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.

The presence of foreign bodies in packaged food is a serious concern for both final consumers (allergies, injuries, choking) and food manufacturers (reputation and economic losses). In particular, low-density plastics, glass and wood splinters are hard to detect even by the most advanced X-ray imagers. One solution is Machine-Learning-based Microwave Sensing (MLMWS): a non-invasive, contactless, and real-time method which uses a machine-learning (ML) classifier to analyze the scattered microwaves from the irradiated target object. In this paper, we want to extend our previous work about contaminant detection in cocoa-hazelnut spread jars by proposing an enhanced ML flow to increase the accuracy of the ML classifier. For the first time in this case study, we use a multi-class classifier, we train it with scattering parameters measured at multiple microwave frequencies, with a new pre-processing scaler, data augmentation, quantization-aware training and a pruning schedule. The results show a contaminant detection multi-class accuracy of 94.167% with a latency of 26 µs when targeting an AMD/Xilinx Kria K26 FPGA. Finally, we released our datasets publicly to OpenML.1

This paper investigates the motivations for developing underwater gait assistance robots and explores methods that leverage the benefits of partial submersion for rehabilitation. An overview of the current state of underwater gait rehabilitation robots is provided, highlighting the successes and limitations of existing designs. The study surveys key approaches in gait characterization, actuation, sensing, system modeling, and control strategies to identify those best suited for underwater environments. Emphasis is placed on how buoyancy and drag can be harnessed during hydrotherapy to improve gait rehabilitation outcomes. The review identifies research gaps and outlines future opportunities for designing effective underwater robotic systems that complement therapeutic needs.

This article provides a research viewpoint on the training procedure of a Bidirectional Encoder Representations from Transformers (BERT) model starting from the beginning, employing the Tensor Processing Unit (TPU), with a particular emphasis on the Spanish language. This article focuses on the techniques and strategies, such as the whole word masking (WWM) technique, that are essential for achieving successful BERT training in multiple languages, including English. The goal is to provide efficient development of natural language processing applications for understanding the Spanish language. The BEThiz model exhibits exceptional proficiency in comprehending contextual cues and producing accurate responses for masked inputs, surpassing other models within the same category and language. This characteristic sets our model apart and solidifies its role as a particularly potent tool in the field of natural language processing.

Large language models (LLMs) have achieved high accuracy in diverse NLP and computer vision tasks due to selfattention mechanisms relying on GEMM and GEMV operations. However, scaling LLMs poses significant computational and energy challenges, particularly for traditional Von-Neumann architectures (CPUs/GPUs), which incur high latency and energy consumption from frequent data movement. While high energy consumption is a crucial challenge for serves, these issues are exacerbated in edge applications with energy-constrained computational resources. DRAM-based near-memory architectures offer high energy efficiency and throughput, but their basic processing elements have limited operations due to stringent area, power, and timing constraints. This work introduces CIDAN-T3D, a novel Processing-in-Memory (PIM) architecture optimized for LLMs. It features an ultra-low-power Neuron Processing Element (NPE) with high compute density (#Operations/Area), enabling efficient in-situ execution of LLM operations by leveraging high parallelism within DRAM. CIDAN-T3D minimizes data transfers, enhances locality, and achieves significant gains in throughput and energy efficiency. Experiments show a 1.3× throughput and 21.9× improvement in energy efficiency compared to existing near-memory designs, making CIDAN-T3D a scalable and efficient solution for LLM-based Gen-AI applications.

Escalating threats from increasingly sophisticated ransomware attacks pose significant risks to data integrity, financial stability, and operational continuity for organizations across sectors. Addressing these threats requires a novel detection framework that overcomes the limitations of traditional signature-based and machine learning-based approaches, particularly their vulnerability to adaptive ransomware tactics and resource constraints. The proposed Dynamic Threat Imprint Analysis (DTIA) method leverages real-time behavioral analysis combined with adaptive learning mechanisms, enabling it to detect both known and previously unseen ransomware variants with high accuracy. Experimental evaluation demonstrates that DTIA achieves a 98.7% detection rate for known ransomware while effectively adapting to new variants, maintaining a 95% detection rate for previously unseen threats. Additionally, DTIA operates with minimal impact on system performance, showcasing scalable and efficient detection capabilities suited for deployment in environments with diverse computational resources. These findings demonstrate the potential of DTIA as a sophisticated and adaptable solution for the evolving challenge of ransomware detection, enhancing cybersecurity resilience through advanced behavioral analysis and dynamic learning capabilities.

Static random access memory (SRAM) plays a vital role in the overall power consumption of system-on-chip (SoC), particularly in the portable applications. Supply voltage overscaling is one of the most commonly used design methods to reduce the power consumption of SRAM, which degrades the speed and stability. This paper proposes a high-speed/stability 10T SRAM (HS10T) cell with high read stability and write ability for low-voltage operation. A discharge transistor and the power-gating structure are used in SRAM to improve the write ability and reduce write delay with relatively low supply voltage. Meanwhile, the read-decoupling structure can improve the read static noise margin (RSNM) by separating the reading circuit from the writing circuit. The simulation results show that the delay and power-delay-product (PDP) of the proposed SRAM cell are reduced by 86.22% and 82.56% respectively compared to the latest similar work. Additionally, the average write margin (WM) of the HS10T SRAM cell has also been improved by 4.14% while ensuring high energy efficiency.

Contemporary academic research covers a wide range of topics, illustrating the complex and dynamic challenges and opportunities faced by societies worldwide. This paper examines the influence of literature in forming cultural identities and the significant effects of technology on conventional frameworks. Present academic works provide indepth perspectives on the integration of humanistic considerations and technological progress, highlighting their joint importance in addressing modern societal problems.

Today's trend in AR application customization focuses on addressing individual user needs, leveraging advancements in sensing technologies such as biosensors and eye trackers, alongside the evolution of generative AI models. These innovations facilitate the creation of dynamic and personalized content, including videos and images, tailored to specific models and rules. To address this evolving landscape, this research proposes a framework for Context-Aware AR Content Generation designed for cultural scenarios. This framework incorporates biosensors to monitor emotional states, eye-tracking to assess visual focus, and geospatial data to consider location and time. By integrating these inputs with generative AI, the framework not only generates and adapts culturally relevant AR content but also employs AR technology to provide historical context about the content elements on the user's device. Our framework aims to minimize the need for extensive user prompts and improve accessibility for individuals who may find textual or voice commands challenging.

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.

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation. In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry.

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, creating an urgent need for rapid and accurate diagnostic tools. Deep learning (DL) techniques, particularly those applied to medical imaging modalities such as chest X-rays (CXR) and computed tomography (CT) scans, have emerged as powerful tools for detecting COVID-19. This survey provides a comprehensive review of the current advancements in deep learning models designed for COVID-19 detection. We discuss commonly used imaging datasets, popular model architectures, and key techniques employed to address the unique challenges posed by COVID-19 diagnosis, including limited data availability, class imbalance, and model interpretability. We also explore emerging methodologies, such as multi-modal learning, federated learning, and synthetic data generation, which offer promising solutions to these challenges. Furthermore, we highlight the critical role of explainable AI in enhancing model transparency and trustworthiness, which is essential for clinical adoption. Finally, we examine the potential for deep learning models to be integrated into clinical workflows, improving efficiency and diagnostic accuracy in real-world settings. Through this survey, we aim to provide researchers and clinicians with a detailed understanding of the state-of-the-art in DL-based COVID-19 detection, along with insights into 1 future directions that could enhance the role of artificial intelligence in pandemic response and healthcare.

Big Data has revolutionized how organizations across industries handle vast quantities of information, enabling valuable insights that can drive business growth. However, with these advancements come numerous challenges, including data management complexities, security concerns, and the integration of various technologies. This paper discusses the overarching challenges in Big Data and provides a strategic framework to resolve them. By understanding key strategic points, methodology, use cases, and implementation steps, organizations can effectively navigate these challenges, ensuring the successful activation of Big Data initiatives. The paper also highlights tools and technologies essential to Big Data operations, potential risks, and mitigation strategies.

The escalating sophistication of ransomware attacks necessitates the development of innovative detection methodologies capable of identifying and mitigating such threats with high precision. The introduction of Quantum Heuristic Analysis (QHA) represents a significant advancement in this domain, leveraging quantum computational principles to enhance anomaly detection mechanisms. Through the integration of quantum heuristics, QHA effectively identifies complex data patterns and subtle anomalies, thereby offering a robust defense against evolving ransomware threats. Empirical evaluations demonstrate that QHA achieves a detection accuracy exceeding 98%, surpassing traditional signature-based approaches. Furthermore, the framework's design, which operates without human intervention, reduces the potential for human error and allows for continuous monitoring and detection, thereby enhancing the overall resilience of cybersecurity infrastructures. The scalability of QHA has been validated across varying dataset sizes, maintaining consistent performance metrics, and its adaptability to diverse encryption techniques demonstrates its robustness. These findings suggest that QHA holds substantial promise for integration into existing cybersecurity infrastructures, providing a proactive and efficient approach to ransomware detection.

The uniaxial tensile mechanical stress (MS) is induced up to 1.4 GPa on the junctionless channel of the twin junctionless nanowire (JL-NW) gate-all-around (GAA) field-effect transistors (FETs) using a four-point bending technique. The variation of the electrical parameters is measured before and during induced MS to analyze the performance. The ON-state current, carrier mobility, threshold voltage, and subthreshold swing are directly proportional to the induced MS due to the reduced energy band gap and intervalley scattering effect. The reduced subthreshold swing shows low power consumption and better switching ability in advanced CMOS technologies. In addition, the change of drain current shows highly piezoresistive sensing ability in nanoelectromechanical sensor applications. Thus, this study demonstrates the importance of mechanical stress engineering for performance improvement in CMOS technology, piezoresistive sensing applications, and device reliability.

Emerging threats in cybersecurity require innovative approaches to counter increasingly complex attacks targeting critical systems. A novel detection framework has been proposed, leveraging the concept of Proactive Cryptographic Residue Mapping to address challenges posed by obfuscated and polymorphic ransomware variants. Through systematic analysis of bytecode sequences and cryptographic residues, the methodology integrates entropy-driven insights with advanced feature extraction to distinguish malicious encryption behaviors. Automated classification pipelines enhance accuracy while minimizing computational overhead, ensuring scalability across diverse deployment environments. Experimental results demonstrate robust detection performance, achieving high accuracy and low falsepositive rates across multiple ransomware families. The use of sequential analysis further mitigates the impact of variantspecific obfuscation, enabling reliable identification of malicious patterns. Processing efficiency evaluations highlight the framework's suitability for resource-constrained applications, offering practical implications for real-time threat mitigation. Comparative analyses against traditional methods underline significant improvements in both accuracy and robustness. Scalability assessments indicate consistent performance across varying dataset sizes and computational configurations. Entropy distribution studies provide deeper understanding of cryptographic behaviors, reinforcing the methodological foundations. Error rates under simulated noise levels reveal opportunities for further refinement, especially in challenging environments. The proposed framework presents a significant contribution to automated cybersecurity solutions, balancing analytical sophistication with operational feasibility.

In this paper, bikeshare data from Chicago on weather-friendly days in 2019 and 2020 were analyzed to investigate the variation in bikeshare travel before and during the pandemic. Our results show that bikeshare trips during the pandemic were much longer than prior to the pandemic. The increased rate of bikeshare usage was unbalanced spatially and varied significantly for different user types. Bikeshare was used remarkably more by casual users than by subscribers, and the increase occurred much more in the outskirts of the city. The increase in bikeshare travel was associated with a reduction in travel by ridehailing and public transit, especially in the urban periphery. The correlation of bikeshare use with the bus system was much less significant than with the rail system. Bike lanes/facilities had a mixed effect on bikeshare travel. Weekend bike trips increased in areas where there was no bike lane. Weekday trips, on the contrary, increased in the vicinity of bike greenways.

Nowadays, electro-thermal transport behavior analyzing is important in emerging nanoscale devices because thermal management is a critical issue in improving the device's performance and cooling strategies. In this article, a comparative study for electro-thermal performance analysis of junctionless and an inversion mode nanowire gate-all-around (GAA) field-effect transistors (FETs) is presented in advanced technology nodes by considering nonlocal effects. The junctionless device showed ~15.2% better thermal reliability and ~26.7%/37.6% better HCI/BTI lifetime compared to the inversion mode device. The transient behavior of electro-thermal reliability is also investigated for both devices; where the devices turn on for a short time within a duty cycle, the devices showed better thermal reliability and ~52.8%/68.2% HCI/BTI lifetime improvement. This study also provides strategies for thermal management in advanced node devices.