) selection using Angular Zone-based two-hop four-neighbor information (AZ-2-H-4-NI) approach, and Store-Carry-Retransmit (SCR) approach to cope with the frequent link disconnection problem. By combining both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication patterns, the total probability of connectivity in cluster and cell-based vehicular node configurations has been computed. The performance of TS-CAGR is evaluated in an urban vehicular environment by considering performance parameters: throughput, end-to-end delay, packet delivery ration and routing overhead. The comparative performance evaluation attests to the superiority of the TS-CAGR protocol over the existing protocols.

Complexity Review of NIMBY Conflict: Characteristics, Mechanism and Evolution Simulation

In the process of modernization and urbanization, some government projects or facilities with negative externalities have caused the psychology of residents nearby to “Not in My Backyard” (NIMBY). That is, adopting strong and resolute, sometimes highly emotional collective opposition or even resistance behavior. This triggered a NIMBY conflict. From the perspective of Complexity, this study re-examines the characteristics and evolution mechanism of NIMBY conflict and draws the following conclusions: (1) NIMBY conflict is a complex system that interacts between multiple subjects and the environment; (2) Adaptability is the driving force for the evolution of NIMBY conflict. Through detectors, regularizers, and effectors, NIMBY subjects can be encouraged to gradually adapt to changes in the external environment and maximize their own interests; (3) In NIMBY conflict, the government conflict response method is more important than the intervention time. Residents’ communication efficiency and connection probability will affect residents’ behavior choices. The lower the residents’ communication efficiency, the less likely it is to form a NIMBY conflict.

Control Mechanisms for Mobile Devices

In this paper we consider control mechanisms for mobile devices in a stochastic environment. In particular, we consider a device in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional space subject to Brownian perturbations where a control mechanism moves the device towards its target location at a speed which is a function of its displacement. For this scenario, we construct stochastic differential equations for the mobility process and solve for the steady state probability density function of displacement. From this we are able to give general solutions to key metrics such as displacement outage (the long term probability of exceeding a given distance from the target), connectivity probability (derived from the SNR distribution in a Rayleigh channel with pathloss), the mean time at which the device first exceeds a given distance from the target, and the mean kinetic energy required by the control mechanism. We evaluate these metrics for important special cases of the control mechanism and also study the optimization problem of minimizing kinetic energy over the parameters of the control function.