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An accurate estimation of tourism demand is of great significance to tourism management. The seasonal and non-stationary features present a significant challenge in developing tourism demand estimation. An effective tourism demand forecasting model is important to address this problem. This paper proposes a novel model for tourism demand forecasting based on developed mechanism-guided attention. The model consists of three sections: the first section defines the degree of stationarity of the demand time series, the second section develops a guided attention mechanism for improving the feature recognition of neural networks; the third section generates forecasting results of tourism demand, in the meanwhile, the developed fully connected recurrent neural network is adopted to identify complex features of tourism demand time series. The proposed model is helpful in identifying the features of seasonality and non-linearity in tourism demand data, the model maintains good forecasting accuracy with the appropriate training process. Case studies show that the proposed method can forecast the daily tourism demand of Macau, China accurately compared with other traditional forecasting methods. • A novel model basing a guided attention mechanism is proposed to forecast tourism demand. • Identify non-stationary feature in tourism demand time series using Fully connected recurrent neural network. • Use adaptive evolutionary algorithms to evaluate the tourism demand forecasting results in different scenarios. • Adopt NSGA-II to optimize the mapping matrix of guided attention mechanism. • Validate the proposed model using open data sets with complex characteristics. [ABSTRACT FROM AUTHOR]

• A 14-layer model with high predictive performance is proposed. • The model fuses three types of multi-source and multi-frequency features. • The model addresses three limitations of LSTM. • The model is validated through four comparison experiments on four stock indices. • Each component of the model improves the predictive performance. The most widely used model in stock price forecasting is the long short-term memory network (LSTM). However, LSTM has its limitations, as it does not recognize and extract features well and has a representational bottleneck. Furthermore, the factors affecting stock prices are multi-source and multi-frequency information, making neural network models difficult to handle. In this paper, we introduce a feature fusion residual LSTM (FFRL) model to answer these two questions – how to compensate for the three limitations of LSTM and how to fuse the multi-source and multi-frequency information. FFRL consists of three modules to improve the three limitations of LSTM, namely the feature selection module, feature extraction module, and residual module. To learn features from multi-source and multi-frequency information, FFRL applies the feature selection module to emphasize important features and the feature extraction module to extract deeper features. We demonstrate significant performance improvements of FFRL over comparison models, ablation networks, and visualization methods on a variety of Chinese stocks. [ABSTRACT FROM AUTHOR]

• A 14-layer model with high predictive performance is proposed. • The model fuses three types of multi-source and multi-frequency features. • The model addresses three limitations of LSTM. • The model is validated through four comparison experiments on four stock indices. • Each component of the model improves the predictive performance. The most widely used model in stock price forecasting is the long short-term memory network (LSTM). However, LSTM has its limitations, as it does not recognize and extract features well and has a representational bottleneck. Furthermore, the factors affecting stock prices are multi-source and multi-frequency information, making neural network models difficult to handle. In this paper, we introduce a feature fusion residual LSTM (FFRL) model to answer these two questions – how to compensate for the three limitations of LSTM and how to fuse the multi-source and multi-frequency information. FFRL consists of three modules to improve the three limitations of LSTM, namely the feature selection module, feature extraction module, and residual module. To learn features from multi-source and multi-frequency information, FFRL applies the feature selection module to emphasize important features and the feature extraction module to extract deeper features. We demonstrate significant performance improvements of FFRL over comparison models, ablation networks, and visualization methods on a variety of Chinese stocks. [ABSTRACT FROM AUTHOR]

• In the proposed IVIF-BWM, we build weight-determining models concerning expert's attitudes. • A bi-objective programming model is proposed to derive experts' dynamic weights. • Develop an IVIF-DBWM to determine the global weights of all criteria. • IVIF-SPA is proposed to evaluate the overall resumption risk of each alternative. Currently, China has achieved a remarkable achievement on the containment of COVID-19, which creates a favorable condition for the gradual resumption of normal life. However, COVID-19 infections continue to rise in many nations and some sporadic cases occur from time to time in China, which still poses some risks to the resumption. Hence, it is imperative to develop some reasonable techniques to assess the resumption risk. This paper aims to investigate an integrated interval-valued intuitionistic fuzzy (IVIF) technique to adroitly assess the resumption risk based on DEMATEL (decision making trial and evaluation laboratory), BWM (best-worst method) and SPA (set pair analysis). This integrated technique is called IVIF-DBWM-SPA, where the IVIF-DBWM (combined by the IVIF-DEMATEL and IVIF-BWM) is used to determine the global criteria weights and the IVIF-SPA is employed to generate the ranking order of the alternatives. The IVIF-DEMATEL and IVIF-BWM are used to determine the weights of dimensions and the weights of criteria under each dimension, respectively. In this IVIF-BWM, two bi-objective programming models are constructed by regarding experts' pessimistic and optimistic attitudes, respectively. Combined experts' intrapersonal and interpersonal uncertainties simultaneously, a bi-objective programming model is proposed to derive the dynamic weights of experts. Based on the determined weights of experts and criteria, an IVIF-SPA is developed to assess the risk levels of all alternatives. The validity of the proposed technique is demonstrated with a real case of college resumption risk assessment amid COVID-19. Some sensitivity and comparison analyses are provided to show the merits of the proposed technique. [ABSTRACT FROM AUTHOR]

Accurate and timely crop yield prediction is difficult to achieve due to the nonlinear and dynamic spatial–temporal correlations included during the crop growth process. The latest approaches utilize deep learning techniques to implicitly capture sequential dependencies but fail to consider prior knowledge. In this paper, a novel Knowledge-guided Spatial–Temporal Attention Graph Network (KSTAGE), which incorporates prior knowledge to model dynamic temporal relationships and geospatial topology structures, is proposed. The proposed KSTAGE first utilizes a 3D convolutional neural network (CNN) to embed the initial spectral feature. Following its development, a new Knowledge-guided Temporal Multi-head Attention Algorithm (KTMA), the core of KSTAGE, is applied to automatically generate temporal attention weights from a self-attention mechanism under the guidance of prior knowledge. Furthermore, a new strategy for aligning self-attention scores with the prior distribution is developed. Finally, a location-aware Spatial Attention Graph Network utilizing geospatial knowledge is proposed to aggregate the spatial neighborhood features for the final yield prediction. The experimental results obtained on county-level yield prediction tasks in China and the contiguous United States (CONUS) demonstrate that the proposed KSTAGE achieves obvious improvements over the baselines. [ABSTRACT FROM AUTHOR]

Changes in stock markets impact the optimal policies for governments, businesses, and even individual households. Causal networks summarizing pairwise directed dependencies between stocks can be used to characterize these changes, as well as to identify influential stocks whose variations have unusually strong effects on the movements of other stocks. In this paper, we apply Granger causality tests in sliding windows to build directed causal networks representing the interactions of stock markets in the world's second largest economy, China, during recent economic, trade and epidemiological crises. We propose identifying influential stocks by using distance correlation and surrogate tests, rather than linear Pearson correlation and asymptotic distributions, to quantify the strength of general nonlinear dependencies between network centrality and financial indicators. We find that, at the onset of a crisis, influential stocks gain their position by themselves being attentive to the stock market. As the crisis progresses, the stock market steadily adjusts to give a stronger leadership role to influential stocks at the expense of non-influential stocks. Interestingly, the industries to which influential stocks belong are closely related to the nature of crisis events. Finally, we show that industries which contained influential stocks represented better investments. [ABSTRACT FROM AUTHOR]

• A novel multi-objective scheduling model is developed for the emergency rescue planning problem. • The proposed model minimizes the fire extinguishing time, delay time, and number of fire rescue vehicles. • The factor of fire spreading speed is firstly considered in our optimization model. • A revised multi-objective discrete gravitational search slgorithm is designed to produce Pareto solutions. • An empirical case study in Heilongjiang Province, China, is applied. The implementation of emergency scheduling of priority-based rescue vehicles for extinguishing forest fires is a complex optimization problem facing with many challenges and difficulties to optimize the operational costs and to improve the efficiency to make robust decisions. The main challenges are to minimize the number of fire engines while minimizing firefighting time and firefighting delay time, simultaneously. The main difficulties to make these decisions are to consider the severity of each fire point with regards to the limited resources of vehicles. Hence, this paper motivates to develop a new multi-objective scheduling model for extinguishing the fire of forests considering rescue priority with the limited rescue resources. To make an efficient and robust solution for the proposed problem, another novelty is to propose a hybrid optimization algorithm which is a modified discrete gravitational search algorithm. To confirm the applicability of the proposed problem, an actual forest fire emergency scheduling in Heilongjiang Province, China, is simulated. The proposed hybrid optimization algorithm is tested to check the feasibility the Pareto solutions and it is compared with a set of well-known and recent algorithms to show its efficiency. Finally, after a comprehensive discussion, the results prove that this work provides an accurate and effective tool for performing the emergency scheduling for extinguishing the forest fire. [ABSTRACT FROM AUTHOR]

As grade differences between concentrates increase, long-term concentrate ingredient planning (LCIP) becomes a crucial issue in ensuring continuous production and maximizing the concentrate utilization. Comparing with the existing studies, the number of LCIP ingredient stages is not deterministic and the decision variables and constraints are also will also change in accordance with the preceding ingredient list. Given these features of LCIP, this paper presents the concept of unpredictable multistage dynamic optimization (UMDO) and establishes an LCIP model that comprehensively considers production constraints, ingredient list duration, and concentrates inventories. A multistage stochastic object coding (MSOC) that establishes a mapping relationship between the coding sequence and the feasible solution space at each scheduling stage is further proposed. A multi-agent differential evolution (DE) algorithm based on the application of sequential simulation is proposed to optimize the high-dimensional population of the MSOC, enabling a globally optimal scheme in which the feasibility of the ingredient plan at each stage is ensured. Finally, the actual inventory concentration data collected from a copper industry in China are used to validate the effectiveness of the proposed planning methodology. [ABSTRACT FROM AUTHOR]

• A novel fuzzy c-means algorithm is proposed for attribute-associated data clustering. • The parameters of proposed algorithms are fully investigated through synthetic datasets. • The proposed algorithm performs better compared with others on synthetic, real-world, and tunnel boring machine datasets. Partitioning data into internally homogeneous parts is an important problem when mining in situ engineering data. In this paper, a polynomial regression-based fuzzy c-means (PR-FCM) clustering algorithm that utilizes the functional relationships among the attributes of the input dataset is proposed. In this algorithm, a polynomial regression equation is taken as the center of each cluster instead of the cluster prototype used in conventional FCM, and the difference between a sample and a cluster prototype is defined as the distance between the actual value of one attribute and the corresponding predicted value provided by its own polynomial regression equation. An alternating optimization method is designed to optimize the new clustering objective function of the proposed algorithm. A series of experiments on synthetic and real-world datasets are conducted to evaluate the performance of the PR-FCM algorithm, which exhibits higher effectiveness and possesses more advantages than the original FCM algorithm. The PR-FCM algorithm is applied to tunnel boring machine (TBM) operation data from a TBM project in China. The experimental results show that the proposed algorithm can effectively cluster TBM operation data. [ABSTRACT FROM AUTHOR]

To achieve sustainable management of municipal solid waste, selecting an appropriate waste to energy (WTE) technology is pivotal. The WTE technology selection is a complicated issue because it involves a range of conflicting criteria and a rich diversity of stakeholders. Moreover, the complexity will be multiplied when considering that the preference information provided by different people with respect to different criteria takes different forms and granularities. To tackle the WTE technology selection problems with multi-granular hybrid information, this paper proposes a new group decision-making framework. The proposed framework provides a reasonable solution to the WTE technology selection problems through four stages. In the first stage, three information transformation mechanisms are established to transform information in different forms and granularities into unified belief structures. In the second stage, a symmetrical cross-entropy measure-based weight determination model is developed to calculate the criteria weights. In the third stage, by combining information transformation mechanisms and criteria weights, the analytical evidential reasoning algorithm is extended to generate group opinions. Finally, according to the generated group opinions, the expected utilities of alternatives are calculated to compare and rank the alternatives. To illustrate the implementation process of the proposed decision-making framework, an application about the WTE technology selection in China is performed. Besides, a comparative experiment is conducted to show the flexibility and reliability of our proposal. [ABSTRACT FROM AUTHOR]

• Integrate the impact of uncertain unit adjustment cost and risk preference of the moderator on RMCCM. • To achieve stability, reliability and robustness, some general robust RMCCM are proposed in three cases by using robust optimization. • Compared with other methods, the proposed method can get better decision results. • That's because the proposed method can well describe the MCC problem under uncertain conditions. Due to the uncertain circumstances in which decisions are made, the moderator may be unable to provide an accurate unit adjustment cost to each decision maker, making the unit adjustment cost uncertain. This paper formulates a consensus approach to reduce the adverse effects of parameter uncertainty and estimation errors. First, the mean–variance (MV) theory is employed to build the risk minimum cost consensus model (RMCCM) to reflect the risk preference of the moderator in the consensus progress. Second, due to existing estimation errors of the mean and covariance matrix of unit adjustment cost in RMCCM, new RMCCM variants are proposed using robust optimization. To identify a solution that performs best in the worst case, the robust counterpart RMCCM problem is equivalent to a tractable problem. Finally, a case study of urban housing demolition in China is used to verify the feasibility and applicability of the proposed method, and then sensitivity analysis and comparative analysis are presented. [ABSTRACT FROM AUTHOR]

• Context-Dependent DEASort is proposed based on BWM for multi-criteria sorting problem. • Categories can be simply obtained without predefined classification categorical rules. • A common set of weight model is introduced for evaluating attractiveness of each DMU. • Case study concerning risk assessment of Yangtze River Economic Zone is provided. • Sensitivity analysis is conducted to illustrate the stability of the proposed method. With the increasing urban economic development and awareness of environment protection, ecological risk assessment (ERA), as a management mode combined with ecological research and risk assessment, has become a highly relevant topic in environment sustainable development. In this paper, we propose a novel sorting model based on Data Envelopment Analysis (DEA) and Best Worst method (BWM) to solve multi-criteria sorting problems and apply it to ERA. The proposed Context-Dependent DEASort method based on the idea of Context-Dependent DEA to position decision-making units (DMUs) into diverse categories in uncertain circumstance. It also takes experts' preference into full account as well, which makes the sorting solution more flexible and reasonable. Besides, a common set of weight-based model is introduced to deal with the situation when evaluating attractiveness and progress of each DMU. The common weight set model can ensure to provide an overall evaluation context compared to the original Context-Dependent DEA model, which only distinct the DMU from a single virtual DMU. Furthermore, a case study concerning ecological risk assessment of Yangtze River Economic Zone in China is provided to illustrate the applicability of the developed environment. Finally, comparative analysis and sensitivity analysis are carried out to demonstrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Automatic understanding of floor plan images is a key component of various applications. Due to the style diversity of rural housing design, the latest learning-based approaches cannot achieve satisfactory recognition results. In this paper, we present a new framework for parsing floor plans of rural residence that combines semantic neural networks with a post-processed room segmentation. First, we take case studies from typical residential buildings in China's rural areas and provide a novel image dataset, called RuralHomeData , containing 800 rural residence floor plans with accurate man-made annotations. Based on the dataset, we propose a new deep learning-based recognition framework using a joint neural network to predict the geometric elements and text information on the floor plan simultaneously. Our insight is that walls and openings (doors and windows) are the basic elements corresponding to the room boundary that a closed 1D loop must form a certain room. Then the semantic information (e.g., the room function) of room regions can be obtained through text detection and identification. Furthermore, we use the MIQP algorithm to divide the area containing multiple room type texts into multiple room areas. Finally, the input floor plan can be transformed into a room layout graph with room attributes and adjacent relationships. The proposed algorithm has been tested on both urban and rural datasets, and the experimental results demonstrate our efficiency and robustness compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As the density of railway operations increases annually, train delays typically result in a greater degree of economic loss. If we can accurately assess the current evolution of delayed trains, railway authorities will be able to make timely decisions and reduce travel anxiety among passengers. In this paper, a deep learning model integrating wavelet transform (WT), fully connected neural network (FCNN), and long short-term memory (LSTM) network is proposed to address the short-term delay prediction problem for trains. Its predictive performance is enhanced by introducing two front train selection schemes, two methods for converting train operating time features into time-series data, and five commonly used wavelet functions. For validation purposes, the model is tested using real-world operational data from two distinct high-speed railway lines in China, and common machine learning algorithms are compared, including random forest (RF), gradient boosting regression tree (GBRT), XGBoosting (XGB), and support vector regression (SVR). The final experimental results demonstrate that our proposed model outperforms the benchmark models and can be effectively applied to practical prediction problems. • We investigate the spatio-temporal effects of delay propagation, categorizing delay data attributes into front trains and target trains. Two feature selection schemes are proposed to assess the impact of front train features on prediction performance. • Wavelet Transform (WT) is employed for the first time in train delay prediction to eliminate random interference. The model's prediction performance is enhanced through denoising and expanding feature dimensionality. • Real data from two Chinese high-speed railways validate the model's accuracy, providing practical guidance for its application. [ABSTRACT FROM AUTHOR]

Massive and complex longitudinal data processing of extended target-effective observations highlights a major challenge: existing methods are concerned with the difficulty of improving prediction robustness and ensuring relatively low computing costs. This paper proposes a novel hierarchical tensor processing error correction gated recurrent unit-effective observation prediction lower- and upper-bound estimation ensemble model. First, we introduce a continuous-time tensor value function to integrate time and context information and create a dynamic variable coefficient embedding layer of continuous-time tensor decomposition, including time granularity, observation, Gaussian component, and context information through a B-spline function approximation. The main advantages of this method include accurate normalized distance squared, effective threshold delimitation, and effective prediction boundary estimation. Second, to solve the error interference caused by the physical factors of multivariate observations, the lower and upper interval boundaries of prediction interval (PI) are deeply integrated into the proposed tensor model through the error factor correction method. The experimental results for aviation datasets from Central and Southern China demonstrate that the proposed model is better than other benchmark models in terms of PI output quality, prediction performance, and computing resource consumption. [ABSTRACT FROM AUTHOR]

• An efficient scheme, TrackU, is designed to accurately track the mobility behaviours without any GPS data. • A drift adjusting algorithm is designed to achieve accurate positioning results through WiFi positioning. • An optimization model is designed to minimize power consumption while maintaining the tracking accuracy. • An activity detection algorithm is proposed to help TrackU to infer user's daily activities by merging adjacent segments. • A trace inferring algorithm is proposed to infer to the user's travel mode (hovering, walking, or vehicle). The concerns about location privacy has received considerable attention with the development of Location-based Services (LBSs) over the recent years. Most smartphone users ignore the fact that Apps can infer their locations through accessing WiFi list, although they carefully set location-related permissions to preserve their privacy. Therefore, it is crucial to the public to investigate severe such consequence of WiFi list leakage. In this paper, we develop a tracking scheme for Android, called TrackU, which validate that it is possible to obtain user's location data as well as their location-related behaviors, just by monitoring the WiFi list without any GPS data. Firstly, it periodically scans available Access Points (APs) nearby and queries the geo-location of the device from LBS providers. Secondly, a drift adjusting algorithm proposed obtains the exact locations considering a set of factors, such as historical location information, average signal strength and changing of WiFi list. To preserve the battery life, an optimization is made to dynamically adjust the positioning interval. Based on the obtained data, we design an activity detection algorithm precisely to infer users' daily activities, and identify their travel modes, i.e., hovering, walking, and vehicles. Finally, we implement TrackU and carry out a series of experiments with 39 volunteers from seven cities of China. The experiment results show that our design can detect 91.6% of activities by monitoring the WiFi list, and accurately recognize 94.6% of user's travel mode. [ABSTRACT FROM AUTHOR]

Haze has become a frequent disastrous weather condition in China. Its formation consists of evolution process of several pollutants in certain meteorological conditions with complex relationships among these factors of haze formation. How to explore the complex relationships among these multi-dimensional factors as well as effectively predict them have become a key issue in research community. The research in this paper presents a method to quantitatively reveal causality in the formation of haze, which can be used to effectively predict haze pollution. In order to make the complicated relationship among different factors be interpretable, an PS-FCM (Primary Sub-Fuzzy Cognitive Maps) model is proposed and its multi-dimensional causality solution is demonstrated. By considering the formation of haze as an evolving process with time, we explore and discover the causality based on time series data of haze pollution with PS-FCM. Thus, a multi-dimensional time series data mining method based on the PS-FCM is developed to investigate the formation of haze. We validate our model by comparing with other machine learning method via experimental data and discuss the performance of PS-FCM under different transformation functions. The results explicitly show the quantitative causality among the different factors in haze formation. [ABSTRACT FROM AUTHOR]

In order to manage the railway dangerous goods transportation system (RDNGTS) successfully, an explicit and effective previous accident analysis and accident control approach is essential and necessary. In this study a Fault Tree and Fuzzy D-S Evidential Reasoning combined approach is proposed to analyze the RDNGTS accident, which can solve the uncertainty modeling and information fusion problems existing in RDNGTS accident analysis. The approach has six steps: (i) identify causes of accident and calculate their weights based on Fault Tree, (ii) establish the Fuzzy Belief Structure model of causes of accident, (iii) handle initial qualitative and quantitative data, (iv) fuse the pre-processing data based on Fuzzy D-S Evidential Reasoning algorithm, (v) allocate Confidence Level of fuzzy intersection and, (vi) rank the final Fuzzy Belief Structure of each component based on trapezoidal fuzzy numbers and triangular fuzzy numbers. A historical lithium battery railway transportation accident happened in 2016 in China is applied as the background to examine the approach mentioned in this paper. The results show that professional skills and attitudes of transportation staffs are the weakest component in this lithium battery railway transportation accident. Managers of RDNGTS in China should pay more attentions to the professional skills and attitudes of transportation staffs. Some measures such as improve the awareness of safety and protection, train and examine the professional skills of transportation staffs, may be helpful in curbing the negligent working attitude of transportation staffs. Furthermore, the results also show that D-S Evidential Reasoning could provide a unified modeling framework for uncertain, incomplete, inaccurate and even ignorant information. It could solve the limitations in probability reasoning processes effectively. [ABSTRACT FROM AUTHOR]

In many countries, wind power is being developed as a primary source of renewable energy. However, it is difficult to describe and forecast wind speed features, which are stochastic and intermittently complex. Moreover, it is important both to obtain precise forecasts of wind speed for wind power generation and to determine their contribution to regional economies. In this paper, we propose a novel approach for wind speed forecasting. The proposed model is a hybrid that uses the wavelet analysis technique (WAT) for denoising, a negative correlation learning neural network (NCL-NN) ensemble, and an ensemble structure optimized using particle-swarm optimization (PSO). We name the approach WAT-NCL-PSO. We define a novel fitness function to optimize the performance of the NCL-NN ensemble. Then, we rebuild the new NCL-NN ensemble using the contribution rates (CRs) by applying the PSO algorithm. We compiled wind speed datasets from six wind power generator sites in western China and used them to test the performance of the proposed model. Further, we analyzed the model's performance in terms of its robustness and time complexity. Finally, to illustrate the effectiveness of the proposed approach, we compared its performance with that of the back-propagation neural network (BPNN), support vector machine (SVM), bagging, AdaBoost, random forest (RF), long short-term memory (LSTM), seasonal autoregressive integrated moving average (SARIMA), SVM with ensemble empirical mode decomposition (EEMD-SVM), and NCL-NN with wavelet denoising (WAT-NCL) models. The simulation results demonstrate that the performance of the WAT-NCL-PSO model is superior to other methods in terms of forecasting accuracy for short-term wind speeds. [ABSTRACT FROM AUTHOR]

Abstract: The integration of high dimensional geo-visualization, geo-data management, geo-process modeling and computation, geospatial analysis, and geo-collaboration is a trend in GIScience. The technical platform that matches the trend forms a new framework unlike that of GIS and is conceptualized in this paper as a collaborative virtual geographic environment (CVGE). This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the conceptual and system framework development. The other is a prototype system development according to CVGE frameworks for air pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dispersion models, air pollution distribution/dispersion visualization in geographically referenced environments, geospatial analysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can join in the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collaborations supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualization of the output of the modeling, and geo-analysis. [Copyright &y& Elsevier]

Abstract: As a newly introduced interconnection network for parallel computing, the locally twisted cube possesses many desirable properties. In this paper, mesh embeddings in locally twisted cubes are studied. Let LTQ n (V, E) denote the n-dimensional locally twisted cube. We present three major results in this paper: (1) For any integer n ⩾1, a 2×2 n−1 mesh can be embedded in LTQ n with dilation 1 and expansion 1. (2) For any integer n ⩾4, two node-disjoint 4×2 n−3 meshes can be embedded in LTQ n with dilation 1 and expansion 2. (3) For any integer n ⩾3, a 4 ×(2 n−2 −1) mesh can be embedded in LTQ n with dilation 2. The first two results are optimal in the sense that the dilations of all embeddings are 1. The embedding of the 2×2 n−1 mesh is also optimal in terms of expansion. We also present the analysis of 2p ×2q mesh embedding in locally twisted cubes. [Copyright &y& Elsevier]

Firefly algorithm (FA) is an effective optimization technique based on swarm intelligence, which has been successfully applied to various practical engineering problems. In this paper, a new dynamic FA (called NDFA) is proposed for demand estimation of water resources in Nanchang city of China. First, a dynamic parameter strategy is used to avoid manually adjusting the step factor. Second, three estimation models in different forms (linear, exponential and hybrid) are developed in terms of the historical water use and local economic structure. Third, normalization method is utilized to eliminate the influences of different units of data. In the experiments, water use in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 are used for finding the optimal weights of the models, and the rest of data (2013–2015) are applied to test the models. Computational results show that all five FA variants can achieve promising solutions. The proposed NDFA obtains better performance than four other FA variants, and its prediction accuracy is up to 97.91%. Finally, the water demand in Nanchang city from 2017 to 2020 is predicted. [ABSTRACT FROM AUTHOR]

Denoeux proposed an operator called the cautious conjunctive rule (CCR) to combine non-dogmatic belief functions (i.e., the frame is considered a focal element) from reliable dependent sources, which can occur in practice. However, in cases such as uncertain multiple attribute and group decision analyses, belief functions may denote data that vary in importance. This paper extends CCR as a weighted CCR (WCCR) to combine belief functions in consideration of their relative weights in such situations. Properties of WCCR are analyzed, proven, and demonstrated using numerical examples. In particular, the consideration of relative weights enables WCCR to combine dogmatic belief functions. The normalized WCCR is further constructed and used to assess the trustworthiness of a hospital information system employed in many hospitals in Anhui Province, China. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes one calibration method for the agent-based continuous double auction (CDA) stock market by scaling analysis based on the work by Pasquini and Serva (1999) [12]. We design and build an agent-based CDA stock market, which uses the same trading mechanism as the Chinese stock market. We also perform a scaling analysis of the absolute returns in both the artificial and real stock markets. The results show volatility correlations as power laws in all the markets. More importantly, the power-law exponent is not unique, and all such exponents follow a multi-scale behavior. All exponents trend to the theoretical value 0.5 with increasing scaling index γ. Scaling character is an important intrinsic quality of the stock market, and this method can be used in calibrating the agent-based stock market model. [Copyright &y& Elsevier]

Abstract: This paper shows that the Zadeh’s extensions of sendograph-metric-continuous fuzzy-valued functions are sendograph-metric-continuous fuzzy functions. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to develop the filter theory of general residuated lattices. First, we extend some particular types of filters and fuzzy filters in BL-algebras and MTL-algebras naturally to general residuated lattices, and further enumerate some relative results obtained in BL-algebras or MTL-algebras, which still hold in general residuated lattices. Next, we introduce the concepts of regular filters and fuzzy regular filters to general residuated lattices, which are two new types of filters and fuzzy filters, and derive some of their characterizations. Finally, we discuss the relations between (fuzzy) regular filters and several other special (fuzzy) filters, and also characterize some special classes of residuated lattices by filters or fuzzy filters. [Copyright &y& Elsevier]

Abstract: Managerial decision-making processes often involve data of the time nature and need to understand complex temporal associations among events. Extending classical association rule mining approaches in consideration of time in order to obtain temporal information/knowledge is deemed important for decision support, which is nowadays one of the key issues in business intelligence. This paper presents the notion of multi-temporal patterns with four different temporal predicates, namely before, during, equal and overlap, and discusses a number of related properties, based on which a mining algorithm is designed. This enables us to effectively discover multi-temporal patterns in large-scale temporal databases by reducing the database scan in the generation of candidate patterns. The proposed approach is then applied to stock markets, aimed at exploring possible associative movements between the stock markets of Chinese mainland and Hong Kong so as to provide helpful knowledge for investment decisions. [Copyright &y& Elsevier]

Abstract: In this paper, we derive a new class of flexible threshold asymmetric Generalized Autoregression Conditional Heteroskedasticity (GARCH) models. We use this tool for analysis and modeling of the properties that are apparent in many financial time series. In general, the transmission of volatility in the stock market is time-varying, nonlinear, and asymmetric with respect to both positive and negative results. Given this fact, we adopt the method of fuzzy logic systems to modify the threshold values for an asymmetric GARCH model. Our simulations use stock market data from the Taiwan weighted index (Taiwan), the Nikkei 225 index (Japan), and the Hang Seng index (Hong Kong) to illustrate the performance of our proposed method. From the simulation results, we have determined that the forecasting of volatility performance is significantly improved if the leverage effect of clustering is considered along with the use of expert knowledge enabled by the GARCH model. [Copyright &y& Elsevier]