A scalable and explainable framework for detecting Ponzi schemes in Ethereum smart contracts using a stacking model

Blockchain technology has reshaped digital finance, enabling decentralized applications (DApps) on platforms like Ethereum. However, these innovations have also facilitated fraudulent schemes such as Ponzi schemes, which deceive users with false promises of high returns. These schemes cause financial losses and weaken trust in blockchain systems. Existing detection methods face key challenges, including limited labeled data, over-reliance on transaction history, and failure to identify scams early. To address these issues, we propose a framework that combines static and dynamic features of smart contracts for early Ponzi detection. Our feature set includes opcode patterns, developer behavior, temporal trends, and metadata, crafted to work independently of transaction data. We enhance feature representation using TF-IDF, CountVectorizer, and Word2Vec for deeper semantic understanding. These features are used to train multiple machine learning and deep learning models such as Random Forest, XGBoost, CNNs, and BiGRUs. A stacking ensemble with a neural meta-learner integrates predictions for improved performance. The model achieves 99% accuracy and an AUC of 0.9522 on a curated Ethereum dataset, handling class imbalance through oversampling and synthetic data generation. We also employ SHAP for model explainability, offering insights into feature importance and promoting transparency. Our framework is scalable and supports real-time monitoring of contracts, helping prevent financial damage by detecting fraud at deployment. This solution enhances the security and reliability of decentralized finance platforms

Shared manufacturing and the sharing economy ideal: Strategic limits in a fragmenting world

This study offers a strategic critique of shared manufacturing (SharedMfg), a concept rooted in the broader sharing economy (SE) and promoted as a mechanism for optimizing industrial capacity through peer-to-peer coordination. While such frameworks emphasize digital platforms, scheduling efficiency, and resource pooling, they frequently neglect the deeper constraints that govern the real-world feasibility of manufacturing. In particular, SharedMfg models are often constructed atop idealized abstractions, treating manufacturing units as modular, cyber-physical assets within an Industry 4.0 ecosystem, while overlooking the material, energetic, and geopolitical foundations on which all manufacturing ultimately depends. Extending beyond critique, we explore the conceptual underpinnings of SharedMfg within its systemic context, a prelude to the layered pyramid model advanced in this study. This paper argues that manufacturing does not evolve autonomously, but rather reflects the socio-political order in which it is embedded. To address this oversight, we propose a layered conceptual framework – a manufacturing transformation pyramid – that begins not with coordination, but with the substrate: matter, energy, and institutional structure. We contend that genuine transformation in manufacturing systems must be grounded in these foundational realities, rather than in digital optimization alone. Absent this grounding, SharedMfg/SE risks becoming a transient theoretical exercise, bounded by the specific conditions of its historical moment and detached from the structural realities that shape industrial capacity

Estimating different velocities in wrist movements from information contained in surface electromyography: Application of a machine learning technique

The study of surface electromyography (sEMG) has several approaches. It is used to classify upper and lower extremity movements by identifying the muscle groups that have been excited to generate movements. In general, movements have certain properties related to the type of movement, the force, and the speed at which they are performed. The hypothesis of this study is that information about different speeds is contained in sEMG signals. Participants performed wrist movements at different speeds, following verbal instructions to alternate between fast and slow movements. Our objective was to estimate whether there is information in the sEMG signal that can be associated with the different speed conditions; therefore, binary differencing (two classes) was chosen to test this. These two conditions (fast and slow) were used as classes for analysis and classification based on surface electromyography signals. The moving window method was used to extract sEMG envelopes at two different speeds performed by the test subjects. A linear discriminant analysis model was created to estimate the velocities with the resulting model. Finally, cross-validation was performed to estimate sensitivity (76.67%), specificity (91.2%), and accuracy (approximately 87%)

Performance evaluation of systematic and nonsystematic polar encoding using FPGA under a practical multipath channel

Polar code is currently implemented in high-speed high performance modern communication systems. The polar code enabled these systems to transmit near Shannon’s limit with minimum bit error rate (BER). However, the performance of polar encoding is enhanced using a systematic encoding technique that convolves the data, providing higher noise immunity. This paper provides the hardware implementation of systematic and nonsystematic encoding using an FPGA to provide fully parallel operation for maximum processing speed. Both techniques' performance is evaluated under the additive white Gaussian noise (AWGN) channel and a practical indoor multipath channel. Both techniques showed a significant BER improvement compared to BPSK. The tests showed that the systematic performance is superior to the nonsystematic technique

A new compact CPW-UWB antenna for advanced healthcare monitoring applications

This paper presents the design and simulation for a microstrip antenna, specifically designed to operate within the ultra-wideband (UWB) frequency range of 3.4226 GHz to 13.4 GHz. The antenna employs a coplanar waveguide (CPW) feeding technique and features a slotted patch mounted on an FR4 substrate. The findings of this study indicate that the antenna effectively spans the entire UWB frequency spectrum, achieving an operational bandwidth of 9.9774 GHz while upholding an input reflection coefficient of less than -10 dB. Notably, this design exhibits bidirectional radiation patterns, distinguishing it from traditional planar or microstrip patch antennas. Its lightweight construction, advantageous emission characteristics, and broad frequency range render this compact antenna highly suitable for various medical applications, including Wireless Body Area Networks (WBANs) and healthcare stations. Accordingly, it presents a promising technological solution for enhancing wireless communication and sensing capabilities in future healthcare initiatives

Using IoT applications for detection of the overvoltage and undervoltage in electrical systems

A significant advancement in managing systems is the development and implementation of an IoT-based voltage monitoring system. The study aims to treat the issue of voltage fluctuations, including overvoltages and undervoltages, which pose risks to electrical devices and infrastructure. Utilizing the ZMPT101B voltage sensor on the Blynk platform and the ESP 32 microcontroller unit, this system offers a solution for real-time monitoring and alerting of voltage situations. The system's structure allows for data collection, processing, and transmission, enabling users to receive notifications on their mobile phones through a user-friendly interface. Extensive testing at voltage levels has confirmed the accuracy and reliability of the system in detecting voltage differences. The results showed a high level of reactivity and efficacy in warning users about possible hazards, which improved electricity efficiency and safety. Further developments in proactive electrical system management are anticipated as future work concentrates on enhancing the system's scalability, predictive capabilities, and integration with smart grid technology. This study demonstrates how IoT technologies have the power to completely transform electrical system monitoring and maintenance, with major advantages for sustainability, safety, and dependability

The effect of climate on water resources in Iraq using AI

Climate change is increasingly affecting global water resources, considering their availability, quality, and distribution. The temperature rise, altered rainfall pattern, and extreme weather incidents further water challenges brought gains in vulnerable but dry areas. In this regard, the study adopted the utilization of AI, in particular, machine learning approaches for climate adaptation sciences concerning water resources. The models of decision tree, Naive Bayes, and linear regression evaluate relationships between temperature, humidity, wind speed, evaporation, and subsequent water balance using climatic data from 1991 to 2021 for three Iraqi governorates: Diwaniya, Najaf, and Karbala. The discovered trend indicates that rising temperature causes an increase in evapotranspiration brought about by water deficiency that persists. The application of AI in the research reflected that while the models can capture long-term phenomena at a gross scale, they are limited in making precise predictions, thereby making it imperative to develop solutions with ensemble learning and deep neural networks. Another thing gleaned from the study is the importance of AI as a complementary tool for water resource management based on data in the face of climate change. Another factor worthy of attention would be how to address the limits of the present when it comes to using data, model interpretability, and interdisciplinary integration so that we can define and implement sustainable climate adaptation options for tomorrow's water security. This study fills the gap in knowledge as it adopted a novel model using AI to predict the effect of climate change on water resources in Iraq. This study also opened a wide gate for future research in this domain

Design and mathematical modeling of new electric vehicles

This paper presents the modeling and optimization of automatic control in electric vehicles (EVs). The performance and overall cost reduction of electric vehicles could be enhanced in multi-speed transmission with some challenges, such as avoiding jerk gearshift that will sometimes demonstrate to be incredible in the event of motor and clutch saturations. This work introduces explicit definitions to understand the jerk gearshift resulting from actuators or motor saturations. The gear shift includes transferring transmission torques from one friction clutch to another. The study of the influence of planetary gear sets on the gear shift dynamics trajectory with impact on the non-jerking. To improve the electric vehicle's performance, the number of gears in automatic transmission is being minimized, as the trucks which continuously increased. The structure of a multi-speed transmission could be optimized by double transitions shift with less difficulty. The simulations result illustrates that the non-overlapping of clutches' inertias phase in the dual transitions shifting could efficiently reduce the shift jerks. The torque phase overlap with the inertia phase of other clutches could be controlling the power loss at law level because of using less shift times. Additionally, this proposal offers tools to compare the transmission architecture through the conceptual designs for new electric vehicles

The analysis of engineering adaptation to global challenges and strategic planning for the future

A global shift towards green and sustainable solutions requires adapting emerging engineering technologies to evolve novel engineering solutions. Such solutions are incumbent to meet the emerging challenges ranging from smart infrastructural needs to sustainable engineering designs for energy production, agriculture, healthcare, and Information Technology. This review encompasses engineering strategies being practiced to serve global challenges around climate change, innovative energy storage, greener buildings, and renewable energy sources. Similarly, the role of recent technologies like artificial intelligence, quantum computing, intelligent robotics, electric vehicles, smart agriculture, automated healthcare, and bioinformatics is elaborated. Further, the role of strategic engineering planning and its adaptation for innovation and tapping digital transformation is discussed. This review also emphasized fostering an ecologically responsible and engineering-inclusive future outlook via tapping into interdisciplinary research and embracing digital technologies into existing engineering frameworks

Modeling the interaction of virtual agents in distributed artificial intelligence systems

Modern distributed artificial intelligence (AI) systems utilize a significant number of virtual agents that must work collaboratively to solve complex tasks. However, existing technologies for organizing their interaction are characterized by certain shortcomings: high computational complexity, simplified operating conditions, poor adaptability to changes, and significant problems in accounting for the diversity of virtual agents and their emotional reactions during decision-making. The purpose of the study is to develop a new approach for organizing virtual agent operations in distributed AI systems that aims to improve their cooperation, coordination efficiency, and adaptability. The methodological foundation of the study was an innovative approach that combined a specialized emotion model containing 100 virtual agents in a two-dimensional space with a complex network of connections between them, with machine learning methods to enhance virtual agent coordination. Computer modeling methods were applied using experiments in the Python programming environment. The research results demonstrate that effective communication methods between virtual agents significantly improve their coordination, and conflicts during task execution are substantially reduced through adaptive mechanisms. The innovative emotion model can achieve high accuracy levels and contribute to the formation of new system behavior that includes sharp changes in collective decision-making processes. It also identifies essential parameters of virtual agent cooperation to ensure stable system operation. The comprehensive approach based on combining rule-based logic with machine learning can effectively improve virtual agent coordination, especially under conditions of their diversity. The AI system demonstrates real capacity for large-scale changes, but is imperfect in reflecting negative emotional states. Such AI system research results are essential for developing autonomous systems, intelligent networks, and collaboration platforms for virtual agents