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The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

To compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.

Twenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.

The onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.

Muscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.

While postdoctoral research (postdoc) training is a common step toward academic careers in science, technology, engineering and mathematics (STEM) fields, the role of postdoc training in social sciences is less clear. An increasing number of social science PhDs are pursuing postdocs. This paper aims to identify factors associated with participation in postdoc training and examines the relationship between postdoc training and subsequent career outcomes, including attainment of tenure-track faculty positions and early career salaries.

Using data from the National Science Foundation Survey of Earned Doctorates and Survey of Doctorate Recipients, this study applies propensity score matching, regression and decomposition analyses to identify the role of postdoc training on the employment outcomes of PhDs in the social science and STEM fields.

Results from the regression analyses indicate that participation in postdoc training is associated with greater PhD research experience, higher departmental research ranking and departmental job placement norms. When the postdocs and non-postdocs groups are balanced on observable characteristics, postdoc training is associated with a higher likelihood of attaining tenure-track faculty positions 7 to 9 years after PhD completion. The salaries of social science tenure-track faculty with postdoc experience eventually surpass the salaries of non-postdoc PhDs, primarily via placement at institutions that offer relatively higher salaries. This pattern, however, does not apply to STEM PhDs.

This study leverages comprehensive, nationally representative data to investigate the role of postdoc training in the career outcomes of social sciences PhDs, in comparison to STEM PhDs. Research findings suggest that for social sciences PhDs interested in academic careers, postdoc training can contribute to the attainment of tenure-track faculty positions and toward earning relatively higher salaries over time. Research findings provide prospective and current PhDs with information helpful in career planning and decision-making. Academic institutions, administrators, faculty and stakeholders can apply these research findings toward developing programs and interventions to provide doctoral students with career guidance and greater career transparency.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

This chapter discusses female managers’ and entrepreneurs’ views on lifelong learning. The main empirical data were drawn from interviews with 67 women participating in training and coaching programmes in South Savo, Finland, in 2017–2021. Many of the women belonged to the working sandwich generation (WSG). The particular focus was on how lifelong learning relates to these women’s careers, wellbeing at work, work–life balance and search for meaningful lives. A model integrating women’s earning, learning and meaning aspects of work and life was developed. The findings of the study show that considering women’s fragmented work careers, lifelong learning is often crucial for them. For an individual, opportunities for lifelong learning and meaningful work assure personal development, wellbeing at work and a sustainable career. For employing organisations, offering opportunities for learning and meaningful work for their employees constitutes a competitive advantage.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Aging population is on the rise around the world. Strategies to improve quality of life in this population are being implemented. Exercise is one of those strategies that has been proven to be effective as it produces many health benefits. The purpose of this paper is to determine the effects of Khon exercise on functional fitness in older persons.

In total, 44 older people aged 60–65 years were recruited through a senior club in an urban area. They were divided into two groups: the Khon exercise group (performed exercise for 12 weeks, 60 min/day, 3 times/week) and the control group (engaged in routine physical activity). The Senior Fitness Test, which consisted of chair stand, arm curl, 2-min step, chair sit and reach, back scratch, 8-ft up and go, and body mass index, was performed before and at 12 weeks after the exercise.

After 12 weeks of training, significant differences in chair stand, 2-min step, chair sit and reach, and 8-ft up and go tests were noted between the exercise and control groups.

These findings showed that Khon exercise has positive effects on lower body strength and flexibility, aerobic endurance and balance. Hence, it is recommended for health promotion among older persons.

Rooting in the literature on training and laying on Kirkpatrick model, this paper aims to explore key drivers of corporate training to identify how they can be combined into an integrated framework of learning for human capital development.

By adopting the constructivist grounded theory, this contribution analyzes the experience carried out in the last ten years by Virvelle, an Italian corporate training firm.

Results show the rise of five core categories, g1iving rise to an integrated model of Kirkpatrick. Their dynamic interplay led to a new orientation of Kirkpatrick model giving rise to a metalearning ecosystem.

Managerial implications have identified key factors on which building and implementing appropriate corporate training programmes capable of triggering co-generative processes of value creation. Particularly, the essential role of learning quality culture, digital technology and personalization are detected in integrating not only hard but furthermore soft shades of learning. Concerning theoretical implications, the emergence of key structural and systems enabling dimensions for learning, and contextual mechanisms involved in reshaping training effectiveness and achieving integrated learning outcomes are detected. The main limitation of this study lies in the need to generalize results: the conceptualized framework needs to be empirically tested.

The value of this research is built along three main points. The first is the integration among the core categories that an integrated learning system can be built on, promoting learning quality culture through positive feedback loops. The second is represented by the chance to enhance an integrated mutual knowledge development among engaged actors, thereby shaping a more holistic and multidimensional learning model. The third is related to the transversal role that digital technology plays in all phases of the training process as it integrates and enriches them.

This paper aims to examine the influence of big data (BD) on human resource management (HRM). It defines how these data can be a useful tool in the decision-making process of companies’ human resources to obtain a sustainable competitive advantage.

This paper emphasizes the need to develop a holistic approach to emphasize these relations. Starting from these observations, the document proposes empirical research employing Eurostat data to test the benefits of BD in HRM decisions that optimize the relationship between training, productivity, and well-being.

The findings estimate HRM decisions and their impact in a broader macroeconomic and microeconomic perspective.

BD research is emerging as a crucial discipline in human resources. To overcome this problem, the paper develops an analysis of the literature on cleaner production and sustainability context; it creates a conceptual framework to clarify whether the existing studies consider the growing intensity of BD on human resources.