The swift growth of the Industrial Internet of Things (IIoT) offers tremendous potential to boost productivity, facilitate real-time decision-making, and automate procedures in various industries. However, as industries increasingly adopt IIoT, they face paramount data security, privacy, and system integrity challenges. Artificial intelligence (AI), Blockchain, and quantum cryptography are gaining significant attention as solutions to address these challenges. This paper comprehensively surveys advanced technologies and their potential applications for securing IIoT ecosystems. It reviews findings from 196 sources, including peer-reviewed journal articles, conference papers, books, book chapters, reports, and websites published between 2021 and 2025. The survey draws insights from leading platforms like Springer Nature, ACM Digital Library, Frontiers, Wiley Online Library, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. This paper explores AI-driven approaches to anomaly detection, predictive maintenance, and adaptive security mechanisms, demonstrating how machine learning (ML) and deep learning (DL) can identify and mitigate threats instantly. It also examines Blockchain technology, emphasizing its decentralized nature, immutability, and ability to secure data sharing and authentication within IIoT networks. The paper discusses quantum cryptography, which utilizes quantum mechanics for theoretically unbreakable encryption, ensuring secure communications in highly sensitive industrial environments. The integration of these technologies is analyzed to create a multi-layered defense against cyber threats, highlighting challenges in scalability, interoperability, and computational overhead. Finally, the paper reviews the current research, limitations and challenges, and future directions for securing IIoT with these advanced technologies. This survey offers valuable insights to researchers, engineers, and industry practitioners working to secure the expanding IIoT infrastructure.

The accumulation of pollution and any kinds of contamination on the glass cover of the solar cell affects the efficiency of the photovoltaic (PV) systems. The contamination on the glass cover can absorb and reflect a certain part of the sunlight irradiation, which can decrease the intensity of the light coming in through the glass cover. With the study, it was planned to develop self-cleaning coatings for the PV systems. It was aimed to prevent or reduce the contamination-induced efficiency loss of the existing PV systems. In the scope of the project, SiO₂/WO₃ and SiO₂/WO₃/ZnO composites were coated from their solutions on the glass substrates using a dip-coating technique. WO₃ was selected as a photocatalyst semiconductor. Under the UV light irradiation, WO₃ could absorb the photons of the UV light, generating the photoinduced charge carriers. The photoexcited charge carriers provide both the photoinduced hydrophilicity on the surface of the coating and the photocatalytic degradation of the organic contaminants accumulated on the surface of the coating, which allows water droplets to spread and flow on the surface of the cover glass to remove the contaminations. However, the recombination rate of the photoexcited charge carriers on the WO₃ film was high. In order to suppress the recombination of the photoinduced charge carriers, WO₃ was coupled with SiO₂ and ZnO. Both of these semiconductors improved the photocatalytic activity of the WO₃ film. Although SiO₂ has superior features in terms of the light transmission, it was not very effective under UV light as a photocatalyst alone. The widely preferred photocatalyst ZnO was added into the composite film structure to enhance the photocatalytic activity. The self-cleaning mechanism of the film coatings on a solar cell was investigated through the photocatalytic dye removal efficiency on the as-prepared film samples. There was a slight decrease in the light transparency and the solar cell efficiency because of the WO₃ content of the composite film. On the other hand, coupling the SiO₂/WO₃ film with ZnO enhanced the photocatalytic activity, and it suppressed the reduction effect of the WO₃ phase on both the light transparency and the solar cell efficiency. The photocatalytic dye removal efficiency was increased to over 90% after 240 min of UVA light irradiation. In addition, the solar cell coated with the SiO₂/WO₃/ZnO film provided almost the same solar cell efficiency as the uncoated solar cell. The water contact angle measurement also exhibited the photocatalytic degradation of the model contamination on the glass cover of the solar cell under the UVA light irradiation. Graphical Abstract

This study investigates the application of Convolutional Neural Networks (CNNs) for forecasting inflation trends in Egypt, aiming to enhance the accuracy of economic predictions by capturing complex, non-linear temporal dependencies in time series data. Traditional econometric models, such as ARIMA and VAR, often struggle to model volatile and dynamic economic conditions, prompting the exploration of deep learning techniques. The proposed CNN-based model leverages historical inflation data from 1960 to 2023, sourced from the World Bank, to predict future inflation trends. The methodology involves data preprocessing, feature extraction using convolutional layers, and prediction through fully connected layers, optimized using the Adam optimizer. Performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²), demonstrate the model's robustness, with an RMSE of 9.2113 and an R² of 0.8911 on the testing dataset. The results indicate a steady upward trend in inflation from 2024 to 2030, with rates rising from 12.45% to 16.35%, accompanied by widening confidence intervals reflecting increased uncertainty over longer horizons. The study concludes that CNNs offer a reliable framework for inflation forecasting, outperforming traditional methods in capturing non-linear patterns. Recommendations include integrating additional economic indicators and exploring hybrid models to further enhance predictive accuracy. This research contributes to the growing application of deep learning in economic forecasting, providing valuable insights for policymakers and researchers.

The impact of artificial intelligence and machine learning on financial stability: a socio-legal analysis of the banking sector. The purpose of the present analysis is to identify the impact of artificial intelligence and machine learning on financial stability in the banking sector. The methodology uses the socio-legal model and the technological approach. At the same time, a SWOT analysis is deployed to identify the strengths, weaknesses, opportunities, and threats that artificial intelligence and machine learning carry for the banking industry. The findings suggest a profound potential that these technologies must increase the efficiency, accuracy, and client experience in banking while at the same time providing significant challenges, such as dis-crimination, bias, loss of privacy, and weaker cybersecurity within the sector. Thus, the central message of the analysis is that the future progress of the banking sector with the help of artificial intelligence and machine learning can be achieved if a multi-stakeholder approach that takes full measure of the benefits and risks at the same time is to be implemented. The impact of the analysis is practical – it is a guideline for the policies that will help to implement the development. Meanwhile, the recommendations in terms of the regulatory environment and development of digital literacy and culture can be implemented in the near future. It also affects the academic and theoretical fields, providing an analysis of the question that can be an excellent background for further theoretical developments and policies.

Silicon single-photon avalanche diodes (Si-SPADs) are the most used devices for measuring ultra-weak optical radiant fluxes in many quantum technology fields, such as quantum optics, quantum communication, quantum computing, etc. In all these fields, the detection efficiency is the main parameter, which has to be accurately known for achieving reliable measurements. In this paper we present the improvements performed on the setup described in López et al. (J Mod Opt 62:S21–S27, 2015) for determining the detection efficiency of Si-SPAD detectors with a low measurement uncertainty. The improvement arises from the precise alignment of the Si-SPAD detector and the low deviation reached between the total calculated filter transmission and the individual filter transmission measurements (≤0.05%) performed with an integrating sphere with attached Si-photodiode as standard detector. The relative standard uncertainty of the Si-SPAD detection efficiency measurement achieved is now as low as ~0.16%. Furthermore, the investigation of the detection efficiency homogeneity of two commercial Si-SPAD detectors from different manufacturers and with different sensor diameters is also presented. The obtained homogeneity is ≤2.2% within a region of diameter of 40 μm. The detailed analysis presented in this paper shows the potential for achieving low measurement uncertainties for Si-SPAD detector calibration even in the low photon flux range. The low uncertainties are only to be realized for reproducible measurement conditions, i.e. in specific for equal beam sizes and beam shapes and well as for an irradiation of equal active areas of the detector. This, however, will be difficult to obtain when measurements are performed at different national metrology institutes.

The rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, BMC, and Google Scholar, the study explores the integration of these advanced technologies to address emerging threats. It highlights the use of DL in intrusion detection, anomaly detection, and biometric authentication to protect digital learning platforms. It also examines how CV techniques, such as facial recognition, behavioral analysis, and emotion detection, enhance security and foster adaptive learning environments. The survey also addresses key challenges, including data quality, model interpretability, computational costs, and ethical considerations. By identifying research gaps and proposing future directions, this survey offers valuable insights for researchers, educators, and policymakers aiming to develop robust, scalable, and ethical AI-driven cybersecurity solutions for smart education.