Simulating the fine geometry of Metasurfaces (MTS) structures in a conventional way is a difficult task requiring huge computational resources. On the other hand, the metallization can usually be modeled as a (cascaded) impedance sheet(s) with modulation scale of the order of the operating wavelength. Such modeling, while homogenizing the currents and fields on the surface, is accurate in predicting the far-field as well as the near-field down to a fraction of wavelength from the surface. However, analyzing an impedance sheet lying on a grounded slab with the Method of Moments (MoM) often leads to ill-conditioning; it is certainly the case when the range of impedance spans both the capacitive and inductive domains. Such impedance range is in practice required for Reflective Intelligent Surfaces (RIS), and for some MTS antennas. This paper proposes a preconditioner aiming to solve ill-conditioning issues caused by the wide range of the surface impedance. The preconditioner involves a multiplication by the conjugate of the MoM matrix followed by a block diagonal preconditioner. The preconditioning is implemented in an accelerated scheme relying on FFTs. Given the resulting well-conditioned system of equations matrix, the unknown current distribution is efficiently computed iteratively using the Generalized Minimal RESidual (GMRES) method. A fast convergence is observed independently of the impedance range.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible  

Researchers have sought solutions to interconnect different radio access technologies, enabling them to coexist in the current and future heterogeneous network environments to obtain additional benefits. This paper presents SVORA, an innovative approach that integrates the concept of Virtualised/Open (V/O)-Radio Access Network (RAN) with a Service-Based Architecture (SBA) in order to optimize the energy consumed by the network and minimize the communication delay. In the context of SVORA, the Delay-aware Energy Efficiencybased RAN selection algorithm (DEER) is proposed. DEER employs utility functions and considers Quality of Service (QoS) metrics such as throughput, resource block utilization, delay, and energy consumption measurements when finding the RAN to best support the requested service. DEER is highly flexible, being able to be tuned to support critical delay services, prioritizes energy saving, or observe a balanced approach. Testing shows that the SVORA-based DEER was able to increase the number of users and the aggregate data rate by about 10%, whereas the delay and energy consumption decreased by approximately 90% and 50%, respectively.

Graph embedding has emerged as a powerful technique for representing graph-structured data in low-dimensional vector spaces, enabling efficient analysis and modeling of complex relationships. We examine a range of graph embedding techniques through a twostep approach. First, we broadly explore prominent methods; then, we investigate approaches that modify these methods, focusing on specific issues. Through this approach, we narrate the evolving research landscape and current trends in graph embedding. We further synthesize this information by categorizing it into a taxonomy of embedding models. Finally, we briefly discuss diverse graph types, generalized graph embedding notations, a generalized design space for developing embedding methods, applications of graph embedding across various domains, and open research directions.

This study compares the effectiveness and correctness of grammar error repair with GPT-3.5 to traditional tools. To improve writing quality and communication clarity in a variety of academic and professional contexts, grammar correction is essential. Using assessment measures including accuracy, precision, recall, and F1 score, the study compares the performance of GPT-3.5 with other grammatical correction tools for corrective tasks. In order to evaluate the practical usefulness of the corrections, the data also contains user input and blind testing. The results show that GPT-3.5 outperforms traditional tools in contextual error management and user satisfaction by fixing grammatical errors exceptionally well, even in complicated sentences with contextual nuances. It does, however, have several drawbacks, including as sensitivity to changes in input and sporadic overcorrection. The study recommends enhancing contextual learning and decreasing overcorrection to further enhance algorithms. It also looks at how GPT-3.5 might be used to improve writing assistance and language-learning materials, outlining potential future research areas to maximize grammar correction AI models.

The increasing complexity of regulatory frameworks across industries presents significant challenges in ensuring compliance, especially as legal and ethical guidelines evolve rapidly. A novel approach to enhancing automated systems' ability to generate legally and ethically sound responses is presented, focusing on the modification of GPT-Neo to incorporate real-time, compliance-driven information retrieval. Through the integration of an external retrieval system, the model dynamically accesses domain-specific legal documents and generates responses that align with complex regulatory requirements. Key metrics such as precision, recall, and compliance score demonstrate significant improvements in the model's capacity to handle diverse compliance scenarios across healthcare, finance, and data privacy domains. The system's ability to retrieve and apply relevant documents during the response generation process allows for a high level of adaptability in meeting evolving legal standards, offering promising implications for compliance automation in highly regulated industries. The findings of this study highlight the effectiveness of combining information retrieval with language model architectures, achieving substantial advancements in accuracy and coherence for compliance-sensitive tasks.

The goal of this study is to reduce rotation angles for faster scans in a gradient-coil-free portable MRI system, employing rotational spatial encoding magnetic fields (rSEM) with non-linear gradients. This is achieved by utilizing a proposed evaluation metric for encoding capability, thereby making optimization feasible. The local k-space-based evaluation metric for each sub-region of image is calculated by summing the area covered by the spokes in the local k-space when the SEM rotates; each spoke corresponds to one angle. This evaluation metric is expected to correlate closely with the image quality of the reconstructed images and is applied for angle selection to accelerate scans in rSEM systems. Greedy algorithm is used for the selection. Both simulations and experiments were conducted to validate the effectiveness of the evaluation method and the selected angle set. The results demonstrate that the proposed evaluation metric for the encoding capability of rSEM has correlation coefficient greater than 0.90 with image quality metrics of the corresponding reconstructed images. Using the angle set selected by greedy algorithm with proposed method, higher image quality with reduced angles is achieved. This further validates the accuracy of the proposed evaluation metric and underscores the necessity of rapid assessment for feasible optimization. A significant reduction of rotation angles is achieved by rotation angles selection using the proposed evaluation method. This facilitates fast scans in gradient-coil-free portable MRI systems with rotating SEM while maintaining high image quality. This advancement is crucial for the practical application of this type of portable MRI.

Flexure bearings provide precise, low-maintenance operation but have a limited range of motion compared to conventional bearings. Here we introduce a new class of bearing – the metamorphic flexure bearing – that retains the advantages of precision, low wear, and low hysteresis over its limited flexure-bearing range, but provides an extended range of motion as needed. This extended range of motion is achieved via a position-activated transition to a conventional sliding or rolling bearing. To demonstrate the operating principles of this new class of bearing, we describe, design, assemble, and test a linear-motion metamorphic flexure bearing using three categorically-different transition mechanisms: a compression spring, a constant-force spring, and a pair of magnetic catches. This design paradigm has the potential to provide various benefits, such as reduced wear, reduced downtime, cost savings, and increased safety, in areas ranging from precision manufacturing to healthcare robotics to biomedical implants.

Ferroelectric Superconducting Quantum Interference Device (Fe-SQUID) has recently emerged as a viable option to realize superconducting computing due to its voltage-controlled switching, which is essential to build large-scale digital circuits. This is the first work to model Fe-SQUID-based logic circuits and develop standard cell libraries compatible with existing EDA tool flows. We provide a comprehensive evaluation of the power consumption and performance of a wide range of Fe-SQUIDbased circuits, benchmarking them against the state-of-the-art 5 nm FinFET-based circuits. Our 5 nm FinFET transistor model is validated against industrial measurements. The validation is conducted not only at room temperature but also at extremely low temperatures, down to 10K, for fair comparisons against Fe-SQUID superconducting circuits. Our findings revealed that contrary to CMOS-based circuits, circuits realized using Fe-SQUID dissipate significantly more power. This presents a substantial challenge within the constraints of limited cooling power budgets in state-of-the-art cryostats. Open source: we will release our developed Fe-SQUID compact model, Fe-SQUID standard cell libraries, and Fe-SQUID-based circuits and make them publicly available in a github repository.

The integration of increasing amounts of volatile renewable energy sources leads to a growing amount of uncertainty. In addition, the number of small-scale renewable units grows rapidly, and the resulting decentralization of supply and demand patterns affects especially the underlying electricity networks. Consequently, reliable and performative forecasts are required, especially for network operators, to cope with the stochastic nature of highly decentralized electricity systems. Therefore, this paper addresses the challenge of selecting among a set of competing forecasting models to predict congestions in power systems with high shares of distributed and uncertain generation. Here, an event-based evaluation framework is applied to different, inter alia, copula-based forecasting models, and compared to other probabilistic metrics, with regard to the resulting congestions. To demonstrate the applicability of this event-based approach, empirical data for a high-voltage distribution network in Germany is used together with intra-day weather forecasts. Furthermore, the different forecasting models are benchmarked to each other with regard to their computational performance.

Performance of MIMO precoder for heterogeneous networks can be hindered by a lack of accurate channel state information. The single-parameter Norm Ball Mismatch Model (NBMM) has traditionally been used to tackle this problem, but its lack of performance has prompted several alternatives including the Sparsity Enhanced Mismatch Model (SEMM) and Orthogonalized SEMM (OSEMM), where its dual parameters allow for greater flexibility in dealing with channel mismatch error. The OSEMM was designed to deal in particular with the problem of high correlated channels but needs to be redesigned for multiuser (MU) transmission where every user is a service user and a victim simultaneously, especially when the users' channels are highly correlated. The impact of correlated channels is more detrimental to OSEMM-based methods than to the NBMM based ones due to the small channel gains resulted from the "decorrelation". Three multiuser OSEMM (MUOSEMM) based schemes are proposed herein that progressively attain better transmission performance than NBMM based schemes. Algorithmic details of the MUOSEMM schemes are given and simulation results are presented to demonstrate their superior performance to that of NBMM, especially in the case where users' channels are highly correlated.

We demonstrate that data-driven system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein architecture. To demonstrate the approach we develop a parsimonious Hammerstein model for a nonlinear CMOS differential amplifier. We train this model on a combination of direct current (DC) and transient Spice (Xyce) circuit simulation data using a novel sequential strategy to identify the static nonlinear and linear dynamical parts of the model. Simulation results show that the Hammerstein model is an effective surrogate for the differential amplifier circuit that accurately and efficiently reproduces its behavior over a wide range of operating points and input frequencies.

The concept of “metaverse” first appeared in Neal Stephenson’s 1992 science fiction novel “Snow Crash,” describing it as a reality shared by millions of users (Stephenson, 2003). Metaverse is a collaborative virtual space using the virtual and environment, created by the combination of the internet and virtual reality (VR), where users can interact and engage with each other and objects (Mozumder et al., 2022). It is a virtual world that allows users to create and explore their own digital identities and experiences and instantly connect with others (Tukur et al., 2024). It is a dynamic and evolving environment that provides new opportunities for discovery, creativity, and connection (Rillig et al., 2022; Allam et al., 2022).

Mutual coupling model is crucial in designing multiple-input-multiple-output (MIMO) antennas since mutual coupling will degrade overall MIMO performance from distorted radiation patterns and reduced antenna efficiencies. Typically, full-wave simulations have to be employed, which is often timeconsuming. Here, the artificial neural network (ANN) method is developed to reduce the modelling time. Compared to previous ANN methods that directly use model parameters as input, a novel physical preprocessing approach is proposed to incorporate antenna correlation information before the network training. As a proof of concept, the mutual coupling model of a nonuniform strongly-coupled array is realized. Furthermore, we use the trained networks for capacity estimation and power allocation with the water-filling algorithm, showing favourable model performance. The proposed physically preprocessed ANN model significantly outperforms traditional analytical solutions and direct modelling networks in terms of prediction accuracy, dataset construction costs, and network convergence, which could facilitate the fast optimization and design of advanced antenna arrays for MIMO communications.

This paper proposes an AC Fault Ride-Through (AC-FRT) control for VSC-Based HVDC systems in accordance with modern grid codes. The presented strategy also provides a backup energy controller for AC faults for cross-control MMCs where the converter total energy is normally regulated through the AC side current. The proposed AC-FRT control is capable of positive and negative sequence voltage support and properly limits current set-points to the specified safe limits accordingly with a tunable response time. Furthermore, the backup energy control allows for a distinct response for AC-FRT operation, independent of the energy controller employed during normal operation. The effectiveness of the proposed methods are validated using time-domain simulations in a Multi Terminal Direct Current (MTDC) grid. Furthermore an analysis is carried out to determine the effects of energy control in Modular Multilevel Converters (MMC) and the AC-FRT controls for different fault scenarios on the AC and MTDC grids interlinked by the converter. The aforementioned analyses will be used to attain a heuristic tuning method for the converter's AC-FRT operation. The AC-FRT control's time constant is the tuning parameter in 2 level VSCs where in the case of an MMC, the backup energy controller can also be designed in tandem with the AC-FRT time constant in order to attain a reasonable balance between AC fault current response time and MTDC grid dynamic variations.

Vehicular Ad-Hoc Networks (VANETs) has drawn great attention due to their potential role in enabling Intelligent Transportation System (ITS). VANETs using a connected-car technology forms an important part of ITS as it provides communication between different neighboring vehicles and infrastructure. The architecture of VANETs includes vehicles and road side units (RSU) to provide secure communication and preventing its architecture from dangerous attacks various algorithms are discussed. A comparison between impacts of attacks on security and privacy of VANET is discussed. Therefore, we comprehensively presented the classification of attacks on the basis of their behavior, trust, architecture and infrastructure. Then their possible cryptographic solutions and analysis between them will be demonstrated. To ensure safety and security in VANETs different type of cryptographic schemes are introduced and their working are discussed. Three main solutions are discussed that are further applied with new technologies. As we have noticed from recent studies the growth in research and the application of these schemes in different projects and real time applications.

Objective. Non-invasive solutions for providing artificial sensory feedback to lower-limb prosthesis users are compact and convenient for clinical translation because they do not require additional surgery. However, they are mostly simpler feedback schemes characterized by limited information bandwidth and low spatial resolution. Additionally, feedback is often assessed using specialized tasks and conditions, which sometimes promote the use of feedback, limiting comprehensive psychophysical and ecological insights. Approach. This study introduces OmniFeel, a novel feedback system composed of eight vibration motors and a sensorized insole, to intuitively convey omnidirectional foot pressure information. It was evaluated psychophysically to test pattern recognition (static and dynamic) and holistically by tracking biomechanical, task load, and user experience outcome measures during an ecological walking task that resembled real-life scenarios. The holistic assessment included walking in a building (overground walking, stairs) with and without a parallel cognitive task and with and without feedback. Ten able-bodied participants, two participants with transtibial amputations (TT1 and TT2), and one with transfemoral amputation (TF1) took part in both assessments. Main results. The feedback scheme was easy to interpret, with a high success rate in recognizing six static and four dynamic spatial patterns, even before systematic training (81.5 ± 7.87% and 95.75 ± 4.42%, respectively). Functional evaluation demonstrated that feedback decreased the task load (NASA-TLX) in most conditions and participants. In two participants with lower-limb amputation, the feedback also improved stance time symmetry (from 55.88% to 74.34% in TF1 and 65.41% to 74.71% in TT2) and substantially increased confidence in TF1, especially in stair ambulation. Significance. The proposed feedback conveys rich information about the foot sole pressure and yet it is easy to interpret. The holistic assessment demonstrates that the feedback can improve the use of a lower limb prosthesis in realistic conditions, but that this depends on the task and participant characteristics.

Incorporating audio comprehension into language models represents a significant leap forward in the pursuit of more dynamic and contextually aware artificial intelligence systems. The novel integration of audio knowledge into the Mistral LLM offers an innovative approach to enhancing its interpretative capabilities, enabling it to process and understand auditory information alongside textual data. Through architectural modifications such as the introduction of an audio encoder and attention-based fusion mechanism, the model demonstrated marked improvements in tasks ranging from transcription accuracy to sentiment analysis and contextual summarization. The empirical evaluation revealed that the audio-augmented model achieved lower Word Error Rates across multiple datasets and higher BLEU scores for audio-textual comprehension, showcasing its ability to handle complex and diverse linguistic environments. Moreover, the model exhibited robustness to background noise and proficiency in cross-language transcription, further highlighting its versatility and potential applications. Although the integration of audio knowledge increased the computational demands of the model, the resultant enhancement in audio comprehension justifies this trade-off, presenting a valuable tool for applications requiring sophisticated spoken language understanding.

Healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues [1]. This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML [2].