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The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

Owing to limit in transportation and production, the connection of FRP rebars is an unavoidable problem. A coaxial joining scheme for FRP rebars using winding wet fabrics and fiber bundle/belt composite is presented in this paper. The experimental results indicate that the connection method is effective. And it has the following characteristics: small size, light weight, high strength, corrosion resistance, easy to construction etc. Facing the development of the engineering application in the future, some works need further research are putting forward.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

The paper investigates whether stock liquidity of firms is valued by lending banks revealing that firms with higher liquidity in the capital market pay lower spreads for the loans they obtain. This relationship is causal as evidenced by using the decimalization of tick size as an exogenous shock-to-stock liquidity in a difference-in-differences setting. Reduction in financial constraint and improvement in corporate governance induced by higher stock liquidity are potential mechanisms through which liquidity impacts loan spreads. These higher liquidity firms also receive less stringent nonprice loan terms, for example, longer loan maturity and less required collateral.

The purpose of this paper is to review the available reduced order modeling approaches in the literature for predicting the flow and specially temperature fields inside data centers in terms of the involved design parameters.

This paper begins with a motivation for flow/thermal modeling needs for designing an energy‐efficient thermal management system in data centers. Recent studies on air velocity and temperature field simulations in data centers through computational fluid dynamics/heat transfer (CFD/HT) are reviewed. Meta‐modeling and reduced order modeling are tools to generate accurate and rapid surrogate models for a complex system. These tools, with a focus on low‐dimensional models of turbulent flows are reviewed. Reduced order modeling techniques based on turbulent coherent structures identification, in particular the proper orthogonal decomposition (POD) are explained and reviewed in more details. Then, the available approaches for rapid thermal modeling of data centers are reviewed. Finally, recent studies on generating POD‐based reduced order thermal models of data centers are reviewed and representative results are presented and compared for a case study.

It is concluded that low‐dimensional models are needed in order to predict the multi‐parameter dependent thermal behavior of data centers accurately and rapidly for design and control purposes. POD‐based techniques have shown great approximation for multi‐parameter thermal modeling of data centers. It is believed that wavelet‐based techniques due to the their ability to separate between coherent and incoherent structures – something that POD cannot do – can be considered as new promising tools for reduced order thermal modeling of complex electronic systems such as data centers

The paper reviews different numerical methods and provides the reader with some insight for reduced order thermal modeling of complex convective systems such as data centers.

Due to ginger holds a special and indispensable place in Chinese cuisine, understanding consumers’ preferences for organic ginger is of significance, especially given the growing interest in organic food products and sustainable agriculture. This study thus examines Chinese consumers’ preference for fresh ginger and the sources of their preferences heterogeneity for organic ginger consumption.

The study is using choice experiment (CE) method and mixed logit (MXL) modeling with 1,312 valid samples. The participants are regular consumers who are 18 years old or above and had bought fresh ginger within the past 12 months.

The results show that consumers prefer organic product certification labeling ginger to conventional ginger, preferred to purchase ginger at wet markets to at supermarkets or online, and preferred either ginger with regional public brand or private brand to unbranded ginger. Results also indicate that age, education level, income, purchasing experience of organic and branded ginger, and cognition of ginger health benefits are the sources of heterogeneity in consumer preferences for organic ginger.

This study contributes to ginger growers, marketers and policy makers. This study tracks how consumers' preferences change under different attribute combinations, capture the complex preference structure of consumers, and help reveal the motivations behind consumers' preferences for organic ginger. These findings will be crucial for developing marketing strategies, promoting organic products, and meeting consumer needs.

Lebanese meals rich in vitamin C are taken for granted to contain this vitamin without consideration of its losses during the cooking and storing processes. This paper aims to examine the impact of different cooking pots, refrigeration and conventional reheating or via microwaving (MWR) on vitamin C depletion.

Two samples of three meals rich in vitamin C (AB: Aadas Bhamoud made of lentils and Swiss chard; CS: cauliflower stew; ML: Meloukhieh made of Jew's mallow) were analyzed in triplicates when they were raw, cooked in double based stainless steel (DBSS) or pressure cookers (PCs), refrigerated at 4 ^○C for 48 h, and when reheated in an open pot or in a microwave reaching 70 ^○C. The titration with 2,6‐dichlorophenolindophenol method was used for vitamin C analysis.

Relative vitamin C losses throughout the processing stages were 37.64, 65.43 and 79.00 percent for ML, CS and AB, respectively. DBSS tended to deplete vitamin C less than PC. AB lost 34.4 and 49.2 percent vitamin C with DBSS and PC, respectively; CS lost 52.3 and 57.5 percent with DBSS and PC, respectively; and ML lost 16.3 and 27.4 percent with DBSS and PC, respectively. Vitamin C loss at refrigeration was significant for both cooking pots used for the meals AB and ML but not for CS. Reheating resulted in further significant losses across meals and reheating methods.

The study highlights the importance of avoiding unnecessary cooking practices to minimize vitamin C depletion and more accurately estimating its daily intake.

The study presents for the first time the quantification of vitamin C losses in Lebanese meals subjected to different processing types and stages.

The purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes.

To improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal.

The experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method.

The proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages.

A K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

This paper aims to classify the inductorless Chua’s circuits into two types from the topological structures and construct a chaotic circuit under this new classification framework.

In this paper, two types of inductorless Chua’s circuit models are presented from topological structure, among which the first type of inductorless Chua’s circuit (FTICC) model is much closer to the original Chua’s circuit. Under this classification framework, a new inductorless Chua’s circuit that belongs to the FTICC model is built by replacing LC parallel resonance of the original Chua’s circuit with a second order Sallen–Key band pass filter.

Compared with a paradigm of a reported inductorless Chua’s circuit that belongs to the second type of inductorless Chua’s circuit (STICC) model, the newly proposed circuit can present the attractors which are much more closely to the original Chua’s attractors. The dynamical behaviors of coexisting period-doubling bifurcation patterns and boundary crisis are discovered in the newly proposed circuit from both numerical simulations and experimental measurements. Moreover, a crisis scenario is observed that unmixed pairs of symmetric coexisting limit cycles with period-3 traverse through the entire parameter interval between coexisting single-scroll chaotic attractors and double-scroll chaotic attractor.

The newly constructed circuit enriches the family of inductorless Chua’s circuits, and its simple topology with small printed circuit board size facilitates the various types of engineering applications based on chaos.