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In order to solve the problems of machine translation efficiency and translation quality, this paper proposes a research method of machine English translation evaluation system based on BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301 respectively, which is much higher than Google translation. A model of machine translation evaluation system based on neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation. [ABSTRACT FROM AUTHOR]

We propose a mathematical model to solve the railway transit network design problem, considering the total time spent in the travel of all the passengers on the network as the optimization goal. The number of passengers to be transported, train accommodating capacity, station capacity, railway section capacity, and available train number is taken as the constraints in the model. Then, we design a heuristic algorithm - fuzzy plant growth simulation algorithm to solve the model. A computing case, based on the data of the railway network of part of East China is presented to prove the efficiency and effectiveness of the proposed model and algorithm. The approach presented in this paper can help to design the service plan in the view of the railway network for the railway operators. [ABSTRACT FROM AUTHOR]

This paper considers the large-scale order batching problem (OBP) in picker-to-parts manual picking warehouses with new features of multiple blocks and ultra-narrow aisles, observed from one of the largest online retailers in China. To address these large instances, we develop a two-stage intelligent heuristic order batching algorithm which can help online retailers get satisfied batching solutions effectively. In the first stage, we cluster orders into groups according to the between-block routes applying Hong's the route-packing-based order batching procedure (RBP) method. In the second stage, based on the former order groups, we construct batches according to the within-block narrow-aisles through heuristic search. [ABSTRACT FROM AUTHOR]

Abstract Abnormal detection of user behavior is essential for Electric Management Information System to ensure its stable operations. In this paper, we propose a user profile based abnormal behavior detection algorithm based on spatial-temporal data mining. First, the user log recording the operation and corresponding timestamps are generated and aggregated according to the user catalog. Then the semantic tags to describe the users are defined and detected from the user log files. Next user profile is generated based on the detected semantic tags. Finally, we detect the abnormal behavior by comparing the user profile with the profile of the group that user belongs to. The algorithm proposed in this paper can detect the regular abnormal behavior such as working late or working overtime, which is proofed by experiment results. [ABSTRACT FROM AUTHOR]

A bi-level programming model is constructed for the oilfield development in this paper with the objective to maximize the total benefit. The management level of the oil company is regarded as the leader in this model, which makes the global programming and assigns the allocated investments to four oilfield development modes (the follower). The lower levels work out their own optimized plans according to the tasks and the investment constraints, and then feed back to the upper level. To solve this mixed integer nonlinear programming problem, the Interactive Intuitionistic Fuzzy methods are employed to obtain the solution by combining with the intuitionistic fuzzy sets and establishing a scoring function. A practical example from some Oilfield in China is used to illustrate the application of the method for the "14th Five-year Plan". [ABSTRACT FROM AUTHOR]

Abstract: This paper uses the method that combines the statistical analysis with the grey correlative algorithm to set up the evaluation index system. The data of ten cities are collected in Shaanxi, and the principal component analysis is adopted to pick up the key factors which influence the urbanization in Shaanxi Province, and then the grey correlative analysis is used to calculate the degree that the key factors influenced the urbanization tenor. Finally, this paper puts forward relevant policy suggestions. [Copyright &y& Elsevier]

Abstract: The study has put forward a water pollution management model as a GIS tool in the long-term water projects regulating process and pollution control plan for the Huaihe River Basin in China. In this paper, we applied GIS network analysis technology to water pollution management, which established digital stream network model and river water environmental database, integration and manage of water projects regulating related data resource. This paper designed a stream network analysis platform based on GIS support. Based on algorithms provided in the GIS foundational software such as shortest path analysis, connectivity component analysis, upstream/downstream trace and buffer analysis, we built an algorithm library including water projects regulating-oriented algorithms for upstream/downstream tracking of water pollution. [Copyright &y& Elsevier]

Abstract: Building energy computer simulation software is a useful tool for achieving sophisticated design and evaluation of the thermal performance of buildings. For successful thermal and energy simulation of buildings, it requires hourly weather data such as dry bulb air temperature, relative humidity, solar radiation, wind speed, etc. Nowadays, an urban city faces a problem of an urban heat island which causes the urban area to have a higher air temperature than the rural region. Since the currently available weather dataset used in building simulation software mainly comes from weather stations located in remote and rural areas, the impact of the urban heat island on thermal and energy performance of buildings may not be effectively reflected. This paper reports an approach to construct a modified typical meteorological weather file, taking into account the urban heat island effect in the summer season. Field measurements have been carried out in the summer months and the corresponding urban heat island intensities were then determined. With a morphing algorithm, an existing typical meteorological year weather file was modified. An office building and a typical residential flat were modeled with a renowned building energy simulation program EnergyPlus. Computer simulations were conducted using the existing and modified typical meteorological year weather files. It was found that there was around a 10% increase in air-conditioning demand caused by the urban heat island effect in both cases. The implications of this and further work will also be discussed in this paper. [Copyright &y& Elsevier]

Abstract: Remote sensing data has an important application value and scientific significance .in retrieval of lake surface temperature (LST). On September 6, 2008, China successfully launched two optical small satellites (respectively called HJ-1A, HJ-1B.) to monitor and predicate environment and disaster. HJ-1B satellite includes an infrared camera (IRS). The camera has a thermal infrared band which can dynamically monitor LST of large river basins in 4 days. HJ-IRS is a new sensor, so there are few papers researching on the retrieval of LST.The main goal of this paper is to introduce a practical method to obtain LST from HJ-IRS thermal data. In this paper, we reviewed primary algorithms of LST retrieval and mainly analyzed Qin′s mono-window algorithm retrieving LST from Landsat TM6 (Thematic Mapper) thermal data. This algorithm included two empirical relationships about atmospheric transmittance and atmospheric average temperature. But these relationships are only suitable for TM6. So on the basis of the spectral response function of HJ-IRS therma1 band we use radiance transfer software MODTRAN4 to simulate and calculate new coefficients in the two relationships. New algorithm requires three parameters: emissivity, vertical water vapor column and atmospheric temperature about 2 meters above the ground. We use two images of Lake Taihu to retrieve LST and validate the improved algorithm. Bias of LST retrieved from HJ-IRS data and the field measurements are smaller than 1.2K, which means this algorithm is applicable for HJ-IRS therma1 band to retrieve LST. [Copyright &y& Elsevier]

Abstract: Distinguished from former literature which focuses on the static or balanced analysis, this paper established the timevarying parameters model based on state space model to find the dynamic relationship between China''s energy consumption and economic growth. After applying Chow test, unit root test and cointegration test, this paper estimated the parameters of state space model with Kalman Filter Algorithm, then found and analyzed their internal regularity. [Copyright &y& Elsevier]

Abstract: This paper develops a methodology of proper scale analysis for regional industry development, which can be used in industry planning with the consideration of regional resource capacities. In the face of different data sources and even data scarcity, alternative methods based on linear programming and quadratic programming algorithms for calculating the resource intensity factors are designed. Based on the empirical study of industrialization, initial scenarios of industry development were set. Using HSY algorithm, sensitive industries that may cause exceeding regional resource capacity can be identified, and the risk of exceeding can be predicted and expressed in probability. Furthermore, a proper scale range can be designed for these sensitive industries according to resource capacity. Taking the case of Dalian city in China, this paper estimated the regional urban development plan, various resources capacities were studied, and land resources were estimated to be the most critical resource for the city. The land resource depletion intensities of different industries are calculated by quadratic programming algorithm. Under the constraint of 427.56km2 available industrial land resources, the electronic and power industries have the most significant impact on total land use, if the scale of power industry exceeds 1.27 billion USD, the probability of land resource capacity breakthrough will be 50%. [Copyright &y& Elsevier]

Abstract: Spatial distribution of forest types is the base and precondition of conducting forest landscape pattern. In current practical forest production, the attainment ways of forest distributing map have very strong subjectivity or are restricted by environmental factors. The purpose of spatial statistics is to describe the distribution characteristics of the things in the space. Conditional simulation algorithm is one kind of effective means of conducting spatial interpolation in the spatial statistics. The forest type is a regionalized categorical variable, and perhaps there are some regions with patch fragmentation of forest types in research regions. For these reasons, this paper used the sequential indicator simulation (SIS) algorithm of categorical variables to simulate the spatial distribution of forest types. The SIS had very great chanciness when the adscription of forest type of simulated point was confirmed, and hence the precision of the SIS was relatively low. This paper presented the improved method for the SIS. Using the sample plots of the China''s Northeast Wangqing Forestry Bureau and the Guangxi Zhuang autonomous region as the tested materials, the simulation of forest distributing map was successfully implemented with the improved sequential indicator simulation (ISIS) algorithm, and thus obtaining a simulation precision of about 80%. Results of precision analysis indicated that the ISIS enhanced the simulation precision. Therefore, the ISIS was feasible and effective. Using the sample plots as the tested materials, the ISIS might be used as an effective way of obtaining the forest distributing map. [Copyright &y& Elsevier]

In response to the appeal for environmental protection and the efficient utilization of energy, local governments in China actively promote time-of-use (TOU) electricity tariffs for manufacturing enterprises. Motivated by a real-life industrial scenario of large special-purpose pressure vessel production, this paper addresses an energy-efficient two-stage flow shop scheduling problem under TOU tariffs to minimize the total electricity cost and the mean tardiness. Since the problem is computationally intractable, we focus on developing a multi-objective discrete differential evolution (MDDE) algorithm. Specifically, based on the optimal properties of the problem, we tailor an encoding scheme that consists of two job sequences and an idle time vector. The novel mutation and crossover operators are designed to generate the trail individuals, and the hypervolume contribution indicator is incorporated into the bi-criteria selection operator to measure the quality of the solution. Furthermore, two neighborhood structures are designed to iteratively improve the non-dominated solutions in the external archive set. We evaluate the performance of the MDDE algorithm via extensive computational experiments. The experimental results indicate that the MDDE algorithm can obtain the good approximate Pareto front, and it outperforms the well-known NSGA-II, SPEA2 and MOEA/D algorithms in solution quality. • A new energy-efficient scheduling problem under TOU tariffs is studied. • The encoding scheme is tailored based on the optimal properties. • Neighborhood search can improve the quality of approximate Pareto front. • The proposed MDDE algorithm performs well in real-life industrial scenario. [ABSTRACT FROM AUTHOR]

Abstract: Internet search has become an important part in people''s daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query “Palace” contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user''s intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user''s search needs well, enhance user''s satisfaction, provide an effective and rational model for optimizing search engines and improve user''s search experience. [Copyright &y& Elsevier]

Abstract: It is difficult to evaluate the performance of the different companies feasibly and fairly only depending on experts. An evaluation model based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is built to solve this problem. In this model, the method of data processing and the evaluation algorithms are analyzed detailed. By means of the evaluation model, eight electric power supply bureau of Chongqing are evaluated in this paper, and the results show feasibly and reasonably. [Copyright &y& Elsevier]

Abstract: As smart grid promoted in China, Internet services are increasingly finding their way to home applications and gradually changing the traditional lifestyles. The home gateway is identified as a major component of the smart home network, which acts as a single point of connection between the home and outside world. In order to guarantee quality for the services including IPTV and VoIP, which requires high bandwidth and real-time support, the QoS guaranteed mechanism in home gateway should be considered. In this paper, a mechanism of home gateway is proposed to extend smart home network to be compatible for smart grid integration. The proposed mechanism classifies the traffic and manages resources by utilizing a novel scheduling algorithm to meet the user requirements on the home network QoS. Through the experiments of implementing the mechanism, performances of the proposed mechanism such as loss ratio, packets delay, and jitter are obtained. [Copyright &y& Elsevier]

Abstract: Subjective question is capable of examining the adopting ability of knowledge of the student, but the assessment for it suffers from a number of questions. Based on the analysis of automated assessment of Chinese subjective questions, in this paper we have proposed a novel automated assessment algorithm for subjective questions automated assessment using sentence similarity method. The results of the research indicate that this algorithm is promising. [Copyright &y& Elsevier]

Abstract: Ground subsidence, mainly caused by over exploitation of groundwater and other underground resources, such as oil, gas and coal, occurs in many cities in China. The annual direct loss associated with subsidence across the country is estimated to exceed 100 million US dollar. Interferometric SAR (InSAR) is a powerful tool to map ground deformation at an unprecedented level of spatial detail. It has been widely used to investigate the deformation resulting from earthquakes, volcanoes and subsidence. Repeat-pass InSAR, however, may fail due to impacts of spatial decorrelation, temporal decorrelation and heterogeneous refractivity of atmosphere. In urban areas, a large amount of natural stable radar reflectors exists, such as buildings and engineering structures, at which radar signals can remain coherent during a long time interval. Interferometric point target analysis (IPTA) technique, also known as persistent scatterers (PS) InSAR is based on these reflectors. It overcomes the shortfalls in conventional InSAR. This paper presents a procedure for urban subsidence monitoring with IPTA. Calculation of linear deformation rate and height residual, and the non-linear deformation estimate, respectively, are discussed in detail. Especially, the former is highlighted by a novel and easily implemented 2-dimensional spatial search algorithm. Practically useful solutions that can significantly improve the robustness of IPTA, are recommended. Finally, the proposed procedure is applied to mapping the ground subsidence in Suzhou city, Jiangsu province, China. Thirty-four ERS-1/2 SAR scenes are analyzed, and the deformation information over 38,881 point targets between 1992 and 2000 are generated. The IPTA-derived deformation estimates correspond well with leveling measurements, demonstrating the potential of the proposed subsidence monitoring procedure based on IPTA technique. Two shortcomings of the IPTA-based procedure, e.g., the requirement of large number of SAR images and assumed linear plus non-linear deformation model, are discussed as the topics of further research. [Copyright &y& Elsevier]

Abstract: Coal mine industry, which produces the most abundant and widely-distributed fossil fuel, is the deadliest in casualties. A recent report shows that the casualty rates of coal production per million tones are 4.36 in China, 2.14 in Ukraine, 2.03 in Russia, 0.088 in USA and 0.08 in Australia respectively. Consequently, safety is a critical issue for the mining industry, especially for the underground mining companies. The modernization and informatization of mining companies have significantly improved the operational environments (and hence safety) for the underground miners. Some advanced technologies, such as the intelligent response and rescue systems, have contributed significantly to the reduction of mining fatalities and accidents. Typical underground positioning and three dimensional (3D) modelling techniques are discussed in this paper first. A RFID/INS integrated positioning system has been tested to evaluate its performance and different positioning algorithms have been implemented. Experiments show that the simple centre of origin (CoO) positioning algorithm can achieve a positioning accuracy of 7.9 m. The integrated fingerprinting algorithm can achieve 1.7 m accuracy by using an Extended Kalman Filter. A new underground structure modelling method and a collision detecting method are developed. A prototype intelligent response and rescue system has been implemented in Da Zhuang coal mine, China. The results indicate that both the underground positioning technique and the 3D modelling and representing technique developed can satisfy the requirements of underground rescue in an emergency situation. [Copyright &y& Elsevier]

The financial situation of listed companies has a great impact on the construction of stock portfolio. However, some traditional portfolio models only consider the fluctuation of stock price and ignore the impact of the financial situation of listed companies on the portfolio, which will affect the effectiveness of the portfolio. To fill the gap, a new portfolio model based on the KMV model and a multiobjective water cycle algorithm is proposed to further improve the framework of portfolio construction. Firstly, the KMV model is used to evaluate the financial situation of listed companies, and then the optimization algorithm directly uses both the results of KMV and the fluctuation of stock price to build a more reasonable portfolio. To evaluate the validity of the model, the data of 100 A-share listed companies collected from China were used in two experiments. The results of the experiments show that the model can determine the stock portfolio according to the internal financial information and stock history information of listed companies, which not only can improve the effect and stability of the investment portfolio, but also can play a guiding role in the performance evaluation of listed companies. • This paper brings the listed companies' financial data into the portfolio. • The multiobjective optimization algorithm is applied to build complex portfolios. • The KMV model is used to evaluate the default probability of listed companies. [ABSTRACT FROM AUTHOR]

Collaborative multicenter pickup and delivery problems with time window assignment (CMPDPTWA) are introduced in this paper and formulated and solved as a two-stage optimization problem. First, the collaborative mechanism achieves optimal transportation resource configuration in a multicenter logistics network. Second, open-closed mixed pickup and delivery routes satisfying the demands and time windows of customers are proposed to enhance transportation efficiency and reduce the total logistics operating cost. A set of candidate-time windows is assigned to the corresponding customers through time window assignment (TWA) strategy to increase the operational efficiency of logistics network. A bi-objective programming model for CMPDPTWA is formulated to minimize the number of vehicles and total operating cost. An improved k -means clustering algorithm is used to cluster customers according to their proximities to pickup and delivery centers for reducing the computational complexity to solve the abovementioned problem. A self-learning non-dominated sorting genetic algorithm-II (SNSGA-II) is proposed to optimize the open-closed mixed pickup and delivery routes in CMPDPTWA. An update mechanism for the probabilities of crossover and mutation is devised to improve the efficiency of searching the solution space, and thus to accelerate the convergence of SNSGA-II. The performance of the proposed algorithm is compared with that of other algorithms, and the results indicate that it can generate near-optimal solutions for CMPDPTWA. A game theoretical method involving a cost gap allocation model and the strictly monotonic path strategy is proposed to find the best profit allocation scheme and the optimal coalition sequence for stabilizing the collaborative coalition. Finally, the proposed method is applied to a case study using the real-world logistics network in Chongqing City, China. Six scenarios with and without the collaborative mechanism and TWA strategy are simulated and compared with one another to verify the efficacy of the proposed method. The results show that the collaborative mechanism and TWA strategy can improve the utilization of transportation resources and operational efficiency. Thus, they can facilitate an efficient and sustainable urban logistics network. • Collaborative multicenter pickup and delivery problems with TWA is studied. • A bi-objective optimization model is developed based on collaborative networks. • A hybrid method with k -means clustering and SNSGA-II is devised to solve the model. • Profit allocation and coalition stability are tested under different game methods. • A case study is conducted to show the applicability of proposed approach. [ABSTRACT FROM AUTHOR]

The Yunnan-Guizhou-Guangxi region is well known for its abundant mineral resources, and low-temperature hydrothermal mineralization represented by the elemental association of gold, arsenic, antimony and mercury is widely developed there. Many studies on the geological-geochemical characteristics of gold have been conducted, but a comprehensive understanding of the antimony geochemical pattern is still lacking. This paper studied the Sb distribution characteristics and the cause of geochemical anomalies based on the geochemical data of stream sediments and rocks in the study area. In addition, the geochemical data of Au, As, Sb and Hg were centered and log-ratio transformed to eliminate the closure effect, and then random forest regression (RFR) with Au, As and Hg as the characteristic variables was used to investigate the ore-related geochemical anomalies of Sb. Seven geochemical provinces were delineated from the original geochemical data, and they are not entirely consistent with the known deposits. Sb moves from the rocks to the stream sediments during weathering. The variation trend in the Sb background values in stream sediments in each tectonic unit is consistent with that in the rocks themselves, implying that Sb in the stream sediments is inherited from the background rocks. The distributions of Sb predicted by RFR are similar to the distribution pattern of Sb in stream sediments. Of the three elements considered, the influence of As on the variations in the Sb geochemical background is the greatest, followed by Au and then Hg. The geochemical anomalies extracted by the residuals produced in this algorithm are consistent with where the known Sb metallogenic district is located, indicating that this method of recognizing geochemical anomalies is feasible and effective and has theoretical and practical significance. [ABSTRACT FROM AUTHOR]

With the development of gas-fired generators, the increasing gas-electricity systems bring a new challenge to accurately analyze pressure or flow rate fluctuation in gas pipeline networks caused by gas-fired generators ramping. This paper proposes a hybrid physical and data driven modeling approach, which can take full advantage of physical laws and measurement data to characterize the transient behavior of gas flow in pipeline networks. Based on the linear graph theory, the gas flow dynamics and network topology are uniformly represented in the directed graph, so that the state-space model can be extracted automatically. Considering that the increasing number of model parameters with networks scale, a decentralized parameters identification algorithm is designed to enhance computational efficiency. The overall network is partitioned into a certain number of non-overlapping subnetworks, and each one formulizes the parameter identification as a nonlinear optimization problem solved by an iterative Levenberg-Marquardt algorithm. To demonstrate the effectiveness of the proposed method, a real-world gas network from a steel industrial park in China is studied, and simulation results are compared with the commercial simulation software Apros™. The results indicate that the proposed method improves the accuracy in gas network transient modeling markedly. • A hybrid physical and data driven transient modeling for gas networks is proposed. • The state space model is developed based on the linear graph theory. • A decentralized parameter identification algorithm is designed for efficiency. • Real steel COG network demonstrates the effectiveness of proposed. [ABSTRACT FROM AUTHOR]

In this paper, a method of assessing water quality from satellite data is introduced. The composite pollution index (CPI) was calculated from measured chemical oxygen demand (COD) and nutrient concentration. The relationships between CPI and 240 band combinations of SeaWiFS water-leaving radiance were analyzed and the optimal band combination for estimating CPI was chosen from the 240 band combinations. An algorithm for retrieval of CPI was developed using the optimal band combination, (L 443 × L 510)/(L 412 + L 490). The CPI was estimated from atmospherically corrected SeaWiFS data by employing the algorithm. Furthermore, the CPI value range for each water quality level was determined based on data obtained from 850 samples taken in the Pearl River Estuary. The remotely sensed CPIs were then transferred to water quality levels and appropriate maps were derived. The remotely sensed water quality level maps displayed a similar distribution of levels based on in situ investigation issued by the State Ocean Administration, China. This study demonstrates that remote sensing can play an important role in water quality assessment. [Copyright &y& Elsevier]

Production tests of natural gas hydrate in deepwater have been successfully conducted twice in China in 2017 and 2019. However, exploitation disturbance caused huge threat to the security of production well, as hydrate production was characterized by complicated phase variation in coupled seepage process, which made conventional analysis approach of oil and gas production well futile. This paper aimed at proposing a mechanical analysis method for casing in hydrate production test well based on the self-developed phase transition solver, which was employed to address exploitation disturbance induced by the varying hydrate saturation, reservoir strain state and physical parameters. By developing a multi-field coupling model and the corresponding algorithm, dynamic changes of casing boundary condition were achieved, and temporal-spatial distribution of casing stress was revealed. A case study was presented to illustrate the application of the proposed methodology. Results indicated that casing stress presented a distribution of "8" and a trend of decreasing first and then increasing with the progress of production test, reaching 112.95 MPa at the 60th day. Meanwhile, this trend was affected by production pressure and in-situ stress inhomogeneity. Moreover, as in-situ stress inhomogeneity, initial hydrate saturation and initial pore pressure changed from 1.05 to 1.3, 0.3 to 0.6 and 19 MPa–15 MPa, casing maximum stress increased by 2.3 times, 13.9% and 11.2%, respectively. The proposed approach could be used to provide theoretical reference for safety design of hydrate production test well. [Display omitted] • A novel methodology to research temporal-spatial distribution of casing stress under hydrate exploitation disturbance was proposed. • A multi-field coupling model and corresponding algorithm for achieving dynamic boundary of casing were developed. • Mechanical characteristics of casing under different hydrate production pressures were revealed. [ABSTRACT FROM AUTHOR]

Large volumes of data have been accumulated in identical or very similar contexts of decision making. To generate accurate and explanatory decision recommendations by using these data, this paper proposes a data-driven multi-criteria decision making (MCDM) method based on machine learning (ML) algorithms and the evidential reasoning (ER) approach. In the method, based on the assessments of all historical alternatives, a comparison framework is designed to determine the most appropriate ML algorithm with the highest predictive accuracy. An optimization model is then constructed to connect the appropriate ML algorithm with the ER approach. Through the optimization model, the difference between overall assessments derived from the ER approach and the predicted results derived from the appropriate ML algorithm is minimized to learn criterion weights. The learned criterion weights are used to generate accurate and explanatory decisions. Such a combination takes advantage of high predictability of ML algorithms and favorable interpretability of the ER approach simultaneously. To demonstrate the validity and applicability of the proposed method, it is used to aid the diagnosis of thyroid nodules for a tertiary hospital located in Hefei, Anhui, China. Its merits are further highlighted by its comparison with two traditional ER approaches and the appropriate ML algorithm. • Data-driven decision making based on ER approach and ML algorithms is proposed. • ML algorithms are compared based on their linkage with ER approach. • An optimization model is constructed to make explanatory decisions based on ML. • The proposed method is used to help radiologists diagnose thyroid nodules. • The proposed method is compared with decision making learning from accuracy and AUC. [ABSTRACT FROM AUTHOR]

Superposition coding (SC) with successive interference cancellation (SIC) is considered a promising non-orthogonal multiple access (NOMA) technique for improving spectral efficiency recently. In contrast, the residual interference after SIC at the receiver severely degrades the BER performance when applied in underwater acoustic (UWA) channels. In this paper, we propose a receiver that combined SIC and turbo equalization for superposition coding based downlink UWA orthogonal frequency-division multiplexing (OFDM) system, where a near-far user pair is mainly considered. We design an SC signal detector based on the minimum mean square error (MMSE) algorithm and eliminate the far user information with high power after detection. After SIC, the turbo equalization composed of an MMSE equalizer and LDPC decoder is adopted for the near user symbols with residual interference from the SIC process. Simulation results and recorded data from the Yellow Sea, China, show that the proposed receiver has a performance gain of at least 1 dB under different modulation pairs compared with traditional SIC receiver based on maximum a posteriori probability principle (MAP SIC). The error-free two-user downlink communication result shows a maximum sum-rate of 8kbps over the horizontal range of downlink 3.55km has been achieved in the Yellow Sea test. • Iterative Turbo structure and implementation algorithm for downlink near user receiver. • Different superposition coding modulation pairs can be processed. • The proposed receiver can achieve error-free communication through iteration. • Receiver was successfully verified by simulation and sea test. [ABSTRACT FROM AUTHOR]

The workforce scheduling problem in call centres is defined as assigning a number of employees to various overlapping shifts during a specified planning horizon, considering some regulations and preferences, so as to minimize the labour cost. This is often difficult to solve to optimality due to its combinatorial structure. In this paper, we propose an enhanced artificial bee colony (EABC) algorithm to solve the problem. After introducing a good-quality solution generated by a heuristic algorithm to the initial population, the solutions are further exploited by four elaborate neighbourhood structures, which are designed based on the decomposed structure of the problem and deeply embedded in the solution evolution mode. Furthermore, a modified abandoning mode is employed in the onlooker stage. Using real instances from Chengxi Call Centre in Suqian, China, the experimental results show that the proposed algorithm can achieve (sub-)optimal solutions for large-scale problems. Furthermore, a comparative evaluation of EABC is carried out against a hybrid artificial bee colony (HABC) and simulated annealing (SA). The results show that EABC outperforms the other two algorithms for all problem instances. In addition, we analyse the influence of the weekend-off fairness on the labour cost. • The study firstly deals with scheduling call centres with weekend-off fairness. • We decompose the problem structure and explore relationships between hierarchies. • An enhanced artificial bee colony algorithm is tailored for solving the problem. • The proposed algorithm can obtain the (sub-)optimal solutions of the problem. [ABSTRACT FROM AUTHOR]

Industrial enterprises are the main body of national scientific and technological innovation activities, and the improvement of their research and development (R&D) output capacity plays a pivotal role in the enhancement of national innovation capabilities. The R&D process is an input–output process, and its results have various forms. In this study, we construct a multi-objective R&D investment allocation optimization model from three dimensions representing China's innovation capability, and propose a novel multi-objective mutation flower pollination algorithm (MOMFPA) to solve the model. We employ a two paired-sample T-tests to test the difference hypothesis of the model, and the test results show that the model is effective and reasonable. The MOMFPA and the weighting algorithm are respectively used for empirical analysis, and the optimized results obtained by the MOMFPA are found to be better than those obtained by the weighting algorithm, thereby demonstrating the validity and application value of the MOMFPA. Moreover, the multi-objective model is used to predictively optimize China's future R&D investment. The forecasting results indicate that China should focus on increasing the proportions of R&D investment in high-tech equipment manufacturing and electronic information technology, while reducing the proportions of R&D investment in traditional industries, such as mining, food, paper, and metallurgy. • This study innovatively put forward a solution to settle multi-objective problems. • We set up a multi-objective industrial enterprise R&D resource optimization allocation model. • We adopt the improved Technique for Order Preference by Similarity to Ideal Solution method to choose the Pareto optimal solution. [ABSTRACT FROM AUTHOR]

To realize the fuzzy control of regional economy, combiuing fuzzy control with fuzzy evaluation method, this paper constructs a new index system featured with dual-space for competition of regional economy based on recent research achievements in regional economics field. It also presents the fuzzy evaluation method and fuzzy control rule based on lots of statistical data and experiences of experts. The result shows that the sequence of regional competition evaluated by fuzzy control is accordant with the developmental reality of Chinese economy. The application of fuzzy control rule makes the evaluation quicker, simplier and more objective. Thus, the intelligentized evaluation on regional economy is realized. [ABSTRACT FROM AUTHOR]

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 — sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics. • We proposes a new hybrid approach based on the thresholding technique to overcome the image segmentation problem. • We developed an integrated slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. • ISMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms. • The experimental results demonstrate that our proposed algorithm outperforms SMA under Kapur's entropy. • Our algorithm could outperform all other algorithms in the fitness values, SSIM, PSNR, UQI, CPU time and (Std). [ABSTRACT FROM AUTHOR]

• A top-down prediction method of urban heat supply based on the grey system theory is proposed. • The posterior difference ratio is minimized by genetic algorithm. • The comprehensive annual growth rate of the heat supply is proposed. Urbanization in China is bringing distinct growth in heating energy consumption. Hence, the key to ensuring the responsibility of energy structure reform is to reasonably predict urban heat supply. This paper presents a top-down prediction method based on the grey system theory. The prediction method is embedded in a genetic algorithm optimization with the objective function of a posterior difference ratio in order to achieve the highest accuracy. In the methodological framework, the first step is to screen the explanatory variables from the preliminary factors by employing grey relational analysis. Then, the grey models of the dependent and explanatory variables are combined to establish the grey system state equation. Finally, the posterior difference of the results is examined to determine the optimization process. The case study focuses on 16 preliminary factors and 3 cities in China. According to the prediction results, the improved prediction accuracy shows the advantages of optimization process as well as updated training data. The comprehensive annual growth rate of the heat supply is finally proposed to integrate all of the prediction possibilities. The top-down prediction method reveals the future tendency of annual heat supply so as to assist urban planning and energy structure reform. [ABSTRACT FROM AUTHOR]