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Smart manufacturing, as a buzzword in recent years, is increasingly attracting attention in academia and industry. However, it is challenging for most manufacturing enterprises as they are uncertain about the ROI of smart manufacturing, and they are currently confronting many difficulties and hurdles. In previous studies, most researchers tended to emphasize larger companies while small and medium enterprises (SMEs) received limited attention. To comprehensively understand the barriers and challenges of smart manufacturing adoption faced by manufacturing companies especially SMEs, the author of this paper conducted a systematic review to identify more than 80 barriers and challenges which are classified into five categories: nature of SMEs, technology, organization, environment, and financial. The top factors include high investment, lack of skilled workforce, lack of strategy and awareness, and security issues. Furthermore, the author pointed out that we should not neglect some less frequently mentioned factors, such as limited bandwidth and speed of internet transfer, and the energy needed for data transmission.

In last years several mathematical methods were successfully used for financial time series modeling. The main problem is to check whether irregularities of data are generated by a stochastic process or they are due to some deterministic chaos and to the presence of low-dimensional strange attractor. We focus on a test based on the correlation dimension. In particular we examine the time series of the daily closure prices of the Italian car industry "FIAT" shares.

The aim of this paper is to develop a forecasting method with global interval input and output for interval-valued financial time series by combining the VAR$(p)$ process, the volatility information and neural network, namely VAR-NN. To reflect the volatility information, four types of interval-valued data volatility information from both the relative and absolute perspectives are constructed. Furthermore, the neural network is combined to produce the parameters. The developed forecasting model is finally applied to the highest and lowest hourly prices of the Shanghai Composite Index prediction. Numerical study shows the feasibility and validity of the developed improved VAR model.

Increasing resource efficiency can potentially deliver important economic and environmental benefits. Many of these benefits are regularly foregone because the financial sector's capacity to adequately take the opportunities and risks arising from resource utilization and related climate change aspects into account has so far remained relatively undeveloped. Focusing on the case of Germany, a number of barriers to the inclusion of resource efficiency and climate change aspects into financial services' considerations are presented. Corresponding measures for improving the capacity of the financial sector to better integrate resource efficiency considerations and climate change related risks into its operating procedures are introduced. The measures encompass the areas of risk controlling, company reporting, institutional reporting requirements, as well as additional supporting measures.

The MSME sector is the most demanded, vibrant and dynamic sector which contributes highly to country’s manufacturing output, employment and exports. As this sector contains more than 80% of the total enterprises of the economy, its labor intensity is much higher than that of any other large enterprise. MSME is an important pillar of Indian economy as this sector is continuously contributing greatly to the growth and development of Indian economy by creating around 70 million employment, manufacturing more than 6000 products, contributing about 45% of manufacturing production and 40% from exports. Presently, the MSME sector is mostly dominated by microenterprises. This signifies that this sector is inviting and giving opportunities to new people for earning and being independent through its available schemes. Therefore, this paper is focused on the financial performance analysis of MSME sector.

Nonperforming Assets (NPA) are the major problem for today’s Banking Industry, and it is being beheld that they are growing with time. So, through this study, the variables that lead to NPA are found. For the study, both bank-specific and macroeconomic variables are taken, to see whether the bank-specific too formulate an effect on NPA or they came out to be insignificant. The variables that lead to NPA are Management Efficiency, Asset Management, GDP, REER, NEER, Inflation, Unemployment and Real Interest Rate. The research for banks’ profitability to see if the same variables lead to NPA, moreover, influence profitability, but the result came out to be that there is only Asset Management, which impacts both. In the end, equity exploration on the four significant variables is conducted, to determine which bank has underperformed or overperformed on which variable.

This study is about the interrelations between three factors which are financial distress, health condition and social relation with mental illness among students in Universiti Utara Malaysia. Mental illness affects individuals feeling, mood as well as thinking processes. This condition eventually disturbs the ability and functions that relates towards others. People with same diagnosis face different experience. A mental illness is not based on one specific event, therefore may include a stressful job or home life, victim of a crime, entering the college life or workforce, marriage, children, divorce, job changes or a job loss. Statistical Package software for Social Science (SPSS) Version 25.0 was used to analyse the data. Results showed that financial distress, health condition and social relation have a positive relationship towards mental illness.