Search Results (372)

With the growing accessibility of tools such as online surveys and web scraping, longitudinal social network data are more commonly collected in social science research along with non-network survey data. Such data play a critical role in helping social scientists understand how relationships develop and evolve over time. Existing dynamic network models such as the Stochastic Actor-Oriented Model and the Temporal Exponential Random Graph Model provide frameworks to analyze traits of both the networks and the external non-network covariates. However, research on the dynamic latent space model (DLSM) has focused mainly on factors intrinsic to the networks themselves. Despite some discussion, the role of non-network data such as contextual or behavioral covariates remain a topic to be further explored in the context of DLSMs. In this study, one application of the DLSM to incorporate dynamic non-network covariates collected alongside friendship networks using autoregressive processes is presented. By analyzing two friendship network datasets with different time points and psychological covariates, it is shown how external factors can contribute to a deeper understanding of social interaction dynamics over time. Full article

Background: The efficacy of real-time fMRI neurofeedback (NFB) depends critically on how feedback is presented and perceived by the participant. Although various visual feedback designs are used in practice, there is limited evidence on the impact of modality on learning and performance. We conducted a feasibility study to compare the effectiveness of different feedback modalities, and to evaluate the technical performance of NFB across two scanner field strengths. Methods: In a single-session study, nine healthy adults (6 men, 3 women) voluntarily adapted the activation level of the primary sensorimotor cortex (SMC) to reach three predefined activation levels. We contrasted a continuous, signal-proportional feedback (cFB; a thermometer-style bar) with an affect-based, categorical feedback (aFB; a smiling face). A no-feedback transfer condition (noFB) was included to probe regulation based on internal representations alone. To assess technical feasibility, three participants were scanned at 7T and six at 3T. Results: Participants achieved successful regulation in 44.4% of trials overall (cFB 46.9%, aFB 43.8%, noFB 42.6%). Overall success rates did not differ significantly between modalities and field strengths when averaged across the session; given the small feasibility sample, this null result is inconclusive and does not establish equivalence. Learning effects were modality-specific. Only cFB showed a significant within-session improvement (+14.8 percentage points from RUN1 to RUN2; p = 0.031; d_z = 0.94), whereas aFB and noFB showed no evidence of learning. Exploratory whole-brain contrasts (uncorrected) suggested increased recruitment of ipsilateral motor regions during noFB. The real-time pipeline demonstrated robust technical performance: transfer/reconstruction latency averaged 497.8 ms and workstation processing averaged 296.8 ms (≈795 ms end-to-end), with rare stochastic outliers occurring predominantly during 7T sessions. Conclusions: In this single-session motor rt-fMRI NFB paradigm, continuous signal-proportional feedback supported rapid within-session learning, whereas affect-based categorical cues did not yield comparable learning benefits. Stable low-latency operation was achievable at both 3T and 7T. Larger, balanced studies are warranted to confirm modality-by-learning effects and to better characterize transfer to feedback-free self-regulation. Full article

This paper analyzes different approaches for the mathematical modeling and optimization of process parameters in the hard turning process of 42CrMo4 steel using a hybrid approach combining response surface methodology (RSM), multi-criteria decision making (MCDM), and machine learning through, support vector regression (SVR) with one-factor-at-a-time (OFAT) sensitivity analysis. Controlled process parameters such as cutting speed, depth of cut, feed, and insert radius are applied to conduct the experiments based on a full factorial experimental design. RSM was used to develop models that describe the effect of controlled parameters on surface roughness and cutting forces. Special emphasis was placed on the analysis of standardized residuals to evaluate the predictive capabilities of the RSM-developed model on an unseen data set. For all four outputs considered, analysis of the standardized residuals shows that over 97% of the points lie within ±3 standard deviations. A multi-criteria optimization technique was applied to establish an optimal combination of input parameters. The SVR model had high performance for all outputs, with coefficient of determination values between 89.91% and 99.39%, except for surface roughness on the test set, with a value of 9.92%. While the SVR model achieved high predictive accuracy for cutting forces, its limited generalization capability for surface roughness highlights the higher complexity and stochastic nature of surface formation mechanisms in the turning process. OFAT analysis showed that feed rate and depth of cut have been shown to be the most important input variables for all analyzed outputs. Full article

In this paper, a two-phase spatio-temporal point process (STPP) defined on a countable metric space and characterized by a conditional intensity function is introduced. In the first phase, the process is memoryless, generating completely random point patterns. In the second phase, the location and occurrence time of each event depend on the spatial configuration of previous events, thereby inducing spatio-temporal correlation. Theoretical results that characterize the distributional properties of the process are established, enabling both efficient numerical simulation and Bayesian inference. A statistical inference framework is developed, for the setting in which the STPP is observed at discrete calendar dates while the spatial locations of events are recorded, their exact occurrence times are unobserved, i.e., interval-censored. This partial observation scheme commonly arises in ecological and epidemiological applications, such as the monitoring of plant disease or insect pest spread across a spatial grid over time. The methodology is illustrated through an analysis of the spatio-temporal spread of sugarcane yellow leaf virus (SCYLV) in an initially disease-free sugarcane plot in Guadeloupe, FrenchWest Indies. Full article

Uncertainty quantification in science and engineering has become increasingly important due to advances in computational mechanics and numerical simulation techniques. In this work, the relationship between uncertainty in soil material parameters and the variability of failure loads and displacements of a shallow foundation is investigated. A Drucker–Prager constitutive law is implemented within a stochastic finite element framework. The random material variables considered are the critical state line slope c, the unload–reload path slope $\kappa$, and the hydraulic permeability k defined by Darcy’s law. The novelty of this work lies in the integrated stochastic u–p finite element framework. The framework combines Drucker–Prager plasticity with spatially varying material properties, and Latin Hypercube Sampling. This approach enables probabilistic prediction of failure loads, displacements, stresses, strains, and limit-state initiation points at reduced computational cost compared to conventional Monte Carlo simulations. Statistical post-processing of the output parameters is performed using the Kolmogorov–Smirnov test. The results indicate that, for the investigated configurations, the distributions of failure loads and displacements can be adequately approximated by Gaussian distributions, despite the presence of material nonlinearity. Furthermore, the influence of soil depth and load eccentricity on the limit-state response is quantified within the proposed probabilistic framework. Full article

The economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions. Full article

Fractional stochastic differential Equations (FSDEs) with time delays and non-instantaneous impulses describe dynamical systems whose evolution relies not only on their current state but also on their historical context, random fluctuations, and impulsive effects that manifest over finite intervals rather than occurring instantaneously. This combination of features offers a more precise framework for capturing critical aspects of many real-world processes. Recent findings demonstrate the existence, uniqueness, and Ulam–Hyers stability of standard fractional stochastic systems. In this study, we extend these results to include systems characterized by FSDEs that incorporate time delays and non-instantaneous impulses. We prove the existence and uniqueness of the solution for this system using Krasnoselskii’s and Banach’s fixed-point theorems. Additionally, we present findings related to Ulam–Hyers stability. To illustrate the practical application of our results, we develop a population model that incorporates memory effects, randomness, and non-instantaneous impulses. This model is solved numerically via the Euler–Maruyama method, and graphical simulations effectively depict the dynamic behavior of the system. Full article

The integration of low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) into the maritime Internet of Things (MIoT) offers an effective solution to overcoming the limitations of connectivity and transmission reliability in conventional MIoT, thereby supporting marine data acquisition. However, the highly dynamic ocean environment necessitates a theoretical framework for system-level performance evaluation before practical deployment. In this article, we consider a LEO satellite and UAV-assisted MIoT (LSU-MIoT) network and develop an analytical framework to evaluate its transmission performance. Specifically, marine devices and relaying buoys are modeled as a Matérn cluster process on the sea surface, UAVs as a homogeneous Poisson point process, and LEO satellites as a spherical Poisson point process. Signal transmissions over marine, aerial, and space links are characterized by Nakagami-m, Rician, and shadowed Rician fading, respectively, with the two-ray path loss model applied to sea and air links for accurately capturing propagation characteristics. By leveraging stochastic geometry, we derive analytical expressions for transmission success probability and end-to-end delay of regular and emergency data under the time division multiple access and non-orthogonal multiple access schemes. Simulation results validate the accuracy of derived expressions and reveal the impact of key parameters on the performance of LSU-MIoT networks. Full article

In natural systems, astrophysics, biological physics, and social physics, 1/f fluctuations are observed across a wide range of systems. Focusing on the case of music, we propose and verify a physical mechanism for generating these fluctuations. This mechanism is based on amplitude modulation (AM) and demodulation (DM), where the 1/f spectral law appears not in the raw waveform but in its demodulated amplitude envelope. Two distinct yet complementary processes generate the required AM: (i) stochastic synchronization among oscillators, modeled via an extended Kuramoto framework that captures perpetual synchronization–desynchronization cycles, and (ii) frequency-selective resonance, modeled by spectral accumulation of eigenmodes in acoustic or structural environments. Numerical simulations demonstrate that both mechanisms, acting alone or in combination, robustly generate 1/f spectra spanning several digits when demodulation is applied and that the classical Kuramoto critical point is not essential for its emergence. While this analysis focuses on 1/f fluctuations in musical performance and acoustics, we also note that 1/f fluctuations inherent in musical scores may be similarly described by the AM/DM mechanism. Full article

Nowadays, renewable energy sources are gaining importance, yet global energy demand is primarily met by burning fossil fuels. Fluctuations in fossil fuel availability, driven by geopolitical tensions, supply–demand changes, and natural disasters, can lead to sudden energy price spikes or supply shortages, adversely affecting the global economy. Despite its negative impact on carbon emissions and climate change, Heating Oil (HO) offers advantages over other fossil fuels in efficiency, reliability, and availability. Accurate time series prediction models for HO are crucial for stakeholders. This study proposes a novel hybrid model, integrating the Chaotic Adaptive Fitness-Distance Balance-based Stochastic Fractal Search (AFDB-SFS) algorithm with a Bidirectional Long-Short Term Memory (Bi-LSTM) network, for HO close price prediction. The dataset comprises daily observations of five financial time series (close, open, high, low, and volume) over 4260 trading days, yielding a total of 21,300 data points (4260 days × 5 variables). During the feature extraction stage, financial signal processing methods such as Demand Concentration Curve (DCC) and traditional technical indicators are utilized. A total of 189 features are extracted at appropriate intervals for each indicator. Due to the large number of features, the AFDB-SFS algorithm then efficiently identifies the most compatible feature subsets, optimizing the Bi-LSTM model based on three criteria: maximizing R², minimizing RMSE, and minimizing feature count. Experimental results demonstrate the proposed hybrid model’s superior performance, achieving high accuracy (R² of 0.9959 and RMSE of 0.0364), outperforming contemporary models in the literature. Furthermore, the model is successfully implemented on the Jetson Orin Nano Developer Platform, enabling real-time, high-frequency HO price predictions with ultra-low latency (1.01 ms for Bi-LSTM), showcasing its practical utility for edge computing applications in commodity markets. Full article

We propose a fair transmission expansion cost allocation (CA) algorithm and a fair process to build alternative transmission expansion plans. We define fairness such that each participant’s payment does not exceed its own benefit and the total payment equals the total TEP cost. In our framework, excessive payments over generator benefits are minimized. Owners of renewable energy resources (RES)s can choose the point of interconnection via the CA algorithm; owners in the same interconnection queue may form an intermediate coalition to persuade owners of expensive bottleneck plans to change at reduced allocation cost. Fairness is implemented using stochastic cooperative game theory (SCGT); the fair CA is obtained by recursively minimizing the largest unfairness, which is the difference between payments and benefits, through coalitions. Benefits consider transmission usage, transmission-induced gains, and the variability of RESs and demand. We design spatially and temporally correlated RESs and demand scenarios using Gibbs sampling specialized for long-term interconnection studies, validate plausibility against a benchmark from the Global Probabilistic Mid-term Load Forecasting Competition 2017, and verify fairness by showing that entities with greater benefits pay larger costs. Full article

In 5G cellular networks, end-to-end data transmission delay is a key metric for evaluating network performance. High-frequency signal fading and complex transmission links often lead to increased delays. Pinching-antenna optimizes signal propagation through directional transmission, enhancing signal quality and reducing delay. Therefore, this paper analyzes the end-to-end transmission delay performance of 5G cellular networks assisted by pinching-antenna. Specifically, the data transmission process is modeled as a two-hop link, where data is first transmitted from the base station to the relay station (RS) via a 5G high-frequency transmission link, and then from the RS to the user equipment via a dielectric waveguide-based pinching-antenna link. We derive the statistical characteristics of the service processes for both the 5G high-frequency transmission link and the dielectric waveguide link. Considering traffic arrivals and service capabilities, we then precisely define the network’s end-to-end delay using stochastic network calculus. Through numerical experiments, we initially evaluate the impact of various network parameters on the performance upper bound and provide system performance. The experimental results show that the pinching-antenna-assisted 5G cellular network significantly reduces end-to-end delay compared with the traditional decode and forward relay, further confirming the substantial advantage of pinching-antenna in optimizing delay performance. Full article

This study assesses the quality of local public administration in European cities using an analytical algorithm based on the B-TOPSIS approach. It draws on the Quality of Life in European Cities survey, which includes five questions on citizens’ satisfaction with local administration, rated on a simplified four-point verbal scale with an option to skip. To process this type of group data, the study extends B-TOPSIS to handle ordinal scales, uncertainty, and missing responses. The method is applied to data from 2023 and compared with 2019 to detect temporal changes in satisfaction. The framework compensates for incomplete information, integrates a Monte Carlo-based protocol for robust results, enhances the ranking through almost first-order stochastic dominance, and supports cross-survey comparison. The results show that Zurich, Luxembourg, and Antalya rank highest in satisfaction, while Rome and Palermo rank lowest. Residents of medium-sized and very large cities report higher satisfaction, with EU and EFTA cities outperforming those in the Western Balkans. Overall, satisfaction levels have remained stable since 2019. These findings offer both methodological contributions and practical insights into governance quality and sustainability, constructing a unified performance index from dispersed survey responses. Full article

Stochastic geometry provides a powerful analytical framework for evaluating interference-limited cellular networks with randomly deployed base stations (BSs). While prior studies have examined limited channel state information at the transmitter (CSIT) and low-resolution analog-to-digital converters (ADCs) separately, their joint impact in multi-user multiple-input multiple-output (MIMO) systems remains largely unexplored. This paper investigates a downlink cellular network in which BSs are distributed according to a homogeneous Poisson point process (PPP), employing zero-forcing beamforming (ZFBF) with limited feedback, and receivers are equipped with one-bit ADCs. We derive a tractable approximation for the achievable spectral efficiency that explicitly accounts for both the quantization error from limited feedback and the receiver distortion caused by coarse ADCs. Based on this approximation, we determine the optimal feedback rate that maximizes the net spectral efficiency. Our analysis reveals that the optimal number of feedback bits scales logarithmically with the channel coherence time but its absolute value decreases due to coarse quantization. Simulation results validate the accuracy of the proposed approximation and confirm the predicted scaling behavior, demonstrating its effectiveness for interference-limited multi-user MIMO networks. Full article

With the increasing emphasis on energy-efficient computing, edge devices accelerated by graphics processing units (GPUs) are gaining attention for their potential in scientific workloads. These platforms support compute-intensive simulations under strict energy and resource constraints, yet their computational efficiency across architectures remains an open question. This study evaluates the performance of GPU-based edge platforms for executing the stochastic simulation algorithm (SSA), a widely used and inherently compute-intensive method for modeling biochemical and physical systems. Execution time, floating point throughput, and the trade-offs between cost and power consumption are analyzed, with a focus on how variations in core count, clock speed, and architectural features impact SSA scalability. Experimental results show that the Jetson Orin NX consistently outperforms Xavier NX and Orin Nano in both speed and efficiency, reaching up to 4.86 million iterations per second while operating under a 20 W power envelope. At the largest workload scale, it achieves 2102.7 ms/W in energy efficiency and 105.3 ms/USD in cost-performance—substantially better than the other Jetson devices. These findings highlight the architectural considerations necessary for selecting edge GPUs for scientific computing and offer practical guidance for deploying compute-intensive workloads beyond artificial intelligence (AI) applications. Full article