AI-based monkeypox detection model using Raspberry Pi 5 AI Kit

Monkeypox is a zoonotic disease that originated from monkeys and then spread to humans; this disease recently popped up globally with increased risks of spreading from human to human and clinical presentation similar to other pox-like diseases. Quick and right identification is fundamental for containment and treatment that will minimize the spread of the disease. The current conventional diagnostic techniques include PCR which takes time, and money, and often needs sophisticated laboratories that cannot be easily accessed in developing countries. This work describes the creation and application of a monkeypox detection algorithm orchestrated on the Raspberry Pi 5 AI Kit. Developed based on convolutional neural networks (CNNs), the model enables one to distinguish actual monkeypox lesions in the images. The Raspberry Pi 5 AI Kit allows for edge computing solutions to be implemented, making the entire solution mobile, affordable, and perfect for locations with low connectivity. Extensive data collection and data preprocessing were performed, and the final dataset with monkeypox and skin lesion images consisted of more than 5000 verified images. 94% accuracy was obtained by the model, making it superior to the model available in literature. The implementation proves that powerful AI technologies can be applied to low-cost hardware to become a valuable weapon in the monkeypox frontline workers’ arsenal and advance the efforts against monkeypox infections

Blockchain technologies and their application in security software development

Current factors like the rising frequency of cyber threats and vulnerabilities on centralized platforms indicate the inefficiency of conventional network security frameworks, leading to new solutions such as the blockchain. This review systematically reviews developments of blockchain technologies in the context of security software (2021-2023) to evaluate its efficacy and challenges and explore the future potential. 77 peer-reviewed papers from ScienceDirect, IEEE Xplore and Scopus; adopting the PRISMA guideline, records were screened down from 1,532 to 77. Empirical evaluations (35%), case studies (28%), and theoretical frameworks (37%) using Joanna Briggs Institute tools and the Newcastle-Ottawa Scale were used in mitigating bias. Our results show that blockchain has strengths that add to data integrity (89% of studies) and security of the Internet of Things (IoT) ecosystem (28 studies) and supply chains (15 studies). Nevertheless, blockchain-based authentication has reduced latency by 284% (342 ± 112 ms) compared to a traditional system and has tradeoffs with scalability and performance. Research is skewed towards finance (47%), missing healthcare (9%), and critical infrastructure (6%). It does not include sufficient interoperability standards, post-quantum cryptographic validation, etc. The adaptive regulations are urged for policy implications for editable blockchains and hybrid Artificial Intelligence (AI) blockchain architectures. Interoperability should be taken care of by cross-chain protocols, scalability trilemmas and real-world adversarial testing must be addressed by the researchers and practitioners must put priority on scalability. This review, in its totality, brings out the singular role of blockchain in complementing the existing security solutions instead of replacing them. It calls for cross-disciplinary involvement and partnership in harnessing technical innovation in a regulatory framework to tackle cybersecurity threats through outsider and insider security approaches

New microstrip bandpass filter design with sharp roll-off based on rectangular split resonators

This paper presents a compact bandpass filter (BPF) with a triple rectangular split resonator optimized for high performance at a central frequency of 3.682 GHz. The filter achieves an exceptional voltage standing wave ratio (VSWR) of 1.088 and a return loss of 27.48 dB, demonstrating superior impedance matching. Additionally, the filter exhibits minimal insertion loss with S21?=0.38dB, ensuring efficient signal transmission. The design boasts a sharp roll-off rate of 87 and a narrow transition band of 0.196 GHz, making it suitable for high-selectivity applications. The compact size of the filter, measuring only 24?mm×24?mm, enhances its applicability in modern communication systems with limited space requirements

Reconfigurable metasurface based on graphene optical antennas for dynamic beam steering

Metasurface represents a transformative advancement in photonics due to its exotic abilities to control electromagnetic wave properties. The integration of graphene and metasurface propels metasurface to new heights of compact footprint, reconfigurability, and multi-functionality. In this article, a reconfigurable metasurface based on graphene optical antennas is designed as a reflective surface that controls the beam steering by tuning graphene’s Fermi energy based on the concept of a phase-shifting surface. The results demonstrated that the designed metasurface can dynamically steer the reflected beam at different reflection angles, in addition to their capability to reflect a single beam and three beams. The metasurface exhibits high gain and directivity at different reflection angles. These steering capabilities provide a potentially efficient method for developing and simplifying dynamic reconfigurable beam-steering systems

Wavelet decomposition and statistical characterization for unbalance detection in rotating systems

Vibration analysis is a crucial tool for the early detection of faults in rotating machines, as it allows for the prevention of major damage and avoids significant costs associated with these faults. This study examines the phenomenon of imbalance in rotating machines, using signals generated on a test bench at the Santander Technological Units, where specific fault conditions were replicated. The signals obtained were analyzed using wavelet decomposition, from which key characteristics were extracted, such as root mean square (RMS), peak value, kurtosis, and mean absolute value (MAV). These characteristics were then compared using box plots to evaluate the separation between signals from unbalanced machines and those in a fault-free state. This analysis allowed us to identify significant differences between the two conditions, demonstrating the effectiveness of the approach in detecting faults due to imbalance

A method of representing design solutions in complex systems through model-parametric spaces

On the side of highly complicated systems, it is necessary to have powerful frameworks that can present solution design integration and visualization in the best possible manner, dealing with clarity, scalability, and adaptability. This work aims to formulate an innovative approach for modeling design solutions using model-parametric spaces to create a systematically structured yet convenient method for dealing with multidimensional design complexities. This investigation is conducted within a mixed-method research design combining qualitative assessments of system architecture with quantitative modeling techniques to formulate parametric spaces where design variables and their interrelations are parameterized systematically. The validation of that methodology was done in a way that involves simulated operational scenarios and expert-driven evaluation, which shows the robustness and versatility of the understanding achieved using the approach in different fields of study. Results reveal the utility of model-parametric spaces in vastly increasing the interpretability, modularity, and optimization capability of complex design processes. Therefore, the study argues that this methodology framework is a solid and reasoned basis for decisions in the systems engineering domain and positively explains both research and industrial applications. These future research trajectories determined by this study include further extensive validation within actual project settings to make the developed software more applicable and impactful in the physical world

Enhancing the optimization of resource distribution for eMMB and URLLC services within 5G wireless network architectures

The complex dilemma of resource allocation and management in the 5G network priority system, particularly for eMBB and URLLC services, is a pressing and critical issue that necessitates comprehensive research and strategic actions to enhance the performance and user experience of modern digital communications. This situation urgently requires the development of innovative spectrum sharing strategies, prioritization methods, and adaptive algorithms to cope with real-time fluctuations in network conditions. The fusion of machine learning and artificial intelligence can significantly enhance these methods by predicting traffic trends and proactively adjusting resources, ensuring that both eMBB and URLLC services meet their respective quality of service standards. This paper introduces a Q-learning-based particle swarm optimization algorithm for efficient resource allocation techniques. The implementation of edge computing can further alleviate some of these challenges by performing data processing close to the user, thereby reducing latency and improving the response time of URLLC applications while meeting the high throughput requirements of eMBB

Driven gamification by AI in a time series healthcare case study: Statistical intervention analysis

With artificial intelligence (AI), gamification has emerged as a promising strategy for improving patient engagement and rehabilitation outcomes. This study investigates the impact of AI models. (GRU, TCN, and ARIMA models ) Driven gamification on stroke rehabilitation by analyzing engagement metrics, functional independence improvement, and motivation scores. A simulated 180-day recovery dataset. SHAP analysis and performance comparisons provide insights into model interpretability and the influence of interventions performed using R. Results indicate that AI-driven gamification significantly enhances patient engagement, improving rehabilitation outcomes. The study provides a data-driven foundation for integrating AI-driven gamification in healthcare interventions. Also, this research simulates the application of a game-based cognitive therapy on day 91 and studies its effects using AI models for intervention analysis on simulated stroke recovery data with time series modeling as a forecasting model

Integration of cloud computing and artificial intelligence to optimize economic management processes: a systematic review

This systematic review examines existing literature on the role of AI-driven cloud computing in optimizing economic management processes, identifying key trends, benefits, challenges, and future research directions. The study adheres to the PRISMA framework to systematically collect and analyze research from academic databases, including Scopus, Web of Science, IEEE Xplore, and Google Scholar. Findings reveal that AI-powered cloud solutions offer scalability, real-time data analytics, cost reduction, and automation of business processes. However, challenges such as data security risks, ethical concerns, and regulatory constraints hinder full-scale adoption. The study also highlights emerging trends, including AI-driven financial forecasting, intelligent automation, and Explainable AI (XAI) models, which facilitate transparent decision-making. Additionally, the research identifies gaps in the literature, particularly in the adoption of AI within public sector economic management and regulatory frameworks. The discussion compares these findings with existing studies, exploring theoretical and practical implications for businesses, policymakers, and researchers. Key recommendations include the need for robust cyber-security frameworks, ethical AI governance, and industry-specific AI applications. Future research should focus on longitudinal studies, cross-sectoral analyses, and the role of AI in sustainable economic growth. This review contributes to the growing body of knowledge on AI-cloud integration, offering insights to drive effective and responsible adoption in economic management

A cross-sectional statistical survey analysis on consumer perceptions of domestic relative to foreign goods in Iraq

The research aims to identify the most important factors affecting the acquisition of local or imported goods by designing a questionnaire form that was distributed to a sample of shoppers in Iraq. The Chi-square test was used in the statistical analysis, while the characteristics that were used in the analysis were demographic characteristics, namely sex, age, educational attainment, profession, culture, intelligence, economic status, marital status, number of children, residence, in addition to the personal characteristics that the consumer is accustomed to and other aspects that were addressed. The analysis showed a set of conclusions, in general, that the majority of shoppers prefer cheaper goods if they are of the same quality, and there is a preference for some local needs over imported ones, such as dairy, meat, sweets, and curtains, while imported goods are preferred over local ones when purchasing clothes and furniture