Cyber Physical Systems

Traditional environmental monitoring, which relies on manual sampling and laboratory analysis, often suffers from slow response times, high operational costs, and limited spatial or temporal resolution. These constraints hinder timely and informed decision-making, particularly in the face of accelerating environmental change. This study investigates the potential of digital technologies—primarily Internet of Things (IoT) sensors and Artificial Intelligence (AI)—to modernize environmental monitoring systems focused on air quality, water, and soil. A comparative design was employed to evaluate traditional methods against digital systems, incorporating IoT-enabled data collection and AI-driven analytics, supported by big data infrastructure. Key environmental indicators included PM2.5 concentration, soil moisture, water pH, temperature, and carbon emissions. The results showed significant improvements: measurement accuracy increased by approximately 20%, response time was reduced by 97.9%, and data processing speed surged by more than 19,900%, effectively reducing processing durations from several hours to near real-time. Operational costs decreased by over 50%. Additionally, predictive models powered by AI allowed for early warnings, while real-time data acquisition through IoT improved responsiveness to environmental threats. Although blockchain was not used directly for measurement or analysis, it played a critical role in ensuring data integrity, transparency, and traceability—factors essential to building trust in digital monitoring frameworks. Despite ongoing challenges such as scalability, energy consumption, and connectivity in rural regions, the findings highlight the potential of integrated digital tools to create more adaptive, efficient, and sustainable environmental management systems. These smart technologies present a path toward proactive governance and resilient ecosystem stewardship.The objective of this study is to investigate how the integration of advanced digital technologies such as IoT, AI, big data, and cloud computing, can revolutionize environmental monitoring and management. By assessing these tools’ potential to enhance data accuracy, responsiveness, and stakeholder collaboration, the research aims to develop proactive, transparent, and cost-effective strategies that address the complex challenges of ecological resilience and sustainable resource use in both urban and rural settings. The objective of this study is to investigate how the integration of advanced digital technologies such as IoT, AI, big data, and cloud computing, can revolutionize environmental monitoring and management. By assessing these tools’ potential to enhance data accuracy, responsiveness, and stakeholder collaboration, the research aims to develop proactive, transparent, and cost-effective strategies that address the complex challenges of ecological resilience and sustainable resource use in both urban and rural settings.



This study aimed to investigate the role of digital technologies in enhancing the effectiveness of environmental monitoring systems, particularly in relation to air, water, and soil quality. The results demonstrate that integrating IoT-based sensors, AI-driven analytics, cloud computing, and blockchain infrastructure can substantially improve measurement accuracy, reduce operational delays, and support faster and more informed decision-making. Real-time monitoring of parameters such as PM2.5 concentration, water pH, and soil moisture content proved notably more precise and reliable than traditional sampling methods, offering practical benefits for both environmental assessment and resource management. In particular, the ability to detect fluctuations in soil moisture and water quality at higher temporal resolution enabled quicker response to environmental risks, which is critical in ecosystems sensitive to drought, contamination, or land degradation. The automation of data collection and processing also led to significant gains in cost efficiency and processing speed, further confirming the operational advantages of digital transformation in environmental systems. Moreover, the application of AI-based predictive models supported proactive intervention, allowing environmental authorities to anticipate potential hazards and take early action before adverse impacts escalate. Nevertheless, the implementation of such technologies remains dependent on infrastructure readiness, reliable network connectivity, and energy efficiency—factors that may limit scalability in certain rural or underdeveloped areas. While tested in Iraq, these findings are applicable to other regions with similar environmental challenges, pending infrastructure upgrades. For example, in Iraq’s rural regions, limited broadband infrastructure, frequent network disruptions, and inconsistent mobile coverage posed significant challenges to continuous data transmission from IoT devices. These connectivity issues resulted in occasional data loss and reduced the overall effectiveness of real-time monitoring efforts. Furthermore, as digital systems become more deeply embedded in environmental governance, considerations around data ownership, system interoperability, and long-term sustainability will need to be addressed. Based on the findings, future research should explore strategies for optimizing low-power digital monitoring frameworks, enhancing sensor durability in diverse terrain, and developing governance mechanisms that ensure data transparency and equitable access. Such efforts are essential for building resilient, responsive, and inclusive systems capable of supporting long-term environmental stewardship.

Based on the comparative performance analysis, IoT-based real-time sensing combined with AI-powered predictive analytics proved to be the most effective in improving measurement accuracy and response time. These tools are highly recommended for environmental monitoring applications, particularly in water and soil resource management. Blockchain, while essential for ensuring data transparency and integrity, had a relatively lower direct impact on measurement accuracy and operational efficiency, and thus is recommended primarily as a supplementary tool for secure data governance rather than for core monitoring tasks

An effective method to attain improved security in identifying harmful activity in cloud computing during the past number of years has been A system for detecting intrusions (IDS). Efforts are being made on an intrusion detection system (IDS) to detect and classify network-level intrusions using machine learning (ML) techniques. Using a Bidirectional Long Short-Term Memory (Bi-LSTM) model, this paper proposes an intelligent cybersecurity intrusion detection system that can recognize complex attack patterns in network data. To apply a comprehensive data pre-processing pipeline over the CICIDS2017 dataset, it has performed numerical feature extraction, z-score normalization, and one-hot encoding for recognizing multiclass labels and SMOTE for the solution of class-imbalance problem. While its performance outpaced all others, the Bi-LSTM model improved at capturing both forward and backward time dependencies and obtained A 99% F1-score, a 98.51% accuracy rate, a 99% precision rate, and a 98% recall rate. Training and validation curves indicated strong generalization, and the normalized confusion matrix confirmed high classification accuracy across diverse intrusion types. A comparative analysis showed that the Bi-LSTM model outperformed traditional classifiers such as Naïve Bayes and Deep Multilayer Perceptron, establishing its effectiveness for advanced intrusion detection in intelligent cybersecurity systems. The study offers practical advice for choosing the best IDS models depending on certain network settings and security needs.

In order to improve cybersecurity and stop criminal activity, network traffic anomaly detection is essential. Anomaly detection is an essential part of cybersecurity, which is necessary to find complex and until undiscovered network threats that frequently evade detection techniques based on signatures and heuristics. This study proposes a comprehensive machine learning framework employing the Random Forest (RF) algorithm, combined with an advanced data preprocessing pipeline encompassing data cleaning, encoding of categorical features, class balancing using SMOTE, feature selection, and normalization to enhance model input quality. The method is tested on the well-known CICIDS2017 dataset, which records a wide variety of current cyberattacks and safe network activity. The suggested RF model performs exceptionally well, attaining 99.88% accuracy, precision, recall, and F1-score. According to comparative findings, the RF model performs much better than baseline methods like K-Nearest Neighbors and Linear Regression, which obtained far lower evaluation metrics. In increasingly complex digital settings, these findings highlight the model's scalability, durability, and applicability for real-time intrusion detection, which helps to create cybersecurity defenses that are more resilient and adaptable.

Helpful discussions with Kazushige Goto and John McCalpin are gratefully acknowledged. Thanks to Dan Stanzione for his notes on cloud computing, Ernie Chan for his notes on scheduling of block algorithms, and John McCalpin for his analysis of the top500. Thanks to Elie de Brauwer, Susan Lindsey, and Lorenzo Pesce for proofreading and many comments. Edmond Chow wrote the chapter on Molecular Dynamics. Robert van de Geijn contributed several sections on dense linear algebra. Contents

A major hindrance in industry modernization is the interoperability issues between existing legacy and new systems. Legacy systems normally have security vulnerabilities and are cost prohibitive to upgrade/replace. Thus, a feasible solution is needed to equip legacy systems with secure emerging technologies. This paper presents a low-cost ARM processor based Inter-Technology Bridging Gateway (ITBG). Its modular design makes it easily customizable for any industrial application. As a use case, this paper presents design, implementation, compliance testing and validation of ITBG for synchrophasor technology in smart grid. It bridges technological gap between legacy and new phasor devices by supporting two-way protocol translation between IEEE C37.118.2 and IEC 61850-90-5 standards. Further, it ensures system security by using IEC recommended GDOI security mechanism. Experiments in a laboratory based physical testbed successfully validated ITBG for different time-critical synchrophasor applications.

Public disclosure of private data such as mobile phone numbers, dates of birth, identity card information, and financial data on social websites represents a significant threat to cybersecurity. Both external and internal intrusions infringe on users' privacy, and the available security practices are not preventing unauthorized access. Substandard encryption, poor access controls, and weak policy enforcement form the core deficiencies of handling private data in this study. An in-depth analysis, comprising industry surveys, interviews with experts, and case studies of eCommerce and digital service companies, is carried out to assess the prevailing practices and their shortcomings. It is seen that most of the companies do not have adequate policies and technical measures, and user data is extremely susceptible to misuse. To fill this gap, a Citizen Digital Identity Portal (CDIP) is envisioned as an eGovernance project sponsored by the government. This solution combines encryption, access controls, and API-based identity confirmation to enable companies to confirm user identities without revealing or holding personal information. The threat of abuse by both internal and external entities is therefore greatly minimized. The study concludes that companies cannot guarantee data security alone and that a policy-driven centralized model must be in place. The CDIP model helps bridge the gap between technology and cybersecurity rules. It offers a flexible and powerful way to protect personal information in today's digital world.

Quantum computing and artificial intelligence are transforming the cybersecurity landscape, dictating a reassessment of cryptographic resilience in the face of emerging threats. This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key distribution method and National Institute of Standards and Technology (NIST) approved quantum-resistant algorithms. NIST is recognised globally as the leading institution in this domain, and the algorithms are selected though a decadelong process. By integrating AI-driven red teaming, automated penetration testing, and real-time anomaly detection, the research develops a robust framework for assessing and mitigating security risks in quantum networks. The findings demonstrate that AI can be effectively used to simulate adversarial attacks, probe weaknesses in cryptographic implementations, and refine security mechanisms through iterative feedback. The use of automated exploit simulations and protocol fuzzing provides a scalable means of identifying latent vulnerabilities, while adversarial machine learning techniques highlight novel attack surfaces within AIenhanced cryptographic processes. This study offers a comprehensive methodology for strengthening quantum security and provides a foundation for integrating AIdriven cybersecurity practices into the evolving quantum landscape.

This interview explores the transformative impact of military drone technology on global security, warfare, and ethical responsibility. Robert Junqueira argues that while drones represent disruptive technological advancement with devastating potential—from precision strikes to autonomous swarms—the core challenge remains fundamentally human. He critiques the notion of "autonomous" drones, insisting that these systems are programmed devices embodying human volition, and that responsibility for their actions must remain exclusively with humans. The discussion addresses how AI-enabled drones are reshaping military strategy and international relations while examining the social, political, and philosophical implications of attributing agency to machines rather than their human operators. Handle: https://hdl.handle.net/10316/120106

A Deus pela vida e por tudo mais que tem me concedido ao longo dela. Ao professor Dr. Fabrício Junqueira pela orientação, motivação e pelos conselhos. Também pela paciência e dedicação para o esclarecimento das dúvidas e incertezas que surgiram durante a realização deste trabalho. Aprendi com ele que o 'não' nós já temos e precisamos persistir indo sempre em busca do 'sim'. Ao professor Dr. Paulo Eigi Miyagi pelas orientações para a elaboração do projeto de doutorado e pelo apoio técnico na escrita de artigos científicos publicados ao longo deste projeto. Ao Marcosiris Pessoa pelo apoio e contribuições nos experimentos práticos realizados no LSA. Ao Edson Watanabe pela amizade, dicas e conselhos ao longo desses anos. Às servidoras Marisa e Regianne da secretaria do PPGEM pelo apoio acadêmico e administrativo. Do programa DINTER USP/IFSC, agradeço aos coordenadores pela organização e realização do projeto, e aos professores pelos ensinamentos e compartilhamento de experiências. À Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) pelo financiamento do programa DINTER. Aos colegas do DINTER que em muito contribuíram, principalmente durante o curso das disciplinas ofertadas pelo programa. Aos servidores do Campus Lages do IFSC pelo apoio e motivação para a realização deste trabalho. Aos colegas do Campus Tubarão do IFSC que me acolheram pelo processo de remoção, justamente na etapa final do desenvolvimento deste projeto. À minha esposa Patrícia pelo apoio e incentivo, principalmente nos momentos de turbulência do trabalho, e também pela compreensão pelas faltas cometidas devido a dedicação ao desenvolvimento deste trabalho. Não posso deixar de reconhecer e de agradecer pela preocupação e os cuidados em torno do nosso bem-estar e de nossa saúde. Aos meus pais Erfriedo Bruno Pisching (in memorian) e Sirlei Pisching pela educação e o apoio incondicional que sempre me deram para o meu crescimento pessoal e profissional. Aos meus sogros Ariovaldo e Mônica pelo incentivo para a realização deste trabalho. Aos meus irmãos, amigos, parentes e a todos que, direta ou indiretamente, colaboraram para a execução deste projeto.

Given the scale of consequences attributable to cyber attacks, the field of cybersecurity has long outgrown ad-hoc decision-making. A popular choice to provide disciplined decision-making in cybersecurity is Game Theory, which seeks to mathematically understand strategic interaction. In practice though, game-theoretic approaches are scarcely utilized (to our knowledge), highlighting the need to understand the deficit between the existing state-of-the-art and the needs of cybersecurity practitioners. Therefore, we develop a framework to characterize the function and assumptions of existing works as applied to cybersecurity and leverage it to characterize 80 unique technical papers. Then, we leverage this information to analyze the capabilities of the proposed models in comparison to the application-specific needs they are meant to serve, as well as the practicality of implementing the proposed solution. Our main finding is that Game Theory largely fails to incorporate notions of uncertainty critical to the application being considered. To remedy this, we provide guidance in terms of how to incorporate uncertainty in a model, and what forms of uncertainty are critical to consider in each application area, and how to model the information that is available in each application area. CCS Concepts: • Security and privacy → Network security; Intrusion detection systems; • Mathematics of computing → Mathematical analysis.

Executive Summary
The war launched by the Russian Federation against Ukraine has caused the most severe geopolitical rupture in Europe since 1945, bringing high-intensity conventional confrontation back to the forefront and testing the resilience of the entire Western security architecture. Predictions of a swift victory collapsed, and the conflict evolved into a war of attrition and adaptation, where success was not determined by numerical or technological superiority, but by the ability to integrate resources, intelligence, logistics, and willpower into a coherent and resilient system.
From a geopolitical perspective, the conflict has accelerated the transition toward a competitive multipolar order, strengthening NATO and reinforcing Euro-Atlantic cohesion, while also prompting states in the Global South to adopt more autonomous positions. Europe reacted with unity, yet revealed structural vulnerabilities: dependence on the United States, industrial fragmentation, and slow decision-making. For Romania and the entire Eastern Flank, the war has confirmed that geography is both an asset and a vulnerability, demanding urgent investment in national resilience.
At the military level, the war has shown that doctrine and technology must be supported by the human factor and by support infrastructure — otherwise they lose their value. Operations combined archaic tactics (trenches, artillery barrages) with cutting-edge innovations (mass drone deployment, integration of satellites and encrypted communications). Information superiority, logistical resilience, and coordinated international support enabled Ukraine to withstand the assault and even reclaim territory, while Russia was forced to reconfigure its doctrine, mobilization system, and defence industry.
In essence, the conflict has demonstrated that military power can no longer be separated from geopolitical power, and victory is no longer decided exclusively on the battlefield, but by the economic, informational, and political networks that sustain it. The fundamental lesson is that in the 21st century, resilience, adaptability, and the capacity to mobilize durable external support are more decisive than raw massed force.
The Russia–Ukraine war fits into the broader framework of the conflict between the older trend of globalization with its postmodernist dimension (including its DEI, “Cancel”, #MeToo, “Black Lives Matter” components, etc.) and multipolarity, represented mainly by the Global South and sovereigntism. The way this war ends will significantly influence the future course of human society. And let us not forget China.

Renewable energy sources and further electrification of energy consumption are key enablers to decrease greenhouse gas emissions, but they also introduce increased complexity to the electric power system. The increased availability of automation, information and communication technology, and intelligent solutions for system operation have transformed the power system into a smart grid. To support the development process of smart grid solutions on system level they have to be tested in a holistic manner covering the multi-domain aspect of a such complex systems. This paper introduces the concept of holistic power system testing and discuss first steps towards a corresponding methodology that is being developed in the European ERIGrid research infrastructure project.

With the rising demand of home automation and sensor networks, the IEEE 802.15. 4 specification outlines a new class of physical and MAC layer protocols targeted at low power devices, personal area networks, and sensor nodes. Based on IEEE 802.15. 4, many upper layer protocols are proposed. The ZigBee is the most popular one. However, the ZigBee itself is not compatible with the IP-based network. It is a great challenge to integrate these two kinds of networks together. In this paper, we proposed an ...

Abstract:
This research examined the incorporation of Artificial Intelligence (AI) competencies to enhance female students' engagement and involvement in brick/block laying and concreting training at technical institutions in Nigeria.  Historically, these sectors have been mostly male, with female students being dissuaded from engaging in vocational education within building trades.  Nonetheless, the emergence of AI technologies, including robots, automation, and predictive analytics, presents substantial opportunities to enhance these vocational abilities, making them more interesting, efficient, and accessible.  The document delineated essential AI tools and competencies that might augment brick/block laying and concreting education, including AI-powered construction simulations, automated bricklaying robots, and AI-driven safety and quality control systems.  Notwithstanding the potential advantages of AI integration, obstacles include gender stereotypes, restricted access to technology, and insufficient infrastructure persist as significant impediments.  The research emphasised the need for focused interventions, including the formulation of gender-sensitive curriculum, allocation of resources for AI, and collaborations with the construction sector, to foster a more inclusive and supportive educational atmosphere for female students.  Recommendations are offered to address these difficulties, including infrastructure enhancement, curriculum revisions, early STEM education for females, and governmental assistance.  The paper contends that by tackling these challenges, AI can significantly enhance female students' engagement and involvement in technical education, hence enabling women to make substantial contributions to the construction industry.

Este artículo analiza la reconfiguración que las tecnologías emergentes están produciendo en la relación entre organizaciones terroristas y grupos de crimen organizados en América Latina (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023). La evidencia encontrada revela un proceso de "metamorfosis y adaptación mediante el cual la incorporación de innovaciones tecnológicas está haciendo borrosa las las fronteras operativas, tácticas y estratégicas entre estos fenómenos. Los resultados muestran que tecnologías como criptoactivos, comunicaciones cifradas y plataformas digitales funcionan como catalizadores de convergencia, mientras que la inteligencia artificial y los sistemas autónomos representan factores de transformación emergentes. El trabajo busca explicar cómo estos desarrollos tecnológicos facilitan la colaboración entre grupos anteriormente diferenciados, generando organizaciones híbridas que desafiaban los sistemas de seguridad tradicionales, confrontando a las estructuras del Estado.

The increasing reliance on the internet has heightened exposure to cyber threats, with malicious URLs being a significant concern. This research focuses on building a machine learning-based system to detect such URLs effectively. A Random Forest classifier is utilized, leveraging critical features like URL length, special character frequency, and the presence of IP addresses to identify harmful URLs. The system is integrated into a user-accessible web application developed using Flask, enabling real-time URL analysis. Users can input a URL, and the system evaluates its safety, categorizing it as either "safe to proceed" or "not safe to proceed." By training the model on a labeled dataset, the system ensures accurate differentiation between benign and malicious URLs. This solution offers a faster and more efficient means of safeguarding users during web browsing, showcasing the potential of machine learning in enhancing cybersecurity.

The proliferation of malicious Uniform Resource Locators (URLs) poses a significant cybersecurity threat, enabling phishing, malware distribution, and data breaches. Traditional detection methods like blacklisting struggle to keep pace with evolving threats. This study develops and evaluates a digital forensic tool leveraging machine learning (ML) to detect malicious URLs. Using a dataset of 450,176 URLs (79.8% benign, 23.2% malicious), we engineered features such as URL length, special character count, and secure HTTP usage. Ensemble ML models, including Random Forest, Decision Tree, AdaBoost, and Extra Trees, achieved perfect classification scores (accuracy, precision, recall, and F1-score of 1.0), outperforming Gaussian Naive Bayes (accuracy 0.775) and K-Nearest Neighbors (accuracy 0.772). Despite concerns of potential overfitting, the tool demonstrates robust potential for realtime URL filtering and forensic investigations. This framework advances proactive cybersecurity by identifying zeroday threats, offering interpretable features for threat attribution, and informing policy development.

An Adaptive Energy Management System
(AEMS) is proposed for smart grids, which integrates machine
learning techniques with internet of things connectivity so as to
improve predictive control ability, response times and even the
efficacy of operations. The system effectively manages
residential energy sources and distributed energy assets by
means of load forecasting based on Long Short-Term Memory
(LSTM), dynamic demand response, controlling assets with big
data. This concept was tested out on a mock-up and showed
that the LSTM model had the highest accuracy: a Mean
Absolute Percentage Error (MAPE) of just 2.14 percent. This
allowed for optimal load scheduling, cutting household energy
costs by 28.17 percent. The AEMS also improved utilization of
renewable energy, with 89.2% solar and 72% less dependence
on the grid. Grid stability was improved when deviation in
frequency and voltage dropped by more than 70 percent. In
addition, the system is capable of triggering 92 demand
response events every month. When tested for scalability, it
was found that the latency was still acceptable (<250 ms) and
packet loss low (<2%) even under 200-node configurations;
this shows that it can be used at scales large enough to be
meaningful. In conclusion, this paper introduces a flexible and
intelligent platform for improving the performance of smart
grids. The proposed solution can integrate renewable energy
more efficiently into grid systems, generate savings for users at
the same time as strengthening cyber electrical reliability.

The Internet of Things (IoT) is already transforming industries, cities, and homes. The economic value of this transformation across all industries is estimated to be trillions of dollars and the societal impact on energy efficiency, health, and productivity are enormous. Alongside potential benefits of interconnected smart devices comes increased risk and potential for abuse when embedding sensing and intelligence into every device. One of the core problems with the increasing number of IoT devices is the increased complexity that is required to operate them safely and securely. This increased complexity creates new safety, security, privacy, and usability challenges far beyond the difficult challenges individuals face just securing a single device. We highlight some of the negative trends that smart devices and collections of devices cause and we argue that issues related to security, physical safety, privacy, and usability are tightly interconnected and solutions that address all...

This paper outlines a first proposal of a formal semantics for the JADEL programming language. JADEL is an agent-oriented programming language based on JADE that has been recently proposed to ease the adoption of JADE, and to promote its use. In previous works, JADEL was specified at the syntax level, and only an informal semantics was given. The major contribution of this paper is to outline a formalization of the semantics of JADEL to complement previous works and to allow reasoning on JADEL agents and multi-agent systems. First, the paper provides a brief recall on JADEL by describing its main abstractions and their specific syntactic constructs. Then, a discussion of the proposed operational semantics based on transition systems is described. Finally, a detailed operational semantics of only some relevant constructs is given. The validity of the proposed approach is discussed in the conclusion of the paper, together with directions of future developments.

Everyday safety issues in kitchens have become an area of concern because of the attendant increase in
kitchen accidents in the domestic and commercial kitchens. Most people usually access and use kitchens
for meal preparation which makes them prone to kitchen accidents. However, there are some risks
involved in such situations, for instance, the occurrence of a gas cylinder leak. And that is where we come
in, how the Internet of things can be used to reduce the risk of gas explosions particularly in the kitchen.
In this respect, IoT efforts such as remote monitoring of the entire kitchen will be able to prevent these
accidents. In order to test this theory and its results, the research will include the use of both hardware
and software. Hardware to be utilized consists of Servo motors, Flame sensors, temperature, humidity,
and gas sensors, Arduino UNO, load cell Node MCU, and the like. In software perspective, mobile
applications and integrated Node MCU have been applied. The output from our system is SMS. The gas
leaks in the kitchen are also a concern since the system can be able to offer surveillance which in the event
of any leak, it may cut bluntly the reaction time. In the case of gas leakages either during the day or night,
there is a likelihood that someone may unknowingly switch on a source of light, a move that may cause
an explosion. The master power supply will immediately kill these lights and save everybody from that
with the associated alert. This means that alert can raise an alarm for the time and response of the users.
Informing and alerting the user to kitchen appliances, such as Cylinder. The design of this research
project aims at highlighting the potential of presenting the IOT based system for communication through
SMS which is the repository of the parametric assistance. In a comparison to the existent in the field
works, the developed system shows a very high accuracy rate almost 97%.

This paper presents a systematic mapping study classifying existing scientific contributions on synergies of Model Driven Engineering (MDE), Artificial Intelligence/Machine Learning (AI/ML), and DevOps, with the overall objective of supporting the continuous development of Cyber-Physical Systems (CPSs). We collected papers from bibliographic sources and selected primary studies to analyse. Then, we characterised and classified the current state of the art, focusing on 1) main aspects already tackled at the intersection of at least two of the three studied areas, and 2) findings emerging from the analysis as a framework for potential future research, notably regarding the integration of the three studied areas. The results reveal that few approaches combine MDE, AI/ML, and DevOps for software and systems engineering. In contrast, several approaches have combined two of them, specifically MDE and DevOps. Approaches combining AI/ML with MDE or DevOps are also becoming more frequent and will most likely continue to progress in the future. These synergies cover a range of engineering activities, from requirements and design to monitoring, maintenance, and evolution. Open research challenges include advancing AI/ML, MDE, and DevOps integration, supporting scalable, data-oriented solutions, proposing new continuous engineering methods, and adapting DevOps practices to diverse systems.

This study delves into the relationship between cyber-physical systems (CPS) and economic security, with particular emphasis on how networking technologies facilitate more efficient data integration. It investigates how CPS adoption is reshaping national economies by influencing productivity levels, altering labor market structures, and introducing new cybersecurity challenges. Employing a hybrid research design that merges cross-sectional data evaluation with expert consultations, the research offers a comprehensive view of the implications of CPS implementation on sectoral productivity, employment trends, and macroeconomic resilience. CPS are positioned in the study as strategic innovations powered by data intelligence, underlining both their promising opportunities and associated threats. The findings support the development of informed policy measures that aim to enhance benefits while reducing potential risks. Ultimately, the work contributes to the evolving discourse on CPS by offering a balanced analysis of their socio-economic impacts and outlining actionable recommendations for decision-makers and industry stakeholders to capitalize on CPS innovations effectively.

Phishing is a common form of cyberattack where fraudulent websites are used to deceive users into revealing sensitive information. Detecting phishing sites is crucial for enhancing online security. This study presents a machine learning-based system for phishing detection using URL features. A balanced dataset containing phishing and legitimate samples was used to train and evaluate the models. The system extracts 23 essential features from URLs and employs machine learning models such as Random Forest, SVM, Gradient Boosting, and MLP for classification. Among these, the Random Forest model achieved the highest accuracy of 96.17%. The system is deployed as a web application using Flask, providing real-time detection and easy user interaction. This research highlights the effectiveness of machine learning in improving cybersecurity through accurate and efficient phishing detection.

The escalating complexity and widespread deployment of autonomous systems, ranging from advanced industrial robotics to intelligent urban infrastructure, necessitate a paradigm shift in software engineering. These systems demand not only high adaptability but also rigorous security and transparent decision-making. This paper proposes a unified Software Intelligence Framework that seamlessly integrates Neural Program Synthesis (NPS), Quantum-Secure DevOps (QSD), and Explainable AI (XAI) to meet these multifaceted demands. The framework leverages advancements in NPS for AI-driven code generation, QSD for fortifying software lifecycles against emerging quantum threats, and XAI for ensuring interpretable and trustworthy decisionmaking in critical autonomous operations. We present a comprehensive literature review of the state-of-the-art in each domain, detailing their respective challenges and synergistic potential. The proposed architecture unifies these components into a continuous pipeline for specification-to-code generation, secure deployment, and runtime adaptation. A hypothetical smart city infrastructure scenario illustrates the practical application and benefits of this convergent framework, demonstrating its capacity for rapid code adaptation, post-quantum security, and human-understandable explanations of autonomous behavior. We further discuss the technical challenges inherent in such integration, along with robust evaluation strategies and the profound ethical, operational, and security implications of deploying AI-generated, quantum-secure systems in sensitive contexts. This work lays the foundation for a new multidisciplinary field essential for developing adaptable, robust, and trustworthy autonomous systems.

With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains the required level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces are limited in their adaptability, a Cognitive Human Machine System (CHMS) aims to perform dynamic, real-time system adaptation by estimating the cognitive states of the human operator. Nevertheless, to reliably drive system adaptation of current and emerging aerospace systems, there is a need to accurately and repeatably estimate cognitive states, particularly for Mental Workload (MWL), in real-time. As part of this study, two sessions were performed during a Multi-Attribute Task Battery (MATB) scenario, including a session for offline calibration and validation and a session for online validation of eleven multimodal inference models of MWL. The multimodal inference model ...

One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventiona...

Cyber-attacks that may pose a threat to critical infrastructures (CI) have the potential to cause wide-ranging negative effects ranging from economic losses to service interruptions and social chaos. Türkiye is in a high-risk group against cyber-attacks due to its strategic geographical location and rapidly digitalizing infrastructures. Denial of service attacks on energy grids, ransomware threats in health infrastructures and data breaches on banking systems are concrete examples of the cyber security vulnerabilities that Türkiye faces. The study addresses the global dimension of cyber threats by taking into account large-scale cyber-attacks experienced internationally and examines the local effects of these threats with examples specific to Türkiye. The study evaluates Türkiye's current policies, legal regulations and defense strategies against threats. In the fight against cyber threats, the development of domestic and national technologies, strengthening cooperation at global, regional, national, international and institutional levels, international information sharing and the establishment of proactive response mechanisms against cyber threats are among the prominent strategic measures. In this context, cyber security is not only a technical issue; it is a necessity in terms of national security, economic sustainability and social stability.

Cyber-attacks that may pose a threat to critical infrastructures (CI) have the potential to cause wide-ranging negative effects ranging from economic losses to service interruptions and social chaos. Türkiye is in a high-risk group against cyber-attacks due to its strategic geographical location and rapidly digitalizing infrastructures. Denial of service attacks on energy grids, ransomware threats in health infrastructures and data breaches on banking systems are concrete examples of the cyber security vulnerabilities that Türkiye faces. The study addresses the global dimension of cyber threats by taking into account large-scale cyber-attacks experienced internationally and examines the local effects of these threats with examples specific to Türkiye. The study evaluates Türkiye's current policies, legal regulations and defense strategies against threats. In the fight against cyber threats, the development of domestic and national technologies, strengthening cooperation at global, regional, national, international and institutional levels, international information sharing and the establishment of proactive response mechanisms against cyber threats are among the prominent strategic measures. In this context, cyber security is not only a technical issue; it is a necessity in terms of national security, economic sustainability and social stability.

Manufacturing systems under Industry 4.0, and their transition towards Industry 5.0, take into account the Quintuple Helix innovation model, associated with the sustainable development goals (SDGs) set by the UN and Horizon 2030, in which companies focus on operational efficiency in terms of the use and minimisation of resources for the protection of the environment. In this respect, the implementation of the circular economy model, which requires engineers to acquire appropriate competencies, enabling companies to establish this model at the manufacturing level. Moreover, competence has always been a priority for both the professional and the company. In this sense, connectivism has been called a learning theory for the digital era; this is the reason why a review of the state-of-the-art developments of this paradigm focused on engineering has been carried out. In this sense, the potential of the digital transformation in instruction to formulate an engineering model based on neuro...

This paper presents a Systematic Literature Review (SLR) that explores the intersection of Quick Response (QR) codes, cryptographic models, and card payment systems. With the rapid evolution of digital payment infrastructures, QR-based transactions have become increasingly prevalent due to their ease of use, low cost, and accessibility. However, the integration of QR codes into card payment systems introduces significant security and privacy concerns, necessitating robust cryptographic solutions. This review systematically analyzes existing research from 2015 to 2025, focusing on the technological frameworks, security protocols, and vulnerabilities associated with QR code transactions and card-based payments. Key themes identified include symmetric and asymmetric encryption models, public key infrastructures (PKIs), tokenization, and blockchain-based enhancements. The review also evaluates the performance, scalability, and usability of various cryptographic schemes in the context of real-time payment authentication and fraud prevention. Through a comprehensive analysis of over 25 peer-reviewed publications, this study highlights research gaps, particularly in lightweight cryptographic models suitable for mobile and low-resource environments. The paper concludes by proposing a conceptual framework for a secure and efficient payment architecture that integrates QR codes with advanced cryptographic techniques, aiming to inform future research and development in secure digital payment ecosystems.

One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

The widespread use of industrial automation Internet of Things (IoT) devices has seen great transformation in operational efficiency and decision-making. But these interconnected environments have nevertheless raised a major concern about their security challenges. Blockchain technology is a promising solution to the challenges of infrastructure because of its decentralized, transparent and tamper proof characteristics. We discuss in this paper how blockchain based security frameworks are being used in IoT devices for industrial automation right now. It first conducts an in depth analysis of existing approaches and their pros and cons before concluding with research gaps. In addition, it explores integration of blockchain with involving technologies for IIoT ecosystems’ security.

Providing innovative resource-efficient solutions able to mitigate temporal interference among cloud services, concurrently sharing the same underlying platform, is crucial to deploy highly time-sensitive applications at the edge of the network where resources are strongly restrained, and timing constraints are stringent. A notable example is provided by the allocation of virtualized network functions in the radio access network of modern mobile networks, such as 5G. This paper describes a kernel mechanism that can be applied to the design of an architecture providing fine-grain control of the temporal interferences among concurrent real-time services while avoiding overheads related to machine virtualization. On top of them, a model is proposed to meet the required endto-end application performance through tuning of parameters in the underlying novel architecture. We show that theoretical latency/load curves match closely with experimental data gathered from a real implementation carried out using both a networking microbenchmark and a real IMS application.

Supply chain 4.0 denotes the fourth revolution of supply chain management systems, integrating manufacturing operations with telecommunication and Information Technology processes. Although the overarching aim of supply chain 4.0 is the enhancement of production systems within supply chains, making use of global reach, increasing agility and emerging technology, with the ultimate goal of increasing efficiency, timeliness and profitability, Supply chain 4.0 suffers from unique and emerging operational and cyber risks. Supply chain 4.0 has a lack of semantic standards, poor interoperability, and a dearth of security in the operation of its manufacturing and Information Technology processes. The technologies that underpin supply chain 4.0 include blockchain, smart contracts, applications of Artificial Intelligence, cyber-physical systems, Internet of Things and Industrial Internet of Things. Each of these technologies, individually and combined, create cyber security issues that should be addressed. This paper explains the nature of the military supply chains 4.0 and how it uniquely differs from the commercial supply chain, revealing their strengths, weaknesses, dependencies and the fundamental technologies upon which they are built. This encompasses an assessment of the cyber risks and opportunities for research in the field, including consideration of connectivity, sensing and convergence of systems. Current and emerging semantic models related to the standardization, development and safety assurance considerations for implementing new technologies into military supply chains 4.0 are also discussed. This is examined from a holistic standpoint and through technology-specific lenses to determine current states and implications for future research directions.

This paper deals with two issues: development of some advanced smart grid applications, and implementation of advanced testbeds to evaluate these applications. In each of the development cases, the role of the testbeds is explained and evaluation results are presented. The applications cover the synchrophasor systems, interfacing of microgrids to the main grid, and cybersecurity solutions. The paper hypothesizes that the use of the advanced testbeds is beneficial for the development process since the solution product-tomarket cycle may be shortened due to early real-life demonstrations. In addition, solution users' feedback to the testbed demonstration can be incorporated at an early stage when making the changes is not as costly as doing it at more mature development stages.

получена 2 ноября 2017 Аннотация. Рассматривается задача проверки допустимости конфигураций модульных вычислительных систем реального времени (МВС РВ). Конфигурация считается допустимой, если все работы успевают выполниться на МВС РВ в рамках своих директивных интервалов. Предложена обобщенная модель функционирования МВС РВ и метод построения на её основе модели для конкретной конфигурации. Модель представляет собой сеть временных автоматов с остановкой таймеров. По вычислению сети автоматов предлагается строить временную диаграмму (ВД) функционирования МВС РВ, необходимую для проверки допустимости. В работе обосновывается корректность предложенного подхода. Из спецификаций на МВС РВ был выделен ряд требований, применимых к моделям МВС РВ и их компонентов на выбранном уровне абстракции. Модели считаются корректными, если удовлетворяют этим требованиям. Доказано, что если все модели компонентов системы удовлетворяют соответствующим требованиям, то модель МВС РВ, построенная согласно предложенному подходу, удовлетворяет требованиям к модели системы в целом (то есть корректна), а также является детерминированной. Под детерминированностью понимается однозначность построения ВД сети автоматов, соответствующей заданной конфигурации. Это позволяет использовать для проверки допустимости конфигурации любое вычисление соответствующей сети автоматов, что крайне важно для эффективности предложенного подхода, так как число возможных вычислений сети автоматов растет экспоненциально с числом работ в системе. Выполнение требований корректности к моделям компонентов системы может быть проверено автоматически с использованием верификатора и подхода автоматов-наблюдателей. Все разработанные нами модели компонентов системы удовлетворяют соответствующим требованиям, что было доказано с помощью верификатора UPPAAL. Если пользовательские модели компонентов системы удовлетворяют требованиям корректности, то они могут быть включены в модель МВС РВ, которая при этом останется корректной и детерминированной.

Global supply networks, once designed for maximum efficiency and Just-in-Time (JIT) delivery, are now shown to be highly vulnerable due to the escalating polycrisis of geopolitical instability, climatic disruptions, and pandemics. This vulnerability results in significant costs: a recent study estimates total losses exceeding $2.3 trillion in global production during major disruptions, such as ElectroLean Inc.'s disastrous $1.2 billion failure during Southeast Asian floods. This study addresses systemic vulnerability by proposing and experimentally validating a transformational framework: Antifragile Supply Chain Management (A-SCM). A-SCM is a six-pillar system designed to actively gain strength from instability, going beyond simple resilience (recovery). We demonstrate how combining Strategic Redundancy and Optionality, Enhanced Visibility and Sensing, Decentralization and Modularity, Adaptive Capacity, Ecosystem Collaboration and Trust, and Continuous Learning and Stress Testing enables organizations to not only withstand shocks but also turn them into engines for innovation and competitive advantage. Case evidence highlights its effectiveness: while vulnerable JIT systems collapse, A-SCM practitioners like MediTech Global turned the Suez Canal blockage into a €85 million EBITDA gain through strategic near-shoring and ecosystem flexibility. Implementing this approach requires reevaluating metrics-such as adopting Mean Time To Improve (MTTI) and Optionality Value-and creating environments that reward smart risk-taking. This study offers a comprehensive framework for this vital transformation, exploring pathways, challenges, and sectoral adjustments. The evidence is clear: in the tumultuous early 21st century, survival depends on moving beyond fragile efficiency. Embracing antifragility is a critical strategic shift-turning disruptions into lasting competitive advantages, structural improvements, and ongoing innovation.

The Constrained Application Protocol (CoAP) is a lightweight application layer protocol for the Internet of Things. CoAP is based on HTTP mechanisms to build RESTful web services. In contrast to HTTP, CoAP was designed for machine-to-machine (M2M) communication and uses a binary representation. This allows efficient transport and processing in resource-constrained networks such as Wireless Sensor Networks (WSN). Due to the analogy to REST, a mapping between CoAP and HTTP is possible. In this paper one of the first translating CoAP-HTTP proxies is presented, that provides HTTP clients transparent access to CoAP resources and vice versa. Furthermore, caching relieves CoAP servers, which solves a key challenge for the Internet of Things: to allow a constant/permanent availability of resources from a network of constrained devices, which are required to minimize data transmissions due to their noticeable restrictions in power consumption. We describe, which issues of the translation we found during the time of implementation and testing, and explain how the proxy handles these issues. Finally, an evaluation, using real WSN hardware, is given, and an approximation scheme on how much transmissions can be saved by caching resources is provided.

Decomposition is one of the commonly used techniques applied in the design of Petri net-based cyber-physical systems. Such an operation permits the splitting of the initial system into sequential components that can be further implemented as an integrated or distributed system. This paper is focused on the decomposition of the modelled CPS toward its further implementation as an integrated system, namely an FPGA device. The adequate decomposition method is presented and explained in detail. Moreover, the proposed idea is explained by the real-life example of the beverage production and distribution system. The results of the experiments are presented and discussed.

This study proposes a cyber-physical framework for the integration of a heterogeneous swarm of robots, sensors, microrobots, and AR for structural health monitoring and confined space inspection based on the application's unique challenges. The structural issues investigated are cracks in the walls, deformation of the structures, and damage to the culverts and devices commonly used in buildings. The PC and augmented reality interfaces are incorporated for human-robot collaboration to provide the necessary information to the human user while teleoperating the robots. The proposed interfaces use edge computing and machine learning to enhance operator interactions and to improve damage detection in confined spaces and challenging environments. The proposed swarm inspection framework is called HeSARIC.

The object of research is IEEE 802.11 (Wi-Fi) networks, which are often the targets of a group of attacks called &quot;evil twin&quot;. Research into this area is extremely important because Wi-Fi technology is a very common method of connecting to a network and is usually the first target of cybercriminals when they attack businesses. With the help of a systematic analysis of the literature focused on countering attacks of the &quot;evil twin&quot; type, this work identifies the main advantages of using artificial intelligence systems in the analysis of network data and identification of intrusions in Wi-Fi networks. To evaluate the effectiveness of intrusion detection and cybercrime analysis, a number of experiments as close as possible to real attacks on Wi-Fi networks were conducted. As part of the research reported in this paper, a method is proposed for detecting cybercrimes in IEEE 802.11 (Wi-Fi) wireless networks using artificial intelligence, namely a model built on the bas...

The food manufacturing industry is one of the key manufacturing sectors across the world and is a vital strategic element in all national economies. Currently, it's fifth most automated industry in the organised sector, but it has one of the lowest levels of robotization i.e. 3%. Previously, Industrial robots were only used for packaging and palletizing, now numerous types of industrial robots are currently being utilized in food processing industries (FPI), generally they are focused on the handling high-volume but single-product lines. The availability of highly effective robots, such as the Delta class of robot swiftly perform pick-and-place operations with food products on the production lines and, Articulated robots carrying out specific tasks that demands high level of dexterity and strength. The major advancement in the use of industrial robots is the ability to handle products that are not wrapped, it includes stacking pancakes, waffles, bread, and pizza dough before loading them onto a conveyor or feeding into a wrappers machine. Modern manufacturing techniques like three-dimensional (3-D) printing has simplified the designing and manufacturing of complex grippers used for precise and soft handling of delicate food products. The lighter, compact, and smaller gripper reduces the mass of the tool, allowing for better product handling with less wear and tear. These robots also comply with IP67 rating that makes them more hygienic with no ingress of dust and water in it. The application of industrial robots does not only increase the productivity, but also ensures the food safety and security. Cobots are becoming an essential component of the FPI, particularly in the service sector. With the inclusion of cyberphysical systems (CPS), human operators could instruct robots to carry out activities, ensuring the significant role of industrial robots in the Industry 4.0 revolution.

While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case. Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependencies between the physical and cyber networks. As a result, the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks. Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems. Firstly, they rely on the assumption of a uniform and strong level of interdependency. That is, once a node within a network fails its counterpart fails immediately. Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure, that now serve as a cyber-physical system. The last is responsible not only for the physical operation (e.g., flow of vehicles) but also for the continuous data transmission and subsequently the cyber operation of the entire transportation network. Therefore, the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects. In this paper, we propose a new robustness assessment approach for modern transportation networks and their underlying interdependent physical and cyber network, subjected to earthquake events. The novelty relies on the modelling of interdependent networks, in the form of a graph, based on their interdependency levels. We associate the serviceability level of the coupled physical and cyber network with the damage states induced by earthquake events. Robustness is then measured as a degradation of the cyber-physical serviceability level. The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure. Furthermore, we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level. Results indicate an improvement in robustness level (i.e., 41 %) by adopting the proposed robustness improvement indicator. The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdependencies and key node protection strategies. The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.