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Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

Nowadays, a prominent research area is the development of competitive advantages in companies, due to their environmental commitment and orientation. Based on resource-based view (RBV) and institutional theory (InT), this paper aims to investigate the influence of internal and external orientation on businesses' sustainable performance while considering the effect of sustainable supply chain management (SSCM) practices.

Data from 351 manufacturing companies in the Kingdom of Saudi Arabia have been collected and analysed through structural equation modelling (SEM) using the partial least squares (PLS) method.

The results indicated that both internal and external environmental orientation have important effects on SSCM practices, which in turn have a considerable beneficial effect on environmental, social and economic performance.

Although SSCM is constantly gaining ground in the literature, most SSCM research and models examine its effects, antecedents or motivation, mainly adopting a qualitative approach. Research on the topic adopting a large-scale empirical approach is still limited. In this context, this study contributes to the SSCM management literature by exploring the role of environmental orientation in facilitating the adoption of SSCM practices and improving companies' performance.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

Several waste bin sanitation initiatives have been introduced in Ghana to address the surge in indiscriminate solid waste disposal in households. What is not known are the behavior factors that determine the acceptability and use of waste bins. This study aimed to identify the determinants of waste bin acceptability and use in Ghana using the theory of planned behavior (TPB).

Data on waste bin acceptability and usage were collected from 881 households in the Volta and Oti regions of Ghana. The data were analyzed using the partial least squares-structural equation modeling technique in SmartPLS 3 software.

The coefficient of determination (R-squared value) of the original TPB and the extended model explained 39.9 and 44.7% of the variance in waste bin acceptability and use intentions, respectively. The results revealed that attitudes (ß = 0.114, t = 3.322, p < 0.001), subjective norms (ß = 0.306, t = 6.979, p < 0.001) and perceived moral obligation (ß = 0.352, t = 8.062, p < 0.001) significantly predicted household waste bin acceptability and use behavior intentions, but perceived behavioral control (ß = −0.003, t = 0.064, p < 0.949) did not influence behavior intentions significantly.

The study provides valuable insights into the behavioral factors to be prioritized by waste management service providers to improve household waste bin acceptability and usage.

This is one of Ghana's first studies investigating the behavioral determinants of waste bin acceptability and usage.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

In 2020, the logistics market in Malaysia was valued at USD 37.60 billion, and it is projected to grow to more than USD 55.0 billion by 2026 at a compound annual growth rate (CAGR) of more than 4%. However, more information is needed about the impact of green logistic practice determinants by the local SMEs on the market share. This study serves as a focal point by examining the factors involved by offering a conceptual framework of determinants and their potential outcomes. This study contributes by demonstrating a conceptual, theoretical framework derived from the synthesis of two theory such as the Resource-Based View theory and the Diffusion of Innovation Theory. At the same time, it offers a holistic approach with an in-depth understanding of the Technological and Organizational factors of SMEs. The relationship between the implementation of green practices and organizational performance is also explored.

Achieving social sustainability has become a critical challenge in global supply chain networks, particularly during complex crises such as terrorism. The purpose of this study is to explore how institutional forces influence the social sustainability approaches of logistics service providers (LSPs) in high terrorism-affected regions (HTAR). This then leads to investigating how the key factors interact with Institutional Theory.

An exploratory multiple-case study research method was used to investigate six cases of different-sized logistics LSPs, each in an HTAR. The data was collected using semistructured interviews and triangulated using on-site observations and document analysis. Thematic analysis was used in iterative cycles for cross-case comparisons and pattern matching.

The findings interact with Institutional Theory and the three final-order themes. First, management processes are driven by coopetition and innovation. Second, organizational resources, structure and culture lead to an ineffective organizational design. Finally, a lack of institutionalization creates institutional uncertainty. These factors are rooted in many other first-order factors such as information sharing, communication, relationship management, capacity development, new process developments, workforce characteristics, technology, microlevel culture and control aspects.

This study answers the call for social sustainability research and enriches the literature on social sustainability, Institutional Theory and LSPs in HTARs by providing illustrations showing that institutional forces act as driving forces for social sustainability initiatives by shaping the current management processes. Conversely, the same forces impede social sustainability initiatives by shaping the current organizational designs and increasing institutional uncertainty.

Kriging surrogate model has demonstrated a powerful ability to be applied to a variety of engineering challenges by emulating time-consuming simulations. However, when it comes to problems with high-dimensional input variables, it may be difficult to obtain a model with high accuracy and efficiency due to the curse of dimensionality. To meet this challenge, an improved high-dimensional Kriging modeling method based on maximal information coefficient (MIC) is developed in this work.

The hyperparameter domain is first derived and the dataset of hyperparameter and likelihood function is collected by Latin Hypercube Sampling. MIC values are innovatively calculated from the dataset and used as prior knowledge for optimizing hyperparameters. Then, an auxiliary parameter is introduced to establish the relationship between MIC values and hyperparameters. Next, the hyperparameters are obtained by transforming the optimized auxiliary parameter. Finally, to further improve the modeling accuracy, a novel local optimization step is performed to discover more suitable hyperparameters.

The proposed method is then applied to five representative mathematical functions with dimensions ranging from 20 to 100 and an engineering case with 30 design variables.

The results show that the proposed high-dimensional Kriging modeling method can obtain more accurate results than the other three methods, and it has an acceptable modeling efficiency. Moreover, the proposed method is also suitable for high-dimensional problems with limited sample points.