The long-term drift is well known as one of the most challenging issues regarding gas sensing and applications involving electronic noses, undermining the reliability and accuracy of these systems over time, as the pattern recognition models lose their capability of performing gas discrimination. In this work, we adopt a probabilistic view of the drift problem. This perspective yields a novel univariate scheme for drift compensation, which is grounded on probabilistic modelling. The potential usefulness of the proposed approach is highlighted through experimental results on the gas classification task, which reveal a superior and more stable performance when compared to traditional component correction multivariate methods. Besides its stable performance, the proposed method also presents several desired attributes, such as computational simplicity, continuous adaptability and suitability for online applications. The experiments were conducted using a dataset lasting 7 months, comprising measurements from three different gases at different concentration levels in an array containing 17 polymeric sensors.

A new proof of Goldbach's Conjecture will be presented in this paper using equations like (a + b) = 2 √ A1 in the first quadrant of the space coordinate system with n dimensions. We shall show that (a + b) is equal to the sum of the two numbers n raised to the power of N by summing (a + b) for any real numbers. It follows that for every pair of real numbers (a+b) must be equal to n n N in quarter of square whose side length is equal to √ 2 times n raised to the power of N. Our method hereby formulates the magnitude of N as n ∈ N , in which n is an infinite positive integral set. Concerning the Goldbach Conjecture, our approach provides original viewpoint and prospects for forking paths in pure mathematics.

In this paper, a new control algorithm was developed for a body weight support system (BWS). BWSs are used for rehabilitation purposes and to help patients regain the ability of walking after conditions like stroke or other neurological disorders. For designing the controller, different approaches, model predictive control; classical PID control and LQR control, have been used and the results are compared.

— The meaningful integration of environmental and neuroimaging information made available by multiple research centers is of significant interest for the study of psychiatric disorders, but is made difficult by having to deal with endogenous and exogenous confounders. To address these open challenges, we developed a pipeline for the extraction of relevant, confounder-free brain-environment latent information with a multimodal convolutional neural network autoencoder (CNN-AE) model. In our study, the model was applied to brain functional connectivity (FC) and environmental data from the multi-site PRONIA cohort, composed of controls and individuals affected with recent-onset depressive (ROD) and psychotic (ROP) disorders. We identified site as a confounder for brain features and diagnosis and sex as outcomes of interest; therefore, we designed the multimodal CNN-AE to integrate brain-environment latent representations, incorporating a strategy for mitigating site effects in the multi-site FC data, facilitating the downstream classification of diagnosis and sex. The site harmonization strategy was designed based on disentanglement representation learning with a site deconfounding block (DB) leveraging a cross-covariance (xcov) penalty. A comparison was performed between the CNN-AE with the site DB placed i) in the brain-related branch, and ii) in the fusion branch, and the CNN-AE without DB, following iii) the state-of-the-art harmonization ComBat model. Our experimental results demonstrate that the CNN-AE with a DB placed directly at the brain-related branch surpassed the state-of-the-art ComBat model in removing site effects and improved the identifiability of sex and diagnosis effects from the fused brain-environment latent space. These findings highlight the potential of embedding the deconfounding processes directly into deep learning (DL) analysis pipelines, offering a more effective approach for addressing confounding variables in neuroimaging studies.

Human digital twin (HDT) is envisioned as a system interconnecting physical twins (PTs) in the real world with virtual twins (VTs) in the digital world, enabling advanced humancentric applications. Unlike optimizing the quality of experience (QoE) of users in single-modal signal transmission for conventional services, users' QoE in multi-modal signal transmission required by HDT is difficult to guarantee. To tackle this, we study an optimization of QoE in multi-modal transmission, focusing on joint visual and haptic signal feedback transmissions from VT to its PT, for providing immersive interactions in HDT. To evaluate a synthesized performance of visual and haptic experiences, we design a comprehensive QoE model, taking into account video quality, continuous video quality switching rate and average haptic feedback error. Then, to maximize QoE with a guarantee on synchronization between visual and haptic signal transmissions, we dynamically optimize bandwidth allocation, bitrate and rendering mode of the video, and haptic signal's compression threshold. To this end, we propose a deep reinforcement learning based algorithm, called VisHap. Furthermore, we build an HDT multi-modal interaction platform for collecting an authentic dataset, and by using it, we conduct experiments, showing that VisHap is not only feasible but also outperforms the counterparts.

Optical flow is crucial for robotic visual perception, yet current methods primarily operate in a 2D format, capturing movement velocities only in horizontal and vertical dimensions. This limitation results in incomplete motion cues, such as missing regions of interest or detailed motion analysis of different regions, leading to delays in processing high-volume visual data in real-world settings. Here, we report a 3D neuromorphic optical flow method that leverages the time-domain processing capability of memristors to embed external motion features directly into hardware, thereby completing motion cues and dramatically accelerating the computation of movement velocities and subsequent task-specific algorithms. In our demonstration, this approach reduces visual data processing time by an average of 0.3 seconds while maintaining or improving the accuracy of motion prediction, object tracking, and object segmentation. Interframe visual processing is achieved for the first time in UAV scenarios. Furthermore, the neuromorphic optical flow algorithm's flexibility allows seamless integration with existing algorithms, ensuring broad applicability. These advancements open unprecedented avenues for robotic perception, without the trade-off between accuracy and efficiency.

Sensing-assisted communication is critical to enhance the system efficiency in integrated sensing and communication (ISAC) systems. However, most existing literature focuses on large-scale channel sensing, without considering the impacts of small-scale channel aging. In this paper, we investigate a dual-scale channel estimation framework for sensing-assisted communication, where both large-scale channel sensing and small-scale channel aging are considered. By modeling the channel aging effect with block fading and incorporating CRB (Cramér-Rao bound)-based sensing errors, we optimize both the time duration of large-scale detection and the frequency of small-scale update within each subframe to maximize the achievable rate while satisfying sensing requirements. Since the formulated optimization problem is non-convex, we propose a two-dimensional search-based optimization algorithm to obtain the optimal solution. Simulation results demonstrate the superiority of our proposed optimal design over three counterparts.

This research paper introduces the Adaptive Genetic Optimization Algorithm (AGOA) as a novel solution for optimizing supply network planning. AGOA leverages the principles of genetic algorithms to adaptively improve network efficiency and cost-effectiveness. Through comprehensive simulations and real-world case studies, AGOA demonstrates superior performance compared to traditional methods. This paper provides a detailed overview of the algorithm, its implementation, and its potential impact on the supply chain industry.

Contributions: This paper presents an innovative course curriculum designed to bridge the gap between electric circuit knowledge and control engineering principles. The curriculum emphasizes hands-on learning through op-amp circuit design projects, fostering a deeper understanding of both theoretical concepts and practical applications. Background: Traditional educational approaches often treat electric circuits and control systems as separate disciplines, hindering students' ability to grasp the interconnectedness of these fields. This disconnect can impede the holistic understanding required for effective circuit design and feedback control system development. Intended Outcomes: The proposed course aims to equip students with a comprehensive understanding of op-amp circuit design principles, proficiency in analyzing and optimizing frequency responses and bandwidth using techniques like zero-pole placement, the ability to translate theoretical knowledge into practical applications in industrial settings, and a stronger appreciation for the synergistic relationship between electric circuits and control engineering. Application Design:The course features a series of three illustrative examples that progressively introduce students to selecting resistor and capacitor values to achieve desired DC gains and frequency responses, analyzing feedback loops using open-loop transfer functions and zero-pole placement techniques for bandwidth optimization, and designing controller architectures for industrial applications by applying the principles learned in previous exercises. Findings: Through student engagement and project implementation, the course demonstrates a significant improvement in students' understanding of both circuit design and control engineering concepts. The hands-on approach fosters deeper comprehension, practical skills, and a more integrated perspective on these interconnected disciplines.

Decision-Making is a fundamental topic in the domain of Autonomous Driving where significant challenges must be tackled due to the variable behaviours of surrounding agents and the wide array of encountered scenarios. The primary aim of this work is to develop a hybrid Decision-Making architecture able to be validated on a real vehicle that marries the reliability of classical techniques with the adaptability of Deep Reinforcement Learning approaches. To address the crucial transition from simulated training environments to real-world applications, this research employs a Curriculum Learning approach, facilitated by the deployment of Digital Twins and Parallel Intelligence technologies, significantly narrowing the Reality Gap and enhancing the applicability of learned behaviors. The viability of this approach is evidenced through Parallel Execution, wherein simulated and real-world tests are conducted simultaneously. Specifically, our approach consistently exceeds the performance benchmarks established by existing frameworks in the Reinforcement Learning literature within SMARTS, achieving success rates greater than 91%. Furthermore, it completes various scenarios in CARLA up to 50% faster than Autopilot, demonstrating improved comfort and safety.

Local electricity markets are increasingly seen as a key factor in democratising the energy system. While there has been a significant amount of research on the technical and economic aspects of these markets, it is still not clear how well they align with the social acceptance of potential participants. The concept of fairness in these markets has been gaining attention, but there has not been a clear discussion or definition of what constitutes fairness in a local electricity market context. Furthermore, some fairness indicators have become popular in the literature on local electricity markets, but have yet to be fully analysed on how well they capture fairness in these markets. This study aims to address these issues by formally defining distributive energy justice in the context of local electricity markets and evaluating how well popular fairness indicators perform based on this definition. The results of this extensive evaluation lead to proposed adjustments to the indicators to make them more suitable for local electricity markets, and a discussion on future research directions for fairness in local electricity markets.

Quantum Key Distribution (QKD) offers a revolutionary method for secure key exchange by exploiting the principles of quantum mechanics. This paper comprehensively reviews the pioneering BB84 protocol, its experimental realization using IBM's Qiskit framework, and the underlying quantum computing concepts. We detail the methodology including qubit encoding, measurement, basis reconciliation, and key sifting, and discuss performance metrics such as the Quantum Bit Error Rate (QBER) and key generation rate. Experimental results obtained on IBM Quantum hardware are presented and analyzed, highlighting the robustness of the protocol and its security implications. The review underscores the importance of integrating QKD with future-proof encryption schemes in a quantum-enabled era.

In-Vehicle Networks (IVNs) have become increasingly complex due to the need to enhance driving experience and provide passengers with a broader range of services. The Controller Area Network (CAN) bus is widely used in modern vehicles, enabling communication between various electronic control units. However, its cybersecurity vulnerabilities require the use of protection mechanisms, such as an Intrusion Detection System (IDS) that can efficiently identify and mitigate threats in CAN networks. In this paper, we perform an extensive and thorough analysis of a CAN dataset, namely CANini. The dataset comes from the augmentation of the CAN-MIRGU dataset, which has been collected from a real vehicle under various driving scenarios. Our analysis offers a comprehensive and extensive characterization of CAN network traffic, examining specifically inter-frame arrival times and patterns in data bytes. We proceeded to test these features by designing a simple anomaly-based IDS that employs a multiclass Decision Tree classifier. Then, a robustness analysis on the IDS has been performed by manipulating the properties of the CAN traffic to find the weaknesses of our selected architecture. Our novel analysis provides critical insights into the behavior and structure of CAN in a typical IVN, which is crucial for the development and validation of a reliable IDS.

In this paper, an adaptive Super Twisting Algorithm (STA) based Second Order Sliding Mode Controller (SOSMC) design using a novel Gated Recurrent Unit (GRU) blended Redial Basis Function Neural Network (RBFNN) structure is proposed. In this GRU blended RBFNN structure, the GRU is placed in 1st hidden layer and the RBF is placed in the 2nd hidden layer to enhance the estimation of unknown function f (x) of the nonlinear dynamical system. The estimated function is used to design an adaptive STA of the SOSMC with adaptive laws for updating STA gains. The closed loop asymptotic stability and finite time convergence of the system is also ensured using the Lyapunov theory. Finally, the controller is implemented on two link Robotic Arm with serial flexible joints to show the performance and efficacy. The experimental results exhibit an excellent tracking performance and robustness of the controller.

In this paper, an optimized hardware architecture is presented for a motor imagery-based Brain-Computer Interface (MI-BCI) tailored for real-time, wearable applications that demand high accuracy and low power consumption. The design captures and filters EEG signals to isolate motor imagery patterns, utilizing Linear Discriminant Analysis (LDA) for feature extraction followed by Euclidean distance-based classification. Key optimizations, including sequential matrix multiplication, pipelining, and an FSM-based control unit, achieve 91.67% accuracy with a power consumption of 2 mW on 180 nm technology, significantly enhancing performance compared to conventional designs.

Plastic pollution in coastal waters is one of the most significant environmental issues facing marine ecosystems today. The accumulation of plastic waste in coastal regions not only disrupts marine life but also affects biodiversity and human health. This study explores the potential of artificial intelligence (AI) in addressing this issue by leveraging drone footage to monitor and manage plastic waste. We developed a machine learning model to automatically identify and classify various types of plastic debris, such as bottles, bags, and microplastics, and used it to detect debris hotspots in coastal regions. The model, trained on a diverse dataset of high-resolution drone images, achieved an accuracy of 89.4%, with strong performance in detecting larger plastic items. By clustering debris into high-concentration areas, the system provides targeted guidance for cleanup operations, improving resource allocation. Compared to traditional methods, the AI-driven approach offers faster, scalable, and more precise monitoring, allowing for continuous surveillance over large, hard-to-reach areas. While challenges remain, particularly in detecting microplastics and addressing environmental variability, the results highlight the potential of AI in enhancing plastic waste management and guiding more efficient conservation efforts. Future research will focus on refining the model’s robustness, integrating real-time monitoring, and exploring the use of AI for autonomous cleanup operations.

The escalating threat of malicious software necessitates innovative detection methodologies to protect critical digital infrastructures. The Contextual Anomaly Graph Analysis (CAGA) framework emerges as a novel approach, leveraging graph-based anomaly detection to identify ransomware activities within complex network environments. By constructing detailed graphs that represent system behaviors, CAGA captures contextual relationships often overlooked by traditional detection systems. The framework's architecture integrates advanced algorithms for graph construction, feature extraction, and anomaly detection, enabling the identification of subtle deviations indicative of ransomware presence. Comprehensive evaluations demonstrate CAGA's high detection accuracy across diverse ransomware variants, including LockBit, BlackCat, and Hive, with minimal false positive rates when tested against benign applications. The framework exhibits scalability, maintaining efficient processing times across varying network sizes, and operates effectively within constrained computational resources. Real-time processing capabilities are achieved through integration with streaming platforms, ensuring prompt detection and mitigation of ransomware threats. Comparative analyses reveal that CAGA outperforms traditional signature-based detection methods, particularly in identifying novel ransomware behaviors. While the framework shows promise, considerations regarding detection latency and performance in diverse real-world scenarios are discussed, highlighting areas for future enhancement. The CAGA framework represents a significant advancement in cybersecurity, offering a robust tool for proactive ransomware detection and contributing to the fortification of digital defenses.

YouTube educational resources can help improve physics classroom instructional practices to promote learning. This study investigated the impact of integrating YouTube-based virtual labs on learners' engagement and understanding in physics classrooms. The study was conducted in a secondary school in Nairobi City, Kenya. The participants were secondary school physics students in classes from one to two. The study applied a mixed research approach. The study participants were grouped into two groups: an experimental group and a control group. Unlike the control group, the experimental group was subjected to YouTube-based virtual labs. The study relied on quantitative data from achievement tests and qualitative data from observations during Virtual labs integrated physics classroom and focus group discussions. The results from achievement tests revealed no significant difference in performance between the experimental and control groups. On the contrary, focus group discussion results showed that incorporating YouTube-based virtual labs in physics classrooms improves learners' understanding. Finally, research findings from focus groups and observations signified that integrating YouTube-based virtual labs in physics classrooms improves learners' engagement.