Since the external environment on a global level is very unstable, recovering from various unexpected shocks becomes a challenging question for all countries. Thus, for each country it is necessary to understand its weaknesses and threats. Further, the preparation for any level of uncertainty in various fields must be imperative. Even for the most unpredictable shocks such as pandemic, cyberthreat, or even war. The aim of the article is to evaluate the state resilience of the Baltic States by creating the national resilience index. A state’s resilience is based on four pillars: economic, social, good governance, and defence. The methodology is based the SAW method, data has been collected from NATO and Eurostat databases. As the result of the study, resilience index has been estimated for each year from 2015 to 2022. Results revealed vulnerability and problematic areas of each country.

The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.

The Malaysian government’s efforts to promote solar photovoltaic (PV) usage among households face a challenge due to its low adoption rate. This study delves into the factors influencing the exponential adoption of solar PV electricity generation among landed residential property owners in Malaysia. The research aims to comprehensively examine the predictors influencing the adoption of solar PV systems among Malaysian households. Hence, the study employs an enhanced Theory of Planned Behavior framework, integrating sustainable energy security dimensions such as availability, affordability, efficiency, acceptability, regulation, and governance. The sample comprised 556 Malaysian residents who owned and resided in the landed properties. The home locations where at least one solar PV installation existed within a residential street. Snowball sampling was employed through referrals, leveraging social and community networks. Collected data was analyzed using the partial least squares structural equation modeling. Attitude, affordability, and acceptability emerged as pivotal factors significantly impacting the intention to use solar PV systems among Malaysian households. This research not only enriches academic discourse but also offers practical implications for policymakers, guiding the formulation of targeted strategies to promote sustainable energy practices and facilitate the widespread adoption of solar PV systems in Malaysia.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

In the rapidly evolving landscape of technological innovation, the safeguarding of Intellectual Property Rights (IPR) emerges as a critical factor influencing economic growth and technological advancement. This study, conducted in the context of organizations operating in the United Arab Emirates (UAE), meticulously explores the intricate dynamics between IPR awareness, enforcement, and their implications for information security practices. The research undertakes a thorough investigation with three primary objectives: a comprehensive examination of IPR awareness, an exploration of the relationship between IPR enforcement and information security practices, and an assessment of the impact of information sensitivity. To achieve these objectives, a sample population of 150 respondents from various sectors was engaged, employing a combination of survey instruments and robust statistical analyses. The findings of the study illuminate a strong positive correlation between IPR awareness and information security practices, underscoring the pivotal role of cultivating IPR awareness among organizations. Furthermore, the enforcement of IPR, intricately connected with a resilient legal framework, regulatory authorities, international agreements, and effective customs and border control measures, is identified as a significant influencer of information security practices. The study employs a statistical model that exhibits a high explanatory power, elucidating approximately 85.9% of the variance in information security practices. In conclusion, the research offers profound implications for organizations, policymakers, and stakeholders in the UAE, advocating for strategies such as education, legal and regulatory support, international collaboration, and robust access control mechanisms to fortify IPR awareness, enforcement, and information security practices. The integration of advanced tools such as the smart PLS software adds depth and reliability to the study’s analytical framework, contributing to its comprehensive insights.

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.