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The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

This study investigates the media activities of firms issuing private equity placements and seasoned equity offerings in Taiwan, as firms have incentives to manage media coverage to influence their stock prices during private equity placement.

We collect a corpus of news stories and transform the news into term sets based on the part of speech. Then, we refer to Cecchini et al. (2010) to classify the news terms into positive, negative, and usual categories. Next, we employ the SVM algorithm to perform the classification tasks and the term frequency method to perform the text mining task. In last, we use a multiple regression model to verify the hypotheses.

We determine that issuing firms in a private placement have substantially more positive news stories and fewer negative news stories than those in public offerings. Furthermore, we evidence that the media management effects of postequity issues are more active than those of preequity issues. Finally, our results demonstrate that the timing and content of financial media coverage among different equity issuance methods may be biased by firm management. According to previous studies, they may attempt to manipulate stock prices to increase the number of highly profitable insider stakeholders.

To our knowledge, this is the first study to investigate that if private placement will associate with more active media management than the public offerings. According to our results of the difference-in-means test, the public offerings market may control news coverage; however, this result is inconsistent with that of the regression results. The private placements market may also exercise media management in the “before announcement day” and “after announcement day” periods by increasing positive news and reducing negative news.

Many scholars and practitioners in the fields of business and management have recently published theoretical and empirical studies on the subject of business culture and its impact on its growth and effectiveness; yet, there has been a dearth of research on the topic of how organizational culture affects productivity. Moreover, there are hardly any theoretical or empirical research that examines these two concepts within the context of a micro-enterprise. This chapter uses a sample of 279 Ancash Region microenterprises to investigate the effect of local entrepreneurial culture on businesses’ overall performance. Among the four types of entrepreneurial cultures studied, only the Hierarchical culture was shown to have no effect on the degree of performance of the microenterprises. There is evidence of a significant causal relationship between the variables studied, the coefficient of determination was; business performance (r² = 0.796), with an SRMR of 0.037, the confirmatory model is relevant within its range of accuracy, while market culture has the greatest impact on business performance.

This paper aims to develop a model that supports public organisations in making informed strategic decisions as to which public services are most suitable to be improved through co-creation. Thus, it first identifies the features that make public services (un)suitable for co-creation and then applies this knowledge to develop a multi-criteria decision support model for the assessment of their co-creation readiness.

The decision support model is the result of design science research. While its structure is determined by a qualitative multi-criteria decision analysis, its substance builds on a content analysis of Web of Science papers and over a dozen empirical case studies.

The model is comprised of 13 criteria clustered into two groups: service readiness criteria from the perspective of service users and service readiness criteria from the perspective of a public organisation.

The model attributes rely on a limited number of empirical cases and references from the literature review. The model was tested by only one public organisation on four of its services.

The paper shifts the research focus from organisational properties and capacity, as the key co-creation drivers and barriers, to features of public services as additional factors that affect the prospect of co-creation. Thus, it makes a pioneering step towards the conceptualisation of the idea of “service readiness for co-creation” and the development of a practical instrument that supports co-creation in the public sector.

Science and technology are transforming our world in ways that have not been seen in a long time, and we live in a rapidly changing world. Despite these changes, as citizens of today, we must not lose sight of the reality that these changes even cause crises that must be managed in order to place ourselves in a true working environment that allows us to survive as employees despite these changes. The main objective of this research is to find the relationship between interpersonal competences and teacher performance in a sustainable university. The methodology used was the quantitative, nonexperimental approach, as the variables will not be deliberately manipulated. In order to verify whether or not there was a relationship between these variables, 84 teachers from the Universidad Nacional Santiago Antunez de Mayolo were surveyed to evaluate the variables under study. The results obtained show a direct and significant relationship (Spearman's Rho = 0.731) between the two variables. Through this research, it was possible to determine that teachers who have developed interpersonal competences have a better performance, while in the relationships between interpersonal competences and the dimensions of teacher performance, a positive correlation was obtained.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.