Epilepsy is a common neurological disorder, and electroencephalogram (EEG) is widely used for its detection and analysis. EEG-based functional brain networks play a key role in revealing epileptic seizure patterns. Multilayer networks, which can capture multiple types of interactions, have shown great potential in this domain. However, previous analysis frameworks often simplified multilayer networks into single-layer subnetworks, ignoring the multilayer structure, or adopted a multiplex analysis framework, ignoring the importance of inter-layer connections. In this study, we proposed a multi-level analysis framework for frequency-based multilayer networks, ranging from supra-adjacency matrix, subnetwork, node, layer, and global levels. We applied this framework to the TUH EEG Seizure Corpus (TUSZ) to investigate the phase synchronization changes between the interictal (between seizures) and ictal (during seizures) phases of epileptic seizures. Compared to the interictal phase, the ictal phase showed increased local connectivity, decreased overall network connectivity, and a shift from random to regular network organization. Meanwhile, cross-frequency coupling (CFC) analysis suggested that δ-γ (frontal and parietal-occipital regions) and θ-α (frontal and right frontal-temporal regions) bands are potentially associated with seizure propagation or termination. These findings provide insights for future analysis of CFC in seizures and intervention strategies by interpreting seizure propagation networks. In conclusion, our framework enables efficient multilayer network analysis in epilepsy, facilitating the identification of key frequency bands and brain regions in CFC analysis.  

In our work, we demonstrate a novel Neural Network (NN) based methodology for generating S-Parameters based on using the meshed and discretized geometry as input to the NN. Our approach would enable a designer to rapidly optimize the performance of vias across an entire substrate. This performance is possible while also achieving a computational time improvement of approximately double that in other NN-based works.

This paper introduces a novel technique based on a planar microwave sensor for contactless monitoring of hydrocarbon contamination in groundwater. Short-chain hydrocarbons are more toxic and volatile, which makes their detection vital for the management of Petroleum Hydrocarbons (PHCs) pollution in aquatic environments. The sensor consists of a tag and reader, which are wirelessly connected; hence, non-contact and highly sensitive sensing is enabled. The tag is fabricated on a flexible substrate for ease in the monitoring of the alkanes. This non-contact nature, coupled with high resolution, makes this sensor ideal for monitoring hazardous and toxic hydrocarbons in groundwater. The detection of Hexane has been studied by varying its concentration in water up to 30% (v/v).

Contemporary control methods for interior permanent magnet synchronous motors frequently depend on mathematical equations or finite element analysis to model motor behavior. However, these approaches are challenging to accurately capture real-world nonlinearities and manufacturing variations, such as manufacturing tolerances and material uncertainties. Therefore, this study proposes a data-driven deep learning approach using long short-term memory networks, trained directly from experimental measurements.The proposed model maintains high accuracy even under challenging driving conditions and successfully captures complex harmonic characteristics. This approach signifies a substantial advancement by training directly from actual motor behavior as opposed to relying on idealized analysis models.The results demonstrate a promising pathway toward accurate and efficient motor control systems that better align with real-world conditions, providing a foundation for improving the design and control of electric drives across various applications.

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference. In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way.

Cross-topology generalization is essential for improving the practicality of deep learning models in power system analysis (PSA) tasks. While large pre-trained models hold substantial promise for PSA, existing research lacks insight into which neurons are pivotal for topology generalization, leading to inefficient fine-tuning processes. This letter reveals the adaptation mechanism of deep PSA models to topological changes by introducing a causally-guided framework to analyze functional differences among neurons. A subset of neurons, termed topology-sensitive neurons, is identified as selectively activated by specific topological inputs and shown to causally influence model performance. Using integrated gradient methods, these neurons are localized and targeted with a masked low-rank adaptation (LoRA) strategy, which prioritizes parameter updates based on causal impact rather than indiscriminate fine-tuning. Experimental results demonstrate that focusing on topologysensitive neurons improves fine-tuning efficiency, reducing trainable parameters by 89% while maintaining high accuracy.

This paper presents the design, fabrication, and application of a strain gauge system for measuring mechanical strain in various materials. Utilizing constantan wire and polyimide film as a substrate, we demonstrate the process of creating a functional strain gauge and its calibration. Experimental results reveal a linear relationship between applied strain and electrical resistance change, confirming the gauge’s effectiveness for real-world applications.

This paper proposes a control system to guide drones in delivering packages autonomously. The objective is to guide the quadrotor to take off from a ground vehicle, go to the delivery point, return to the ground vehicle, and land on it. The drone should disregard the ground vehicle as its leader when flying toward the delivery point and resume its leadership after delivering the parcel. To ensure this, the leader-follower formation control paradigm is adopted, allowing controlling the follower to track the leader's movement as a reference trajectory. To fully accomplish the delivery task, the delivery drone will follow two leaders at two different moments: the delivery point in the delivery step and the ground vehicle when returning to it. The delivery drone should also avoid obstacles, such as buildings and trees, to which the well-known potential field theory is adopted, thus completing the controller design. Results of an experiment on a lab scale are presented and discussed, which validates the proposed control system. Consequently, the correctness of the underlying hypothesis that the leader-follower control paradigm is a feasible solution to control a delivery drone in accomplishing package delivery is confirmed. This is the main contribution of the study. Moreover, a possible generalization of the proposed control system for a set of delivery drones is outlined.

The rapid evolution of language models has significantly enhanced their ability to generate human-like text; however, challenges persist in achieving complex syntactic and contextual understanding. Introducing Dynamic Syntax Transfer (DST), a novel methodology that dynamically adjusts syntactic structures in response to contextual variations, thereby enabling large language models to produce text with improved grammatical accuracy and contextual relevance. The implementation of DST within an open-source language model architecture has demonstrated notable advancements in syntactic precision and contextual coherence, as evidenced by comprehensive quantitative and qualitative analyses. These findings demonstrate DST's potential to advance context-aware syntax modeling, offering a promising avenue for future research and applications in sophisticated language generation systems.

Soliton crystal microcombs, as a new type of Kerr frequency comb, offer advantages such as higher energy conversion efficiency and a simpler generation mechanism compared to traditional soliton microcombs. They have a wide range of applications in fields like microwave photonics, ultra-high-speed optical communication, and photonic neural networks. In this review, we discuss recent developments of soliton crystals microcombs and analyze the advantages and disadvantages of generating soliton crystal microcombs utilizing different mechanisms. First, we briefly introduce the numerical model of optical frequency combs. Then, we introduce the generation schemes for soliton crystal microcombs based on various mechanisms, such as utilizing an avoided mode crossing, harmonic modulation, bi-chromatic pumping, and the use of saturable absorbers. Finally, we discuss the progress of research on soliton crystal microcombs in the fields of microwave photonics, optical communication, and photonic neural networks. We also discuss the challenges and perspectives of soliton crystal microcombs.

In this paper we continue our previous investigation about energy minimization in the flow of fluids through tubes and networks of interconnected tubes of various geometries. We will show that the principle of energy minimization holds independent of the geometry of the tubes and networks of such interconnected tubes and independent of the type of fluid in such geometries where in this regard we consider generalized Newtonian fluids. We consider in this investigation the flow of Newtonian fluids through tubes and networks of interconnected tubes of elliptical, rectangular, equilateral triangular and concentric circular annular cross sectional geometries. We also consider a combination of geometric factor with a fluid type factor by showing that the principle of energy minimization holds in the flow of some non-Newtonian fluids (namely power law, Ellis and Ree-Eyring fluids) through tubes and networks of interconnected tubes of elliptical cross sections. The relevance of this study extends beyond tubes and networks of fluid flow to include for instance porous media and electrical networks.

The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions.

This letter introduces a novel approach for online bad data detection in distribution system state estimation (DSSE) by integrating compressive sensing (CS) with a modified largest normalized residual (LNR)-based detector. To the best of the authors' knowledge, this is the first work to develop a bad data detection method specifically for CS-based DSSE in unobservable distribution networks. The paper derives a closed-form solution for the compressed sensing problem, which is then used to quantify the error statistics in CS-based DSSE estimates. These statistics enable the design of a modified LNR-based detector using eigen decomposition, significantly improving anomaly detection. Extensive simulations on IEEE 37-bus and 123-bus unbalanced distribution systems demonstrate that the proposed method consistently outperforms the conventional LNR approach, achieving superior detection rates even with a limited number of measurements. This robust approach effectively detects data anomalies from both random errors and cyber-attacks, making it highly suitable for practical DSSE applications.

Software engineers integrate third-party components into their applications. The resulting software supply chain is vulnerable. To reduce the attack surface, we can verify the origin of components (provenance) before adding them. Cryptographic signatures enable this. This article describes traditional signing, its challenges, and changes introduced by next generation signing platforms.

It is commonly known that gray matter, a tissue type with high concentrations of neuronal cell bodies, is indicative of brain health. Nonetheless, there has not been a lot of research on the relationship between gray matter volume and incidental memory. In my study, I analyzed the anatomical images of the participants of the Magic, Memory, and Curiosity research project, a study dedicated to the influence of magic tricks on a subject's curiosity and incidental memory. Using the provided masks, I utilized FSL to calculate the ratio of gray matter volume to total brain volume. After doing so, I calculated the p-value and r-value for the correlation between the volume ratio and the eight different memory scores. The recollection and recall scores turned out to be a moderate combination of positive and negative correlation coefficients, and an appreciable number turned out to be statistically significant. It follows that there is a correlation between gray matter volume and recall as well as recognition memory. In the future, scientists could measure the volume ratio of a brain region specifically associated with memory, such as the hippocampus or amygdala. Perhaps, measuring the gray matter thickness or using another set of measures would have also yielded more significant results.

In hospitals, the efficient management of infectious diseases is crucial yet challenging, given the complexity and high-pressure environment that can contribute to bacterial resistance. This paper introduces a system based on the Retrieval Augmented Generation (RAG) Large Language Model for antimicrobial treatment protocols, leveraging the Antimicrobial Treatment Guide from the Albert Einstein Israelite Hospital in São Paulo, Brazil, to standardize and improve antimicrobial therapy adherence to local guidelines providing accurate and timely responses when needed. The system comprises four modules: Document Processing, Query Processing, Large Language Model, and Prompt Management, and utilizes the RAG methodology to generate responses based on external data without requiring training. The methodology involves the manual segmentation of protocols into manageable text chunks, transformed into vectors, and integrated into a vector database for efficient retrieval. Evaluation metrics include the Jaccard index and a System Usability Scale (SUS), supplemented by a behavior change questionnaire. The system achieved a high SUS score of 82, indicating excellent usability, and the model successfully retrieved the correct protocol in 83% of cases. The results demonstrate that the RAG methodology significantly aligns antimicrobial protocols with clinical practices, enhancing diagnostic accuracy and treatment specificity. This approach shows promise in bridging the gap between clinical guidelines and practice, potentially reducing the risk of antibiotic resistance and improving patient outcomes.

Deploying a data-driven model in a real environment is challenging due to the fact that the distribution of the input data is usually not the same as that of the training data. The distribution change can considerably degrade the reliability and robustness of the model output. In this study, we develop a chance constrained back-mapping approach to transform the features of input data with a varying distribution into the distribution of the training dataset, based on the Kantorovich transportation. Since the Kantorovich method cannot guarantee a truthful backmapping of samples with highly variable size and distribution, we introduce a constraint to restrict the transformation by ensuring a prospect certainty of high accuracy of the model output. In addition, to reflect the data variability, the upper bound of the constraint is considered as a random parameter whose distribution is estimated online using the rolling average method. Furthermore, chance-constrained optimization is used to satisfy this constraint in a probabilistic manner. Therefore, the proposed approach enssures a high certainty of high accuracy of the trained model when deployed in a variable environment. Both analytical and experimental verification of the approach are carried out. The results of synthetic and real datasets demonstrate a clear improvement in terms of accuracy and robustness compared with the existing methods.

The transition from idealized descriptions of memristors to physical implementations, such as resistive random access memory (RRAM) devices, is crucial for realizing many neuromorphic computing concepts in hardware. However, the intrinsic variability and fabrication tolerances of real-world RRAM devices present significant challenges for designing reliable circuits. This study focuses on addressing these challenges in the context of a memristive Hindmarsh-Rose circuit, a model for simulating the spiking and bursting behavior of biological neurons. To evaluate the robustness of the circuit in maintaining oscillatory behavior the Variability Intensity Model (VIM) is introduced, which enables a two-stage analysis with respect to the absolute tolerances and the dynamic variabilities. The results show that high conductance states are the most sensitive parameters, while low conductance states are more robust, optimally matching the behavior of real RRAM devices where relative variability decreases with increasing conductance. In addition, the tolerance analysis shows that spiking dynamics are generally robust to parameter variation, while bursting behavior is more sensitive. These results establish parameter constraints for actual RRAM devices as a basis for designing robust RRAMbased implementations of the Hindmarsh-Rose circuit.