Search Results (81)

In this paper, we report the design and verification methodology of a SystemVerilog-based I²C protocol Verification IP (VIP) based not only on assertion-based verification but also on the new checkout error injection techniques. The resulting VIP is designed as a set of loose-coupled modules for protocol description, transaction generation, and automatic protocol checking with SystemVerilog Assertions (SVAs). Timing, multi-master arbitration, and error recovery related to I²C protocol verification challenges are achieved using embedded assertion monitors and focused error injection scenarios on the testbench. The paper describes the inclusion of assertion-based monitors used for checking real-time protocol compliance as well as the ability for systematic error injection to expose corner-case bugs and verify the strength of the DUT and the verification environment. We present our experimental results that demonstrate the effectiveness of proposed strategies on coverage, bug leakage, and reduction in debug cycles. The approach also provides a useful guideline for verification engineers who need to build protocol VIPs or wish to improve the efficiency of their verification flow with assertion-led methods. Full article

The reliability of power supply systems is of utmost importance for telecommunications. In our daily lives, we are used to having constant access to the power grid with negligible risks. Standards and practices established over the years guarantee minimal problems for the household consumer and accidents in their electrical appliances. Often, the biggest inconvenience of a power failure for the average person is having to set the clock on the stove or use the flashlight on their phone. However, we rarely realize how fragile the balance on which all this is based is, but telecom companies are fully aware of this fact. Regardless of whether the problem comes from natural phenomena, accidental or intentional damage, or defects in the equipment, the equipment used in telecommunications technologies is extremely sensitive, and it is necessary to take protective measures. Full article

Teaching of embedded systems, including communication protocols such as SMBus, is commonly faced with difficulties providing the students with interactive and personalized, practical learning experiences. To overcome these shortcomings, this report presents a new conceptual framework that exploits generative artificial intelligence (GenAI) via customized DV-GPT. Coupled with prepromises techniques, DV-GPT offers timely targeted support to students and engineers who are studying SMBus protocol design and verification. In contrast to traditional learning, this AI-based tool dynamically adjusts feedback based on the users’ activities, providing greater insight into challenging concepts, including timing synchronization, multi-master arbitration, and error handling. The framework also incorporates the industry de facto standard UVM practices, which helps narrow the gap between education and the professional world. We quantitatively compare with a baseline GPT-4 and show significant improvement in accuracy, specificity, and user satisfaction. The effectiveness and feasibility of the proposed GenAI-enhanced educational approach have been empirically validated through the use of structured student feedback, expert judgment, and statistical analysis. The contribution of this research is a scalable, flexible, interactive model for enhancing embedded systems education that also illustrates how GenAI technologies could find applicability within specialized educational environments. Full article

Walnut (Juglans regia L.) exhibits a high sensitivity to water deficit, making it crucial to comprehend this characteristic in order to optimize irrigation strategies to improve its productivity. Deficit irrigation is widely used under drought conditions to achieve water savings goals. This study examines the impact of sustained deficit irrigation (SDI) strategies—applying 33%, 50%, or 75% of the crop water demand—on yield and quality parameters of two walnut cultivars (Chandler and Cisco) over a three-year monitoring period. These treatments were compared against control trees receiving full irrigation at 100% of crop water requirements (C₁₀₀). The nut yield was significantly and proportionally reduced under the SDI treatments. In the experiment, the average yield for cv. Chandler amounted to 6.7, 6.4, and 12.2 kg tree⁻¹ under SDI₃₃, SDI₅₀, and SDI_75, respectively, which were less than 13.9 kg tree⁻¹ in the C₁₀₀ plot. Similarly, cv. Cisco yielded 8.0, 11.6, 11, and 15.6 kg tree⁻¹ under SDI₃₃, SDI₅₀, SDI₇₅, and C₁₀₀, respectively. These findings indicate that the cultivar Cisco exhibits greater tolerance to moderate and intermediate levels of water deficit. Furthermore, the SDI treatments notably influenced several morphological and physicochemical kernel parameters. The key affected attributes include the weight, size, color, profiles of specific sugars, and mineral content (notably potassium, iron, and zinc), as well as the composition of unsaturated fatty acids (palmitoleic and cis-vaccenic) and polyunsaturated fatty acids (linoleic and α-linolenic), with pronounced effects observed particularly under the SDI₇₅ treatment. Thus, deficit irrigation did not drastically affect the kernel quality parameters, and it is also possible to augment them by selecting the appropriate water stress level. Therefore, for both walnut cultivars, approximately 25% of the irrigation water (SDI₇₅), equivalent to an average of 1681 m³ ha⁻¹, can be conserved relative to the total crop water requirement without negatively impacting walnut tree performance in the short to medium term. Here, we show the key role of adjusting irrigation practices by stressing the benefits of SDI that can save water, foster water productivity, and boost walnut health-promoting phytochemicals. Full article

Entanglement swapping is a crucial step in quantum communication, generating long-distance entanglements between quantum users for quantum network applications, such as distributed quantum computing. This study focuses on the efficiency of entanglement swapping strategies in quantum networks, particularly in multi-user concurrent quantum communication. Since multi-user concurrent quantum communication consists of multiple point-to-point quantum communications, we first analyze the challenges faced by existing entanglement swapping strategies in this scenario and then propose Parallel Segment Entanglement Swapping (PSES) to address them. PSES utilizes a tree-like model to divide the path into segments and execute entanglement swapping in parallel across them, thereby enhancing the generation rate of long-distance entanglement. Furthermore, we analyze the impact of resource contention on entanglement swapping in multi-user concurrent quantum communication and propose Multi-user PSES (M-PSES) to alleviate this negative impact. M-PSES leverages the entanglement swapping trigger signal and resource locking mechanisms to mitigate resource contention. The simulation results show that PSES performs superiorly to existing entanglement swapping strategies in point-to-point quantum communication, and M-PSES can achieve better performance than PSES in multi-user concurrent quantum communication. Full article

Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies that integrate biochemical biomarkers, advanced imaging techniques, and machine learning models relevant to osteoarthritis. We evaluate the diagnostic utility of cartilage degradation markers (e.g., CTX-II, COMP), inflammatory cytokines (e.g., IL-1β, TNF-α), and synovial fluid microRNA profiles, and how they correlate with quantitative imaging readouts from T2-mapping MRI, ultrasound elastography, and dual-energy CT. Furthermore, we highlight recent developments in radiomics and AI-driven image interpretation to assess joint space narrowing, osteophyte formation, and subchondral bone changes with high fidelity. The integration of these datasets using multimodal learning approaches offers novel diagnostic phenotypes that stratify patients by disease stage and risk of progression. Finally, we explore the implementation of these tools in point-of-care diagnostics, including portable imaging devices and rapid biomarker assays, particularly in aging and underserved populations. By presenting a unified diagnostic pipeline, this article advances the future of early detection and personalized monitoring in joint degeneration. Full article

With the continuing development of technology, telepresence services have emerged as an essential part of modern communication systems. Concurrently, the rapid growth of fog computing presents new opportunities and challenges for integrating telepresence capabilities into distributed networks. Fog computing is a component of the cloud computing model that is used to meet the diverse computing needs of applications in the emergence and development of fifth- and sixth-generation (5G and 6G) networks. The incorporation of fog computing into this model provides benefits that go beyond the traditional model. This survey investigates the convergence of telepresence services with fog computing, evaluating the latest advancements in research developments and practical use cases. This study examines the changes brought about by the 6G network as well as the promising future directions of 6G. This study presents the concepts of fog computing and its basic structure. We analyze Cisco’s model and propose an alternative model to improve its weaknesses. Additionally, this study synthesizes, analyzes, and evaluates a body of articles on remote presence services from major bibliographic databases. Summing up, this work thoroughly reviews current research on telepresence services and fog computing for the future. Full article

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. Ewing sarcoma), and predicting mutation signatures (e.g., TP53, RB1). Moreover, we highlight the use of liquid biopsy and circulating tumor DNA (ctDNA) as emerging diagnostic biomarkers, coupled with point-of-care molecular assays, to enable early and accurate detection in low-resource settings. The review concludes by discussing translational barriers, including data harmonization, regulatory challenges, and the need for multi-institutional datasets to validate AI-based diagnostic frameworks. This article synthesizes current advancements and provides a forward-looking view of precision diagnostics in musculoskeletal oncology. Full article

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, algorithm evaluation, ethics, and the curriculum. AI is used in preoperative planning and predictive analytics in the postoperative period that bridges theory and practice. AI and MR are key to supporting innovation and scalability in orthopedic education. However, technological innovation relies on collaborative partnerships to develop equitable, evidence-informed practices that can be implemented in orthopedic education. For sustained impact, innovation must be aligned with pedagogical theories and principles. We believe that orthopedic medical educators’ future critical role will be to enhance the next generation of competent clinicians. Full article

Networked microgrids (NMG) are gaining popularity as an example of smart grids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware, such as Cisco 4331 Integrated Services Routers (ISR). We evaluate QoD in NMG control data by measuring RTT under varying relative network congestion levels. The results reveal that high-variance background traffic leads to greater RTTs, surpassing the industrial communication response time requirement specified by the European Telecommunications Standards Institute (ETSI) by over 25 times. Full article

As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed threats due to the presence of redundant and irrelevant information for the IDSs. Furthermore, recent IDSs that utilize artificial intelligence are often presented as black boxes, offering no explanation of their internal operations. In this study, we develop a solution to the identified challenges by presenting a deep learning-based model that adapts to new attacks by selecting only the relevant information as inputs and providing transparent internal operations for easy understanding and adoption by cybersecurity personnel. Specifically, we employ a hybrid approach using statistical methods and a metaheuristic algorithm for feature selection to identify the most relevant features and limit the overall feature set while building an LSTM-based model for intrusion detection. To this end, we utilize two publicly available datasets, NF-BoT-IoT-v2 and IoTID20, for training and testing. The results demonstrate an accuracy of 98.42% and 89.54% for the NF-BoT-IoT-v2 and IoTID20 datasets, respectively. The performance of the proposed model is compared with that of other machine learning models and existing state-of-the-art models, demonstrating superior accuracy. To explain the proposed model’s predictions and increase trust in its outcomes, we applied two explainable artificial intelligence (XAI) tools: Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), providing valuable insights into the model’s behavior. Full article

This research investigates the core competency indicators required for battery electric vehicle (BEV) sales personnel to effectively contribute to the growth of the BEV industry and the transition toward sustainable mobility. As global efforts to reduce carbon emissions intensify, this study identifies the necessary competencies to equip BEV sales teams in navigating the complexities of BEV adoption. This study employs a structured Delphi methodology, gathering insights from a panel of 15 industry professionals, to define and validate key competency dimensions. These competencies are categorized into four main dimensions—professional knowledge, professional ability, professional attitude, and personal traits—and further subdivided into 20 sub-dimensions and 58 specific indicators. Essential competencies include technical expertise in BEV technology, communication skills, customer relationship management, sales techniques, and proficiency in after-sales services. The findings emphasize the significant role of continuous learning, work attitude, and the integration of digital tools in driving sales effectiveness and customer trust. Furthermore, the competency framework developed in this study aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (industry, innovation, and infrastructure), SDG 11 (sustainable cities and communities), and SDG 4 (quality education). The framework offers practical insights for recruitment, training, and performance evaluation, ensuring that BEV sales personnel are well-prepared to foster the widespread adoption of electric vehicles, thereby contributing to a sustainable and low-carbon future. Full article

The Serial Peripheral Interface (SPI) protocol plays a crucial role in wearable and IoT devices, enabling high-speed communication between microcontrollers and peripherals such as sensors, displays, and connectivity modules. With the increasing complexity of modern devices and system-on-chip (SoC) designs, robust verification methods are essential to ensure functionality and reliability. This paper utilizes the Universal Verification Methodology (UVM) to develop a scalable and reusable testbench for SPI verification. The process encompasses test planning, simulation, emulation, and top-level verification to validate multi-slave coordination and error-handling scenarios. The results demonstrate the critical importance of UVM in ensuring the performance and dependability of SPI in advanced electronics, contributing to the reliable integration of the protocol in future devices. The verification results demonstrated a functional coverage of 83.33% and 100% assertion coverage, confirming our approach’s robustness. Analysis of the uncovered functional bins revealed that specific edge cases, such as timing violations and multi-slave arbitration conflicts, require additional test scenarios for full verification. Furthermore, our testbench successfully identified and handled critical fault conditions, such as clock jitter, bus contention, and framing errors, ensuring reliable SPI operation in real-world deployments. These findings highlight the effectiveness of UVM-based verification in improving the reliability and robustness of SPI communication in modern low-power, resource-constrained embedded systems. Full article

Data in-transit from an at-rest state can be securely transmitted and managed within a network. Background: This work covers the design and simulation of an Enhanced Network Topology (ENT) with multiple components that connects multiple locations of an imaginary organisation structure. The components of the network locations are mix-vendor components. The focus is on how classless IP address allocation, routing protocols, hierarchical network topology, and the updated Transmission Control Protocol/Internet Protocol (TCP/IP) model are put together to enhance performance and security. The simulation is performed using Cisco Packet Tracer where the packet transfer and connections within the network are examined. Methods: The Variable Length Subnet Mask (VLSM) approach is applied to a network design to secure data and information. We provided and detailed the implementation of subnetting, routing protocols, the updated TCP/IP model, and simulation within Cisco Packet Tracer. Our paper demonstrates the applicability of a single IP address range (0-255) where only the fourth octet changes to serve to secure information across networks through creating subnets. Results: The results of the simulation are further analysed; the security protocols are summarised. Conclusions: Our work has potential to be applied to Supervisory Control and Data Acquisition (SCADA) networks, Internet of Things (IoT) and Cloud networks; a useful resource for academia and industry professionals. Full article

A state-of-the-art multi-omics approach was applied to improve our understanding of the aetio-pathogenesis of a highly prevalent, performance-limiting disorder of racehorses: mild-to-moderate equine asthma (MMEA). This is a prerequisite to improving prophylactic, management, and therapeutic options for this condition. Although a number of risk factors have been identified, options for intervention are limited. This study applied a multi-omic approach to reveal key inflammatory pathways involved in inflammatory cell recruitment to the lower airways and highlight distinct MMEA inflammatory profiles. We compared bronchoalveolar lavage fluid (BALF) cell gene and protein expression data from horses with non-inflammatory BALF cytology with those isolated from horses with neutrophilic, mastocytic, mixed neutrophilic/mastocytic, and eosinophilic/mastocytic inflammation. The analyses on transcriptomic/proteomic data derived from BALF from horses with neutrophilic cytology showed enrichment in classical inflammatory pathways, and horses with mastocytic inflammation showed enrichment in pathways involved in hypersensitivity reactions related to nonclassical inflammation potentially mimicking a Th2-immune response. The mixed eosinophilic/mastocytic group also presented with a nonclassical inflammatory profile, whereas the mixed neutrophilic/mastocytic group revealed profiles consistent with both neutrophilic inflammation and hypersensitivity. Our adopted multi-omics approach provided a holistic assessment of the immunological status of the lower airways associated with the different cytological profiles of equine asthma. Full article