A compact spiral cavity backed substrate integrated waveguide (SIW) Multiple Input multiple output (MIMO) antenna is presented in this paper. The edge-shaped spiral on top of the SIW cavity acts as a dipole antenna. The dual spiral arms are excited from their symmetrical connecting center. The unit cell comprising of dual spiral arms is rotated such that each unit cell is orthogonal to each other forming a compact 4x4 MIMO SIW antenna. The proposed design shows a wide bandwidth of 930 MHz (13.74 GHz to 14.67 GHz) and 67.68% impedance bandwidth. The overall size of proposed MIMO SIW antenna is 0.9 λ o x 0.9 λ o x 0.024 λ o , where λ o is the operating wavelength. A good return loss of-18.4 dB at 14.17 GHz is achieved. The metal pins of plus shape at the center improves the isolation to 19.6 dB at resonant frequency. A pattern diversity is achieved by the top spiral radiating arms and it's complementary structure at the bottom of the cavity. The beam-width of the proposed antenna is 90° varying from-45 deg to +45 deg, useful for reliable signal transmission and reception. Thus proposed antenna design is a symmetrical compact design working at Ku band suitable for radio telescope application.

The circulatory system ensures continuous blood flow to all body tissues. During gestation, progressive changes in the female body and immune system lead to homeodynamic alterations, characterized by increased venous return and cardiac output, enhancing blood flow to meet tissue metabolic demands. This paper presents the Homeodynamic Maternal-Placental Molecular Communication Model (HMP-MC) in the cardiovascular network to accurately represent circulatory components and interactions. The model also analyzes the dynamics of exosomes carrying Zika virus particles throughout pregnancy, integrating new parameters from gestational physiological changes. We quantify the risk of pregnancy over different stages accounting the impact of infection on the placenta viral. This interdisciplinary approach explores cardiovascular interactions during pregnancy analysis over time with the quantification of pregnancy risk.

We present the training and validation dataset to be utilized in the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. This challenge is being virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria, and the Medical Imaging and Signal Analysis Hub (MISAHUB) in collaboration with the 9th International Conference on Computer Vision Image Processing (CVIP 2024) organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India.

Modern academic research encompasses a broad spectrum of subjects, reflecting the intricate and evolving challenges and opportunities that societies encounter globally. This study delves into the role of literature in shaping cultural identities and the profound impact of technology on traditional structures. Current scholarly contributions offer deep insights into the blend of humanistic concerns and technological advancements, underscoring their combined relevance in tackling contemporary societal issues.

The development of increasingly sophisticated language models has led to substantial progress in generating coherent and contextually relevant text for a wide range of applications. Despite these advancements, challenges persist in ensuring that models can integrate newly acquired knowledge without undergoing resource-intensive retraining, which severely limits their adaptability and scalability in dynamic environments. Dynamic Knowledge Synchronization (DKS) emerges as a novel framework addressing this limitation through the introduction of selective, modular updates that allow models to retain their core knowledge while incrementally adapting to new information. By compartmentalizing the knowledge architecture and integrating reinforcement-based feedback mechanisms, DKS offers an efficient method to incorporate new data without compromising the stability and coherence of previously learned knowledge. The study presented here demonstrates the efficacy of DKS in overcoming the rigidity of traditional large-scale training processes through modular parameter adjustments and selective synchronization procedures. Experimental results showed significant improvements in model adaptability, knowledge retention, computational efficiency, and response consistency when compared to conventional fine-tuning or retraining methods. The novel architecture introduced through DKS not only provides a scalable approach to continual learning but also enhances the model's ability to operate effectively across diverse and evolving domains, thereby extending its applicability to real-world scenarios requiring frequent and incremental knowledge updates. Furthermore, the incorporation of attention-based filters and targeted parameter adjustments in DKS contributes to reducing computational overhead, enabling more efficient deployment in resource-constrained environments. This innovative approach redefines the framework for updating and managing knowledge within language models, offering a more sustainable solution for long-term learning while maintaining high responsiveness and consistency in performance.

Although deep learning (DL) has led to several breakthroughs in many disciplines of science, technology, engineering, and mathematics (STEM), a fundamental understanding of why and how it is empirically successful remains elusive. In attacking this fundamental problem and unraveling the mysteries behind DL's empirical successes, significant innovations toward a (unified) theory of DL have been made. Even though these innovations encompass nearly fundamental advances in optimization, generalization, and approximation, no work to date has quantified the testing performance of a DL-based algorithm employed to solve a pattern classification problem. In overcoming this fundamental challenge in part, this paper exposes the fundamental testing performance limits of feedforward neural network (FNN)-based binary classifiers trained with hinge loss. For binary classifiers based on hinge-loss-trained deep FNNs with ReLU and Tanh activation, we derive their fundamental asymptotic testing performance limits that we validate using extensive computer experiments.

Recent advances in stable diffusion models have revolutionized text-to-image generation. However, these models struggle with spatial relationship comprehension in remote sensing scenarios, limiting their ability to generate spatially accurate imagery. We present a novel framework for generating remote sensing images from spatial relationship descriptions with precise spatial control. Our approach introduces a two-stage pipeline: first, a spatial relationship semantic structuring model converts formalized spatial relationship descriptions into controlled layouts; second, an enhanced diffusion model incorporates positional prompts and a layout attention mechanism to generate the final image. The positional prompts explicitly encode spatial information, while the layout attention mechanism enables focused region learning. Comprehensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art approaches in both spatial accuracy and image quality.

This paper introduces the MagNetX -an extension of the MagNet database to investigate transient hysteresis of magnetic materials. By employing automated data acquisition and measurement systems, detailed transient phenomena are observed and evaluated across a wide range of waveforms and operational conditions. Results reveal significant core loss variations during frequency transitions, and the B-H loop analysis further demonstrates loop area changes during transient phases. The platform enables advanced data-driven modeling of core losses and hysteresis loops over a wide range of amplitudes, frequencies, temperatures, and duty cycles under transient conditions, which are critical for practical circuit design when steady-state models are inadequate, such as modeling the magnetic core behaviors in ac-dc converters, motor drives, or power amplifiers.

Silicon photonics is the most viable and cost-effective platform for next generation optical interconnects. One of the key optical devices in these interconnects is an optical modulator which, along with photodetectors, effectively defines performance of the whole optical link. However, due to its centrosymmetric structure, silicon does not offer linear electro-optic effects, leaving plasma dispersion effect as the only feasible modulation mechanism for high speed applications. The performance of plasma dispersion-based modulators is affected by several design variables, affecting various modulator figures of merit (FOMs) where performance trade-off exists. This necessitates design optimization, requiring reasonably accurate mathematical predictive models for all the FOMs. This paper presents a rigorous full 3D model for the junction capacitance of silicon-on-insulator interleaved junction optical phase modulators with submicron dimensions. Junction capacitance is instrumental in calculating power consumption per bit, modulation speed and efficiency of optical modulators. Solution of Drift-Diffusion and Poisson's equations were carried out on 3D finite-element-mesh and Maxwell's equations were solved using Finite-Difference-Frequency-Domain (FDFD) method on non-uniform rectangular grid to calculate confinement factor. Whole of the modeling process has been detailed and all the coefficients required in the model are presented. Model validation suggests < 10 % RMS error.

In the context of high fossil fuel consumption and inefficiency within China's energy systems, effective demand-side management is essential. This study examines the thermal characteristics of various building types across different functional areas, utilizing the concept of body coefficient to integrate their unique structural and energy use traits into a demand response framework supported by real-time pricing. We developed a Stackelberg game-based bi-level optimization model that captures the dynamic interplay of costs and benefits between integrated energy providers and users. This model is formulated into a Mixed Integer Linear Programming (MILP) problem using Karush-Kuhn-Tucker (KKT) conditions and linearized with the Big M method, subsequently solved using MATLAB and CPLEX. This approach enables distinctive management of heating loads in public and residential areas, optimizing energy efficiency while balancing the interests of both providers and users. Furthermore, the study explores how the proportion of different area types affects the potential for reducing heat loads, providing insights into the scalability and effectiveness of demand response strategies in integrated energy systems. This analysis not only highlights the economic benefits of such strategies but also their potential in reducing dependency on traditional energy sources, thus contributing to more sustainable energy system practices.

Cybersecurity threats have escalated rapidly in recent years, with ransomware posing one of the most critical challenges to digital infrastructures globally. Traditional detection mechanisms, while effective against known threats, often lack the adaptability needed to address the fast-evolving tactics employed by ransomware attackers. The RansomGuard framework introduces a transformative, multi-layered approach to ransomware detection, integrating Dynamic Encryption Pattern Analysis (DEPA), Real-Time Behavior Profiling (RBP), and Heuristic Risk Scoring (HRS) to achieve unprecedented levels of accuracy and reliability. By employing adaptive analytical techniques, RansomGuard effectively distinguishes between benign and malicious activities, minimizing false positives and optimizing detection speed. Extensive evaluation across diverse ransomware families demonstrates RansomGuard's ability to achieve high detection rates while maintaining computational efficiency, ensuring its suitability for real-time deployment in dynamic and resourceconstrained environments. This study not only showcases a novel solution to the challenges associated with ransomware detection but also sets a new standard for cybersecurity frameworks aimed at proactive threat mitigation, positioning RansomGuard as a critical advancement in ransomware defense strategies.

Within the color space of 3-coloring, we build deterministic algorithms for plane graphs with no more than 4 to find base configurations that appear in the n-DJC group, and thus find new triplets bringing 3-inseparable vertices. This paper provides the basic theory of the n-DJC group, including the removable conflict group between DJC and the rotation of DJC in the group. We can identify 3-uncolorable vertices according to unreasonable removable conflicts or frozen rotations. And we point out that such n-DJC groups can appear in the color space of planar graph with degree no more than 4.

Multi-antenna technology has been leveraged in the past decades to meet the continuous demand for increased system capacity. User densification and high data rate provision further necessitates the efficient and fair allocation of resources among the users to reap the full benefit of multi-antenna systems. Conventionally, the resource allocation has been optimized using channel state information of users, which poses a performance trade-off considering large overhead in dense system configuration. In contrast, the position information carries the potential for efficient resource allocation with minimal acquisition cost, and can be enhanced by utilizing the power of machine learning (ML) frameworks. This work focuses on applying ML model for fair resource allocation based on position coordinates of the users in the system. Specifically, we evaluate the performance of our previously proposed coordinates-based resource allocation scheme through ML [1] in an interference-limited scenario, along with a fairness metric applied for optimized resource allocation. Results show that coordinatesbased resource allocation through ML can be successfully used for both noise-limited and interference-limited systems, and performs consistently well under different propagation channel conditions. Surprisingly, the learning frameworks used for coordinates-based resource allocation require similar training time, despite the complexity arising due to interference consideration, and has a moderate storage footprint as an implementation cost in the system.

This paper contains a short introduction to Newtonian gravitation. The main focus is on some C++ code.

The Generative Cryptographic Pattern Disruption Analysis (GCPDA) framework offers a novel approach by analyzing cryptographic patterns to identify malicious activities. Through comprehensive evaluations, the framework demonstrated detection accuracies exceeding 95% across various ransomware families, including WannaCry, Locky, CryptoLocker, Petya, and Cerber. Comparative analyses revealed that GCPDA outperforms traditional detection methods, achieving a detection accuracy of 97.0% with a false positive rate of 2.5%. The framework maintained low false positive rates across different operating systems, with slightly higher rates observed in Linux environments. Resource utilization assessments indicated moderate CPU and memory usage during peak detection activities, suggesting suitability for real-time deployment. However, a gradual decline in detection rates for successive generations of polymorphic ransomware highlights the need for continuous refinement. The GCPDA framework represents a significant advancement in ransomware detection, offering a robust and adaptable solution to the evolving threat landscape.

This paper introduces a new receiver coil configuration that dramatically enhances energy transfer efficiency in Dynamic Wireless Power Transfer (DWPT) systems. By reimagining the traditional coil layout, we propose the Vertical Coil (VC) design, which leverages multiple vertically aligned coils wound around a ferrite core to capture magnetic flux more effectively than the conventional DDQ horizontally oriented receiver coils. Full scale VC and DDQ models have been studied by simulation and small, bench-models have been realized and studied both experimentally and by simulations. The VC configuration not only results in a higher peak efficiency when the receiver is right above the transmitter, it also spans a larger range of efficient energy transfer. Our findings demonstrate that the VC receiver has the potential to offer better performances and higher energy transfer efficiency, thereby contributing to the development of dynamic wireless power transfer technology and the EV industry.

5G-based mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the benefits of fusing line-of-sight (LoS) 5G signals with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urban areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G single-bounce reflections (SBRs) and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with a low-cost inertial measurement unit (IMU) and a wheel encoder. Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized extended Kalman filter (EKF) within the 5G-based positioning research community. We performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were generated using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic NLoS and multipath scenarios. For the same trajectories, real motion data were collected from two different low-cost IMUs. Our integrated positioning solution was capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when SBR signals are not considered, which is only around 92% of the time.

— 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.