Search Results (153)

The increasing integration of photovoltaic (PV) systems into modern power grids poses significant operational challenges, including variability in solar generation, fluctuations in demand, degradation of power quality, and reduced reliability under uncertain conditions. Addressing these challenges requires advanced control strategies that can manage uncertainty while coordinating storage, inverter-level actions, and power quality functions. This paper proposes a unified stochastic Model Predictive Control (SMPC) framework for the optimal management of photovoltaic (PV) systems under uncertainty. The approach integrates chance-constrained optimization with Value-at-Risk (VaR) modeling to ensure system reliability under variable solar irradiance and demand profiles. Unlike conventional deterministic MPCs, the proposed method explicitly addresses stochastic disturbances while optimizing energy storage, generation, and power quality. The framework introduces a hierarchical control architecture, where a centralized SMPC coordinates global energy flows, and decentralized inverter agents perform local Maximum Power Point Tracking (MPPT) and harmonic compensation based on the instantaneous power theory. Simulation results demonstrate significant improvements in energy efficiency from 78% to 85%, constraint satisfaction from 85% to 96%, total harmonic distortion reduction by 25%, and resilience (energy supply loss reduced from 15% to 5% under fault conditions), compared to classical deterministic approaches. This comprehensive methodology offers a robust solution for integrating PV systems into modern grids, addressing sustainability and reliability goals under uncertainty. Full article

This study presents a practical framework for the maximal covering location problem (MCLP) under uncertainty. The approach combines possibility theory with chance-constrained programming to represent both imprecision and randomness in demand. Demand is modeled as fuzzy random variables. Using the Zadeh extension principle, both the fuzzy and fuzzy random formulations are transformed into equivalent deterministic mixed-integer programs. Clear linearization steps are provided for the objective function and constraints. Two specifications are examined to reflect different attitudes toward risk. The first specification uses possibility measures, reflecting an optimistic stance, while the second uses necessity measures and represents a conservative approach. Computational experiments conducted in an urban facility context show that increasing the possibility or probability level results in more conservative solutions and a smaller amount of covered demand. In contrast, lower thresholds lead to more exhaustive coverage with greater exposure to uncertainty. In the deterministic scenario, full coverage becomes attainable as the number of facilities increases. Under uncertainty, the models balance coverage with robustness based on the chosen risk tolerance levels. The proposed framework serves as a flexible decision support tool, enabling planners to align facility location choices with their risk tolerance while maintaining tractability with standard optimization solvers. Full article

Within the field of assisted reproductive technologies (ARTs), embryologists regularly face the critical task of identifying embryos with the highest likelihood of implantation and survival. To help aid and standardize this practice, many embryo selection strategies have been developed to give the best chance of pregnancy success. Over the years, there has been a large increase in experimental studies conducted within this area of research. This increase has allowed for the formation of significant and plausible theories of embryo development, especially in cases where the most prominent factors seem identical. These advancements have both expanded the typical process of traditional treatments and have even paved the way for new techniques. The exact combination of all these relevant factors has not been fully elucidated into a single all-encompassing scheme for embryo decision. Morphological, genetic, and developmental indicators are well-studied individually, but the exact methods that should be prioritized in each scenario may change with respect to an individual patient. Deciding whether factors like age, egg quality, lifestyle choices, or previous medical history should alter methods of embryo ranking can result in conflict, especially in the case where a choice is being made between two similar embryos. This article reviews the conventional methods along with emerging technologies that provide the tools for embryologists to evaluate and rank embryos with high implantation potential (HIP). By showcasing these methods, including their respective benefits and drawbacks, this article provides information to allow clinicians to make effective decisions by integrating multiple approaches to embryo selection. Full article

To address the economic and reliability challenges of high-penetration renewable energy integration in electricity-heat-hydrogen integrated energy systems and support the dual-carbon strategy, this paper proposes an optimal dispatch method integrating Information Gap Decision Theory (IGDT) and Fuzzy Chance-Constrained Programming (FCCP). An IES model coupling multiple energy components was constructed to exploit multi-energy complementarity. A stepped carbon trading mechanism was introduced to quantify emission costs. For interval uncertainties in renewable generation, IGDT-based robust and opportunistic dispatch models were established; for fuzzy load uncertainties, FCCP transformed them into deterministic equivalents, forming a dual-layer “IGDT-FCCP” uncertainty handling framework. Simulation using CPLEX demonstrated that the proposed model dynamically adjusts uncertainty tolerance and confidence levels, effectively balancing economy, robustness, and low-carbon performance under complex uncertainties: reducing total costs by 12.7%, cutting carbon emissions by 28.1%, and lowering renewable curtailment to 1.8%. This study provides an advanced decision-making paradigm for low-carbon resilient IES. Full article

Host-vector contact rates influence the spread of several vector-borne infections, including avian haemoparasites. To investigate the ecological mechanisms underlying avian disease dynamics, we examined haemoparasite prevalences in relation to bird life-history attributes. Using previously collected data of 1003 birds sampled from an Afrotropical region, we tested the hypothesis that a bird’s behavioural traits and habitat do not influence the chances of infection. Overall, infection prevalence did not differ significantly between gregarious and solitary birds, nor across association categories (wild, mixed, anthropogenic). However, significant differences in infection were detected across haemoparasite genera. Plasmodium, Haemoproteus, and Leucocytozoon showed distinct infection patterns in relation to host behavioural traits and habitats. Moreover, there were significant differences in infection prevalence based on movement patterns (resident, nomadic, migratory) and foraging strata (ground, mixed, aerial). These results enhance our avian parasitology theories, indicating that behavioural traits and habitat also have parasite-genus-dependent impacts on infection prevalence. Our research demonstrates that behavioural characteristics have an unequal impact on haemoparasite prevalence, indicating that no single factor can accurately predict the probability of infection at an Afrotropical setting. Full article

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

To address the challenges of weak power-grid infrastructure, insufficient power supply capacity along mountainous railways, and severe three-phase imbalance caused by imbalanced traction loads at the point of common coupling (PCC), this paper proposes an energy storage configuration method for mountainous railway power grids considering renewable energy integration. First, a distributionally robust chance-constrained energy storage system configuration model is established, with the capacity and rated power of the energy storage system as decision variables, and the investment costs, operational costs, and grid operation costs as the objective function. Subsequently, by linearizing the three-phase AC power flow equations and transforming the model into a directly solvable linear form using conditional value-at-risk (CVaR) theory, the original configuration problem is converted into a mixed-integer linear programming (MILP) formulation. Finally, simulations based on an actual high-altitude mountainous railway power grid validate the economic efficiency and effectiveness of the proposed model. Results demonstrate that energy storage deployment reduces overall system voltage deviation by 40.7% and improves three-phase voltage magnitude imbalance by 16%. Full article

With their traditional focus on financial compensation, tort law systems worldwide struggle with the adverse effects the claims resolution process can have on victims of personal injury. It has therefore been argued that tort law systems should be more emotionally intelligent and more mindful of the non-financial needs of victims. In this debate, the perspective of the wrongdoer has been largely neglected. Drawing from empirical research on the personal experiences of wrongdoers in the Dutch personal injury practice and building on theories of procedural and restorative justice, this contribution argues that, to optimize the societal value of tort law systems, attention should be paid to the wrongdoer’s perspective. A tort law system that lacks sufficient opportunity for wrongdoers to personally make amends is deficient both in terms of morality and justice, as it deprives both victims and wrongdoers of a chance at emotional and moral recovery from the injurious event. We therefore believe this represents a shared future for all of us: towards restorative tort law. Full article

This study explores the integration of 3D printing technology by adult learners in Greek Second Chance Schools (SCS), institutions designed to address Early School Leaving and promote Lifelong Learning. Grounded in constructivist and experiential learning theories, the research examines adult learners’ attitudes toward 3D printing technology through a hands-on STEM activity in the context of teaching scientific literacy. The instructional activity was centered on a physics experiment illustrating Archimedes’ principle using a multimodal approach, combining 3D computer modeling for visualization and design with tangible manipulation of a printed object, thereby offering both digital and Hands-on learning experiences. Quantitative data was collected using a structured questionnaire to assess participants’ perception toward the 3D printing technology. Findings indicate a positive trend in adult learners’ responses, finding 3D printing accessible, interesting, and easy to use. While expressing hesitation about independently applying the technology in the future, overall responses suggest strong interest and openness to using emerging technologies within educational settings, even among marginalized adult populations. This work highlights the value of integrating emerging technologies into alternative education frameworks and offers a replicable model for inclusive STEM education and lays the groundwork for further research in adult learning environments using innovative, learner-centered approaches. Full article

This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders. Full article

The Engel algorithm was created to solve chip-firing games and can be used to find the stationary distribution for absorbing Markov chains. Kaushal et al. developed a matlab-based version of the generalized Engel algorithm based on Engel’s probabilistic abacus theory. This paper introduces a modified version of the generalized Engel algorithm, which we call the modified Engel algorithm, or the mEngel algorithm for short. This modified version is designed to address issues related to non-absorbing Markov chains. It achieves this by breaking down the transition matrix into two distinct matrices, where each entry in the transition matrix is calculated from the ratio of the numerator and denominator matrices. In a nested iteration setting, these matrices play a crucial role in converting non-absorbing Markov chains into absorbing ones and then back again, thereby providing an approximation of the solutions of non-absorbing Markov chains until the distribution of a Markov chain converges to a stationary distribution. Our results show that the numerical outcomes of the mEngel algorithm align with those obtained from the power method and the canonical decomposition of absorbing Markov chains. We provide an example, Torrence’s problem, to illustrate the application of absorbing probabilities. Furthermore, our proposed algorithm analyzes the Monopoly transition matrix as a form of non-absorbing probabilities based on the rules of the Monopoly game, a complete information dynamic game, particularly the probability of landing on the Jail square, which is determined by the order of the product of the movement, Jail, Chance, and Community Chest matrices. The Long Jail strategy, the Short Jail strategy, and the strategy for getting out of Jail by rolling consecutive doubles three times have been formulated and tested. In addition, choosing which color group to buy is also an important strategy. By comparing the probability distribution of each strategy and the profit return for each property and color group of properties, and the color group property, we find which one should be used when playing Monopoly. In conclusion, the mEngel algorithm, implemented using R codes, offers an alternative approach to solving the Monopoly game and demonstrates practical value. Full article

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article