Uncertain Conditions Research Articles

Emergency Dispatch Strategy Considering Spatiotemporal Evolution of Power Grid Failures Under Typhoon Conditions

The increasing climate-change-induced tropical cyclone phenomena expose the power grid to significant operation risks by disrupting the normal operation of grid components. This paper considers the failure mechanism with respect to critical grid components and reveals a novel spatiotemporal revolution of grid failures during the passage of typhoons. Based on the spatiotemporal evolution of grid failures, a threshold-based emergency scenario set and the corresponding two-stage robust optimization-based emergency dispatch model are developed. The robust emergency dispatch strategy is obtained using a column-and-constraint generation (C&CG) algorithm. The simulation results show that the proposed robust emergency dispatch strategy can guarantee a considerable degree of robustness under multiple emergency scenarios driven by uncertain typhoon conditions.

Analysis of steady‐state operation of active distribution network under uncertain conditions

Analysis of steady‐state operation of active distribution network under uncertain conditions

Fault-tolerant partition resolvability of cycle with chord.

In the realm of connected networks, distance-based parameters, particularly the partition dimension of graphs, have extensive applications across various fields, including chemistry and computer science. A notable variant of the partition dimension is the fault-tolerant resolving partition, which is critical in computer science for networking, optimization, and navigation tasks. In networking, fault-tolerant partitioning ensures robust communication pathways even in the event of network failures or disruptions. In optimization, it aids in developing efficient algorithms capable of withstanding errors or changes in input data. In navigation systems, fault-tolerant partitioning supports reliable route planning and navigation services under uncertain or dynamic conditions. This paper focuses on the fault-tolerant partition dimension within the specific context of the cycle with chord graphs, exploring its properties and implications for enhancing the robustness and reliability of networked systems.

Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.

Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed behavior. The origin of this cognitive ability, constitutive of general intelligence, remains elusive. Here, we show that moderate levels of computation noise in artificial neural networks promote zero-shot generalization for decision-making under uncertainty. Unlike networks featuring noise-free computations, but like human participants tested on similar decision problems (ranging from probabilistic reasoning to reversal learning), noisy networks exhibit behavioral hallmarks of optimal inference in uncertain conditions entirely unseen during training. Computation noise enables this cognitive ability jointly through "structural" regularization of network weights during training and "functional" regularization by shaping the stochastic dynamics of network activity after training. Together, these findings indicate that human cognition may ride on neural variability to support adaptive decisions under uncertainty without extensive experience or engineered sophistication.

Neutrosophic Logic-Based Crop Yield Prediction and Risk Assessment using Least Squares Regression

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with actionable insights for decision-making. The results demonstrate that the proposed method effectively estimates crop yield damage with the ability to assess risk under varying conditions, such as extreme weather. This approach can help farmers optimize crop management strategies, minimize losses, and improve overall productivity. Future work will explore the application of this method to other crops and farming systems to expand its utility.

The Development of IV-VIKOR Method Under Hesitant Fuzzy Sets in MCDM

Due to its solution correctness and ease of evaluation, the VIKOR approach has already established itself as a popular multi-criterion decision-making (MCDM) method. This strategy focuses on picking and ranking from a list of viable options in order to assist the decision-maker in determining a final course of action. Additionally, it looks for a middle ground to resolve a problem with competing demands. It produces a ranking list of compromises based on a precise measurement of distance from the ideal response. The hesitant fuzzy set (HFS) has furthermore come under increased study because of its potency and ability in capturing ambiguity and uncertainty. This paper introduces the inversion of VIKOR method (IV-VIKOR) to take uncertain fuzzy conditions into account. These novel concepts serve as the foundation for an IV-VIKOR method that we suggest, and a real-world application is given to demonstrate how well our method works when dealing with MCDM problem with hesitant fuzzy preference data.

Capturing the complexity of veterinarians' antibiotic prescribing practices in the livestock sector: a meta-ethnography across contexts: Veterinarians' antibiotic prescribing in different contexts.

Strategies and policies to tackle the global public health threat of antimicrobial resistance are increasingly addressing antimicrobial use prescribing practices in both the human and animal health sectors. Veterinarians' antibiotic prescribing practices are influenced by different factors and conditioned by the context within which antibiotic prescribing decisions are made, complexifying the implementation of behaviour change interventions. A better understanding of these factors could therefore help in the design and application of such interventions. Meta-ethnography was used to explore the antibiotic prescribing behaviour of veterinarians in different contexts and to construct a new conceptual framework. A search was conducted in PubMed, Web of Science Core Collection and SciELO Citation Index between 2016 and 2024. The final sample consisted of 29 articles, 27 of which were selected from the 561 articles identified in the search and 2 of which were added by the authors. The results were synthesized and presented through four contextual situations influencing antibiotic prescribing by livestock veterinarians: priorities and pressures, uncertain field conditions, systemic challenges and an enabling environment. The results are presented as a conceptual framework that views veterinarians' antibiotic prescribing behaviour as dynamic, adapting in response to the different contextual situations they encounter. The findings provide an integrated and contextualized understanding of veterinarians' antibiotic prescribing behaviours, which could be implemented to facilitate the development and application of future antimicrobial stewardship interventions.

MENEMUKAN IMAN DALAM KETIDAKPASTIAN: REFLEKSI TEOLOGIS BERDASARKAN KITAB HABAKUK

This paper is organized to find faith in uncertainty through theological reflection based on the book of Habakkuk. Habakkuk is a prophet of God in the Old Testament who experienced a difficult time amid King Jehoiakim's reign. The difficulties of life and injustice that occurred during the time of King Jehoiakim made the prophet Habakkuk constantly pray and put his faith in God. This steadfastness of faith needs to be emulated by believers. The modern age in which believers live often brings various challenges, such as the relativism of absolute truth and identity crisis. As a believer living in modern times, I believe that imitating Habakkuk can be done to achieve faith resilience. By having faith resilience as a result of imitating Habakkuk, believers can get through difficult times through their faith. Believers' faith will be trained amid life's uncertainties. Through descriptive qualitative research methods, the author tries to narrate the discovery of faith amid uncertain conditions through theological reflection based on the book of Habakkuk. The author states that the theological reflection of the book of Habakkuk brings believers to have faith that even amid uncertainty, God still answers prayer.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

Humanist Proselytizing in Distruptive Era Based on the Sustainable Development Goals (SDG’s)

Aim/Purpose: The disruptive era is an uncertain condition in social, political, and religious life that creates a new trend regarding monolithic religious understanding. A suitable proselytizing approach based on sustainable development goals is needed to improve the dignity of human life. Background: Social media plays an important role in the spread of a more monolithic religion. This triggered a significant conflict, so efforts are needed to improve religious understanding and experience, one of the fields affected by the disruptive era. Methodology: Various scientific research materials are reviewed to understand how proselytizing strategies are used in the disruptive era. Secondary data analysis was carried out for this qualitative and descriptive study. Contribution: This paper helps understand the right proselytizing strategy to minimize conflicts in the disruptive era by utilizing social media. Findings: The practice of humanist proselytizing through religious moderation and the strengthening of preachers is fundamental to strengthening literal religious understanding in the disruptive era. This study finding helps us understand how to apply proselytizing appropriately in this era of uncertainty. Recommendation for Practitioners: Implementing humanist proselytizing through religious moderation can reduce the monolithic understanding of society due to one of the negative impacts of social media. Recommendation for Researcher: To ensure the relevance of research studies, practitioners and researchers must maintain a collaborative approach with research insights to apply relevant proselytizing strategies in the disruptive era . Impact on Society: Analyze how the proselytizing approach is relevant to society in a disruptive era due to the dark side of social media in the religious. Technology evolves, so society must proactively address this problem with more literacy. In addition, the role of government policymakers, clerics, and ulama is important to reduce the monolithic understanding and cause conflict. Future Research: The study's scope will be better if it can determine the effectiveness of humanist proselytizing through religious moderation with a quantitative approach. In general, this study discusses the best proselytizing strategy in the disruptive era. The significant role of the media can be analyzed to dive deep. Similar studies could be conducted on other religious issues because of technological advancements.

Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

Economic Uncertainty and Impact of Governments’ Spending on Economic Growth: An Empirical Study of Emerging Market Economies*

The COVID-19-led economic crisis has rekindled scholarly interest in the relationship between government spending and economic growth. Further, there is an emerging realm of empirical literature that focuses on the dynamics of this relationship during times of uncertainty. Against this backdrop, this study investigates the effect of government expenditure on economic growth during economic uncertainty and the role of economic confidence in the transmission mechanism of policy shocks utilizing a panel generalized method of moments (GMM) framework for 13 select emerging market economies (EMEs). The findings suggest that the beneficial impact of government spending during economic uncertainty is not significantly different from regular times; however, its effectiveness is enhanced during contractionary periods. Government spending, on the other hand, does not have a statistically significant effect during expansionary phases. Furthermore, unanticipated government spending boosts economic confidence, especially during contractionary phases. Governments in EMEs should implement countercyclical fiscal policies selectively to stimulate aggregate demand and facilitate economic recovery, prioritizing targeted spending in confidence-building initiatives and key sectors. Spending in high multiplier sectors during contractionary periods can boost economic activity and speed recovery, fostering stability and growth amidst uncertain economic conditions. JEL Classification E32, H30, H32

Covariance Control for Uncrewed Aircraft Systems Under Correlated Uncertainty

Uncrewed aircraft systems and advanced air mobility are associated with uncertain conditions, such as wind prediction errors, and they may deviate from dedicated airspaces referred to as corridors. However, further studies on these aspects are required. This study focuses on the covariance control for a stochastic discrete-time linear system under correlated uncertainties. A covariance control problem with chance constraints can be formulated as a convex-programming problem. Numerical simulations of uncrewed aircraft systems path planning can be used to compare two control laws: covariance control laws that consider correlated and noncorrelated noise. Numerical simulations were conducted to verify that the covariance control law considering correlated noise generates a trajectory that appropriately satisfies the constraints. Consequently, the effect of considering correlated noise in the covariance control law was clarified. In addition, covariance control has been demonstrated to generate appropriate trajectories for various state constraints. Moreover, numerical simulations indicated that covariance control facilitated the control of uncertainty, and the effectiveness of the covariance control was evaluated. The insights gained from the results of this study can inform enhanced uncertainty management for improving reliability and safety in real-world applications of uncrewed aircraft systems and advanced air mobility.

Providing a framework to optimize the sustainable construction material supply chain with the possibility of horizontal transfers

The application of Supply Chain Management (SCM) principles has gained considerable attention across various industries to enhance operational efficiency and business performance. In the construction sector, SCM can guide managers in strategic planning and foster collaboration with suppliers, leading to more effective project execution. Despite its importance, limited research has focused on designing sustainable supply chains for construction materials under uncertain conditions. This study aims to bridge this gap by introducing a robust optimization framework that incorporates sustainable development criteria, specifically the minimization of harmful environmental effects and the reduction of worker unemployment. Additionally, the research introduces an innovative concept of horizontal transfers between downstream members, which not only reduces supply deficiencies but also indirectly increases profitability for these members. This article is a part of a Doctoral dissertation. It follows a two-step approach. First, suppliers of construction materials are ranked based on sustainable development criteria using a multi-criteria decision-making method. The best-ranked supplier is then integrated into a mathematical model for optimizing the supply chain design. Given the NP-hard nature of the problem, the CPLEX 12.1 solver with the epsilon constraint method is used to solve small-scale models. For larger-scale problems, three meta-heuristic algorithms are developed: multi-objective ant-lion, multi-objective gray wolf, and multi-objective dragonfly. The results show that while both the ant-lion and gray wolf algorithms deliver comparable average performance, the gray wolf algorithm excels in terms of solution time, making it more effective for real-time problem-solving. This makes the gray wolf algorithm the most efficient choice for addressing large-scale, multi-objective supply chain challenges. This research introduces a novel framework for designing sustainable construction supply chains under uncertain conditions. By focusing on criteria such as minimizing harmful environmental effects and reducing worker unemployment, this study addresses key aspects of sustainable development. Additionally, the innovative horizontal transfer mechanism and the insights into algorithmic performance provide valuable contributions to the field, especially for real-world applications in the construction industry.

MPPT control for PMSG based wind turbine: Adaptive dynamic programming approach

This paper proposes an online adaptive dynamic programming-based controller for the variable-speed wind turbine system, ensuring it tracks the maximum power point under uncertain conditions. This control scheme is composed of two components: the adaptive optimal control component and the high-order disturbance observer-based adaptive sliding mode control component. The adaptive optimal control part is designed using the online adaptive dynamic programming technique, which is one of the algorithms of the reinforcement learning method. This control component has the role of achieving the optimal characteristics for the nonlinear system. Likewise, the high-order disturbance observer is established to estimate system uncertainties and external disturbances. These approximated disturbances are then fed to the adaptive sliding mode control component for system disturbance compensation. As a result, the system, which is affected by the unknown disturbances, achieves robust optimal performance. In addition, the convergence of the adaptive laws and the stability of the overall system are ensured through the Lyapunov theory. Finally, comparative simulations are conducted to validate the advantages of the proposed control scheme in comparison to the existing methods.

Alpha transcranial alternating current stimulation modulates pain anticipation and perception in a context-dependent manner.

Pain perception is closely tied to the brain's anticipatory processes, particularly involving the suppression of sensorimotor α-oscillations, which reflect the system's readiness for incoming pain. Higher sensorimotor α-oscillation levels are correlated with lower pain sensitivity. Alpha transcranial alternating current stimulation (α-tACS) can enhance these oscillations, potentially reducing pain perception, with effects that may be sustained and influenced by the certainty of pain expectations. Hence, this study investigated the immediate and sustained effects of α-tACS on pain anticipation and perception, focusing on how these effects are shaped by the certainty of expectations. In a double-blind, sham-controlled design, 80 healthy participants underwent a 20-minute session of real or sham α-tACS over the right sensorimotor region. Behavioral and neural responses related to pain anticipation and perception were recorded before, immediately after, and 30 minutes poststimulation under both certain and uncertain conditions. Compared with sham stimulation, real α-tACS disrupted the habituation of laser-evoked potentials (N2-P2 complex), particularly under certain expectations, with effects persisting 30 minutes poststimulation. In anticipatory brain oscillations, real α-tACS enhanced somatosensory α1-oscillations and increased midfrontal θ-oscillations in conditions of certainty, with θ-oscillation modulation showing sustained effects. Mediation analysis revealed that α-tACS reduced pain reactivity by enhancing somatosensory α1-oscillations but increased pain reactivity through the enhancement of midfrontal θ-oscillations, with the latter effect being more pronounced. These findings suggest that while α-tACS may provide pain relief through somatosensory α-oscillation augmentation, its stronger and longer-lasting impact on midfrontal θ-oscillations could lead to hyperalgesia, particularly in the context of certain pain expectations.

An optimized decentralized peer-to-peer energy trading system for smart grids incorporating uncertain renewable energy sources through fuzzy optimization

The significant issue in today's world is that the power system is undergoing on account of unafforded power generation and distribution. Consequently, improper utilization of renewable energy, as well as its pricing mechanism of electrical energy, is also a vital issue in transmission and distribution systems (TDS). In addition, in the context of upcoming power systems, ensuring energy stability and effective energy management have become paramount. This paper introduces a novel approach to address these challenges by proposing an optimal demand response grid-connected decentralized peer-to-peer (P2P) energy trading system. This system efficiently manages energy transactions among prosumers, the grid, and renewable energy sources (RES) under uncertain conditions. Fuzzy optimization (FO) is employed to facilitate interconnections between these entities. A fuzzy logic controller with intelligent rules governs the overall strategy and pricing mechanism based on prosumer power deviation (PPD). Inspired by human thinking, these rules prioritize P2P transactions, with surplus energy exported to the main grid (MG) and energy imported from the MG when necessary. Pricing adjusts dynamically with load demand. Simulation results demonstrate the effectiveness of the fuzzy rules across various conditions, ensuring proper system operation. The proposed model also proves that the P2P energy trading system has reduced power consumption by balancing the generated power and the load demand. The proposed system is environmentally friendly, emitting no harmful gases.

Optimal Scheduling of PV Panel Cleaning and Policy Implications Considering Uncertain Dusty Weather Conditions in the Middle East

Airborne dust easily accumulates on the top of solar panel surfaces and reduces the output power in arid regions. A commonly used mitigation solution for dust deposition issues is cleaning PV panels periodically. However, cleaning frequency affects the economic viability of solar PV systems, resulting in a trade-off between cleaning costs and energy loss costs. To address this issue, this study relates several metrics and develops a generic framework based on simulation and optimization to determine the optimal cleaning interval. Based on the computational tests, the optimal cleaning interval in Abu Dhabi is determined to be 34 days, which is longer than the currently recommended cleaning interval of 28 days. This study also identifies that energy recovery is responsive to decreases in unit cleaning costs in the presence of high electricity tariffs, whereas total cost savings show sensitivity when electricity tariffs are low. Finally, this study discusses energy policy implications by presenting an innovative concept involving the introduction of a cleaning subsidy which could reshape energy system cost dynamics, making PV systems economically competitive beyond the conventional levelized cost of electricity.

Data-model driven probability model of branch power flow for distribution networks and its application to analysis of overload and line loss

The fluctuation of highly penetrated distributed generations (DGs) in distribution networks (DNs) increases branch overload risks, which makes the analysis uncertainty of branch power flow more complex. The probability analysis of branch power can grasp the power flow operation characteristics under uncertain conditions, which supports the power flow optimization management and ensures the safe and economic operation of DNs. Therefore, this paper proposes a data-model driven probability analysis method for branch power flow in DNs. An approximate branch power model is first derived to reveal the analytical relationship between branch power and node power. Then, based on the branch power approximation model and the forecasting error, the datasets of branch apparent power are constructed to capture the complex nonlinear characteristics of the power flow. The non-Gaussian distribution characteristics of branch apparent power and line loss are described by the optimal probability fitting method. Finally, the probability level of branch overload is evaluated and the probability distribution of line loss is analyzed. The case studies demonstrate that the branch power approximation model is highly accurate, and the probability distribution of the branch apparent power presents different characteristics. The proposed method can quickly and accurately calculate the branch overload probability level.

Employment uncertainty and parenthood: quantifying the mediating role of union formation

This study aims to quantify the extent to which union formation mediates the relationship between employment uncertainty and the transition to parenthood, taking Italy as a case study. Research on the determinants of low fertility is increasingly shedding light on the negative effects of employment uncertainty. It is generally acknowledged that partnership formation plays a fundamental mediating role, as uncertain employment conditions may hamper the initial formation of a stable union, consequently affecting the likelihood of having a child. Nevertheless, it remains unknown how much of the (total) effect of employment uncertainty on fertility is indeed mediated by union formation. To perform the mediation analysis, we relied on the KHB (Karlson–Holm–Breen) method to accurately assess the effect of employment uncertainty on the transition to parenthood with and without controlling for union formation, i.e. the mediating variable. Results indicate that union formation mediates between 25 and 40% of the negative effect of employment uncertainty on the transition to parenthood, especially among men. Differences by cohort groups are evident, with the youngest cohorts being the most affected by such a mediation. We conclude that union formation is a key element in the relationship between employment uncertainty and fertility as it mediates a non-negligible share of the total effect of employment uncertainty on transition to parenthood.