Starting from the ‘90s, there has been a significant increase in PPP use in the public sector in Europe, benefiting the implementation of infrastructure projects. In Italy, PPP is still much more limited than in such countries as the UK and France: the projects funded are smaller and the sectors involved are less appropriate. Based on the economic literature, European initiatives and international comparisons, the paper examines aspects of regulations that could encourage the appropriate use of PPP and considers the problems with the Italian regulations, while proposing some corrective measures. The main limitations involve: i) the absence of adequate preliminary assessments about the advantages of using PPP rather than the traditional procurement, ii) the relative lack of attention to the contract terms, iii) inadequate safeguards to ensure the bankability of the projects, and iv) limited information transparency and accessibility.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

There is a large literature on public-private-partnership, covering many different areas and aspects. This article deals with a specific but important aspect: the decision-making mechanisms to choose the management of PPP enterprises. In this sector, a suitable choice of managers is of particular importance because the persons chosen must balance the public and private interests. This is often difficult to achieve. Two new procedures are discussed, “Directed Random Choice” and “Rotating CEOs”. In each case, the advantages and disadvantages of the procedure of choosing the managers of PPP enterprises are discussed and evaluated. It is concluded that the two novel mechanisms should be seriously considered when choosing the managers of PPP enterprises.

In a rapidly evolving digital economy, cyberpreneurship has emerged as a pivotal force driving innovation and economic growth. The study applies the Theory of Planned Behaviour in predicting entrepreneurial intention in the context of Malaysia, where the government has actively championed digital entrepreneurship. Drawing from a sample of 473 final-year university students in the Klang Valley region of Malaysia, the study investigates the impact of Individual Entrepreneurial Orientation (IEO) dimensions, namely innovativeness, risk-taking, and proactiveness, on the intention to engage in cyberpreneurship within the context of Digital Free Trade Zones (DFTZ). The study further examines the moderation effect of psychological characteristics incorporating visionary thinking, self-efficacy, opportunism, and creativity to provide a comprehensive understanding of the factors influencing cyberpreneurial intentions. With the moderating variable, the paper presents a comprehensive model to investigate the IEO and psychological characteristics contributing to cyberpreneurship intentions and its impact on engagement in DFTZ. An empirical examination of data and hypotheses found that risk-taking (RISK) and proactiveness (PRO) are significantly related to cyberpreneurial intention. Psychological characteristics significantly proved its moderating role in its interaction with innovatiness (INNO), risk-taking (RISK), and proactivness (PRO) in influencing cyberpreneurial intentions (CYBER_PI). Innovativeness (INNO) without the influence of the moderating variable is not significantly related to cyberpreneurial intentions. Engagement with the Digital Free Trade Zone (DFTZ) through the mediating role of cyberpreneurial intentions (CYBER_PI), the innovativeness (INNO) did not succeed. On the other hand, risk-taking (RISK) and proactiveness (PRO) are found to be significant. The paper contributes to the landscape of e-commerce and digital trade literature by advancing our understanding of the factors driving individuals’ intentions to participate in cyberpreneurship and engage in DFTZ. The findings of this study provide valuable insights for policymakers, educators, and entrepreneurs alike.

In the context of establishing businesses in a new region, neglecting environmental orientation may lead to the omission of crucial motives for entrepreneurs’ migration and the subsequent course of their businesses. This present study aims to investigate the effect of green space quality (GSQ), green campaign (GC), and green attitude (GA) on green entrepreneurship pioneering intention (GEPI). Further, national pride (NP) was added as a moderator. This study utilized a cross-sectional approach using a survey method targeting small and medium-sized enterprise (SME) owners who will be relocated to the new capital city. Partial least square structural equation modeling was employed in the data analysis. The results revealed that GSQ, GC, and GA positively influence GEPI. Also, NP moderates the positive influences of GC and GA on GEPI. Entrepreneurs were motivated to pioneer green entrepreneurship in the new region due to environmental factors. Furthermore, their nationalism reinforces the connection between environmental motivations and the aspirations to undertake such pioneering endeavors. The findings present valuable insights for governments to formulate policies that encourage entrepreneurs to migrate internally and establish new economic nodes. Further, the results demonstrate how nationalism encourages green business pioneering endeavors in an untapped market.

The COVID-19 crisis, which occurred in 2020, brought crisis events back to the attention of scholars. With the increasing frequency of crisis events, the influence of crisis events on stock markets has become more obvious. This paper focuses on the impact of the subprime crisis, the Chinese stock market crash crisis and the COVID-19 crisis on the volatility and risk of the world’s major stock markets. In this paper, we first fit the volatility using EGARCH model and detect asymmetry of volatility. After that, a VaR model is calculated on the basis of EGARCH to measure the impact of the crisis event on the risk of stock markets. This paper finds that the subprime crisis has a significant influence on the risk of the stock market in China, US, South Korea, and Japan. During the COVID-19 crisis, there was little change in the average risk of each country. But at the beginning of the COVID-19 crisis, there was a significant increase in the risk of each country’s stock market. The Chinese stock market crash crisis had a more pronounced effect on the Chinese and Japanese stock markets and a lesser effect on the US and Korean stock markets.