Selecting proper sites for underground dam construction using Multi-Attribute Utility Theory in arid and semi-arid regions

Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam construction remains a challenge.Due to the costly and time-consuming methods of site selection for underground dam construction,this study aimed to present a new method using geographic information systems techniques and decision-making processes.The exclusionary criteria including fault,slope,hypsometry,land use,soil,stream,geology,and chemical properties of groundwater were selected for site selection of dam construction and inappropriate regions were omitted by integration and scoring layers in ArcGIS based on the Boolean logic.Finally,appropriate sites were prioritized using the Multi-Attribute Utility Theory.According to the results of the utility coefficient,seven sites were selected as the region for underground dam construction based on all criteria and experts’opinions.The site of Nazarabad dam was the best location for underground dam construction with a utility coefficient of 0.7137 followed by sites of Akhavan with a utility coefficient of 0.4633 and Mirshamsi with a utility coefficient of 0.4083.This study proposed a new approach for the construction of the subsurface dam at the proper site and help managers and decision-makers achieve sustainable water resources with limited facilities and capital and avoid wasting national capital.

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.

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.

Optimal allocation of hybrid energy storage for microgrids based on multi-attribute utility theory

To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.

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.

Fermatean Hesitant Fuzzy Prioritized Heronian Mean Operator and Its Application in Multi-Attribute Decision Making

In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle uncertain information,Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making(MADM)problems.This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership,non-membership,and priority are considered simultaneously.Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators,this paper proposes the Fermatean hesitant fuzzy Heronian mean(FHFHM)operator and the Fermatean hesitant fuzzyweighted Heronian mean(FHFWHM)operator.Then,considering the priority relationship between attributes is often easier to obtain than the weight of attributes,this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator(FHFPHM),and discusses its elegant properties such as idempotency,boundedness and monotonicity in detail.Later,for problems with unknown weights and the Fermatean hesitant fuzzy information,aMADM approach based on prioritized attributes is proposed,which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results.Finally,a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.

Inversion of Oceanic Parameters Represented by CTD Utilizing Seismic Multi-Attributes Based on Convolutional Neural Network

In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters.

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.

A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.

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.

Temporal Preferences-Based Utility Control for Smart Homes

The residential sector contributes a large part of the energy to the global energy balance.To date,housing demand has mostly been uncontrollable and inelastic to grid conditions.Analyzing the performance of a home energy manage-ment system requires the creation of various profiles of real-world residential demand,as residential demand is complex and includes multiple factors such as occupancy,climate,user preferences,and appliance types.Average Peak Ratio(A2P)is one of the most important parameters when managing an efficient and cost-effective energy system.At the household level,the larger relative magni-tudes of certain energy devices make managing this ratio critical,albeit difficult.Various Demand Response(DR)and Demand Side Management(DSM)systems have been proposed to reduce this ratio to 1.The main ways to achieve this are economic incentives,user comfort modeling and control,or preference-based.In this study,we propose a unique opportunistic social time approach called the Time Utility Based Control Feature(TUBCF),which uses the concept of a utility function from economics to model and control consumer devices.We propose a DR model for residential customers to reduce Peak-to-Average Ratio(PAR)and improve customer satisfaction by eliminating Appliance Wait Time(WTA)during peak periods.For PAR reduction and WTA,we propose a system architecture and mathematical formulation.Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics.Simulation results show that using this strategy can reduce A2P by 80%and improve user comfort during peak hours.

Two-Sided Matching Decision Making with Multi-Attribute Probabilistic Hesitant Fuzzy Sets

In previous research on two-sided matching(TSM)decision,agents’preferences were often given in the form of exact values of ordinal numbers and linguistic phrase term sets.Nowdays,the matching agent cannot perform the exact evaluation in the TSM situations due to the great fuzziness of human thought and the complexity of reality.Probability hesitant fuzzy sets,however,have grown in popularity due to their advantages in communicating complex information.Therefore,this paper develops a TSM decision-making approach with multi-attribute probability hesitant fuzzy sets and unknown attribute weight information.The agent attribute weight vector should be obtained by using the maximum deviation method and Hamming distance.The probabilistic hesitancy fuzzy information matrix of each agent is then arranged to determine the comprehensive evaluation of two matching agent sets.The agent satisfaction degree is calculated using the technique for order preference by similarity to ideal solution(TOPSIS).Additionally,the multi-object programming technique is used to establish a TSM method with the objective of maximizing the agent satisfaction of two-sided agents,and the matching schemes are then established by solving the built model.The study concludes by providing a real-world supply-demand scenario to illustrate the effectiveness of the proposed method.The proposed method is more flexible than prior research since it expresses evaluation information using probability hesitating fuzzy sets and can be used in scenarios when attribute weight information is unclear.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

Utility Impact below Bridge or Culvert Soffit on Open Channel Flow

The background to this research was a flooding incident that occurred in Bridgend, Co. Donegal, Ireland in August 2017. While several properties were flooded, a flooding case study of a single dwelling house adjacent to the Bridgend River at Riverdale, Bunamayne, Co. Donegal, Ireland is used herein. For this study the flooded site shall be referred to as the “Hegarty property”. A structure in the form of a stone arched culvert is located directly adjacent to the two-storey detached dwelling house on the Hegarty Property. While the culvert is referred to locally as a bridge, within this research the word culvert will be used in connection with the structure. The culvert has a concrete surrounded utility (watermain) crossing at a gradient below the culvert soffit on the upstream face of the structure. The utility obstructed flow through the culvert and contributed to the flooding event. Given the implication of climate change and the increased probability of more extreme flooding events, it was decided to explore the case study to ascertain the factors that contribute to flooding events when utilities are positioned at culvert or bridge structures. This work was completed to assist undergraduate students, researchers, and local authorities in a relatively unknown area of flood causation.

SPI软件Import Utility模块在仪表数字化交付中的应用

为满足仪表数字化交付要求,基于Smart Plant Instrumentation(SPI)软件Import Utility导入模块的使用,对数字化交付这项内容采用的主要解决方法和主要流程进行了阐述,提出了在前期设计未使用SPI软件的情况下,设计院如何满足业主数字化交付的需求。

Understanding Expected Utility for Decision Making

In this paper,we present a brief version of de Finetti-Ramsey’s subjective probability theory and provide a rigorous yet intuitively plausible explanation of expected utility using elementary mathematics.In a final section,we take up the case of some“Paradoxes in Expected Utility Theory”and try to reconcile them with the help of subjective probabilities.

Resource utilization of hazardous gypsum sludge in oxidation smelting of lead concentrate

Gypsum sludge,a hazardous waste generated by the non-ferrous smelting industry,presents a significant challenge for disposal and utilization.To investigate the feasibility of substituting gypsum sludge for limestone as a flux for smelting,the effects of calcium sulfate(CaSO_(4))and smelting conditions on oxygen-rich smelting of lead concentrate were studied.The interaction between CaSO_(4)and sulfides facilitates the conversion of CaSO_(4)into CaO,which is crucial for slag formation.The order of the influence of sulfide minerals on the conversion of CaSO_(4)is pyrite>sphalerite>galena.When using gypsum sludge exclusively as the calcium source,under optimal conditions with a CaO/SiO_(2)mass ratio of 0.8,an FeO/SiO_(2)mass ratio of 1.2,a melting temperature of 1150℃,an oxygen flow rate of 1.3 L/min,the recovery rates of Pb and Zn in the lead-rich slag reached 85.01%and 95.69%,respectively,with a sulfur content of 2.65 wt%.The As content in the smelting slag obtained by reduction smelting was 0.02 wt%.Resource utilization of gypsum sludge in lead smelting is a feasible method.

Effects of the kinetic pattern of dietary glucose release on nitrogen utilization, the portal amino acid profile, and nutrient transporter expression in intestinal enterocytes in piglets

Background Promoting the synchronization of glucose and amino acid release in the digestive tract of pigs could effectively improve dietary nitrogen utilization.The rational allocation of dietary starch sources and the exploration of appropriate dietary glucose release kinetics may promote the dynamic balance of dietary glucose and amino acid supplies.However,research on the effects of diets with different glucose release kinetic profiles on amino acid absorption and portal amino acid appearance in piglets is limited.This study aimed to investigate the effects of the kinetic pattern of dietary glucose release on nitrogen utilization,the portal amino acid profile,and nutrient transporter expression in intestinal enterocytes in piglets.Methods Sixty-four barrows(15.00±1.12 kg)were randomly allotted to 4 groups and fed diets formulated with starch from corn,corn/barley,corn/sorghum,or corn/cassava combinations(diets were coded A,B,C,or D respectively).Protein retention,the concentrations of portal amino acid and glucose,and the relative expression of amino acid and glucose transporter m RNAs were investigated.In vitro digestion was used to compare the dietary glucose release profiles.Results Four piglet diets with different glucose release kinetics were constructed by adjusting starch sources.The in vivo appearance dynamics of portal glucose were consistent with those of in vitro dietary glucose release kinetics.Total nitrogen excretion was reduced in the piglets in group B,while apparent nitrogen digestibility and nitrogen retention increased(P<0.05).Regardless of the time(2 h or 4 h after morning feeding),the portal total free amino acids content and contents of some individual amino acids(Thr,Glu,Gly,Ala,and Ile)of the piglets in group B were significantly higher than those in groups A,C,and D(P<0.05).Cluster analysis showed that different glucose release kinetic patterns resulted in different portal amino acid patterns in piglets,which decreased gradually with the extension of feeding time.The portal His/Phe,Pro/Glu,Leu/Val,Lys/Met,Tyr/Ile and Ala/Gly appeared higher similarity among the diet treatments.In the anterior jejunum,the glucose transporter SGLT1 was significantly positively correlated with the amino acid transporters B0AT1,EAAC1,and CAT1.Conclusions Rational allocation of starch resources could regulate dietary glucose release kinetics.In the present study,group B(corn/barley)diet exhibited a better glucose release kinetic pattern than the other groups,which could affect the portal amino acid contents and patterns by regulating the expression of amino acid transporters in the small intestine,thereby promoting nitrogen deposition in the body,and improving the utilization efficiency of dietary nitrogen.