In machine learning, dealing with binary imbalanced data classification is challenging due to unequal class sizes, leading to model bias. We propose a unique method that uses filtering, ADASYN oversampling, and ENN cleaning to balance data, improve minority class accuracy, and boost overall model performance, showing significant improvements in AUC, F1, and G-mean metrics.

The Web of Things (WoT) attempts to overcome the Internet of Things (IoT) fragmentation and interoperability limitations by using standard web protocols. While WoT brings a huge potential for developing new and custom distributed applications, the number of system’s components and system complexity keeps growing and is ever more difficult to manage, along with an increase in resource consumption, which is a critical issue in constrained environments as the IoT. In order to tackle this issue, a framework named Self-Orchestrated Web of Things (SOrWoT) is proposed that integrates Hierarchical Finite State Machine (HFSM) constructs into WoT, allowing for the modeling of behavior in applications, breaking down complex systems, and further promoting changes in the architecting of components. A use case for applying SOrWoT’s constructs to a smart city energy management system is discussed, to showcase the framework’s practical application. Our proposed framework has the potential to improve resource efficiency, cut operational costs, and enhance overall performance by decomposing complex processes into smaller, manageable components. This modular and hierarchical strategy also has the potential to improve the system’s adaptability, resilience, and scalability.

Heart disease caused of death worldwide, efforts are continued to treat this hard disease, one of the treatments could powerfully infected for a large number of patients is stent placement. However, accurately determining the necessity for a stent based on coronary angiogram data is challenging due to the complexity of the decision-making process. There are a large number of metaheuristic and optimization algorithms so far could serve of these kinds of determining. However, the results of necessity of stent using optimization algorithms are not promising a happiness results. The current study attempted to discover a promising result for necessity of a stent for infected patients of heart disease. An image dataset has been used including stent and notstent images. This study also aiming to improve accuracy in assessing stent necessity for heart disease infects by leveraging ResNet-50 for feature extraction, combining with six optimization algorithms utilized to obtain and present the promising results using five comparative metrics like accuracy, sensitivity, specificity, precision and confusion matrix. Among those algorithms LPBSA overwhelming the other proposed algorithms by scoring 100% for all the five metrics, however PSO competed comprehensively for the majority of the results with the score of 100% for all the metrics excepting sensitivity metric obtaining 75%.

Diffusion models have become a cornerstone of generative modeling, excelling in tasks such as image creation, text synthesis, audio generation, and scientific simulations. These models rely on iterative refinement processes to generate high-quality outputs, but their computational demands remain a significant bottleneck. The need for hundreds or thousands of sampling steps often limits their practicality in real-time applications, resource-constrained environments, and energy-efficient systems. In recent years, extensive efforts have been made to enhance the efficiency of diffusion models, addressing their inherent computational complexity. Key advancements include the development of faster sampling algorithms, model compression techniques like pruning and quantization, and knowledge distillation strategies to reduce model size without compromising quality. Additionally, the integration of hardware-specific optimizations, such as GPU and TPU acceleration, has enabled diffusion models to perform efficiently on both cloud platforms and edge devices. This survey systematically examines the state-of-the-art techniques for improving the efficiency of diffusion models. It categorizes these innovations into three primary areas: sampling acceleration, model optimization, and hardware-aware design. Furthermore, it highlights the transformative impact of efficient diffusion models in applications ranging from creative industries and real-time systems to scientific research and sustainable AI. By analyzing current trends and identifying open research directions, this survey aims to provide a roadmap for advancing the scalability and accessibility of diffusion models across diverse domains.

Remote photoplethysmography (rPPG) is an active area of research that has extended beyond heart rate (HR) estimation to estimate more complex cardiac signals such as HR variability and blood oxygen saturation (SpO 2). As these applications extend to more challenging domains, the SNR of the physiological signal is increasingly important. This research is particularly useful for enhancing the accessibility and impact of rPPG technologies in consumer electronic devices, such as RGB cameras. Almost all videos recorded with consumer devices use prevailing codec settings, and such video compression is a source of signal degradation. While prior works have assessed the performance of rPPG algorithms under various video compression rates, they have not explored how specific compression configurations can be strategically optimized to better preserve the integrity of rPPG signals. This paper aims to address this gap by identifying a close connection between the projection plane from the principled plane-orthogonal-to-skin (POS) algorithm to the YCbCr color space commonly used in compression. Namely, these projection planes are equivalent, which is a crucial connection that motivates the consideration of alternative color spaces for video compression in physiological monitoring applications. Improved rPPG signal preservation at low bitrates can be achieved using the RGB24 color space for compression compared to YUV444 and YUV420. These results suggest opportunities to reduce error in rPPG applications by treating the video compression color space as a tunable setting in a system's design.

Algorithmic Memory Imprints introduce a transformative approach to enhancing contextual retention and reasoning capabilities in neural language models through the integration of dynamically structured memory representations. The novel framework leverages hierarchical encoding mechanisms to embed semantic patterns, facilitating improved coherence and adaptability in processing extended textual sequences. By integrating memory imprints into transformer-based architectures, the approach achieves significant reductions in perplexity and enhances accuracy across a variety of natural language processing tasks. The computational efficiency of the methodology is maintained through optimized embedding spaces and recurrent feedback loops, ensuring scalability for large-scale datasets without imposing excessive resource demands. Experimental results highlight marked improvements in token-level coherence, reduced repetition in generated text, and higher semantic diversity, particularly in tasks requiring long-range contextual reasoning. Comparative analysis reveals that memory imprints outperform traditional memory-augmented frameworks in both effectiveness and adaptability while avoiding the pitfalls of increased computational overhead. The methodology also demonstrates superior performance in specialized applications, including summarization, sentiment analysis, and conversational AI, showing its versatility and robustness. Analysis of latent patterns reveals that memory imprints enable models to capture and retrieve complex contextual dependencies with unprecedented precision. By addressing limitations inherent in standard attention mechanisms, the proposed approach redefines the structural organization of memory in neural networks. The framework achieves a balance between enhanced contextual understanding and computational feasibility, positioning it as a critical innovation for advancing artificial intelligence-driven text generation. The findings establish a new standard for leveraging memory mechanisms in neural architectures, paving the way for more coherent and contextually aware language modeling systems.

Numerical methods are essential for simulating transient physical phenomena, providing crucial support to engineers and researchers in fields such as defense, space, and the natural sciences. By modeling these phenomena with partial differential equations (PDEs), deeper insights can be gained. Among the various PDE-solving methods, the Crank-Nicolson technique stands out for its efficiency in handling time-dependent PDEs, offering precise and stable solutions across diverse applications. In this work, we present a 3D GPU-accelerated code optimized for parallel execution in three-dimensional space-time domains. The PDE solver is applied to quantum wave function information processing, specifically in the context of the circular double-slit experiment. Additionally, we utilize the solver to simulate a dual Mach-Zehnder interferometer to model the Fenna-Matthews-Olson (FMO) complex, developing a natureinspired algorithm based on its quantum dynamics. This simulation demonstrates near-perfect intelligence in a photosynthetic system without any neural network. This work may help in the design of solar cells, potentially increasing their efficiency to a greater extent.

This paper presents the application of Infrared Thermography to measure distribution transformer efficiency. The method is very intuitive, practical, and easy to use. The results obtained with the proposed method are compared to those obtained by the standard procedure, showing excellent agreement with them. The paper presents the nature of the losses in energy transformation, the theoretical basis of the proposed method, and the comparison of its results with those obtained from the standard procedure.

Automatic detection of early-stage electrical faults in motors is challenging because physical signals, such as temperature or vibrations, are often linked to regular machine operation. Inter-turn short-circuit (ITSC) faults in the stator winding are a leading cause of irreversible damage. Given industrial demands, there is an ongoing need for innovative approaches that reduce dimensionality and expedite feature extraction from three-phase current signals. This work presents an ITSC multi-fault classification algorithm by modeling the behavior of three-phase stator currents in a complex space. To achieve this, two geometric-based and two optimization-based approaches are employed to parameterize the shape of the complex signal. Rather than directly extracting features from the raw signals, these parameters capture the similarities among incipient failures. The algorithm is evaluated using machine learning classifiers trained on a dataset generated from an experimental test bench and a publicly available dataset. The proposed method achieved an accuracy of 95.30% across 13 categories, demonstrating its robustness and reliability and positioning it as a highly competitive alternative to state-of-the-art techniques.

This study proposes an innovative framework called Evolutionary Generative Design (EGD), which integrates evolutionary computation with machine learning. In conventional evolutionary computation, the mapping between the phenotype space and the genotype space (encoding and decoding) has relied heavily on the designer's experience and expertise, posing significant challenges to automation. EGD utilizes autoencoders to automate the mapping process using existing data, enabling the dynamic generation of design spaces. EGD enables efficient exploration and evolutionary design generation by employing these generated design spaces as genotype spaces within evolutionary computation. The automatic creation of genotype spaces allows for dynamic searches of novel and optimized designs, significantly advancing the design process. To demonstrate the effectiveness of the proposed framework, this study constructs an interactive evolutionary computation (iEC) system and applies it to multiple design scenarios, showcasing improvements in customization. Variational Autoencoders (VAE) are employed as the autoencoder model in this framework. These findings highlight EGD's potential to revolutionize design exploration, paving the way for creative and datadriven design innovation.

The capacity of modern artificial intelligence systems to interpret and respond to intricate human language patterns has introduced significant advancements across diverse domains. However, maintaining contextual coherence over extended and semantically complex interactions remains an area of considerable challenge. The concept of Dynamic Semantic Bridging (DSB) introduces a highly novel approach designed to address this limitation through a recalibration mechanism that enhances contextual alignment within large language models (LLMs). By dynamically adapting to semantic shifts within evolving dialogues, DSB enables LLMs to retain thematic continuity, thereby advancing their ability to respond consistently to complex, multi-turn exchanges. The architectural design of DSB integrates a layered, dual-channel approach to contextual embedding, supporting selective prioritization of both primary and secondary context components within each interaction. Experiments reveal that DSB markedly improves coherence scores and contextual accuracy across varied datasets, with a reduction in semantic drift over long-form conversations, ultimately enhancing the interpretative precision of LLMs. Findings from this study highlight the transformative implications of DSB in applications where context-sensitive responses are essential, positioning DSB as a foundational advancement for improving the adaptability and coherence of LLMs in real-world deployments.

The design of dual-band bandpass filters consisting of series short-circuited wire-bonded multiconductor transmission lines and shunt open stubs is thoroughly carried out in this paper. Two different configurations are studied and closed-form analytical design equations are derived to synthesize Chebyshev filtering functions. A novel, simplified and time-saving synthesis procedure to design dual-band filters with narrow or moderate broad bandwidths is finally presented, attending to the required final specifications and the physical and manufacturing limitations. The usefulness and validity of the proposed analytical equations are illustrated by designing, manufacturing, and measuring three different prototypes, showing an excellent agreement between analytical and measured results.

This work encompasses some relevant issues that arise when using a broadband method to characterize the even and odd mode propagation constants of symmetric microstrip coupled lines using four-port pure-mode differential and common mode S-Parameters. Correct excitations of differential and common modes play a significant role in experimental results. From these 4-port measurements, parameters corresponding to the even and odd modes can be extracted from the multi-mode generalized S-parameters obtained by the vector network analyzer. Differential and common mode sub-matrices are considered as two independent equivalent 2-port transmission lines. The method will be applied to extract the propagation constants of even and odd modes of coupled microstrip transmission lines. A discussion is included on ensuring the correct excitation of the modes in the structure and avoiding possible unwanted effects such as radiation, that negatively affects the final estimation, both in phase and attenuation. Two figures of merit are defined to evaluate how transitions affect the performance of the method. These figures of merit allow a reasonable estimation of how the final experiment is affected by selected excitation of modes. To assess the theory presented, two sets of seven lines of different lengths are manufactured. The experimental results show an excellent agreement, both with electromagnetic simulations and with the analytical model used, for the phase constant in the improved case of 30 • bends, and an improvement in the useful bandwidth of the attenuation constant. This fact verifies the correct functioning of the method. In addition, the measurements are presented in the range of 0.01-67 GHz, which represents a significant advance in bandwidth for this type of structure.

The Distance-based Neighborhood k-Search (DNkS) algorithm, introduced in this article, offers a novel approach to enhancing meter-transformer connectivity modeling in low-voltage grids. By employing a local k-neighborhood search strategy, DNkS effectively subdivides the grid into manageable sections, ensuring robust connectivity assessments. Utilizing metrics such as Adjusted Mutual Information and Accuracy, DNkS demonstrated superior performance in trials, achieving up to 100% accuracy in certain cases, significantly outperforming existing state-of-the-art methods such as deep convolutional time-series clustering and spectral embedding-based meter-transformer mapping. Although DNkS is effective, its performance critically depends on the accuracy of meter and transformer coordinates. In comparative analyses across various network configurations, DNkS consistently outperformed other methods, affirming its utility and effectiveness. The versatile nature of the algorithm would allow its integration into existing systems in various ways, for example, through an API or a web interface. Implementing DNkS promises substantial improvements in the reliability and accuracy of utility network models, directly contributing to enhanced grid management practices.

The rapid evolution of technology has brought forth unprecedented opportunities and challenges in this digital era. Ethical issues in responsible data research have become a principal concern among these challenges necessitating thoughtful consideration and proactive management (Esmer & Arıbaş, 2022). As data becomes a pivotal asset for organizations and individuals alike, the methodologies and practices encompassing its collection, analysis, and storage must be scrutinized to ensure ethical compliance (Edquist et al., 2022). The importance of ethics in responsible data research has been amplified within the realm of cybersecurity where researchers are tasked with securing sensitive information and protecting the privacy of individuals and organizations. Cybersecurity is not just about defending against malicious attacks but also about maintaining the trust of stakeholders through ethical conduct and moral lapses in cybersecurity can result in ramifications such as breaches of confidentiality, loss of data integrity, and violation of public trust (Macnish & Van der Ham, 2020). This implies providing a comprehensive understanding of the ethical deliberations cybersecurity researchers must navigate to balance security and moral standards in an effective manner. The examination of case studies, regulatory frameworks, and best practices can equip cybersecurity practitioners with the knowledge and tools required to uphold ethical standards in their work alongside underscoring the need for continuous ethical vigilance in a rapidly evolving digital landscape while ensuring that the data protection standards align with the broader societal values of fairness, privacy, and respect for individuals' rights (Miller & Bossomaier, 2024).

Accurate parameter estimation such as angle of arrival (AoA) is essential to enhance the performance of integrated sensing and communication (ISAC) in mmWave multiple-input multiple-output (MIMO) systems. This work presents a sensingaided communication channel estimation mechanism, where the sensing channel shares the same AoA with the uplink communication channel. First, we propose a novel orthogonal matching pursuit (OMP)-based method for coarsely estimating the AoA in a sensing channel, offering improved accuracy compared to conventional methods that rely on rotational invariance techniques. Next, we refine the coarse estimates obtained in the first step by modifying the Space-Alternating Generalized Expectation-Maximization algorithm for fine parameter estimation. Through simulations and mathematical analysis, we demonstrate that scenarios with shared AOA achieve a better Cramér-Rao lower bound (CRLB) than those without sharing. This finding highlights the potential of leveraging joint sensing and communication channels to enhance parameter estimation accuracy, particularly in channel or location estimation applications.

In this work, we present single-event upset (SEU) analysis for Forksheet FET (FSFET) based CMOS circuits. Next, we present an array-level power and performance analysis along with the V min evaluation for the FSFET-based SRAM. Physicsbased TCAD and industry-standard BSIM-CMG compact models are calibrated for accurate circuit analysis in SPICE. The impact of varying Heavy-Ion Radiation (HIR) doses and strike orientations is investigated for the FSFETs. The robustness of CMOS inverter against HIR is also reported in terms of failure time (t fail) and output voltage swing (∆VDrop). For the SRAM, we determine the critical Linear Energy Transfer (LET). For FSFET, the individual n-/p-FETs are more vulnerable to the irradiation incident on nearby devices. At the circuit level, in comparison to perpendicular strikes, the ∆VDrop increases by 1.25 V and 2.75 V respectively, for oblique and transverse incidences, at a dose of 2.0 MeVcm 2 /mg. The t fail also increases by 43% and 60% and the SRAM critical LET also decreases by 85% and 57.5%, respectively. The array level SRAM evaluation shows that the FSFET enables reliable operation with low-power consumption, impressive noise margins, and low minimumoperating voltage (V min) values. FSFET SRAM power dissipation during the read and write operations is as low as 7.02 µW, and 3.00 µW respectively. At VDD=0.70 V, the noise margins for hold, read, and write operations are 289.27 mV, 122.89 mV, and 297.79 mV. The V min for read and write operations are 0.30 V and 0.35 V respectively.

The Predictive Code Anomaly Signatures (PCAS) framework represents a significant advancement in the proactive detection of ransomware threats. By integrating static code analysis with sophisticated anomaly detection algorithms, PCAS effectively identifies malicious code patterns indicative of ransomware activity. This innovative approach models code features as multi-dimensional vectors, capturing both syntactic and semantic characteristics to distinguish between benign and malicious code segments. The framework's scalability and modular adaptability facilitate seamless integration into diverse computing environments, enhancing its practical applicability. Empirical evaluations demonstrate that PCAS achieves a high true positive rate and a low false positive rate, indicating its robustness in accurately detecting ransomware threats. Furthermore, the ability of PCAS to bridge static and dynamic analysis paradigms addresses limitations inherent in traditional detection methods, offering a comprehensive solution for preemptive ransomware identification. The implementation of PCAS in cybersecurity infrastructures has the potential to significantly enhance defenses against evolving ransomware threats, providing real-time detection capabilities and enabling prompt responses to mitigate the impact of such incidents. Overall, the PCAS framework represents a substantial contribution to the field of cybersecurity, offering a novel and effective approach to ransomware detection.