Vehicular communication systems can be used to improve the safety level of road users by exchanging safety/warning messages. In this paper, we propose an antenna for a pedestrian-to-vehicle (P2V) device that provides a safety service to road workers on the highway or the road environment. A metasurface antenna system was fabricated and tested first in a controlled laboratory environment in an anechoic chamber. The device is designed to be worn close to the body of the road worker and in this framework the specific absorption rate (SAR) has to be evaluated and compared to the threshold values recommended by standards. Field tests results in an environment representative of the highway are conducted at the Transpolis test tracks are also provided to validate the performances of our system. It is shown that the proposed antenna system consisting of two monopoles backed by a metasurface reflector is an excellent candidate for P2V communications based on IEEE 802.11p systems with desirable radiation pattern, polarization, and safety limits.

In today's Agile and DevOps-driven software development landscape, the need for rapid and accurate bug reporting is more critical than ever. This paper presents a nextgeneration automation tool powered by large language models and machine learning, aimed at innovating the bug reporting process. The tool automates every phase of bug reporting, from failure detection to severity assessment, duplicate detection, and report generation. By addressing the limitations of manual bug reporting such as inconsistency, scalability challenges, and time inefficiencies, the proposed solution enhances the software testing workflow. Initial findings demonstrate significant time savings, reduced manual errors, and improved collaboration between testers and developers. This work establishes a foundation for fully automated bug reporting, poised to accelerate software development cycles while maintaining high-quality standards.

Early detection and identification of risk markers is extremely important in managing certain neurodegenerative diseases. Identifying risk-genes is an important research trend in this regard, along with the field of imaging genomics-to understand the relationship between brain activity and genetics [3] Drawing from these themes, this paper explores the possibility of a connection between individuals' resting brain activity & the presence of the G allele of the Phosphatidylinositol-binding Clathrin Assembly Protein (PICALM) gene, towards early detection of Alzheimer's Disease. The G allele is a known risk factor associated with Alzheimer's Disease [AD] progression. To measure resting brain activity, I used the beta frequency values of the electroencephalogram (EEG) readings of sample patients. A dataset of 79 patients' EEG recordings and PICALM allele data from OpenNEURO was used in this research. From the EEG recordings, each patients' beta frequency attributes were determined using spectral analysis, using Minimum Norm Estimation (MNE) and other related computational libraries. Subsequently, I explored statistical correlation between beta wave characteristics from frontal channels and the presence of PICALM G allele, and observed strong positive correlation between the presence of the G allele and peak resting beta frequencies, specially in certain localized brain areas. This indicates the possibility of developing easily accessible risk screening for AD in the future, using standard EEG scans (e.g. 10-20 channels-as opposed to the more expensive 120/240 channel set ups) on resting patients (i.e. without any need for cognitive tests). I want to build on my findings and explore similar correlations between AD and non AD patients and also conduct research on other medical markers as well (i.e. in addition to the presence / absence of G allele). The result, approach and resources used in this paper will also be useful for new researchers wishing to explore EEG analysis in general and low cost AD screening, in particular.

Stability of multi-vendor power electronics-dominated grids requires proper specifications for converters interoperability, e.g. tuning boundaries for the converters' control loops to be respected by all vendors. The confidentiality and variety of implementation of the converters' control system by different vendors and the nonlinear dynamics of power converters make this task arduous. This paper formulates this research question as a robust stability problem, embedding frequency-dependent uncertainties in the converter control systems and analytically quantifying the amount of uncertainty able to warranty the power grid stability. Grid following converters are considered and modeled with a nonlinear grey-box approach, in contrast with conventional small-signal black-box models which suffer from inaccuracy when their operating point deviates from the nominal one. This paper also demonstrates both analytically and with Hardware-In-the-Loop (HIL) experiments that different converters operating points (e.g. active power injections, ac bus voltages) radically affect their stability, empathizing the necessity of nonlinear converters models to realize accurate power grid stability analyses.

This study explores the influence of both laser spot size and sizing threshold in Surface Scanning Inspection Systems (SSIS) on cluster identification. Reducing the laser spot size enables the detection and differentiation of smaller point defects while simultaneously limiting the specified search distance, collectively influencing cluster identification. A smaller sizing threshold introduces more point defects, enhancing the likelihood of cluster identification. However, this also leads to a shift between clusters and scratches, as well as cluster merging, ultimately resulting in a decrease in the overall quantity of clusters.

In this correspondence, we point out an error in the proof of Theorem 1 of [1] and provide the revised versions of definitions to correct this error. In addition, the statements of similar conditions are also corrected.

Ransomware continues to pose a growing threat to global cybersecurity, with attackers leveraging increasingly sophisticated techniques to evade traditional detection mechanisms. A novel hybrid approach combining Extreme Gradient Boosting (XGBoost) with Recurrent Neural Networks (RNNs) introduces a powerful solution for detecting ransomware through the analysis of opcode sequences. XGBoost's capacity for efficient feature selection, coupled with the RNN's ability to model the temporal structure of sequential data, allows for a comprehensive detection framework capable of identifying ransomware with high accuracy. The proposed method achieves significant improvements over traditional machine learning algorithms, as demonstrated through rigorous experiments and evaluation metrics. By effectively addressing the complexities of opcode-based detection, this approach provides a scalable and adaptable solution for modern ransomware defense, offering enhanced detection performance and the potential for real-world application.

We present a bilayer metasurface design for efficient polarization conversion of linearly polarized electromagnetic waves operating at the center frequency of 280 GHz. The metasurface unit cell consists of a substrate sandwiched by two identical metallic double-split ring resonators (DSRRs). Through careful selection of split opening angle and angular offset between the two rings, a phase shifts up to 2π with more than 80% polarization conversion efficiency across a broad bandwidth is achievable. The wide bandwidth and high efficiency performance are attributed to the Fabry-Perot-like effect i.e. a cavity is formed between the two DSRRs where multiple reflections occur, leading enhanced transmission. A semi-empirical equation to calculate the total transmission has been derived and validated through fullwave numerical simulations. In addition, we demonstrate a lowprofile polarization conversion lens using the proposed meta cells. Experimental and simulation results show that the lens has a gain enhancement exceeding 16 dB with one full 2π phase coverage, operating at the frequency of 280 GHz. The proposed metasurfaces are anticipated to inspire new designs and studies on intricate wave-manipulating devices, finding applications in wireless communications, radar, imaging, and spectroscopy.

In recent years, large language models (LLMs) and generative AI have revolutionized natural language processing (NLP), offering unprecedented capabilities in education. This chapter explores the transformative potential of LLMs in automated question generation and answer assessment. It begins by examining the mechanisms behind LLMs, emphasizing their ability to comprehend and generate human-like text. The chapter then discusses methodologies for creating diverse, contextually relevant questions, enhancing learning through tailored, adaptive strategies. Key prompting techniques, such as zero-shot and chain-ofthought prompting, are evaluated for their effectiveness in generating high-quality questions, including open-ended and multiple-choice formats in various languages. Advanced NLP methods like fine-tuning and prompt-tuning are explored for their role in generating task-specific questions, despite associated costs. The chapter also covers the human evaluation of generated questions, highlighting quality variations across different methods and areas for improvement. Furthermore, it delves into automated answer assessment, demonstrating how LLMs can accurately evaluate responses, provide constructive feedback, and identify nuanced understanding or misconceptions. Examples illustrate both successful assessments and areas needing improvement. The discussion underscores the potential of LLMs to replace costly, time-consuming human assessments when appropriately guided, showcasing their advanced understanding and reasoning capabilities in streamlining educational processes.

Bila theory is constructed by applying modern physics steps, from action proposal to quantization and renormalization. This paper presents the detailed process of developing Bila theory, including fields, interactions, and symmetry groups. Specific applications of this theory in digital communication, image communication, written communication, symbolic communication, interpersonal communication, and machine-to-machine communication are also discussed.

Wireless sensor networks (WSNs) were originally envisioned to consist of a massive number of diminutive, lowcost, and long-life wireless sensors. However, this vision has faced significant obstacles, particularly in transferring sensing information to data centers. This article proposes a novel protocol, termed relaying-as-a-service (RaaS), to leverage public smart devices to relay sensing information to data centers. By offloading major communications and networking operations to these more powerful smart devices and well-developed fifth generation (5G) or sixth generation (6G) networks, the size and cost of sensors can be significantly reduced, and the need for extensive network infrastructure can be eliminated. This reduction in complexity can greatly streamline the development and implementation of WSNs and Internet of Things (IoT) solutions. Furthermore, using low-power sensors enables large-scale frequency-reuse, which can help alleviate the load on the already congested spectrum.

Understanding brain dynamics during motor tasks is a significant challenge in neuroscience, often limited to studying pairwise interactions. This study provides a comprehensive hierarchical characterization of node-specific, pairwise and higher-order interactions within the human brain's motor network during handgrip task execution. Methods: The cortical activity was reconstructed from the scalp EEG signals of ten healthy subjects performing a motor task, identifying five brain regions within the contralateral and ipsilateral motor networks. Using the spectral entropy rate as the basis for the decomposition of dynamic information in the alpha and beta frequency bands, we assessed the predictability of the individual rhythms within each brain region, the information shared between the activity of pairs of regions, and the higher-order interactions among groups of signals from more than two regions. Results: An overall decrease in hierarchical interactions at various orders within the motor network is observed during motor task execution, primarily in the alpha frequency band, due to the well-known sensorimotor mu rhythm. Conclusions and Significance: This work emphasizes the importance of examining brain interactions at multiple levels within the frequency domain using a unified framework which fully captures the complex dynamics of the motor network highlighting the critical role of its hierarchical organization.

The increasing complexity of linguistic tasks has driven a need for more adaptable and contextually aware language models, capable of producing accurate and coherent outputs even when faced with ambiguous or evolving input. Traditional token embedding methods, which rely on static representations, often fall short in capturing the complex relationships between tokens and their contextual dependencies. Introducing a novel approach, Dynamic Token Embedding Sampling (DTES), allows embeddings to be dynamically adjusted during inference based on the evolving input sequence, offering a significant improvement in predictive accuracy and reduction in hallucinations. Through seamless integration into transformer architectures, DTES enhances both the flexibility and efficiency of token processing, ensuring more precise and contextually relevant outputs across various natural language processing tasks. Experimental results demonstrate considerable improvements in key metrics, including accuracy, inference speed, and factual consistency, further highlighting the potential of DTES to optimize language models for real-world applications. The method's scalability and adaptability across diverse linguistic domains make it a compelling advancement in the ongoing effort to improve the performance and reliability of language models in increasingly complex environments.

Amorphous magnetic alloys are now widely used in electronics, electrical engineering, robotics, sensors, etc. They are mainly used in the form of wires, tapes and foils. The technology of manufacturing products from amorphous magnetic alloys is constantly improving and new methods are being implemented. In our work, microspirals (microsprings) made from amorphous microwires are studied. Measurements of hysteresis loops for different types of microspirals have been carried out and the relationship between the hysteresis anisotropy and the parameters of the spirals is discussed. Special attention is given to the study of the magnetic impedance. It is found that the field dependence of the impedance of microspirals is asymmetric, and the sign of the asymmetry is related to the chirality of the spirals. The possibility of using amorphous magnetic microspirals for the development of sensor and microrobotic elements is discussed.

Lower limb exoskeletons play an increasingly important role in rehabilitation and mobility assistance, often requiring real-time human activity recognition (HAR) to deliver seamless assistance and natural interaction with the user. This study evaluates the effectiveness of various sensor types in HAR, employing three deep neural network models (MLP, LSTM, and CNN-LSTM) on an open-source dataset featuring 21 subjects performing six different locomotion activities. The dataset includes data from inertial measurement units (IMUs), goniometers, and marker-based motion capture derived joint angles using inverse kinematics (IK). To ensure real-time applicability, we trained these networks with a very short sliding window (50 ms) of timeseries data from different sensor combinations. Our results show that using bilateral IK joint angle data from all three lower limb joints can achieve an accuracy of 98.98%. Accuracy decreases as fewer joints are used, with 97.77% for the hip+knee combination and 91.79% for the hip joint alone. The addition of a thigh IMU to the IK data consistently improves HAR accuracy across all configurations. Moreover, incorporating derived joint angular velocity notably enhances HAR accuracy, improving accuracy of goniometer up to 10% for a single joint (knee) and 2% for two joints. For bilateral IK data, this improvement goes up to 15% for a single joint (knee).These findings highlight the substantial benefits of utilizing joint angle, velocity, and acceleration data obtainable from wearable exoskeletons and IMUs for real-time HAR and adaptive assistance in lower limb exoskeletons. The insights gained are pivotal for developing minimalistic HAR approaches and optimizing the seamless integration of exoskeletons into everyday activities.

This paper describes the properties of pin Ge1-ySny diodes (y=4.4-10% Sn) grown directly on Si(100) wafers as a way to investigate the impact of eliminating the Ge buffer layers used conventionally for the integration of GeSn devices on Si. The technology offers a simplified and potentially lower-cost alternative for SWIR-LWIR applications. Two device designs are discussed. The first design adopts a layer sequence n-Ge1-ySny/i-Ge1-ySny/p-Ge1-ySny/Si, featuring a single defected bottom interface between the p layer and the Si wafer. This was followed by an even simpler n-Ge1-ySny/i-Ge1-ySny /p-Si heterostructure design. In both cases, the top i/n interface is pseudomorphic and potentially defect-free. The Ge1-ySny layers are produced by CVD reactions of Ge3H8 and SnH4 at temperatures ranging from 300 o C to 390 o C. The n-type electrodes in the samples were doped with As using As(SiH3)3, and the p-type GeSn layers were doped using diborane as the source of B-atoms. All samples were characterized by XRD, RBS, IR-ellipsometry, AFM and TEM. The layers were found to be monocrystalline single-phase alloys exhibiting mostly relaxed strain states and top surfaces devoid of the cross-hatch surface patterns that are typical of Ge1-ySny films grown on Ge buffers. Current voltage I-V curves of fabricated devices over the 4.4-10% Sn range of interest showed that rectifying behavior is readily attained. It appears that the effect of eliminating the Ge-buffer is an increase of only one order magnitude in the density of defects responsible for the dark current, together with an increase in residual doping in the nominally intrinsic layer. The results suggest that these deleterious effects may be further reduced with improved sample designs, particularly at high Sn-concentrations, opening up new alternatives for the effective integration of GeSnand Si technologies.

The advancement of data analysis and classification is crucial for extracting valuable insights from vast datasets across multiple domains. In this work, we present a suite of innovative techniques that enhance these processes by employing cutting-edge algorithms and methodologies. A primary focus is on novel feature extraction methods that ensure relevant data is captured while limiting the influence of noise. We further adopt ensemble learning strategies that amalgamate multiple classifiers, leading to improved predictive accuracy. Data preprocessing plays a vital role in our approach, emphasizing normalization and transformation for optimal dataset readiness. Implementing these techniques in diverse fields such as healthcare, finance, and social media reveals significant improvements in classification accuracy and data insights. Comprehensive evaluations on benchmark datasets confirm the effectiveness and robustness of our proposed methodologies, offering practitioners essential tools and guidelines for addressing complex data-related challenges in their domains.

In engineering science, the strategies of data-based control synthesis are valuable tools that allow to obtain a control law without the need to identify or to model the system through its related physics. However, the inherent presence of noise when acquiring data represents an obstacle in the validity of these data to represent accurately the system. In this paper, Lyapunov stability theory, dissipativity theory and Petersen's lemma are utilized to develop an algorithm. The proposed algorithm uses sum-of-squares programming to design a control law that guarantees asymptotic stability of polynomial systems with noisy data. In addition, the develop method is tested in comparison to a D-K iteration method in order to evaluate its performance in terms of computational costs.