Uncertain Factors Research Articles

Green Vessel Scheduling with Weather Impact and Emission Control Area Consideration

Emissions of maritime transport have been a critical research topic with the substantial growth in the global shipping industry, encompassing both the expansion of the world fleet and the increased distances it has been covering recently. The International Maritime Organization (IMO) has enforced some regulations to mitigate ship Greenhouse Gas (GHG) emissions, which affect vessels’ operational practice, and further affect service reliability. In this paper, some compliance methods (two-speed strategy, fuel switching, and LNG) against Emission Control Areas (ECAs) at the operational level are examined regarding if and how they impact the liner shipping schedule and service reliability; meanwhile, uncertain weather conditions and port times, as the main uncertain factors, are also involved. Then, a bi-objective fuzzy programming model is formulated and solved by the augmented ε-constraint approach, which generates a set of Pareto solutions by balancing the economic and environmental sustainability. Some findings can be concluded through the experimental results, including that, firstly, to meet uncertain weather conditions at sea requires strong robustness; secondly, ECA regulations can negatively affect the liner shipping service level; moreover, slow steaming is an immediate and effective measure to reduce GHG emissions; and, furthermore, ship routing choice could have a significant influence on ship emissions and service reliability.

Progress of EGFR targeted therapy for oral cancer

Because of some uncertain factors such as eating habits and smoking, oral cancer has become one of the serious diseases of the oral and maxillofacial region. Especially in some Chinese cities where betel nut chewing is customary, the incidence of oral cancer has been high. Today, the main treatment methods are still traditional surgical excision, radiation and chemotherapy. However, with the development of medical technology, gene-targeted therapy has become the most important research object. Gene targeting technology is mainly targets the treatment of cancer organ cells, reducing the damage of traditional treatment methods on other areas without lesions. At present, the epidermal cytokine EGFR has been the most studied, and some research results have been put into clinical application. However, in terms of targeted therapy, the development of technology is still incomplete and immature. This article mainly discusses the pathogenic mechanism of EGFR in oral cancer, the synergistic effect of EGFR and other factors on the development of oral cancer, and the research results of EGFR-targeted therapy and combination therapy for oral cancer. The significance of this study is that based on these discussions, it provides reference opinions for treatment that can achieve a good prognosis for oral cancer patients and alleviate the pain of oral cancer patients, and evaluate the current development status of clinical application of EGFR targeted therapy for oral cancer. At present, the targeted therapy technology of oral cancer is still immature. In the future, the role of other factors in the development of oral cancer can be studied, and the influence of the combination of targeted therapy and other therapeutic methods on the treatment results of oral cancer can be studied.

Dynamic analysis and optimal control of a stochastic investor sentiment contagion model considering sentiments isolation with random parametric perturbations

Investor sentiment contagion has a profound influence on economic and social development. This paper explores the diverse influences of various investor sentiments in modern society on the economy and society. It also investigates the interference of various uncertain factors on investor sentiments in the modern economy and society. On this basis, the dual-system stochastic SPA2G2R model was constructed, incorporating positive and negative sentiments, as well as a supervision and isolation mechanism. The global existence of positive solutions was established, and sufficient conditions for the disappearance and steady distribution of investor sentiment were calculated. An optimal control strategy for the stochastic model was put forward, with numerical simulation supporting the theoretical analysis results. A comparison with parameter changes in the deterministic model was also conducted. The research reveals a competitive relationship between different investor sentiments. Enhancing societal guidance mechanisms promotes positive investor sentiment contagion. Timely control by the supervisory department effectively curbs the spread of investor sentiment. Additionally, white noise promotes investor sentiment contagion, suggesting effective regulation through control of noise intensity and disturbance parameters.

Quantum-Secured Space-Air-Ground Integrated Networks: Concept, Framework, and Case Study

In the upcoming 6G era, existing ground networks have evolved toward space-air-ground integrated networks (SAGIN), providing ultra-high data rates, seamless network coverage, and ubiquitous intelligence for communications of applications and services. However, conventional communications in SAGIN still face data confidentiality issues. Fortunately, the concept of Quantum Key Distribution (QKD) over SAGIN is able to provide information-theoretic security for secure communications in SAGIN with quantum cryptography. Therefore, in this paper, we propose quantum-secured SAGIN (Q-SAGIN) which are feasible to achieve proven secure communications using quantum mechanics to protect data channels between space, aerial, and ground nodes. Moreover, we propose a universal QKD service provisioning framework to minimize the cost of QKD services under the uncertainty and dynamics of communications in Q-SAGIN. In this framework, fiber-based QKD services are deployed in passive optical networks with the advantages of low loss and high stability. Moreover, the widely covered and flexible satellite- and UAV-based QKD services are provisioned as a supplement during the real-time data transmission phase. Finally, to examine the effectiveness of the proposed concept and framework, a case study of Q-SAGIN in the Metaverse is conducted where uncertain and dynamic factors of the secure communications in Metaverse applications are effectively resolved in the proposed framework.

The Novel Data-Driven Robust Maximum Expert Mixed Integer Consensus Models Under Multirole’s Opinions Uncertainty by Considering Noncooperators

In group decision-making, ignoring the existence of uncertain factors causes the decision-making problem to lose its practical significance. Based on the maximum expert consensus model (MECM), we considered the uncertainty of the opinions of the three participating roles by introducing noncooperators. Additionally, three different opinion uncertainty sets were constructed to describe the characteristics of opinion uncertainty more accurately. Furthermore, by applying a robust optimization (RO) method to process uncertain sets, we propose mixed-integer robust MECMs, which reduce the risk of uncertain opinions to decision-makers (DMs). Moreover, numerical experiments used in the passenger satisfaction survey of the Shanghai Metro verified the validity of the models proposed in this article. The characteristics of the models were revealed through sensitivity analysis. Finally, to overcome the relatively highly conservative results of the classic RO method, we construct data-driven opinion uncertainty sets and propose data-driven RO models. Hence, DMs with different risk preferences can choose RO models with different risk levels according to the situation.

A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties

As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples.

Cross-regional green certificate transaction strategies based on a double-layer game model

With the implementation of the renewable energy quota system and the development of the green certificate market, the diversification of cross-region trading subjects has brought about the problem of complicating the competition and cooperative relationship between multiple subjects. This problem makes it difficult to balance the conflict of interests of multiple participating subjects in green certificate transactions, and reduces the enthusiasm for trading. To this end, this paper proposes a multi-participant decision model of cross-regional green certificate trading behavior that takes into account the uncertainty of renewable energy generation. In the model, in order to explore the potential full set of strategies of the participating subjects, the cooperative game theory is utilized to analyze the complex interaction between multiple participating subjects in the cross-regional transaction, and the cooperative game model based on green certificates is established. As the participating subjects' information about other participants and their knowledge of the game environment are limited, the decision-making behavior is uncertain. In order to more reasonably portray the subject's game behavior in the actual decision-making scenario and to solve the defect that the traditional evolutionary game model can not reflect the influence of uncertain factors, we use the stochastic evolutionary game theory to dynamically deduce the cooperative strategies of the participating subjects under the limited rationality, and at the same time, introduce the strategy influencing factors to explore the dependence relationship between strategies. In addition, in order to cope with the impact of power generation uncertainty on cross-regional trading and to solve the defects of distributionally robust fuzzy set construction which is subjective and only for the analysis of uncertainty in a single time section, it is proposed to use Bayesian multivariate nonlinear model and mixed-effects model to construct probability distribution fuzzy sets of wind and light power generation uncertainty on the basis of distributionally robust theory. Finally, based on the arithmetic example, the cooperative strategy of multi-participants in cross-regional transactions under finite rationality is deduced, and the impact of uncertainty factors on the cooperative strategy is analyzed.

Bi-objective Optimization for Resource-constrained Robust Construction Project Scheduling

Construction projects are often executed in complex environments, and various uncertain factors (e.g., bad weather, machine breakdown) prolong activity duration in practice, which may cause project completion delays or profit reduction. Moreover, previous studies often optimized net present value or robustness in project scheduling separately, they neglected the linkage between the value of obtaining NPV and the capacity of tackling uncertainties in a project schedule. To achieve optimal profit and enhance the stability of a project schedule simultaneously, this study proposes a bi-objective optimization for resource-constrained robust construction project scheduling problem (RCRCPSP), which aims at optimizing NPV and robustness, and builds a bi-objective optimization model for the RCRCPSP based on five typical cash flow models. Then an ε-constraints procedure embedded with a genetic algorithm is proposed to solve the model. The ε-constraints procedure performs better than the multi-objective genetic algorithm proposed by the previous study through case study. Furthermore, the findings demonstrate that a trade-off relationship exists between NPV and robustness, and cash flow models have little impact on robustness, while the relaxed deadline can improve a project schedule with high robustness. The fruits of this study can help contractors balance the NPV and the robustness in an indeterministic environment.

Just-in-time deep learning for real-time X-ray computed tomography

Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments.

Robust Optimization for Uncertain Pathways: Converting Natural Gas into Liquid Transportation Fuels in an Unpredictable Environment

This journal article, titled "Robust Optimization for Uncertain Pathways: Converting Natural Gas into Liquid Transportation Fuels in an Unpredictable Environment," delves into the pivotal intersection of energy, uncertainty, and optimization within the context of liquid transportation fuel production from natural gas. The study addresses the challenges posed by the inherent unpredictability in the energy landscape, offering a robust optimization framework as a strategic solution.
 The research focuses on the conversion of natural gas into liquid transportation fuels, a process susceptible to dynamic and uncertain factors such as market fluctuations, geopolitical events, and technological advancements. The proposed robust optimization model aims to enhance the decision-making process under these uncertainties, providing a resilient strategy for sustainable fuel production.
 Through a comprehensive analysis, the study incorporates diverse scenarios and uncertain parameters into the optimization model, considering variations in market demand, feedstock prices, and regulatory landscapes. The robust optimization framework optimally adapts to these uncertainties, ensuring that the liquid transportation fuel production pathway remains resilient and economically viable in diverse future scenarios.
 The results demonstrate the effectiveness of the robust optimization approach in mitigating risks and maximizing the economic feasibility of natural gas-derived liquid transportation fuels. The findings have implications for energy policy, industrial planning, and investment decisions, offering a valuable tool for stakeholders navigating the complex and unpredictable terrain of the energy sector.
 In conclusion, this article contributes to the evolving discourse on robust optimization strategies in energy systems, providing insights that are crucial for policymakers, industry professionals, and researchers seeking sustainable and adaptive solutions for the production of liquid transportation fuels from natural gas in an uncertain and dynamic environment.

Investigation of aerosol transportation phenomena in pool scrubbing by combining bubble plume measurements and single-bubble mass transfer analysis

The fission product filtration capability of pool scrubbing is an uncertain factor in severe accident risk analysis. Limited discussions on aerosol transportation phenomena exist due to insufficient understanding of aerosol mass transfer from single bubbles and bubble plume behavior. This study aims to investigate aerosol transportation during pool scrubbing by integrating individual bubble mass transfer into the overall bubble plume.Decontamination factor (DF) measurements were conducted for various nozzle submergences, followed by analysis of bubble diameter distributions in the corresponding bubble plume. Findings revealed that the MELCOR model overestimates bubble break-up, contrary to the expected positive impact on DF. To address this discrepancy, a computational fluid dynamics simulation was employed to analyze mass transfer coefficients considering different bubble diameters and aerosol properties. By integrating the mass transfer of single bubbles, the discrepancy in the DF tendencies was attributed to the underestimation of aerosol mass transfer coefficients in individual bubbles.

Study on the freeze–thaw effect on concrete arch dam using an improved response surface method

The damming materials will be deteriorated during the long-term service of concrete arch dam by weathering, seepage dissolution, freeze–thaw and other factors which will put dams at potential risk. However, there are few researches on the reliability assessment of arch dams considering the effect of material deterioration. In this paper, a prediction model of concrete mechanical properties under freeze–thaw cycles is established using regression analysis. Considering various uncertain factors, a time-varying structural reliability analysis model is established using finite element equivalent stress method and an optimized response surface method with improved sampling strategy and convergence criterion. A case study is later carried out. The results show that, at the early stage of freeze–thaw, the structural failure probability will increase steadily in a small range. However, as the tensile strength of concrete continues to decrease under long-time freeze–thaw action, the reliability of the arch dam structure will suddenly fall off a cliff. The proposed deterioration model and reliability analysis probability model are reasonable and practical and could be serve as a technical support for similar engineering projects.

The run test for two samples in the presence of uncertainty

The run test, which examines whether two samples selected from the same population are random, has been employed. However, the current run test for two samples is based on the assumption of certainty, which is not always valid in practical scenarios. This paper aims to introduce a modified version of the run test for two samples that account for uncertainty. We will develop a statistical approach for the run test that considers uncertain factors such as sample size, level of significance, and observations. To evaluate the effectiveness of the proposed test, we analyze wind power and photovoltaic power data. The analysis of these variables demonstrates that they are randomly selected from the population. The results indicate that the proposed run test is well-suited for addressing uncertainty in renewable energy. By employing this modified test, we can effectively assess the randomness of samples and make reliable conclusions in uncertain conditions.

A Multi-Scenario analysis method for collapse risks due to initial local failure of building structures

Traditional progressive failure analysis is usually computationally expensive and can only be used to analyze a limited number of cases. The resulting mechanical data on structural components are not suitable for displaying collapses or providing an intuitive reference for emergency management. In addition, numerical models do not incorporate uncertain factors in failure development, which makes it difficult to deduce multiple scenarios of future. This paper proposes a multi-scenario progressive failure analysis method based on a sub-region model for emergency management. Firstly, the material model is augmented with a possibility factor to evaluate the failure of components based on internal forces, and thereby deduce different progressive collapse scenarios. Secondly, the building structure is divided into several sub-regions based on the damage correlation of components, so that the degree of regional collapse can be demonstrated at a more macroscopic scale, which is more suitable for emergency management. By analyzing typical failure scenarios, the collapse evolution rules at the sub-region level can be summarized, which can be used for deducing the results of multiple collapse scenarios rapidly. The proposed method can identify the weak positions of buildings, deduce highly probable progressive damage scenarios, and exhibit macro collapse processes. The multi-scenario analysis results can serve as a reference for collapse safety management in risk pre-assessment and help to reconstruct the collapse process in post-disaster analysis.

Investigation on accuracy of numerical simulation of aerodynamic noise of single-stage axial fan

The prediction accuracy of turbomachinery aerodynamic noise, particularly in relation to broadband noise with uncertain factors, has long been a challenging issue. Previous studies have not fully comprehended the factors influencing its prediction accuracy, lacking an objective and comprehensive evaluation method. An improved approach combining orthogonal experiment design and principal component analysis is employed to address these limitations. The evaluation method expands the noise metrics and provides a comprehensive assessment of the accuracy of numerical simulation for aerodynamic noise. The evaluation method is utilized to optimize and quantitatively analyze the impact of the refinement size of the core area on noise prediction for single-stage axial fans. Subsequently, the three metrics, namely, Z1, Z2, and broadband noise Z3, are integrated using PCA to form a new integrated optimal metric Ztotal. The influence of different refinement sizes, particularly on Ztotal, is quantitatively examined. The findings reveal that the mesh size of the stator wake (D area) exhibits the most significant influence on noise prediction accuracy, with a calculated weight of 81.3% on noise accuracy. Furthermore, a comprehensive investigation is conducted on the influence of turbulence models and the wall Y+ value on aerodynamic noise. Detached-eddy simulation and large eddy simulation demonstrate effective capabilities in simulating both upstream and downstream turbulent flow characteristics of the stator, enabling accurate prediction of broadband noise. This study presents a set of numerical simulation schemes that achieve precise prediction of turbomachinery aerodynamic noise.

A bi-objective optimization for finance-based and resource-constrained robust project scheduling

Numerous finance-based projects encounter disturbances from a variety of uncertain factors during their execution, under which both the original baseline schedule and financing alternative become infeasible. However, many previous studies on the finance-based project scheduling problem (FBSP) neglected a potential feature of stochastic activity durations. In this paper, we address the issue of generating a robust project schedule that not only satisfies financing credit and renewable resource limits, but also tackles disruptions due to activity uncertainty. A bi-objective model for finance-based and resource-constrained robust project scheduling problem (FBRCRPSP) is constructed, where the trade-off between profit and robustness is considered. Based on the transformed integer programming model, an exact procedure of ε- constraints is proposed to obtain Pareto-optimal solutions for small-sized instances. For large-scale projects, a non-dominated sorting genetic algorithm with local search (NSGA-II-LS) that deeply explores the neighboring solution space is developed, in which a generic procedure with two new recursion policies is proposed to determine the robustness of schedules. Benchmarking is conducted to evaluate the efficiency of the algorithms via some performance criteria. The results show that all developed approaches have good performance in small-sized instances, and the NSGA-II-LS outperforms the non-dominated sorting genetic algorithm without local search (NSGA-II) in terms of the spread, convergence, and diversity of Pareto-optimal solutions significantly. In addition, some managerial insights are summarized to enlighten project managers.

IMPROVING THE INNOVATIVE STRATEGY OF MANAGEMENT OF AGRICULTURAL ENTERPRISES IN THE CONDITIONS OF GLOBALIZATION

The purpose of the article is to study the peculiarities of the implementation of innovative strategies by Ukrainian farmers, to identify influencing factors, including the influence of global market factors on the effectiveness of the implementation of strategies, and to develop a mathematical approach to assessing the influence of uncertain factors. The methodological basis of the article is the methods and forms of scientific knowledge accepted in science, such as methods: of comparative and abstract-logical analysis, induction and deduction, abstraction, and mathematical formalization. A stratification of the factors of the negative impact of globalization, which slow down the implementation of the innovative strategy and create threats to the activity of agricultural enterprises, is carried out. It is indicated that the main directions of improvement of the innovation strategy should be assessment, consideration, and neutralization of the influence of globalization factors. It is also indicated that this leads to the heterogeneity of the implementation of the innovative management strategy of agricultural enterprises. Prospective directions for the implementation of innovative strategies in agricultural production are stratified. The stages of the implementation of the innovation strategy in the conditions of globalization and the tasks of management for the implementation of the innovation strategy in the global conditions are determined. Peculiarities of the implementation of innovative strategies in Ukraine are revealed. A mathematical model was developed to assess the influence of uncertain global factors, which will allow for reducing risks for the implementation of significant innovative strategies. It is indicated that significant innovative projects could lead to the transformation of agribusiness activities and the need for permanent implementation of innovative processes. It is indicated that thanks to the systemic innovation process, a synthesis of the resource and innovation model of enterprise development could be formed. Taking into account the specifics of innovative strategies and using the developed mathematical approach to assess the influence of uncertain global factors will allow reducing risks in the implementation of innovative strategies and improving their management.

Medium- and Long-Term Prediction of Airport Carbon Emissions under Uncertain Conditions Based on the LEAP Model

As important nodes in the air transport system, it is of great significance for airports to achieve the carbon-peaking goal before 2030 under the target of peaking carbon emissions in China’s civil aviation industry. However, it remains unknown whether airports will be able to realize this ambitious goal due to a variety of uncertain factors, such as the social economy, epidemic impact, and emission reduction measures. According to the possibilities of uncertain factors, 12 uncertain scenarios were constructed. Using the case of Guangzhou Baiyun International Airport (CAN), this study predicted medium- and long-term carbon emission trends under 12 uncertain scenarios based on the Long-range Energy Alternatives Planning System (LEAP) model. Furthermore, the effects of carbon abatement measures and emission reduction responsibilities were analyzed. The results show that CAN cannot guarantee that it will realize the goal under the established abatement policy. If socioeconomic development is rapid, carbon emissions will peak at about 90 kt tons in 2030, and if socioeconomic development is slow, it will plateau at about 1 million tons between 2030 and 2035. What is more, airlines bear the greatest responsibility for reducing emissions, and technological progress measures have the highest abatement potential. This study provides decision support for airport stakeholders in abatement work so as to ensure that airports can achieve the carbon-peaking goal.

T-S fuzzy model based event-triggered change control for product and supply chain systems

Product and supply chain systems are a class of complex nonlinear systems due to the uncertain demand fluctuation and unexpected events, and Takagi–Sugeno (T-S) fuzzy systems are suitable for modelling such product and supply chain systems with different production rates. According to the uncertainty customer demands, different subsystems which describe the corresponding production-inventory processes are orchestrated by the T-S fuzzy rule. Change control is designed to handle the supply chain blocking and event breaking caused by uncertain factors. Then, sufficient conditions are given such that the exponential stability with a prescribed bullwhip effect attenuation level can be guaranteed, and the control synthesis problem is transformed into the solvability of linear matrix inequalities for the product and supply chain systems. An example is introduced to verify the validness and efficiency of the proposed approach.