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Social responsibility (SR) has become critical in facilitating the sustainability of new infrastructure construction (NIC) and is also a nonnegligible aspect in its management. Although studies attempting to explore this issue from various and disparate perspectives have become increasingly popular, no consensus has yet been reached regarding what SR factors affect NIC management. This paper aims to establish an inventory of SR factors for NIC and reveal a comprehensive framework for SR of NIC (NIC-SR) management through an in-depth analysis of the relationships among factors.

This article proposes a mixed-review method that combines the preferred reporting items for systematic reviews and meta-analyses and content analysis methods as a solution.

From 62 chosen publications on NIC-SR published in peer-reviewed journals between 2010 and 2022, a total of 44 SR factors were found. These 44 SR factors were divided into 4 interconnected categories: political, ethics-environmental, legal and economic. Based on the interactions among SR factors and incorporating the impact of the four categories of SR factors on NIC management, an integrated framework from micro to macro was developed.

This paper educates researchers and practitioners about the SR factors that must be considered to improve the sustainability of NIC management and provides practical implications for architectural, engineering and construction (AEC) practices. Furthermore, it serves as an impetus for governments to improve their programs and policies and fulfill social responsibilities.

Our study aims to focus on the application of knowledge mapping to provide pedagogically-structured learners' competences.

We conducted an experiment examined the associations between the pedagogical quality of students’ pedagogically-informed knowledge (PIK) maps, class assignment scores and perceptions of PIK mapping’s uses.

The results showed that higher assignment scores were significantly predicted by higher quality PIK maps, ratings for PIK mapping were significantly higher than other mappings, and the learners’ experience of PIK mapping led to a significant change of attitude towards mapping as a learning activity and to a positive opinion of the value of PIK mapping in particular. Interestingly, there was no significant relation between learners’ opinion ratings of the uses of PIK mapping in learning and their assignment scores.

Questions remain on the generalizability of the findings, and on the features of a PIK map which are particularly useful to a learner. This study investigated the value of PIK mapping in the context of a practical class on the building of simple DIY (do-it-yourself) holographic projectors; it may be thought that the applied nature of the topic was more suited to the PIK mapping of learner competences and intended learning outcomes than a more theoretic classroom topic on holography. A future study is planned to address this issue.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

This study aims to study the moderating effects of private ownership and government support on the relationship between outsourcing and product innovation in entrepreneurial ventures in a transition economy.

The data of 10,296 Vietnamese entrepreneurial ventures from the four rounds of the survey conducted by the General Statistics Office (GSO) of Vietnam to investigate the moderating effects of private ownership and government support on the association between outsourcing and entrepreneurial ventures’ product innovation performance. The Probit regression model is employed to estimate such associations.

Our research uncovered that the impact of outsourcing on the likelihood of product innovation is more significant for entrepreneurial operations characterized by a substantial degree of private ownership and government backing as opposed to those without.

The results of our research indicated that the resource-based perspective and extended resource-based view (ERBV) are essential in examining the impact of gaining resources or skills from external sources on the growth of entrepreneurial enterprises. These ideas have significance and importance not just in industrialized economies but also in countries undergoing transition. Our findings suggest that entrepreneurial enterprises should have the ability to manage a wide range of resources and make decisions about which activities should be handled internally and which should be delegated to other parties.

Our findings also imply that entrepreneurial ventures should be able to control many resources and choose which tasks should be performed in-house and which should be outsourced to third parties.

By adopting and leveraging the resource-based view (RBV) and extended resource-based views (ERBV), our study developed a theoretical model about private ownership and government support for moderate outsourcing’s impact on entrepreneurial innovation in a transition economy.

Our paper studies a central issue with a long history in economics: the relationship between population and economic growth. We analyze the joint dynamics of economic and demographic growth in 111 countries during the period 1960–2019.

Using the concept of economic regime, the paper introduces the notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree (MST) and a hierarchical tree (HT) are constructed to detect groups of countries sharing similar dynamic performance.

The methodology confirms the existence of three country clubs, each of which exhibits a different dynamic behavior pattern. The analysis also shows that the clusters clearly differ with respect to the evolution of other fundamental variables not previously considered [gross domestic product (GDP) per capita, human capital and life expectancy, among others].

Our results indirectly suggest the existence of dynamic interdependence in the trajectories of economic growth and population change between countries. It also provides evidence against single-model approaches to explain the interdependence between demographic change and economic growth.

We introduce a methodology that allows for a model-free topological and hierarchical description of the interplay between economic growth and population.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

This study aims to describe the m-learning experience of school students and teachers during the COVID-19 pandemic and explores the factors influencing the continuance intention of m-learning.

Semistructured interviews of 24 students and 09 teachers of schools in national capital territory (NCT) Delhi, India were conducted over 03 months and transcribed verbatim. A hermeneutic phenomenological design was used to interpret the text and bring out the “lived experiences” of m-learning.

The following 15 themes or factors influencing continuance intention emerged through the hermeneutic circle: (1) actual usage, (2) attitude, (3) context, (4) extrinsic motivation, (5) facilitating conditions, (6) intrinsic motivation, (7) perceived compatibility, (8) perceived content quality, (9) perceived mobile app quality, (10) perceived teaching quality, (11) perceived usefulness, (12) satisfaction, (13) self-efficacy, (14) self-management of learning and (15) social influence.

The study offers insightful recommendations for school administrators, mobile device developers and app designers. In addition, suggestions for effectively using m-learning during disasters such as COVID-19 have been provided. Several future research directions, including a nuanced understanding of m-assessment and online discussions, are suggested to enhance the literature on m-learning continuance.

The study enriches the literature on m-learning continuance. A qualitative approach has been used to identify relevant factors influencing m-learning continuance intention among secondary and higher secondary level (Grades 9 to 12) school students and teachers in India. In addition, a conceptual framework of the relationships among the factors has been proposed. Further, an analysis of the lived experiences of m-learning during the COVID-19 pandemic indicated several issues and challenges in using m-learning during disasters.

Employment exchanges have been playing a significant role in the worldwide labor market for more than a hundred years now. In India, since 1946, millions of aspiring Indian youths have registered themselves with the government-run employment exchanges to find a job. About four million job seekers got registered at 1,000 employment exchanges in India, it is important to analyze the placement statistics of these exchanges. In recent years, new methods of job search have evolved. This study examines whether employment exchanges are effective in the changed scenario?

Using state-level employment exchange data for the period 2010–2011 to 2019–2020, this study analyzes the determinants of job placement at employment exchanges in India. A critical analysis of the functioning of employment exchanges along with recommendations to improve the job search ecosystem in India is also presented in the study.

This study found that increased share of service sector in the state economy negatively impacts placement at employment exchanges.

The absence of focus on the service sector requires policy intervention if Indian employment exchanges are to remain relavant.

The government administration should rethink that ignoring service sector employment potential is unaffordable for an emerging economy and employment exchanges should be aligned accordingly.

About 30 million people are unemployed in India. If employment exchanges are transformed, it can have far-reaching socio-economic advantages.

This study is the first sub-country level study on the institution of employment exchanges. This study comprehensively maps the landscape of career services in India. Empirically establishing the impact of sectoral structure of economy on efficacy of employment exchanges, and makes the case for policy intervention that is needed to keep the employment exchanges relevant in India.

The focus of this paper is to provide an assessment of the impact of imports from China on Indian manufacturing and capture the multifarious dimensions of India–China bilateral trade flows. By examining the comparative disadvantage imports (RCA<1), the paper critically examines their significance on India's industry output and performance and underlines factors beyond trade competitiveness.

For examining the impact of India's manufacturing imports from China on industry performance, four stages of analysis is adopted. First, the imports with RCA <1 have been identified. For these, BRCA was also computed. Second, trends in industry performance associated with high imports from China. Third, for estimating the impact of imports on industry output, augmented production function was specified and estimated with imports from China as a potential determinant. And fourth, comparison of industry performance between India and China.

The impact of imports from China on industry output is positive and significant. A 1% increase/decrease in the share of China in world imports will result in output increasing by 0.31%. The rise in imports from China seems to be on account of non-availability of necessary intermediate and capital goods domestically, thereby making these imports critical and complementary for production. This negates the threat perception of imports from China.

The paper recognizes the need for understanding the firm heterogeneity in import decisions and R&D intensity of imports. Across industries, the drivers for firms' decisions to import are “learning by importing’ and “self-selection” (Camino-Magro et al., 2020). Also, another important dimension at the firm-level analysis is the elasticity of substitution between foreign and domestic inputs. If the elasticity of substitution is low then high import barriers will lead to reduction of domestic output. These firm-level issues are important for effective policy interventions.

One, the inward looking focus of the industry which is exhibited in low export intensity will not provide the necessary impetus to propel the manufacturing sector to a higher technology frontier and translate the productivity gains to export competitiveness. Two, unless the domestic manufacturing is propelled from the current low/medium technology to high technology products, the current policy thrust on “self-reliance” cannot be realized.

Analysis is based on manufacturing imports with RCA<1 from China thereby underlining factors beyond trade competitiveness not covered by RCA methodology. Complementing the quantitative analysis with economic policy developments in China and India and contrasting the same has provided insights into the real factors determining India–China bilateral trade.