Optimal investment based on relative performance and weighted utility

This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance comparison to a predefined reference point. We find the optimal investment strategy by maximizing a weighted average utility of a concave utility and an Sshaped utility via a concavification technique and the martingale method. Numerical results are carried out to show the impact of the extent to which the manager pays attention to the change of relative performance related to the reference point on the optimal terminal relative performance.

A utility and easily fabricated dual-mode fiber film for efficient and comfortable thermal management

Nowadays, the global climate is constantly being destroyed and the fluctuations in ambient temperature are becoming more frequent. However, conventional single-mode thermal management strategies(heating or cooling) failed to resolve such dynamic temperature changes. Moreover, developing thermal management devices capable of accommodating these temperature variations while remaining simple to fabricate and durable has remained a formidable obstacle. To address these bottlenecks, we design and successfully fabricate a novel dual-mode hierarchical(DMH) composite film featuring a micronanofiber network structure, achieved through a straightforward two-step continuous electrospinning process. In cooling mode, it presents a high solar reflectivity of up to 97.7% and an excellent atmospheric transparent window(ATW) infrared emissivity of up to 98.9%. Noted that this DMH film could realize a cooling of 8.1 ℃ compared to the ambient temperature outdoors. In heating mode, it also exhibits a high solar absorptivity of 94.7% and heats up to 11.9 ℃ higher than black cotton fabric when utilized by individuals. In practical application scenarios, a seamless transition between efficient cooling and heating is achieved by simply flipping the film. More importantly, the DMH film combining the benefits of composites demonstrates portability, durability, and easy-cleaning, promising to achieve large-scale production and use of thermally managed textiles in the future. The energy savings offered by film applications provide a viable solution for the early realization of carbon neutrality.

Multi-Objective Low Carbon Vehicle Routing for Cold Chain Distribution with Customer Time Loss Aversion

The elevated refrigeration expenses linked to cold chain distribution contribute to increased overall logistics costs and carbon emissions.Concurrently,the sensitivity of consumers to delivery delays also impacts the design of cold chain distribution operations.This paper considers the logistics costs of cold chain,consumer time loss aversion,and the efficiency of low-carbon distribution to construct a multi-objective cold chain vehicle routing problem.It combines a decomposition-based multi-objective solution algorithm and fruit fly optimization algorithm to solve the proposed model,and validates the algorithm and model through a large number of numerical experiments.Firstly,our computations of the C-metric,IGD value,Hypervolumn,and CPU time demonstrate that the algorithm employed in this study has yielded notable advantages in terms of convergence and the overall performance of the non-dominated solutions.Secondly,we find that increasing logistics satisfaction requires a significant investment in logistics costs,thus requiring a delicate balance between logistics expenditure and service advantages.Finally,we used a typical example to analyze the size of different cost modules in cold chain distribution and find that vehicles can optimize their routes without needing to make extensive diversions to reach distant customers,ultimately leading to reduced fuel consumption and carbon emissions.Besides,the traditional assumption that a higher utilization of logistics vehicles results in increased carbon emissions and fuel usage is not universally valid.Our research contributes to the current balance between cold chain costs and consumer satisfaction in cold chain distribution.Additionally,leveraging multi-objective algorithm design,we provide feasible solutions for current cold chain delivery operations.Further,by incorporating consumer time loss aversion model,we aid in understanding the impact of consumer behavior on the design of cold chain delivery solutions.

Omni-Channel Retailing and Firm Performance from a Customer Lifetime Value Perspective

In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.

PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

Assessing the impact of artificial intelligence on customer performance: a quantitative study using partial least squares methodology

The purpose of this research is to examine the impact of artificial intelligence(AI)on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach,specifically the partial least squares method,to test the hypotheses and explore the relationships between various variables.The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance.Additionally,the results of this study provide valuable insights for both academic and practical communities.This study highlights the importance of specific variables,such as organizational and customer agility,customer experience,customer relationship quality,and customer performance in AI assimilation.By exploring these variables,it contributes significantly to the academic,managerial,and social aspects of AI and its impact on customer performance.

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.

Dynamic Analysis of Customer Demand Based on Intuitionistic Fuzzy Number in Product Planning

To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.

不同海域Custom450高强度不锈钢的腐蚀规律研究

目的 研究Custom450钢在青岛、舟山、厦门及三亚等4个海域大气和飞溅环境中的自然腐蚀规律。方法 采用形貌分析、腐蚀速率、点腐蚀深度分析等方法对Custom450钢在上述4个海域大气和飞溅区的腐蚀规律进行研究。结果 Custom450钢在4个海域大气环境的年平均腐蚀速率相当,且未观察到有点蚀现象发生,舟山海域的年平均腐蚀速率最小,为0.001 41 mm/a,三亚海域的年平均腐蚀速率最大,为0.001 54 mm/a。4个海域飞溅区的年平均腐蚀速率范围为0.0021~0.0028mm/a,舟山海域的年平均腐蚀速率为0.002 1 mm/a,平均及最大点腐蚀深度分别为5.7μm和25.99μm,均低于其他3个海域。结论 Custom450钢在4个海域的大气环境暴露1 a后,未发现明显的腐蚀现象,表明该钢短期内在上述4个海域海洋大气环境中具有良好的耐蚀性。在飞溅环境中的腐蚀速率高于大气环境,且有明显的点腐蚀现象发生,钢中未溶的NbC相会促进点腐蚀现象的发生。

EOS Utility软件在摄影理论可视化教学中的应用

可视化的理论知识更容易被识记、理解,而在摄影理论教学中,运用静态的图形图像,并不能理想地将复杂理论知识及其相互间的关系表征、可视化。运用EOS Utility软件进行摄影理论知识的辅助教学,可将各知识点内涵及其相互间的关系进行动态地表征,实现摄影参数可视化、摄影过程可视化、摄影效果可视化,使摄影知识点及其相互间的关系更容易被识记、理解,提高教学效率。

THE OPTIMAL REINSURANCE-INVESTMENT PROBLEM CONSIDERING THE JOINT INTERESTS OF AN INSURER AND A REINSURER UNDER HARA UTILITY

This paper focuses on an optimal reinsurance and investment problem for an insurance corporation which holds the shares of an insurer and a reinsurer.Assume that the insurer can purchase reinsurance from the reinsurer,and that both the insurer and the reinsurer are allowed to invest in a risk-free asset and a risky asset which are governed by the Heston model and are distinct from one another.We aim to find the optimal reinsuranceinvestment strategy by maximizing the expected Hyperbolic Absolute Risk Aversion(HARA)utility of the insurance corporation’s terminal wealth,which is the weighted sum of the insurer’s and the reinsurer’s terminal wealth.The Hamilton-Jacobi-Bellman(HJB)equation is first established.However,this equation is non-linear and is difficult to solve directly by any ordinary method found in the existing literature,because the structure of this HJB equation is more complex under HARA utility.In the present paper,the Legendre transform is applied to change this HJB equation into a linear dual one such that the explicit expressions of optimal investment-reinsurance strategies for-1≤ρi≤1 are obtained.We also discuss some special cases in a little bit more detail.Finally,numerical analyses are provided.

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

Corneal collagen cross-linking in patients with keratoconus from the Dresden protocol to customized solutions:theoretical basis

Keratoconus is an ectatic condition characterized by gradual corneal thinning,corneal protrusion,progressive irregular astigmatism,corneal fibrosis,and visual impairment.The therapeutic options regarding improvement of visual function include glasses or soft contact lenses correction for initial stages,gas-permeable rigid contact lenses,scleral lenses,implantation of intrastromal corneal ring or corneal transplants for most advanced stages.In keratoconus cases showing disease progression corneal collagen crosslinking(CXL)has been proven to be an effective,minimally invasive and safe procedure.CXL consists of a photochemical reaction of corneal collagen by riboflavin stimulation with ultraviolet A radiation,resulting in stromal crosslinks formation.The aim of this review is to carry out an examination of CXL methods based on theoretical basis and mathematical models,from the original Dresden protocol to the most recent developments in the technique,reporting the changes proposed in the last 15y and examining the advantages and disadvantages of the various treatment protocols.Finally,the limits of non-standardized methods and the perspectives offered by a customization of the treatment are highlighted.

Design Strategies for Aqueous Zinc Metal Batteries with High Zinc Utilization: From Metal Anodes to Anode-Free Structures

Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage due to the excellent safety, environmental friendliness, natural abundance, high theoretical specific capacity, and low redox potential of zinc(Zn) metal. However,several issues such as dendrite formation, hydrogen evolution, corrosion, and passivation of Zn metal anodes cause irreversible loss of the active materials. To solve these issues, researchers often use large amounts of excess Zn to ensure a continuous supply of active materials for Zn anodes. This leads to the ultralow utilization of Zn anodes and squanders the high energy density of AZMBs. Herein, the design strategies for AZMBs with high Zn utilization are discussed in depth, from utilizing thinner Zn foils to constructing anode-free structures with theoretical Zn utilization of 100%, which provides comprehensive guidelines for further research. Representative methods for calculating the depth of discharge of Zn anodes with different structures are first summarized. The reasonable modification strategies of Zn foil anodes, current collectors with pre-deposited Zn, and anode-free aqueous Zn metal batteries(AF-AZMBs) to improve Zn utilization are then detailed. In particular, the working mechanism of AF-AZMBs is systematically introduced. Finally, the challenges and perspectives for constructing high-utilization Zn anodes are presented.

Entrepreneur Education Level and Its Utility Maximization

The education level of entrepreneurs plays an important role in the decision-making process of loan financing. In this paper, the research method of optimization theory is used to study the problem of the education level and maximizing utility of entrepreneurs, and the optimal analytic solution is obtained. On this basis, the influence of the change of bank loan interest rate, the marginal effect of entrepreneur’s effort, the marginal effect of entrepreneur’s investment, the elastic coefficient of entrepreneur’s effort and the elastic coefficient of entrepreneur’s investment on the optimal solution of entrepreneur’s effort and investment amount is discussed.

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several datasets.The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been built.This framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost customers.The improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost customers.The framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature selection.Logistic regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational awareness.The prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and F-measure.The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.

Multi-Objective Cold Chain Path Optimization Based on Customer Satisfaction

To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigeration cost, and time penalty cost, a multi-objective path optimization model of fresh agricultural products distribution considering client satisfaction is constructed. The model is solved using an enhanced Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), and differential evolution is incorporated to the evolution operator. The algorithm produced by the revised algorithm produces a better Pareto optimum solution set, efficiently balances the relationship between customer pleasure and cost, and serves as a reference for the long-term growth of organizations. .