Search Results (355)

This paper presents a dual-core RISC-V architecture designed for energy-efficient AI acceleration at the edge, featuring dynamic MAC unit sharing, frequency scaling (DFS), and FIFO-based resource arbitration. The system comprises two RISC-V cores that compete for shared computational resources—a single Multiply–Accumulate (MAC) unit and a shared external memory subsystem—governed by a channel-based arbitration mechanism with CPU-priority semantics, while each core maintains private instruction and data caches. The architecture implements a tightly coupled Neural Processing Unit (NPU) with CONV, GEMM, and POOL operations that execute opportunistically in the background when the MAC unit is available. Dynamic frequency scaling (DFS) with three levels (100/200/400 MHz) is applied to the shared MAC unit, allowing the dynamic acceleration of CNN workloads. The arbitration mechanism uses SystemC sc_fifo channels with CPU-priority polling, ensuring that CPU execution is minimally impacted by background AI processing while the NPU makes progress during idle MAC slots. The NPU supports 3 × 3 convolutions, matrix multiplication (GEMM) with 10 × 10 tiles, and pooling operations. The implementation is cycle-accurate in SystemC, targeting FPGA deployment. Experimental evaluation demonstrates that the dual-core architecture achieves 1.87× speedup with 93.5% efficiency for parallel workloads, while DFS enables 70% power reduction at low frequency. The system successfully executes simultaneous CPU and AI workloads, with CPU-priority arbitration ensuring no CPU starvation under contention. The proposed design offers a practical solution for embedded AI applications requiring both general-purpose computation and neural network acceleration, validated through comprehensive SystemC simulation on modern FPGA platforms. Full article

Mastitis is an inflammatory disease of mammary tissue that impairs milk production and quality, thereby seriously threatening the economic viability of dairy farms. miRNAs, such as miR-146b, are emerging as new candidates for anti-inflammatory therapy, and their activity modulation may provide a basis for controlling inflammation. However, the exact role and underlying mechanisms of miR-146b in bovine mastitis and associated inflammatory pathways in bovine mammary epithelial cells are still unclear. To clarify this, we established an in vitro model by stimulating MAC-T cells with Lipoteichoic acid and investigated the miR-146b-associated molecular pathways. The data from this study showed increased miR-146b expression after LTA stimulation. Then, the dual-luciferase reporter assay identified TRAF6 as a genuine target of miR-146b. Overexpression of miR-146b repressed the TRAF6 level and NF-κB pathway activation. Consequently, the production and secretion of major pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-6, were significantly repressed, which could be indicative of inhibition of the inflammation in the cell model. Meanwhile, knockdown of miR-146b abolished such benefits. Based on these findings, it is reasonable to conclude that miR-146b mitigates LTA-induced inflammatory responses in bovine mammary epithelial cells by directly targeting TRAF6 and downstream NF-κB signaling. The present study identifies the functional role of miR-146b in the regulation of inflammation in MAC-T cells and highlights its potential as a target in mastitis intervention. Full article

Caseous calcification of the mitral annulus (CCMA) is a liquefactive necrosis of mitral annular calcification (MAC). CCMA is rare and usually asymptomatic, has a benign course, and, when incidentally found, can be misdiagnosed as a thrombus, abscess, cardiac tumor or vegetation. Although rarely, CCMA may complicate with rupture, which can lead to ventricular-atrial fistulization, pseudoaneurysm, severe mitral regurgitation (with possible heart failure and atrial fibrillation) and systemic embolism of caseous material (with cerebral ischemic events). A significant increase in CCMA dimensions and an infectious involvement of liquefactive necrosis make CCMA prone to rupture. To date, only case reports and some case series have been published on CCMA, without focusing on the pathophysiological mechanisms responsible for rupture, nor recommendations for prevention and management. However, despite general concerns about surgical treatment of CCMA because of high perioperative risks, most published cases actually underwent successful cardiac surgery. In the present review, we conducted a systematic review of the studies published in the medical literature up to March 2025, reporting cases of CCMA and its complications, as identified through the PubMed database. We analyzed clinical and biological risk factors for CCMA rupture and its diagnostic criteria, focusing on imaging features differentiating mitral annular calcification from uncomplicated CCMA and ruptured CCMA. To this regard, we focused on the key role of multimodality imaging in the achievement of the correct diagnosis. Finally, we propose a management strategy for CCMA, with the aim to fill a gap in this field in the current literature. Full article

Leptospirosis is a widespread zoonosis of human and veterinary concern. The etiological agent of the disease is the pathogenic bacteria of the genus Leptospira. Transmission typically occurs through mucosal contact and/or injured skin with the urine of infected animals or contaminated environmental sources. Understanding the biology and pathogenesis of leptospires is the main focus of our study. In this work, we characterized a novel protein encoded by the LIC_13056 gene from L. interrogans serovar Copenhageni, having an OmpA-like domain. We show that this coding sequence (CDS), previously assigned as a hypothetical protein with an unknown function, is capable of binding to the cellular receptor α8 integrin subunit, potentially contributing to kidney colonization. Additionally, the protein bound to both purified and normal human serum (NHS) plasminogen (PLG). In both conditions, PLG bound to protein was able to generate plasmin (PLA). Furthermore, rLIC_13056 interacted with the complement system components C4b, C4BP, C8 and C9. The interaction of recombinant protein to the C9 had a negative impact on C9 polymerization. Taken together, the protein LIC_13056, having an OmpA-like domain, appears to be involved in leptospiral pathogenesis via different stages of the infection process; PLA generation together with the inhibition of the membrane attack complex (MAC) may contribute to the immune evasion mechanism of Leptospira, thus facilitating the infection. Full article

Remote sensing images (RSIs) are frequently degraded by atmospheric haze, which introduces color distortion and contrast reduction, thereby impeding downstream applications. Existing models often struggle with non-uniform haze distributions, high computational costs, and the loss of local texture details. To address these challenges, this paper proposes a lightweight Feature Self-Recalibration Network (FSRNet) for efficient remote sensing image dehazing. FSRNet adopts a symmetric encoder–decoder architecture as its backbone and utilizes parameter-free pixel shuffle and unshuffle operations for multiscale feature resampling to preserve complex spatial details. The core of FSRNet lies in the specially designed Feature Self-Recalibration Module (FSRM), which consists of two key components: the Dual-Stage Feature Calibration Block (DFCB) and the Hierarchical Context Aggregation Block (HCAB). Specifically, the DFCB statistically splits features into informative and redundant parts, independently recalibrating them through a simplified channel attention mechanism to enhance representation in heterogeneous haze regions. Simultaneously, the HCAB integrates a non-local haze perception branch and a local detail enhancement branch in parallel, enabling the model to perceive global haze density while preserving fine-grained textures. Experimental results on multiple authoritative synthetic and real-world remote sensing datasets demonstrate that FSRNet achieves state-of-the-art dehazing performance. With only 0.865 M parameters and 8.622 G MACs, FSRNet strikes a superior balance between restoration quality and computational efficiency, making it highly suitable for real-time deployment on resource-constrained platforms. Full article

The In-Wheel Motor represents a non-conventional propulsion architecture in which the electric motor is integrated into the wheel, offering advantages such as improved energy efficiency, individual torque control, and drivetrain simplification. In this study, two architectures, inboard and outboard, were developed using an original three-dimensional motor–brake–suspension–steering assembly model, in which disk brake position and In-Wheel Motor integration act as primary design drivers influencing vehicle dynamics. Both architectures were developed in CATIA V5 and exported to Altair Motion 2025 for multibody dynamics simulations. The study evaluates the impact of inboard versus outboard disk brake positioning on vehicle dynamics and provides a qualitative assessment of the associated architectures in terms of mechanical complexity, serviceability, sealing requirements, bearing load asymmetry, and packaging constraints. The results indicate that the inboard architecture exhibits more linear and stable kinematics and compliance (K&C) behavior compared to the outboard configuration, at the expense of increased mechanical complexity and reduced serviceability. By contrast, the outboard architecture preserves a simpler, more conventional MacPherson-like layout with a lower component count and improved service access but is dynamically outperformed under the imposed geometric constraints of the present study. Full article

Objectives: Addiction disorders remain a major challenge in contemporary psychiatry due to high relapse rates and significant individual and societal burden. Despite advances in addiction neurobiology, current diagnostic frameworks and dominant models offer limited tools for early risk identification and dynamic support of clinical decision-making across the course of treatment. The aim of this narrative review is to introduce the MAC/MAB–RCS model as an integrated conceptual framework for risk stratification and personalized intervention in addiction psychiatry. Methods: The proposed model integrates evidence from four complementary domains: genetic, epigenetic, and stress-axis biomarkers; functional brain network organization; and psychological/psychiatric dimensions relevant to addictive behaviors. These domains are synthesized into a unified conceptual structure designed to capture dynamic regulatory processes underlying addiction vulnerability. Results: At the core of the model lies the Regulatory Control State (RCS), a latent higher-order construct representing an individual’s dynamic regulatory capacity through the integration of cognitive control, emotional regulation, and motivational drive modulation. Disruption of the RCS is conceptualized as a shared transdiagnostic mechanism driving craving escalation, compulsive behavior, and relapse vulnerability, independent of substance class or specific addictive behavior. Conclusions: The MAC/MAB–RCS model aligns with the principles of precision psychiatry by offering a pragmatic, clinically oriented translational framework with potential applicability across clinical settings, bridging neurobiological research and clinical practice. The review discusses its relationship to existing models, potential clinical and systemic applications, key limitations, and priorities for future validation studies. Full article

Smart Network Interface Cards (SmartNICs) represent a paradigm shift in system architecture by offloading packet processing and selected application logic from the host CPU to the network interface itself. This architectural evolution reduces end-to-end latency toward the physical limits of Ethernet while simultaneously decreasing CPU and memory bandwidth utilization. The current ecosystem comprises three principal categories of devices: (i) conventional fixed-function NICs augmented with limited offload capabilities; (ii) ASIC-based Data Processing Units (DPUs) that integrate multi-core processors and dedicated protocol accelerators; and (iii) FPGA-based SmartNIC shells—reconfigurable hardware frameworks that provide PCIe connectivity, DMA engines, Ethernet MAC interfaces, and control firmware, while exposing programmable logic regions for user-defined accelerators. This article provides a comparative survey of representative platforms from each category, with particular emphasis on open-source FPGA shells. It examines their architectural capabilities, programmability models, reconfiguration mechanisms, and support for GPU-centric peer-to-peer datapaths. Furthermore, it investigates the associated software stack, encompassing kernel drivers, user-space libraries, and control APIs. This study concludes by outlining open research challenges and future directions in RDMA-oriented data preprocessing and heterogeneous SmartNIC acceleration. Full article

In the context of tightening sustainability regulations and rising demands for transparent and responsible capital allocation, understanding how digital financial innovations influence market efficiency has become increasingly important. This study examines the impact of Financial Technology (FinTech) solutions and crowdfunding platforms on sustainable market efficiency, volatility dynamics, and risk structures in the United Kingdom. Using weekly data for the Financial Times Stock Exchange 100 (FTSE 100) index from January 2010 to June 2025, the analysis applies the Lo–MacKinlay variance ratio test to assess compliance with the Random Walk Hypothesis as a proxy for informational efficiency. Firm-level proxies for FinTech and crowdfunding activity are constructed using the Nomenclature of Economic Activities (NACE) and Standard Industrial Classification (SIC) systems. The empirical results indicate substantial deviations from random-walk behavior in crowdfunding-related market segments, where persistent positive autocorrelation and elevated volatility reflect liquidity constraints and informational frictions. By contrast, FinTech-dominated segments display milder inefficiencies and faster information absorption, pointing to more stable price-adjustment mechanisms. After controlling for structural distortions through heteroskedasticity-consistent corrections and volatility adjustments, variance ratios converge toward unity, suggesting a restoration of informational efficiency. The results provide relevant insights for investors, regulators, and policymakers seeking to align financial innovation with the objectives of sustainable financial systems. Full article

Wireless Sensor Networks (WSNs) operate under strict energy constraints and are therefore highly vulnerable to radio interference, particularly jamming attacks that directly affect communication availability and network lifetime. Although jamming and anti-jamming mechanisms have been extensively studied, energy is frequently treated as a secondary metric, and analyses are often conducted in partial isolation from system assumptions, protocol behavior, and deployment context. This fragmentation limits the interpretability and comparability of reported results. This article presents a systematic literature review (SLR) covering the period from 2004 to 2024, with a specific focus on energy-aware jamming and mitigation strategies in IEEE 802.15.4-based WSNs. To ensure transparency and reproducibility, the literature selection and refinement process is formalized through a mathematical search-and-filtering model. From an initial corpus of 482 publications retrieved from Scopus, 62 peer-reviewed studies were selected and analyzed across multiple dimensions, including jamming modality, affected protocol layers, energy consumption patterns, evaluation assumptions, and deployment scenarios. The review reveals consistent energy trends among constant, random, and reactive jamming strategies, as well as significant variability in the energy overhead introduced by defensive mechanisms at the physical (PHY), Medium Access Control (MAC), and network layers. It further identifies persistent methodological challenges, such as heterogeneous energy metrics, incomplete characterization of jamming intensity, and the limited use of real-hardware testbeds. To address these gaps, the paper introduces an energy-centric taxonomy that explicitly accounts for attacker–defender energy asymmetry, cross-layer interactions, and recurring experimental assumptions, and proposes a minimal set of standardized energy-related performance metrics suitable for IEEE 802.15.4 environments. By synthesizing energy behaviors, trade-offs, and application-specific implications, this review provides a structured foundation for the design and evaluation of resilient, energy-proportional WSNs operating under availability-oriented adversarial interference. Full article

With the increasing prevalence of microplastics (MPs) and nanoplastics (NPs) in global aquatic environments, their potential ecotoxicological and health impacts have become a major concern in environmental science. Slow sand filtration (SSF) is widely recognized for its low energy demand, ecological compatibility, and operational stability; however, its efficiency in removing small or neutrally buoyant MPs remains limited. In recent years, integrating modified activated carbon (MAC) into SSF systems has emerged as a promising approach to enhance MP removal. This review comprehensively summarizes the design principles, adsorption and bio-synergistic mechanisms, influencing factors, and recent advancements in MAC-SSF systems. The results indicate that surface modification of activated carbon—through controlled pore distribution, functional group regulation, and hydrophilic–hydrophobic balance—significantly enhances the adsorption and interfacial binding of MPs. Furthermore, the coupling between MAC and biofilm facilitates a multi-mechanistic removal process involving electrostatic attraction, hydrophobic interaction, physical entrapment, and biodegradation. In addition, this review discusses the operational stability, regeneration performance, and environmental sustainability of MAC-SSF systems, emphasizing the need for future research on green and low-cost modification strategies, interfacial mechanism elucidation, microbial community regulation, and life-cycle assessment. Overall, MAC-SSF technology provides an efficient, economical, and sustainable pathway for microplastic control, offering valuable implications for a safe water supply and aquatic ecosystem protection in the future. Full article

The Internet of Military Things (IoMT) relies on Long-Range Wide Area Networks (LoRaWAN) for low-power, long-range communication in critical applications like border security and soldier health monitoring. However, conventional priority-based flow control mechanisms, which rely on static classification thresholds, lack the adaptability to handle the nuanced, continuous nature of physiological data and dynamic network states. To overcome this rigidity, this paper introduces a novel, domain-adaptive Fuzzy Logic Flow Control (FFC) protocol specifically tailored for LoRaWAN-based IoMT. While employing established Mamdani inference, the FFC system innovatively fuses multi-parameter physiological data (body temperature, blood pressure, oxygen saturation, and heart rate) into a continuous Health Score, which is then mapped via a context-optimised sigmoid function to dynamic transmission intervals. This represents a novel application-layer semantic integration with LoRaWAN’s constrained MAC and PHY layers, enabling cross-layer flow optimisation without protocol modification. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency relative to traditional static priority architectures. Seamlessly integrated into the NS-3 LoRaWAN simulation framework, the FFC protocol demonstrates superior performance in IoMT communications. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency compared with traditional static priority-based architectures. It achieves this by prioritising high-priority health telemetry, proactively mitigating network congestion, and optimising energy utilisation, thereby offering a robust solution for emergent, health-critical scenarios in resource-constrained environments. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

Real-time peer-to-peer communication in web browsers typically relies on centralized signaling servers, creating single points of failure, privacy vulnerabilities, and censorship risks. We present WebRTC Swarms, a fully decentralized signaling architecture integrated into GRIDNET OS that combines onion-routed relay circuits with designated verifier zero-knowledge authentication and cryptoeconomic incentives. The proposed system empowers peers to discover and connect without exposing identities or IP addresses through an overlay of incentivized full nodes that carry signaling traffic using transmission tokens. We introduce a MAC-based designated verifier ZK authentication protocol allowing peers sharing a pre-shared key to mutually authenticate without revealing the key, ensuring only authorized participants can join sessions while preserving unlinkability to outsiders across sessions. Through formal verification using TLA+, we prove key safety and liveness properties of both the signaling protocol and the authentication mechanism. Empirical evaluation demonstrates near-100% NAT traversal success via incentivized decentralized TURN relaying (compared to approximately 85% for STUN-only approaches), join latencies under 2 s for swarms of dozens of peers, and strong resilience against Sybil and denial-of-service attacks through token-based rate limiting. Our work represents the first practical integration of decentralized WebRTC signaling with designated verifier cryptographic authentication and built-in economic incentives, providing a privacy-first substrate for secure, community-governed communication networks. Full article

Receiver-initiated MAC protocols, such as the IEEE 802.15.4e RIT scheme, are promising for energy-efficient communication in multi-hop wireless sensor networks. However, their practical use requires a better understanding of how multiple contention-avoidance mechanisms interact under realistic network conditions. This study develops an ns-3 implementation of an RIT-compliant receiver-initiated MAC protocol together with a flexible evaluation framework that enables selective activation of representative enhancement strategies, including carrier-sensing options for data and beacon transmissions and randomization of beacon intervals. Four realistic network scenarios were designed to simulate practical deployment settings. Simulation results revealed that the effectiveness of these enhancement strategies varied significantly depending on network load and topology. In particular, beacon interval randomization, although often assumed to improve robustness, was found to degrade performance under low-load conditions, indicating that even widely adopted mechanisms may behave differently depending on operational environments. Conversely, CSMA-based approaches provided consistent improvements in transmission reliability. These observations highlight the importance of considering environmental factors and parameter configurations when enabling enhancement mechanisms. Overall, the proposed platform provides a reproducible and unified environment for fair comparison of receiver-initiated MAC protocols and their optional mechanisms, offering practical insights for selecting appropriate configurations in real sensor network deployments. Full article