Search Results (348)

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

The practical deployment of Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs) is hindered by the lack of a Medium Access Network (MAC) protocol capable of managing their flexible, distance-dependent data rates, despite their high spectral efficiency. This paper proposes and validates a novel rate-adaptive, Time Division Multiplexing (TDM)-based MAC protocol for OFDM-PON systems. A key contribution is the design of a three-layer header frame structure that supports multi-ONU data scheduling with heterogeneous rate profiles. Furthermore, the protocol incorporates a unique channel probing mechanism to dynamically determine the optimal transmission rate for each Optical Network Unit (ONU) during activation. The proposed Optical Line Terminal (OLT) side MAC protocol has been fully implemented in hardware on a Xilinx VCU118 FPGA platform, featuring a custom-designed ring buffer pool for efficient multi-ONU data management. Experimental results demonstrate robust upstream and downstream data transmission and confirm the system’s ability to achieve flexible net data rate switching on the downlink from 8.1 Gbit/s to 32.8 Gbit/s, contingent on the assigned rate stage. Full article

In this study, we develop a stochastic optimal control model for corporate carbon management that synergistically combines emission reduction initiatives with carbon trading mechanisms. The model incorporates two control variables: the autonomous emission reduction rate and initial carbon allowance purchases, while accounting for both deterministic and stochastic carbon pricing scenarios. The solution is obtained through a two-step optimization procedure that addresses each control variable sequentially. In the first step, the problem is transformed into a Hamilton–Jacobi–Bellman (HJB) equation in the sense of viscosity solution. A key aspect of the methodology is deriving the corresponding analytical solution based on this equation’s structure. The second-step optimization results are shown to depend on the relationship between the risk-free interest rate and carbon price dynamics. Furthermore, we employ daily closing prices from 16 July 2021, to 31 December 2024, as the sample dataset to calibrate the parameters governing carbon allowance price evolution. The marginal abatement cost (MAC) curve is calibrated using data derived from the Emissions Prediction and Policy Analysis (EPPA) model, enabling the estimation of the emission reduction efficiency parameter. Additional policy-related parameters are obtained from relevant regulatory documents. The numerical results demonstrate how enterprises can implement the model’s outputs to inform carbon emission reduction decisions in practice and offer enterprises a decision-support tool that integrates theoretical rigor and practical applicability for achieving emission targets in the carbon market. Full article

Credit risk refers to the possibility that a borrower fails to meet contractual repayment obligations, posing potential losses to lenders. This study aims to enhance post-loan default prediction in credit risk management by constructing a time-series modeling framework based on repayment behavior data, enabling the capture of repayment risks that emerge after loan issuance. To achieve this objective, a Residual Enhanced Encoder Bidirectional Long Short-Term Memory (ResE-BiLSTM) model is proposed, in which the attention mechanism is responsible for discovering long-range correlations, while the residual connections ensure the preservation of distant information. This design mitigates the tendency of conventional recurrent architectures to overemphasize recent inputs while underrepresenting distant temporal information in long-term dependency modeling. Using the real-world large-scale Freddie Mac Single-Family Loan-Level Dataset, the model is evaluated on 44 independent cohorts and compared with five baseline models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) across multiple evaluation metrics. The experimental results demonstrate that ResE-BiLSTM achieves superior performance on key indicators such as F1 and AUC, with average values of 0.92 and 0.97, respectively, and demonstrates robust performance across different feature window lengths and resampling settings. Ablation experiments and SHapley Additive exPlanations (SHAP)-based interpretability analyses further reveal that the model captures non-monotonic temporal importance patterns across key financial features. This study advances time-series–based anomaly detection for credit risk prediction by integrating global and local temporal learning. The findings offer practical value for financial institutions and risk management practitioners, while also providing methodological insights and a transferable modeling paradigm for future research on credit risk assessment. Full article

Direct pulp capping using mineral trioxide aggregate (MTA) is commonly used to preserve dental pulp tissue, but the molecular mechanisms underlying reparative dentin formation during this procedure and the restoration of dental pulp homeostasis remain unclear. To elucidate these mechanisms, we investigated the expression and function of intercellular adhesion molecule 2 (ICAM2) in dental pulp cells and tissue. ICAM2 expression in human dental pulp cells (HDPCs) was confirmed by the gene and protein expression analysis. ICAM2 expression during reparative dentin formation after direct pulp capping was investigated using a rat direct pulp capping model. The effect of ICAM2 on odontoblast-like differentiation of HDPCs was assessed using siRNA and magnetic cell sorting (MACS). The gene and protein expression analysis showed that ICAM2 is expressed in approximately 10% of HDPCs. Immunofluorescence staining of rat mandibular bone sections showed that ICAM2 is expressed in dental pulp tissue. During reparative dentin formation, ICAM2 expression significantly increased to nearly three-fold higher than pretreatment levels on the 3 days after direct pulp capping and then returned to normal levels. ICAM2 knockdown by siRNA enhanced odontoblast-like differentiation of HDPCs. In contrast, culture supernatant from ICAM2-positive HDPCs separated by MACS inhibited odontoblast-like differentiation of HDPCs. These findings suggest that ICAM2 may regulate reparative dentin formation in dental pulp tissue. Full article

Environmentally friendly materials with superior structural, physical, optical, and shielding capabilities are of great technological importance and are continually being investigated. In this work, novel multicomponent borate glasses with the composition xTiO₂-10BaO-5Al₂O₃-5WO₃-20Bi₂O₃-(60-x) B₂O₃, where 0 ≤ x ≤ 15 mol%, were produced via the melt-quenching technique. The increase in TiO₂ content results in a decrease in molar volume and a corresponding increase in density, indicating the formation of a compact, rigid, and mechanically hard glass network. Elastic constant measurements further confirmed this behavior. FTIR analysis confirms the transformation of BO₃ to BO₄ units, signifying improved network polymerization and structural stability. The prepared glasses exhibit an optical absorption edge in the visible region, demonstrating their strong ultraviolet light blocking capability. Incorporation of TiO₂ leads to an increase in refractive index, optical basicity, and polarizability, and a decrease in the optical band gap and metallization number; all of these suggest enhanced electron density and polarizability of the glass matrix. Radiation shielding properties were evaluated using Phy-X/PSD software. The outcomes illustrate that the Mass Attenuation Coefficient (MAC), Effective Atomic Number (Z_eff), Linear Attenuation Coefficient (LAC) increase, while Mean Free Path (MFP) and Half Value Layer (HVL) decrease with increasing TiO₂ at the expense of B₂O₃, confirming superior gamma-ray attenuation capability. Additionally, both TiO₂-doped and undoped samples show higher fast neutron removal cross sections (FNRCS) compared to several commercial glasses and concrete materials. Overall, the incorporation of TiO₂ significantly enhances the optical performance and radiation-shielding efficiency of the environmentally friendly glass system, making these potential candidates for advanced photonic devices and radiation-shielding applications. Full article

Diabetic nephropathy (DN) is a leading cause of end-stage renal disease (ESRD) globally. Beyond metabolic and haemodynamic stress, the complement system has emerged as a contributor to glomerular and tubulointerstitial injury. In type 1 diabetes mellitus (T1DM), complement proteins contribute through autoimmune mechanisms, while in type 2 diabetes mellitus (T2DM) they are linked to insulin resistance. In both, complement activation promotes micro- and macrovascular complications through inflammatory pathways that accelerate DN progression. This review summarises the current evidence on the role of complement activation in diabetic nephropathy (DN). First, we outline the mechanisms by which the complement system is activated through the lectin pathway (in which mannoses bind to modified glycosylation structures), the classical pathway (in which C1q recognises immune complexes/damaged self), and the alternative pathway (in which C3 ticks over and amplifies on damaged renal surfaces). Next, we consider the roles of their effector molecules (C3a, C5a, and C5b-9/MAC), and the consequences of regulatory dysfunction (e.g., CD59 dysfunction). When integrated with findings from renal histology, blood and urine biomarkers enable us to evaluate the correlation between prognosis, disease severity, and progression. We will also discuss therapeutic implications, including the rationale behind selective complement inhibition and future intervention strategies. Full article