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This research explores the “transformation” ideas of Japanese Lesson Study (LS) and Open Approach (OA) to create and sustain a Thailand LS incorporated OA (TLSOA) model to successfully adapt to the local contexts. Although LS is spreading globally, previous studies have identified several challenges to its implementation.

The researcher employed a longitudinal research design that involved repeated investigations of a group of participants: from their fourth year as bachelor's degree students until they became eligible coordinators to practice the TLSOA model for teachers' professional development (PD). Data were collected using reflective journals, two types of survey questionnaires, and records of periodical reflective meetings over three cohorts.

As results reveal, the participating teachers' active engagement in the TLSOA model has made a positive impact on their teaching practices, collegiality, and professional self-identification. Students perceived themselves as having enormous changes in their learning behaviors. Those changes are linked to establishing a positive, student-centered, and active learning-based school culture with teachers' beliefs for innovations.

Further studies should focus on the possible conflicts emerging between the different cultures of teaching.

The idea of the TLSOA model is to ensure teachers are well trained to possess sufficient skills.

The findings could be of value for the leaders, educators, policymakers to advocate the TLSOA model as a systematic approach to whole-school improvement and as a channel for spreading effects at the national, the APEC, and the CLMV regional levels.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model.

This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy.

The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market.

The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner.

Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.

This study aims to computational numerical simulations to clarify and explore the influences of periodic cellular lattice (PCL) morphological parameters – such as lattice structure topology (simple cubic, body-centered cubic, z-reinforced body-centered cubic [BCCZ], face-centered cubic and z-reinforced face-centered cubic [FCCZ] lattice structures) and porosity value ( ) – on the thermal-hydraulic characteristics of the novel trussed fin-and-elliptical tube heat exchanger (FETHX), which has led to a deeper understanding of the superior heat transfer enhancement ability of the PCL structure.

A three-dimensional computational fluid dynamics (CFD) model is proposed in this paper to provide better understanding of the fluid flow and heat transfer behavior of the PCL structures in the trussed FETHXs associated with different structure topologies and high-porosities. The flow governing equations of the trussed FETHX are solved by the CFD software ANSYS CFX® and use the Menter SST turbulence model to accurately predict flow characteristics in the fluid flow region.

The thermal-hydraulic performance benchmarks analysis – such as field synergy performance and performance evaluation criteria – conducted during this research successfully identified demonstrates that if the high porosity of all PCL structures decrease to 92%, the best thermal-hydraulic performance is provided. Overall, according to the obtained outcomes, the trussed FETHX with the advantages of using BCCZ lattice structure at 92% porosity presents good thermal-hydraulic performance enhancement among all the investigated PCL structures.

To the best of the authors’ knowledge, this paper is one of the first in the literature that provides thorough thermal-hydraulic characteristics of a novel trussed FETHX with high-porosity PCL structures.

Cyberattacks are becoming increasingly widespread, and cybersecurity is therefore increasingly important. Although the technological aspects of cybersecurity are its best-known characteristics, the cybersecurity phenomenon goes beyond the detection of technological impacts, and encompasses all the dimensions of an organization. This study thus focusses on an additional set of organizational elements. The key elements of cybersecurity organizational readiness depicted here are cybersecurity awareness, cybersecurity culture and cybersecurity organizational resilience (OR). This study aims to qualitatively assess small and medium enterprises’ (SMEs) overall level of organizational cybersecurity readiness.

This study focused on conducting a cybersecurity organizational readiness assessment using a sample of 53 Italian SMEs from the information and communication technology sector. Informed mixed method research, this study was conducted consistent with the principles of the explanatory sequential mixed method design, and adopting a quanti-qualitative methodology. The quantitative data were collected through a questionnaire. Qualitative data were subsequently collected through semi-structured interviews.

Although many elements of the technical aspects of cybersecurity OR have yielded very encouraging results, there are still some areas that require improvement. These include those facets that constitute the foundation of cybersecurity awareness, and, thus, a cybersecurity culture. This result highlights that the areas in need of improvement are exactly those that are most important in fighting against cyber threats via organizational cybersecurity readiness.

Although the importance of SMEs is obvious, evidence of such organizations’ attitudes to cybersecurity are still limited. This research is an attempt to depict the organizational issue related to cybersecurity, i.e. overall cybersecurity organizational readiness.

The research analyzes the influence of three environmental triggers, i.e. awareness, concern and knowledge on environmental attachment and green motivation that affect tourists' pro-environmental behavior in the Pakistan’s tourism industry. Furthermore, this study has analyzed the moderating role of moral obligation concerning environmental attachment and green motivation on tourists' pro-environmental behavior.

Data were gathered via a structured questionnaire by 237 local (domestic) tourists of Pakistan. Furthermore, the data were examined by employing SmartPLS.

Findings demonstrate that all three environmental triggers have a positive and significant relationship with environmental attachment and green motivation. Accordingly, environmental attachment and green motivation promote tourists' pro-environmental behavior. Furthermore, the moderating role of moral obligations has also been incorporated in the study. The finding reveals a strong and positive relationship among environmental attachment and tourists' pro-environmental behaviors during high moral obligations. In contrast, moral obligations do not moderate association between green motivation and tourists' pro-environmental behavior. Therefore, competent authorities should facilitate tourists to adopt environmentally friendly practices; which will ultimately promote pro-environmental behavior.

This study provides useful insights regarding the role of tourism in fostering environmental attachment and green motivation that sequentially influence tourist pro-environmental behavior. Secondly, this research has employed moral obligations as a moderator to identify the changes in tourists’ pro-environmental behavior based on individuals' ethical considerations. Hence, the study provides an in-depth insight into tourists' behavior. Lastly, the present research offers effective strategies for the tourism sector and other competent authorities to increase green activities that can embed the importance of the environment among individuals.

Over the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care infrastructure advancements, and superiority in biomedical sciences and technology to predict potential infection rates should a health pandemic occur. One such commonly relied-upon indicator was that of the Global Health Security (GHS) Index. However, the coronavirus disease 2019 (COVID-19) pandemic has shown how such variables prove to be inaccurate in predicting the infection rates during a global health pandemic. Hence, this paper proposes the utilization of socio-cultural behavioral traits to predict a country's COVID-19 infection rates.

This is achieved by proposing a model involving the classification and regression tree (CART) algorithm and a Poisson regression against the six selected cultural behavioral predictors consisting of individualism, power distance, masculinity, uncertainty avoidance, long-term orientation, and indulgence.

The results show that all the selected cultural behavioral predictors are significant in impacting COVID-19 infection rates. Furthermore, the model outperforms the conventional GHS Index model based on a means squared error comparison.

The authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases.

The authors hope that their works could prove beneficial to public office holders, as well as health experts working in health facilities, in better predicting potential outcomes during a health pandemic, thus allowing them to plan and allocate resources efficiently.

The results are a testament to the fact that sociocultural behavioral traits are more reliant predictors in modeling cross-national infection rates of global health pandemics, like that of COVID-19, as compared to economic-centric indicators.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

This study examines the relationship of positive career shocks and career optimism. The mediating role of career decision-making self-efficacy (CDSE) between positive career shocks and career optimism and the moderating role of consideration of future consequences – immediate (CFC-I) between CDSE and career optimism are checked.

Through cluster sampling, cross-sectional data from 192 professionals of electronic media industry were collected via an electronically administered questionnaire. For preliminary descriptive data analysis, SPSS version 21 was used. SmartPLS version 3.0 was used for testing the proposed hypotheses.

The results showed that positive career shocks have a relationship with career optimism via CDSE. Also, CFC-I moderated the relationship of CDSE and career optimism such that the relationship of CDSE and career optimism was stronger at higher level of CFC-I.

The study provides implications for the career consultants, human resource professionals and senior management of organizations. All these stakeholders can strive to build an inventory of positive career shocks. Also, shifting to a surprised business model of announcing compensations and promotions is another area to work on. The results of this study further suggest disengaging the fresh potential employees in the initial processes of recruitment. Interdepartmental coordination of health and safety department and human resource management department is also a very important implication for this study to highlight the positive aspects of being optimistic.

The study is among the few empirical studies which investigates the relationship between positive career shocks and career optimism via CDSE. Also, in light of the latest call of various empirical works in the domain, this study adds a moderating variable, i.e. CFC-I in predicting career optimism. Furthermore, contrary to the conventional approach of applying students' data on career models, this study tests the proposed career model on data collected from professionals.

This paper aims to identify variables that determine the differing levels of environmental quality on Java and other islands in Indonesia.

Using a quantitative approach, secondary data were sourced from the Central Statistics Agency and the Ministry of Environment and Forestry. The data were obtained through the collection of documentation from 33 provinces in Indonesia. The analytical approach used was discriminant analysis. The research variables are Trade Openness, Foreign Direct Investment (FDI), industry, HDI and population growth.

The variables that distinguish between the levels of environmental quality in Indonesian provinces on the island of Java and on other islands are Industry, HDI, FDI and population growth. The openness variable is not a differentiating variable for environmental quality. The most powerful variable as a differentiator of environmental quality on Java Island and on other islands is the Industry variable.

This study has not classified the quality of the environment based on the Ministry of Environment and Forestry's categories, namely, the very good, good, quite good, poor, very poor and dangerous. For this reason, further research is needed using multiple discriminant analysis (MDA).

Industry is the variable that most strongly distinguishes between levels of environmental quality on Java and other island, while the industrial sector is the largest contributor to gross regional domestic product (GDRP). Government policy to develop green technology is mandatory so that there is no trade-off between industry and environmental quality.

This study is able to identify the differentiating variables of environmental quality in two different groups, on Java and on the other islands of the Indonesian archipelago.