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It is increasingly important to study labour market outcomes for people who are not in employment, education, or training (NEET). Where most studies focus solely on young people, the purpose of this paper is to include both younger and older NEETs to find out if there is any long-term scarring effect, and if the effect is different between these two groups.

This study uses a twin-based estimation method for the first time to measure the long-term effect of economic inactivity on income. The analysis is based on biological twins, in order to control for individuals’ unobservable heterogeneity. It is assumed that twins are similar to each other and the only unobservable heterogeneity is at the family level. Register-based data from Statistics Sweden is used.

The result indicates a significant negative income effect for those who have been in NEET, and is larger for those who have been in NEET for several consecutive periods of time. Individuals who were in NEET during 2001-2003 had on average 62 per cent lower income compared with their twin in 2011. The corresponding number for individuals who were in NEET for just one period was 33 per cent. Hence, time in NEET reduces income. The results show that the long-term scarring effect is not affected by age.

This study utilises for the first time a twin-based estimation method to measure the long-term effect of inactivity. Most studies focus solely on young people, but the authors also include an older group of people.

Economists have been reluctant to interpret as purely causal the relationship between educational attainment and earnings. In an influential paper in which they use quarter of birth as an instrument for educational attainment in wage equations, Angrist and Krueger interpret their estimates as the causal impact of education on earnings. To support this interpretation, they argue that compulsory school attendance laws alone account for the association between quarter of birth and earnings. In this work we present new evidence suggesting that this interpretation may not be well-founded. We document an association between quarter of birth and earnings in cohorts that were not bound by compulsory school attendance laws. Moreover, we find that the association between quarter of birth and educational attainment was weaker in more recently-born cohorts while no similar pattern existed in the association between quarter of birth and earnings. Our results call into question the validity of any causal inferences based on Angrist and Krueger's estimates regarding the effect of education on earnings.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.

The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).

IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.

The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.

DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.

The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

The physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica.

The viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance.

Results show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost.

Suggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment.

This study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

This paper investigates the effect of household size, and in particular of the number of children of different age groups, on poverty, defined as being in a situation of low income. We apply various static and dynamic probit models to control for the endogeneity of the variables of interest and to account for unobserved heterogeneity, state dependence, and serially correlated error components. Using Luxembourg longitudinal data, we show that the number of children of different age groups significantly affects the probability of being poor. However, the magnitude of the effect varies across different specifications. In addition, we find strong evidence of true poverty persistency due to past experience, spurious poverty persistency due to individual heterogeneity, and transitory random shocks.

This paper presents a systematic review of the literature in the domain of maritime disruption management, upon which future research framework and agenda are proposed. Two review questions, i.e. the measures that are employed to manage disruptions and how these contribute to resilience performance, were pursued.

The systematic literature review procedure was strictly followed, including identification and planning, execution, selection and synthesis and analysis. A review protocol was developed, including scope, databases and criteria guiding the review. Following this, 47 articles were eventually extracted for the systematic review to identify themes for not only addressing the review questions but also highlighting future research opportunities.

It was found that earlier studies mainly focused on measures, which are designed using mathematical models, management frameworks and other technical support systems, to analyse and evaluate risks, and their impacts on maritime players at the levels of organisation, transport system and region in which the organisation is embedded. There is, however, a lack of research that empirically examines how these measures would contribute to enhancing the resilience performance of maritime firms and their organisational performance as a whole. Subsequently, a Digitally Embedded and Technically Support Maritime Disruption Management (DEST-MDM) model is proposed.

This review is constrained by studies recorded by the Web of Science only. Nevertheless, the proposed research model would expectedly contribute to enhancing knowledge building in the specific domain of maritime disruption management and supply chain management overall while providing meaningful managerial implications to policymakers and managers in the maritime industry.

This research is perhaps one of the first studies which presents a systematic review of literature in maritime disruption management and proposes a future research framework that establishes the link between disruption management and resilience and organisational performance for empirical validation.

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection.

The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed.

Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type.

The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.