Search Results (73)

Pipelines that satisfy k-anonymity alone remain vulnerable to attribute disclosure under skewed sensitive attributes. We studied real-time anonymization of high-throughput data streams under strict delay budgets (β). We jointly enforced k-anonymity and l-diversity via a delay-aware Monitor–Trigger–Repair controller that selects swap vs. merge by minimizing a weighted objective λΔIL + (1 − λ)ΔRT while bounding overhead with a neighbor cap (c) and a growth cap (γ). On UCI Adult stream replay, we identified operating regions where stricter privacy does not necessarily increase distortion: with moderate-to-high $k$ and sufficiently large $\beta$, groups satisfy $l$ preemptively, reducing reconfigurations and avoiding aggressive generalization, thereby mitigating information loss relative to k-only baselines. Privacy metrics (l-satisfaction rate and entropy) also improved. We further report a focused sensitivity analysis on $\lambda$, $c$, and $\gamma$ and evaluate an entropy-driven adaptive $l_t$ controller, showing that these levers provide interpretable trade-offs between latency and distortion and can suppress excessive reconfiguration and tail latency. Full article

Background subtraction is a core preprocessing step for video analytics, enabling downstream tasks such as detection, tracking, and scene understanding in applications ranging from surveillance to transportation. However, real-time deployment remains challenging when illumination changes, shadows, and dynamic backgrounds produce heavy-tailed pixel variations that are difficult to capture with simple Gaussian assumptions. In this work, we propose a fully parallel GPU implementation of a per-pixel background model that represents temporal pixel deviations with lognormal distributions. During a short training phase, a circular buffer of n frames (as small as $n=3$) is used to estimate, for every pixel, robust log-domain parameters $(\mu ,\sigma )$. During testing, each incoming frame is compared against a robust reference (per-pixel median), and a lognormal cumulative density function yields a probabilistic foreground score that is thresholded to produce a binary mask. We evaluate the method on multiple videos under varying illumination and motion conditions and compare qualitatively with widely used mixture of Gaussians baselines (MOG and MOG2). Our method achieves, on average, 87 fps with a buffer size of 10, and reaches about 188 fps with a buffer size of 3, on an NVIDIA 3080 Ti. Finally, we discuss the accuracy–latency trade-off with larger buffers. Full article

Recent space exploration roadmaps from China, the United States, and Russia highlight the establishment of Mars bases as a major objective. Future deep-space missions will span the inner solar system and extend beyond the asteroid belt, demanding network control systems that sustain reliable communication and efficient scheduling across vast distances. Current centralized or regionalized technologies, such as the Deep-Space Network and planetary relay constellations, are limited by long delays, sparse visibility, and heterogeneous onboard resources, and thus cannot meet these demands. To address these challenges, we propose a dual-center architecture, DualSynNet, anchored at Earth and Mars and enhanced by Lagrange-point relays and a minimal heliocentric constellation to provide scalable multi-mission coverage. On this basis, we develop a federated multi-agent reinforcement learning framework with graph attention (Fed-GAT-MADDPG), integrating centralized critics, decentralized actors, and interplanetary parameter synchronization for adaptive, resource-aware scheduling. A unified metric system: Reachability, Rapidity, and Availability, is introduced to evaluate connectivity, latency, and resource sustainability. Simulation results demonstrate that our method increases task completion to 52.4%, reduces deadline expiration, constrains rover low-state-of-charge exposure to approximately 0.8%, and maintains consistently high hardware reliability across rover and satellite nodes. End-to-end latency is reduced, with a shorter tail distribution due to fewer prolonged buffering or stagnation periods. Ablation studies confirm the essential role of graph attention, as removing it reduces completion and raises expiration. These results indicate that the integration of a dual-center architecture with federated graph reinforcement learning yields a robust, scalable, and resource-efficient framework suitable for next-generation interplanetary exploration. Full article

Accurate prediction of maximum flow latency is crucial for ensuring the efficient transport of latency-sensitive fronthaul traffic in packet-switched Xhaul networks while maintaining the reliable operation of 5G and beyond Radio Access Networks (RANs). Deterministic worst-case (WC) models provide strict latency guarantees but tend to overestimate actual delays, resulting in resource over-provisioning and inefficient network utilization. To address this limitation, this study evaluates a data-driven Quantile Regression (QR) model for latency prediction in Time-Sensitive Networking (TSN)-enabled packet-switched Xhaul networks operating under dynamic traffic conditions. The proposed QR model estimates high-percentile (tail) latency values by leveraging both deterministic and queuing-related data features. Its performance is quantitatively compared with the WC estimator across diverse network topologies and traffic load scenarios. The results demonstrate that the QR model achieves significantly higher prediction accuracy—particularly for midhaul flows—while still maintaining compliance with latency constraints. Furthermore, when applied to dynamic Xhaul network operation, QR-based latency predictions enable a reduction in active processing-node utilization compared with WC-based estimations. These findings confirm that data-driven models can effectively complement deterministic methods in supporting latency-aware optimization and adaptive operation of 5G/6G Xhaul networks. Full article

Background/Objectives: Lidocaine hydrochloride (LD-HCl) is the most commonly used local anesthetic in dentistry, often administered with epinephrine to extend its duration and reduce systemic absorption. However, its relatively short duration of action, the need for repeated injections, and the unpleasant taste may limit patient compliance and procedural efficiency. This study aimed to develop and evaluate a novel injectable nanoemulsion-based in situ gel depot system of LD to provide prolonged anesthetic activity. Methods: LD-loaded nanoemulsions were formulated by high-shear homogenization followed by probe sonication, employing Miglyol 812 N (oil phase), a combination of Tween 80 and soy lecithin (surfactant–co-surfactant), glycerin, and deionized water (aqueous phase). The selected nanoemulsion (S1) was dispersed in a thermoreversible poloxamer solution to form a nanoemulgel. The preparation was evaluated for globule diameter and uniformity, zeta potential, surface morphology, pH, drug content, stability, rheological behavior, injectability, and in vitro drug release. Analgesic efficacy was assessed via tail-flick and thermal paw withdrawal latency tests in Wistar rats. Cardiovascular safety was monitored using non-invasive electrocardiography and blood pressure measurements. Results: The developed nanoemulsions demonstrated a spherical shape, nanometer size (206 nm), high zeta-potential (−66.67 mV) and uniform size distribution, with a polydispersity index of approximately 0.40, while the nanoemulgel demonstrated appropriate thixotropic properties for parenteral administration. In vitro release profiles showed steady LD release (5 h), following the Higuchi model. In vivo studies showed significantly prolonged analgesic effects lasting up to 150 min (2.5 h) compared to standard LD-HCl injection (p < 0.001), with no adverse cardiovascular effects observed. Conclusions: The developed injectable LD in situ nanoemulgel offers a promising, patient-friendly alternative for prolonged anesthetic delivery in dental and operative procedures, potentially reducing the need for repeated injections and enhancing procedural comfort. Full article

Responsiveness—the speed at which a text-to-speech (TTS) system produces audible output—is critical for real-time voice assistants yet has received far less attention than perceptual quality metrics. Existing evaluations often touch on latency but do not establish reproducible, open-source standards that capture responsiveness as a first-class dimension. This work introduces a baseline benchmark designed to fill that gap. Our framework unifies latency distribution, tail latency, and intelligibility within a transparent and dataset-diverse pipeline, enabling a fair and replicable comparison across 13 widely used open-source TTS models. By grounding evaluation in structured input sets ranging from single words to sentence-length utterances and adopting a methodology inspired by standardized inference benchmarks, we capture both typical and worst-case user experiences. Unlike prior studies that emphasize closed or proprietary systems, our focus is on establishing open, reproducible baselines rather than ranking against commercial references. The results reveal substantial variability across architectures, with some models delivering near-instant responses while others fail to meet interactive thresholds. By centering evaluation on responsiveness and reproducibility, this study provides an infrastructural foundation for benchmarking TTS systems and lays the groundwork for more comprehensive assessments that integrate both fidelity and speed. Full article

This study evaluates leadership uniformity—the degree to which the proposer role is evenly distributed among validator nodes over time—in Quorum-based Byzantine Fault Tolerance (QBFT), a Byzantine Fault-Tolerant (BFT) consensus algorithm used in permissioned blockchain networks. By introducing simulated follower timeouts derived from uniform, normal, lognormal, and Weibull distributions, it models a range of network conditions and latency patterns across nodes. This approach integrates Raft-inspired timeout mechanisms into the QBFT framework, enabling a more detailed analysis of leader selection under different network conditions. Three leader selection strategies are tested: Direct selection of the node with the shortest timeout, and two quorum-based approaches selecting from the top 20% and 30% of nodes with the shortest timeouts. Simulations were conducted over 200 rounds in a 10-node network. Results show that leader selection was most equitable under the Weibull distribution with shape $k=0.5$, which captures delay behavior observed in real-world networks. In contrast, the uniform distribution did not consistently yield the most balanced outcomes. The findings also highlight the effectiveness of quorum-based selection: While choosing the node with the lowest timeout ensures responsiveness in each round, it does not guarantee uniform leadership over time. In low-variability distributions, certain nodes may be repeatedly selected by chance, as similar timeout values increase the likelihood of the same nodes appearing among the fastest. Incorporating controlled randomness through quorum-based voting improves rotation consistency and promotes fairer leader distribution, especially under heavy-tailed latency conditions. However, expanding the candidate pool beyond 30% (e.g., to 40% or 50%) introduced vote fragmentation, which complicated quorum formation in small networks and led to consensus failure. Overall, the study demonstrates the potential of timeout-aware, quorum-based leader selection as a more adaptive and equitable alternative to round-robin approaches, and provides a foundation for developing more sophisticated QBFT variants tailored to latency-sensitive networks. Full article

Background: Chronic pain poses a major global health burden, often inadequately managed by conventional analgesics due to limited efficacy and side effects. In this context, plant-based therapies offer a promising alternative. This study aimed to evaluate the antioxidant and analgesic potential of four medicinal plants traditionally used for pain relief: Morus alba, Angelica archangelica, Valeriana officinalis, and Passiflora incarnata. Methods: Phytochemical analyses quantified total phenolic acid, flavonoid, and polyphenolic acid contents in the extracts. Antioxidant activity was assessed using the ABTS radical scavenging assay. Analgesic effects were evaluated in vivo using the hot-plate and tail-flick tests in mice treated for 14 days with plant extracts or paracetamol. Results: Morus alba showed the highest polyphenolic content and strongest antioxidant activity (IC₅₀ = 0.0695 mg/mL). In analgesic tests, Angelica archangelica demonstrated the most significant effect in the hot-plate test (72.2% increase in latency), while Valeriana officinalis had the highest efficacy in the tail-flick test (41.81%), exceeding paracetamol’s performance in that model. Conclusions: While antioxidant activity correlated with polyphenol content, analgesic effects appeared to involve additional mechanisms. These findings support the potential of Angelica archangelica and Valeriana officinalis as effective natural alternatives for pain relief. Full article

Due to the challenges posed by high resolution, substantial background noise, significant object scale variation, and long-tailed data distribution in remote sensing images, traditional techniques often struggle to maintain both high accuracy and low latency. This paper proposes YOLO11-FSDAT, an advanced object detection framework tailored for remote sensing imagery, which integrates not only modular enhancements but also theoretical and architectural innovations to address these limitations. First, we propose the frequency–spatial feature extraction fusion module (Freq-SpaFEFM), which breaks the conventional paradigm of spatial-domain-dominated feature learning by introducing a multi-branch architecture that fuses frequency- and spatial-domain features in parallel. This design provides a new processing paradigm for multi-scale object detection, particularly enhancing the model’s capability in handling dense and small-object scenarios with complex backgrounds. Second, we introduce the deformable attention-based global–local fusion module (DAGLF), which combines fine-grained local features with global context through deformable attention and residual connections. This enables the model to adaptively capture irregularly oriented objects (e.g., tilted aircraft) and effectively mitigates the issue of information dilution in deep networks. Third, we develop the adaptive threshold focal loss (ATFL), which is the first loss function to systematically address the long-tailed distribution in remote sensing datasets by dynamically adjusting focus based on sample difficulty. Unlike traditional focal loss with fixed hyperparameters, ATFL decouples hard and easy samples and automatically adapts to varying class distributions. Experimental results on the public DOTAv1, SIMD, and DIOR datasets demonstrated that YOLO11-FSDAT achieved 75.22%, 82.79%, and 88.01% mAP, respectively, outperforming baseline YOLOv11n by up to 4.11%. These results confirm the effectiveness, robustness, and broader theoretical value of the proposed framework in addressing key challenges in remote sensing object detection. Full article

NAND flash memory has been widely adopted as the primary data storage medium in data centers. However, the inherent characteristic of out-of-place updates in NAND flash necessitates garbage collection (GC) operations on NAND flash-based solid-state drives (SSDs), aimed at reclaiming flash blocks occupied by invalid data. GC processes entail additional read and write operations, which can lead to the blocking of user requests, thereby increasing the tail latency. Moreover, frequent execution of GC operations is prone to induce more pages to be written, further reducing the lifetime of SSDs. In light of these challenges, we introduce an innovative GC scheme, termed SplitGC. This scheme leverages the records of data redundancy gathered during periodic read scrub operations within the SSD. By analyzing these features of data duplication, SplitGC enhances the selection strategy for the victim block. Furthermore, it bifurcates the migration of valid data pages into two phases: non-duplicate pages follow standard relocation procedures, whereas the movement of duplicate pages is scheduled during idle periods of the SSD. The experiment results show that our scheme reduces tail latency induced by GC by 8% to 83% at the 99.99th percentile and significantly decreases the amount of valid page migration by 38% to 67% compared with existing schemes. Full article

Background/Objectives: Essential oils are increasingly recognized for their therapeutic potential, yet Ammoides verticillata essential oil (AVEO) remains relatively unexplored, particularly for its anti-inflammatory and analgesic properties. This study aimed to profile AVEO’s chemical composition and evaluate its antioxidant, anti-inflammatory, and analgesic effects, with a focus on its novel pharmacological actions. Methods: The chemical composition of AVEO was determined using GC-MS analysis, and antioxidant capacity was assessed through in vitro assays. Furthermore, the anti-inflammatory potential was investigated using a carrageenan-induced paw edema model in rats, complemented by the inhibition assays of cyclooxygenase (COX) enzymes. The analgesic effects were evaluated through acetic acid-induced writhing and tail immersion tests. Additionally, a computational study was performed to explore the binding affinity of AVEO’s major constituents to COX-2. Results: GC-MS analysis revealed a rich monoterpene profile dominated by carvacrol (32.51%). It was found that AVEO exhibited significant antioxidant activity. Similarly, in vivo, AVEO showed significant anti-inflammatory effects, achieving a percentage inhibition of 52.23% at 200 mg/kg, comparable to diclofenac, along with potent COX-2 inhibition observed (IC₅₀ = 1.51 ± 0.20, SI = 5.56). Moreover, analgesic tests demonstrated dose-dependent pain relief, in which the dose of 200 mg/kg significantly prolonged tail latency to 14.00 ± 1.45 s and markedly reduced abdominal constriction to 21.17 ± 1.62. Computational analysis further corroborated the high binding affinity of carvacrol and thymol with COX-2 (−7.381 and −6.939 Kcal/mol, respectively). Conclusions: These findings underscore AVEO’s potential as a promising therapeutic agent for managing inflammation and pain. Full article

Background: Pain is a major clinical and socioeconomic problem worldwide. The available therapies are not always effective and are often associated with the multiple adverse effects that reduce their clinical application. Natural compounds are an important group of pharmaceuticals that may be used in pain management. We aimed to investigate the analgesic activity of the sanguinarine–chelerythrine from Coptis chinensis. Methods: The analgesic and anti-inflammatory activity of the sanguinarine–chelerythrine fraction of C. chinensis extract (SC 5 and 10 mg/kg), sanguinarine (SAN 1 and 2 mg/kg) and chelerythrine (CHEL 4 and 8 mg/kg) was assessed in tail flick and formalin tests. A microscopic and macroscopic examination of stomach mucosae was performed. TNFα and MMP-9 levels were measured with ELISA kits. Results: Morphine (MORF), CHEL and SC prolongated the tail withdrawal latency, with comparable analgesic activity between MORF and CHEL 8 mg/kg. MORF, CHEL 8 mg/kg, and SAN 2 mg/kg ameliorated the pain reaction in the neurogenic phase of the formalin test. In the inflammatory phase of the formalin test, all tested substances exerted analgesic activity. SAN, CHEL and SC additionally reduced TNFα and MMP-9 secretion. Conclusions: Our results confirmed analgesic effects of CHEL and SC with CHEL analgesic activity comparable to MORF. All investigated substances exerted significant anti-inflammatory activity without concomitant gastrotoxicity. Full article

Background: Major depressive disorder is a common mental health issue characterized by persistently low mood and high morbidity and mortality. The major pathophysiology is neuroinflammation, as evidenced by elevated cytokine levels. Patients often fail to achieve full remission with the use of currently available antidepressants, prompting the search for new treatment options. Schaftoside (SS), a flavonoid found in traditional Chinese herbs, has both antioxidant and anti-inflammatory properties. However, its antidepressant effects are poorly understood. Methods: Male C57BL/6 mice underwent chronic unpredictable mild stress (CUMS) and lipopolysaccharide (LPS) treatment to induce depression- and anxiety-like behaviors. SS was administered at 40, 80, and 160 mg/kg for 28 days. The effect on depression-like behaviors was assessed using behavioral assays, and ELISA was used to measure pro-inflammatory cytokines in the serum and hippocampus. Results: SS significantly decreased immobility in the forced swim and tail suspension tests, increased sucrose preference in the sucrose preference test, and reduced feeding latency in the novelty-suppressed feeding test. These findings indicate improved depression and anxiety-like behaviors. ELISA showed that SS lowered interleukin-1 beta (IL-1β), IL-6, and tumor necrosis factor-alpha levels in the serum and hippocampus of CUMS mice. Conclusions: Our study indicates that SS has antidepressant and anxiolytic effects, possibly through neuroinflammatory processes, making it a promising therapeutic candidate for depression, and thus deserves further investigation into its mechanisms and clinical efficacy. Full article

This work investigates a new erase scheme in NAND flash memory to improve the lifetime and performance of modern solid-state drives (SSDs). In NAND flash memory, an erase operation applies a high voltage (e.g., >20 V) to flash cells for a long time (e.g., >3.5 ms), which degrades cell endurance and potentially delays user I/O requests. While a large body of prior work has proposed various techniques to mitigate the negative impact of erase operations, no work has yet investigated how erase latency and voltage should be set to fully exploit the potential of NAND flash memory; most existing techniques use a fixed latency and voltage for every erase operation, which is set to cover the worst-case operating conditions. To address this, we propose Revisiting Erase Operation, (REO) a new erase scheme that dynamically adjusts erase latency and voltage depending on the cells’ current erase characteristics. We design REO by two key apporaches. First, REO accurately predicts such near-optimal erase latency based on the number of fail bits during an erase operation. To maximize its benefits, REO aggressively yet safely reduces erase latency by leveraging a large reliability margin present in modern SSDs. Second, REO applies near-optimal erase voltage to each WL based on its unique erase characteristics. We demonstrate the feasibility and reliability of REO using 160 real 3D NAND flash chips, showing that it enhances SSD lifetime over the conventional erase scheme by 43% without change to existing NAND flash chips. Our system-level evaluation using eleven real-world workloads shows that an REO-enabled SSD reduces average I/O performance and read tail latency by 12% and 38%, respectivley, on average over a state-of-the-art technique. Full article

This paper presents Boundary-Aware Concurrent Queue (BACQ), a high-performance queue designed for modern GPUs, which focuses on high concurrency in massively parallel environments. BACQ operates at the warp level, leveraging intra-warp locality to improve throughput. A key to BACQ’s design is its ability to replace conflicting accesses to shared data with independent accesses to private data. It uses a ticket-based system to ensure fair ordering of operations and supports infinite growth of the head and tail across its ring buffer. The leader thread of each warp coordinates enqueue and dequeue operations, broadcasting offsets for intra-warp synchronization. BACQ dynamically adjusts operation priorities based on the queue’s state, especially as it approaches boundary conditions such as overfilling the buffer. It also uses a virtual caching layer for intra-warp communication, reducing memory latency. Rigorous benchmarking results show that BACQ outperforms the BWD (Broker Queue Work Distributor), the fastest known GPU queue, by more than 2× while preserving FIFO semantics. The paper demonstrates BACQ’s superior performance through real-world empirical evaluations. Full article