Search Results (300)

Background/Objectives: Macular Telangiectasia Type 2 (MacTel type 2) is a rare, progressive retinal disease that can lead to central vision loss. Optical coherence tomography (OCT) plays a crucial role in the early diagnosis, monitoring, and prognostic assessment of this condition. This narrative review aims to summarize the clinical features, OCT findings, and current management strategies for MacTel type 2. Methods: A literature search of PubMed, MEDLINE, and Google Scholar was performed for articles published from October 1993 to September 2025 using keywords related to MacTel type 2, OCT, clinical features, and treatment. All relevant clinical studies, including observational studies, clinical trials, and case series, were considered. The literature was screened independently by two authors, and a total of 69 articles were included. Results: Characteristic OCT findings include foveal cavitation, hyperreflective middle retinal layers, inner and outer retinal cavities, ellipsoid zone disruption, and retinal pigment clumps. Central macular thickness is consistently reduced, and structural biomarkers identified on OCT correlate with visual acuity decline. Treatment strategies vary by disease stage: non-proliferative MacTel type 2 currently has no universally effective therapy, although neuroprotective interventions such as ciliary neurotrophic factor (CNTF) show promising results. Proliferative MacTel type 2 is primarily managed with anti-vascular endothelial growth factor therapy (anti-VEGF), demonstrating functional and anatomical improvements. Conclusions: OCT provides essential structural evaluation for monitoring MacTel type 2, while treatment approaches remain stage-dependent. Emerging therapies, including CNTF implants and novel anti-VEGF agents, hold potential for improving outcomes. Full article

Efficient object detection is vital for Remotely Operated Vehicles (ROVs) performing marine debris cleanup, yet existing lightweight designs frequently encounter efficiency bottlenecks when adapted to deeper neural networks. This research identifies a critical “Inverted Bottleneck” anomaly in the Slim-Neck architecture on the YOLO11 backbone, where deep-layer Memory Access Cost (MAC) abnormally spikes. To address this, we propose SvelteNeck-YOLO. By incorporating the proposed EHSCSP module and EHConv operator, the model systematically eliminates computational redundancies. Empirical validation on the TrashCan and URPC2019 datasets demonstrates that the model resolves the memory wall issue, achieving a state-of-the-art trade-off with only 5.8 GFLOPs. Specifically, it delivers a 34% relative reduction in computational load compared to specialized underwater models while maintaining a superior Recall of 0.859. Consequently, SvelteNeck-YOLO establishes a robust, cross-generational solution, optimizing the Pareto frontier between inference speed and detection sensitivity for resource-constrained underwater edge computing. Full article

This paper introduces a novel methodology that integrates 6G wireless Federated Edge Learning (FEEL) frameworks with MAC to PHY cross layer optimization strategies. In the context of mobile edge computing, typically ensuring robust channel estimation within the 6G network use cases presents critical challenges, particularly in managing data retransmissions. Inaccurate updates from distributed 6G devices can undermine the reliability of federated learning, affecting its overall performance. To address this, rather than relying on direct evaluations of the objective function, we propose an AI/ML-assisted algorithm for global optimization based on radial basis functions (RBFs) decision-making process to assess learned preference options. Full article

Slicing functionality makes it possible for an operator to share a 5G physical infrastructure between several virtual networks operated by different institutions. The deployed slices can support a wide range of applications with conflicting QoS targets. The coexistence of these slices on top of a common infrastructure is challenging and remains an open issue. Identifying traffic associated with a given type of slice is required to operate and control network resources in an efficient and secure way. This work proposes new algorithms operating at the physical and MAC layers. The solutions designed identify traffic generated by URLLC and eMBB slices by defining a new LightGBM framework. The algorithms can operate at the base station level in an O-RAN-type architecture. They provide a valuable input to radio resource management and traffic steering procedures. Full article

Dynamic network conditions in precision agriculture motivate a scalable, privacypreserving federated learning architecture that tightly integrates ground-based edge intelligence with a space-assisted hierarchical aggregation layer. In Phase 1, heterogeneous tractors act as intelligent farm nodes that train local models, form capability- and task-aware clusters, and employ Network Quality Index (NQI)-driven scheduling, similarity-based checkpointing, and compressed transmissions to cope with highly variable 3G/4G/5G connectivity. In Phase 2, cluster drivers synchronize with Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites that perform regional and global aggregation using staleness- and fairness-aware weighting, while end-to-end Salsa20 + MAC encryption preserves the confidentiality and integrity of all model updates. Across two representative tasks—nutrient prediction and crop health assessment—our full hierarchical system matches or exceeds centralized performance (e.g., AUC 0.92 vs. 0.91 for crop health) while reducing uplink traffic by ∼90% relative to vanilla FedAvg and cutting the communication energy proxy by more than 4×. The proposed fairness-aware GEO aggregation substantially narrows regional performance gaps (standard deviation of AUC across regions reduced from 0.058 to 0.017) and delivers the largest gains in low-connectivity areas (AUC 0.74 → 0.88). These results demonstrate that coupling on-farm intelligence with multi-orbit federated aggregation enables near-centralized model quality, strong privacy guarantees, and communication efficiency suitable for large-scale, connectivity-challenged agricultural deployments. 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

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

In automated warehousing scenarios, mobile UHF RFID robots typically operate along preset fixed paths to collect basic information from goods tags. They lack the ability to perceive shelf layouts and goods distribution, leading to problems such as missing reads and low inventory efficiency. To address this issue, this paper proposes a cross-layer sensing method for mobile UHF RFID robot reading states based on multiple linear regression-orthogonal least squares (MLR-OLS) and random forest. For shelf state sensing, a position sensing model is constructed based on the physical layer, and MLR-OLS is used to estimate shelf positions and interaction time. For good state sensing, combining physical layer and MAC layer features, a K-means-based tag density classification method and a missing tag count estimation algorithm based on frame states and random forest are proposed to realize the estimation of goods distribution and the number of missing goods. On this basis, according to the read state sensing results, this paper further proposes an adaptive reading strategy for RFID robots to perform targeted reading on missing goods. Experimental results show that when the robot is moving at medium and low speeds, the proposed method can achieve centimeter-level shelf positioning accuracy and exhibit high reliability in goods distribution sensing and missing goods count estimation, and the adaptive reading strategy can significantly improve the goods read rate. This paper realizes cross-layer sensing and read optimization of the RFID robot system, providing a theoretical basis and technical route for the application of mobile UHF RFID robot systems. Full article

In dense Wireless Body Area Network (WBAN) environments, inter-network interference significantly degrades the reliability of medical data transmission. This paper proposes a novel MAC layer interference mitigation strategy that integrates interference-priority-weighted K-means++ clustering with graph-coloring-based time slot allocation. Unlike traditional coexistence schemes, our two-phase approach first partitions the network using a weighted metric combining physical distance and Interference Signal Strength (ISS), ensuring a balanced distribution of high-priority WBANs. Subsequently, we employ an enhanced Priority-Weighted Welch–Powell algorithm to assign collision-free time slots within each cluster. Simulation results demonstrate that the proposed strategy outperforms IEEE 802.15.4, CSMA/CA, and random coloring benchmarks. It reduces inter-network interference by 26.7%, improves priority node distribution balance by 65.7%, and maintains a transmission success rate above 80% under high-load conditions. The proposed method offers a scalable and low-complexity solution for reliable vital sign monitoring in crowded healthcare scenarios. Full article

This research explores the roles of perceived teacher support, L2 grit, and Foreign Language Enjoyment (FLE) in willingness to communicate (WTC) in English among Pakistani university students, thereby filling a contextual gap in Pakistani multilingual society. It utilized a quantitative cross-sectional design based on the WTC pyramid model by MacIntyre et al. and positive psychology. Adapted scales were used to gather data on 1050 multidisciplinary Pakistani English learners, who were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The main findings can be summarized as follows: (1) perceived teacher support had a small but significant direct effect on L2 WTC; (2) L2 grit had a strong and significant direct effect on L2 WTC; and (3) more importantly, FLE had a significant mediating effect. Indirectly, teacher support was the key factor in improving the L2 WTC, as evidenced by a significant increase in FLE. Though the impact of L2 grit was mostly direct, it was also indirect through FLE. This model explained 45.9 percent of the variation in L2 WTC. These findings highlight FLE, a favorable emotion, as the key channel through which environmental support (teacher support) and personal resilience (L2 grit) are translated into communicative willingness. The results confirm the inclusion of positive psychology into the multi-layered L2 WTC model, which emphasizes the importance of FLE in connecting cognition and emotion. This has important pedagogical implications for EFL/ESL contexts in Pakistan, where teachers should create engaging learning experiences, provide multidimensional support, and foster learners’ perseverance to enhance communicative interaction. Full article

Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and fault tolerance, security and trust, time synchronisation, energy harvesting and power management, media access control (MAC) and scheduling, interoperability, routing and topology control, and real-world validation, within a unified comparative framework. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Scopus search identified 60 primary publications published between 2022 and 2025. The analysis shows a clear shift from reactive designs to predictive approaches that incorporate learning methods and energy considerations. Fault detection now relies on deep learning (DL) and statistical modelling, security incorporates trust and intrusion detection, and new synchronisation and MAC schemes approach wired levels of determinism. Regarding applied contributions, the analysis notes that routing and energy harvesting advances extend network lifetime. However, gaps remain in mobility support, interoperability across protocol layers, and field validation. The present work outlines these open issues and highlights research directions needed to mature IWSANs into robust infrastructure for Industry 4.0 and the emerging Industry 5.0 vision. Full article

Industrial Internet of Things (IIoT) deployments increasingly rely on low-cost microcontrollers and single-board computers to stream operational telemetry for monitoring, control, and predictive maintenance, yet the canonical “TLS-to-broker” model does not protect message content from a compromised or curious MQTT broker. This study therefore designs and implements a practical, application-layer end-to-end (E2E) encryption pipeline spanning an ESP32 data client (C++/mbedTLS), an untrusted MQTT broker, and a Raspberry Pi gateway (Python/PyCryptodome) using AES-256-GCM with Additional Authenticated Data (AAD). Sensor measurements are serialized as compact JSON, encrypted and authenticated on the ESP32, framed into a binary record, Base64-encoded for MQTT payload carriage, and verified/decrypted only at the gateway. Experiments on ESP32-WROOM-32 and Raspberry Pi 4 show an average ESP32 packet-preparation latency of 41.754 ms (JSON 1.0 ms; AES-GCM 29.5 ms; Base64 11.2 ms), robust rejection of ciphertext tampering and unauthorized devices via MAC verification and whitelist checks, and 99.72% decrypt-and-store success over a one-hour run (718/720 messages). These results indicate that commodity IIoT hardware can support practical and replicable E2E confidentiality and integrity without sacrificing operational throughput, while eliminating the MQTT broker as a de facto man-in-the-middle. 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 recent years, polymer-based hybrid nanocomposites have emerged as promising alternatives to traditional heavy metal shields due to their low density, flexibility, and environmental safety. In this study, the synthesis of PS-PEG copolymers and the gamma radiation-shielding properties of PS-PEG/As₂O₃, PS-PEG/BN, and PS-PEG/As₂O₃/BN nanocomposites with different compositions are investigated. The goal is to find the optimal nanocomposite composition for gamma radiation shielding and dosimetry. Therefore, the mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), half-value layer (HVL), tenth-value layer (TVL), effective atomic number, mean free path (MFP), radiation shielding efficiency (RPE), electron density, and specific gamma-ray constant were presented. Gamma rays emitted by the Eu source were detected by a high-purity germanium (HPGe) detector device. GammaVision was used to analyze the given data. Photon energy was in the vicinity of 121.8–1408.0 keV. The MAC values in XCOM simulation tools were used to compute. Gamma-shielding efficiency was increased by an increased number of NPs at a smaller photon energy. At 121.8 keV, the HVL of a composite with 70 wt% As₂O₃ NPs is 2.00 cm, which is comparable to the HVL of lead (0.56 cm) at the same energy level. Due to the increasing need for lightweight, flexible, and lead-free shielding materials, PS-b-PEG copolymer-based nanocomposites reinforced with arsenic oxide and BN NPs will be materials of significant interest for next-generation radiation protection applications. 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