Search Results (6,795)

The theory of stochastic processes is the prominent part of advanced probability theory and very influential in various mathematical models having randomness. One of the potential aspects is to investigate the stochastic convex processes. Working in the following direction, this study explores the set-valued super-quadratic processes through a unified approach under the centre-radius order relation, which is a totally ordered relation. First, we discuss some captivating properties and important results, which serve as a criterion. Relying on the newly proposed class of super-quadratic processes, we develop several fundamental inequalities within the fractional framework. Moreover, we present some novel deductions to complement the theoretical results with the existing literature. Also, we have provided the graphical breakdown, applications to the means, information theory, and divergence measures of the main inequalities. Full article

This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. Full article

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys (HETUS) provide a detailed activity-based energy modeling approach, while Bayesian and constraint-based optimization improve data calibration and reduce modeling uncertainties. A Luxembourg-specific stochastic load profile generator links occupant activities to energy loads, incorporating occupancy patterns and daylight illuminance calculations. This study quantifies lighting demand variations across household types, validating results against empirical TUS data with a low mean squared error (MSE) and a minor deviation of +3.42% from EU residential lighting demand standards. Findings show that activity-aware dimming can reduce lighting demand by 30%, while price-based dimming achieves a 21.60% reduction in power demand. The proposed approach provides data-driven insights for energy-efficient residential lighting management, supporting sustainable energy policies and household-level optimization. Full article

Predicting early-age temperature evolution in mass concrete is crucial for controlling thermal cracks. This process involves two distinct physical stages: an initial, hydration-driven heating stage (Stage I) and a subsequent, environment-dominated cooling stage (Stage II). To address this challenge, we propose a novel two-stage hybrid modeling strategy that decouples the underlying physical processes. This approach was developed and validated using a 450-h on-site monitoring dataset. For the deterministic heating phase (Stage I), we employed polynomial regression. For the subsequent stochastic cooling phase (Stage II), a Random Forest algorithm was used to model the complex environmental interactions. The proposed hybrid model was benchmarked against several alternatives, including a physics-based finite element model (FEM) and a single Random Forest model. During the critical cooling stage, our approach demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of $0.24°\mathrm{C}$. This represents a 17.2% improvement over the best-performing single model. Furthermore, cumulative error analysis indicated that the hybrid model maintained a stable and unbiased prediction trend throughout the monitoring period. This addresses a key weakness in single-stage models, where underlying phase-specific errors can accumulate and lead to long-term drift. The proposed framework offers an accurate, robust, and transferable paradigm for modeling other complex engineering processes that exhibit distinct multi-stage characteristics. Full article

The energy transition requires advanced technologies to plan, manage and operate systems with high penetration of renewables, facing the stochastic variability of sources, the massive integration of electric vehicles (EV), coupling with storage and the emergence of new market models and energy communities, in order to ensure flexibility, resilience and economic efficiency in the short and long term [...] Full article

EU production is important for global poultry markets and is concentrated in a few countries, including Italy. The aim of this study is to investigate the technical efficiency of Italian poultry farms in 2019–2022, characterized by the COVID-19 pandemic and avian influenza, which occurred almost simultaneously and presented poultry farms with important economic challenges. In particular, this study aims to observe how efficiently poultry farms utilized their inputs with regards to controllable or managerial factors and exogenous shocks and factors beyond the firm’s control. Data was retrieved from the RICA database, the Italian section of the EU Farm Accountancy Data Network. After a descriptive analysis, a stochastic frontier model was applied to the panel data to estimate production frontier and firm-specific inefficiency factors. Results reveal the relevance of certain cost categories (feed, water, fuel, and electricity) and their increase over the observed period. Current and capital costs have positive and significant impacts on the value of production. As regards the determinants of technical efficiency, a greater endowment of some inputs (labor and farm area) and the sizes of farms in terms of livestock units are correlated with an improvement in the technical efficiency of farms. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions. Unlike traditional analytical methods, which are computationally intensive and often impractical in real time, the proposed system leverages the flexibility of multilayer perceptrons to model both forward and inverse relationships between drop conditions and flight outcomes. In the forward stage, a trained network predicts range, flight time, and impact velocity from predefined release parameters. In the inverse stage, a deeper neural model reconstructs the required release velocity, angle, and altitude directly from the desired operational outcomes. By employing a hybrid workflow—combining physics-based simulation with neural approximation—our approach generates large, high-quality datasets at low computational cost. Results demonstrate that the inverse network achieves high accuracy across deterministic and stochastic tests, with minimal error when operating within the training domain. The study confirms the suitability of neural architectures for tackling complex, nonlinear identification tasks in precision airdrop operations. Beyond their technical efficiency, such models enable agile, GPS-independent mission planning, offering a reliable and low-cost decision support tool for humanitarian aid, scientific research, and defense logistics. This work highlights how artificial intelligence can transform conventional trajectory design into a fast, adaptive, and autonomous capability. Full article

The adverse propagation environment in underground coal mine tunnels caused by enclosed spaces, rough surfaces, and dense scatterers severely degrades reliable wireless signal transmission, which further impedes the deployment of IoT applications such as gas monitors and personnel positioning terminals. However, the conventional power enhancement solutions are infeasible for the underground coal mine scenario due to strict explosion-proof safety regulations and battery-powered IoT devices. To address this challenge, we propose singular value decomposition-based Lagrangian optimization (SVD-LOP) to minimize transmit power at the mining base station (MBS) for IRS-assisted coal mine wireless communication systems. In particular, we first establish a three-dimensional twin cluster geometry-based stochastic model (3D-TCGBSM) to accurately characterize the underground coal mine channel. On this basis, we formulate the MBS transmit power minimization problem constrained by user signal-to-noise ratio (SNR) target and IRS phase shifts. To solve this non-convex problem, we propose the SVD-LOP algorithm that performs SVD on the channel matrix to decouple the complex channel coupling and introduces the Lagrange multipliers. Furthermore, we develop a low-complexity successive convex approximation (LC-SCA) algorithm to reduce computational complexity, which constructs a convex approximation of the objective function based on a first-order Taylor expansion and enables suboptimal solutions. Simulation results demonstrate that the proposed SVD-LOP and LC-SCA algorithms achieve transmit power peaks of $20.8\mathrm{dBm}$ and $21.4\mathrm{dBm}$, respectively, which are slightly lower than the $21.8\mathrm{dBm}$ observed for the SDR algorithm. It is evident that these algorithms remain well below the explosion-proof safety threshold, which achieves significant power reduction. However, computational complexity analysis reveals that the proposed SVD-LOP and LC-SCA algorithms achieve $\mathcal{O}\left(N^3\right)$ and $\mathcal{O}\left(N^2\right)$ respectively, which offers substantial reductions compared to the SDR algorithm’s $\mathcal{O}\left(N^7\right)$. Moreover, both proposed algorithms exhibit robust convergence across varying user SNR targets while maintaining stable performance gains under different tunnel roughness scenarios. Full article

Understanding the dynamics of fire propagation is essential in improving predictive models and developing effective fire management strategies. This study applies computer vision techniques to complement traditional fire behaviour modelling. We employ the Segment Anything Model to achieve the accurate segmentation of experimental fire videos, enabling the frame-by-frame segmentation of fire perimeters, quantification of the rate of spread in multiple directions, and explicit analysis of slope effects. Our laboratory experiments reveal that the ROS increases exponentially with slope, but with coefficients differing from those prescribed in the Canadian Fire Behaviour Prediction System, reflecting differences in field conditions. Complementary field data from prescribed burns in coniferous fuels (C-7) further demonstrate that slope effects vary under operational conditions, suggesting field-dependent dynamics not fully captured by existing deterministic models. Our experiments show that, even under controlled laboratory conditions, substantial variability in spread rate is observed, underscoring the inherent stochasticity of fire spread. Together, these findings highlight the value of vision-based perimeter extraction in generating precise spread data and reinforce the need for probabilistic modelling approaches that explicitly account for uncertainty and emergent dynamics in fire behaviour. Full article

The scarcity of explicit feedback data is a major challenge in the design of recommender systems. Although such data are of a high quality due to users’ voluntary provision of numerical ratings, collecting a sufficient amount in real-world service environments is typically infeasible. As an alternative, implicit feedback data are extensively used. However, because implicit feedback represents observable user actions rather than direct preference statements, it inherently suffers from ambiguity as a signal of true user preference. To address this issue, this study reinterprets the ambiguity of implicit feedback signals as a problem of epistemic uncertainty regarding user preferences and proposes a latent factor model that incorporates this uncertainty within a Bayesian framework. Specifically, the behavioral vector of a user, which is learned from implicit feedback, is restructured within the embedding space using attention mechanisms applied to the user’s interaction history, forming an implicit preference representation. Similarly, item feature vectors are reinterpreted in the context of the target user’s history, resulting in personalized item representations. This study replaces the deterministic attention scores with stochastic attention weights treated as random variables whose distributions are modeled using a Bayesian approach. Through this design, the proposed model effectively captures the uncertainty stemming from implicit feedback within the vector representations of users and items. The experimental results demonstrate that the proposed model not only effectively mitigates the ambiguity of preference signals inherent in implicit feedback data but also achieves better performance improvements than baseline models, particularly on datasets characterized by high user–item interaction sparsity. The proposed model, when integrated with an attention module, generally outperformed other MLP-based models in terms of NDCG@10. Moreover, incorporating the Bayesian attention mechanism yielded an additional performance gain of up to 0.0531 compared to the model using a standard attention module. Full article

As the European Union’s energy systems are transforming towards achieving climate goals, this article examines the energy balances of EU member states. This analysis covers, among other things, the dynamics of energy dependence and strategies for decoupling economic growth from the level of emissions in the European Union (EU), with particular emphasis on Poland, which is strongly influenced by its historical reliance on coal in the energy balance. Using panel data from 1990 to 2022, the article investigates differences in energy dependence between individual countries, shaped by economic structures and national energy policies. The study results confirm significant heterogeneity between member states and emphasize that the stability and direction of decoupling economic growth from greenhouse gas (GHG) emissions are strongly dependent on the composition of the energy mix and vulnerability to external conditions. Based on scenario analysis, potential paths for Poland’s energy transition are assessed. We demonstrate that a high share of renewable energy sources (RES) significantly reduces CO₂ emissions, provided it is accompanied by infrastructure modernization and the development of energy storage. Furthermore, integrating nuclear energy as a stabilizing element of the energy mix offers an additional path to deep decarbonization while ensuring supply reliability. Finally, we demonstrate that improving energy efficiency and demand management can effectively increase energy security and reduce emissions, even in a scenario with a stable coal share. The study addresses a research gap by integrating decoupling analysis with scenario-based stochastic modeling for Poland, a country for which few comprehensive transition assessments exist. The results provide practical guidance for developing resilient, low-emission energy policies in Poland and the EU. Results are reported for 2025–2050 (with 2040 as an interim milestone). Full article

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial networks using 8-adjacency lattices. Our unified framework integrates OOP with stochastic geometry, modeling obstacle placement via Strauss (regular) and Matérn (clustered) processes, and evaluates traversal using the Reset Disambiguation algorithm. Through extensive Monte Carlo experiments, we show that traversal cost increases by up to 40% under strongly regular placements, while clustered configurations can decrease traversal costs by as much as 25% by leaving navigable corridors compared to uniform random layouts. In mixed (with both true and false obstacles) scenarios, increasing the proportion of true obstacles from 30% to 70% nearly doubles the traversal cost. These findings are further supported by statistical analysis and stochastic ordering, providing rigorous insights into how spatial patterns and obstacle compositions influence navigation under uncertainty. Full article

Leader–follower neural networks deployed over wireless platforms are subject to parameter uncertainties and stochastic channel fading. The combined impact of these effects on quasi-synchronization control remains largely unexplored. The paper addresses the problem of quasi-synchronization performance degradation in discrete-time leader–follower neural networks caused by randomly occurring parameter uncertainties and stochastic channel fading. Discrete leader–follower neural networks are constructed in state-space form. Randomly occurring parameter uncertainties in the leader neural networks are described using a Bernoulli probability distribution and time-varying parameter matrices. Channel fading is represented by a finite-state Markovian model that captures state switching. For the follower neural networks, an intermittent impulsive control strategy is designed based on linear matrix inequalities and the Lyapunov stability principle. A computable bound on the synchronization error is derived as well. A simulation study validates that the proposed impulsive control strategy effectively suppresses synchronization error caused by parameter uncertainties and Markovian channel fading, thereby ensuring mean-square boundedness. Compared with an existing method, the proposed approach consumes less control energy but achieves better performance in terms of synchronization error. The average norms of the synchronization error and the control input signal are reduced by 24.00% and 58.64%, respectively. Full article

With the increasing penetration of renewable energy, power system frequency stability faces multiple challenges. In addition to the decline of system inertia traditionally provided by synchronous machines, uncertainties such as wind power forecast errors, converter control characteristics, and stochastic load fluctuations further exacerbate the system’s sensitivity to power disturbances, increasing the risks of frequency deviation and instability. Among these factors, insufficient inertia is widely recognized as one of the most direct and critical drivers of the initial frequency response. This study focuses on this issue and explores the use of battery energy storage system (BESS) parameter optimization to enhance system stability. To this end, a simulation platform was developed in PSS^®E V34 based on the IEEE New England 39-bus system, incorporating three wind turbines and two BESS units. The WECC generic models were adopted, and three wind disturbance scenarios were designed, including (i) disconnection of a single wind turbine, (ii) derating of two turbines to 50% output, and (iii) derating of three turbines to 50% output. In this study, a one-at-a-time (OAT) sensitivity analysis was first performed to identify the key parameters affecting frequency response, followed by optimization using an improved particle swarm optimization (IPSO) algorithm. The simulation results show that the minimum system frequency was 59.888 Hz without BESS control, increased to 59.969 Hz with non-optimized BESS control, and further improved to 59.976 Hz after IPSO. Compared with the case without BESS, the overall improvement was 0.088 Hz, of which IPSO contributed an additional 0.007 Hz. These results clearly demonstrate that IPSO can significantly strengthen the frequency support capability of BESS and effectively improve system stability under different wind disturbance scenarios. Full article