Search Results (121)

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection from speckle noise and land–sea clutter. To address these challenges, we propose a novel end-to-end (E2E) transformer-based SAR ship detection framework, called Flow-Aligned Nested Transformer for SAR Small Ship Detection (FANT-Det). Specifically, in the feature extraction stage, we introduce a Nested Swin Transformer Block (NSTB). The NSTB employs a two-level local self-attention mechanism to enhance fine-grained target representation, thereby enriching features of small ships. For multi-scale feature fusion, we design a Flow-Aligned Depthwise Efficient Channel Attention Network (FADEN). FADEN achieves precise alignment of features across different resolutions via semantic flow and filters background clutter through lightweight channel attention, further enhancing small-target feature quality. Moreover, we propose an Adaptive Multi-scale Contrastive Denoising (AM-CDN) training paradigm. AM-CDN constructs adaptive perturbation thresholds jointly determined by a target scale factor and a clutter factor, generating contrastive denoising samples that better match the physical characteristics of SAR ships. Finally, extensive experiments on three widely used open SAR ship datasets demonstrate that the proposed method achieves superior detection performance, outperforming current state-of-the-art (SOTA) benchmarks. Full article

Content delivery networks (CDNs) face steadily rising, uneven demand, straining heuristic cache replacement. Reinforcement learning (RL) is promising, but most work assumes a fully observable Markov Decision Process (MDP), unrealistic under delayed, partial, and noisy signals. We model cache replacement as a Partially Observable MDP (POMDP) and present the Miss-Triggered Cache Transformer (MTCT), a Transformer-decoder Q-learning agent that encodes recent histories with self-attention. MTCT invokes its policy only on cache misses to align compute with informative events and uses a delayed-hit reward to propagate information from hits. A compact, rank-based action set (12 actions by default) captures popularity–recency trade-offs with complexity independent of cache capacity. We evaluate MTCT on a real trace (MovieLens) and two synthetic workloads (Mandelbrot–Zipf, Pareto) against Adaptive Replacement Cache (ARC), Windowed TinyLFU (W-TinyLFU), classical heuristics, and Double Deep Q-Network (DDQN). MTCT achieves the best or statistically comparable cache-hit rates on most cache sizes; e.g., on MovieLens at $M=600$, it reaches $0.4703$ (DDQN $0.4436$, ARC $0.4513$). Miss-triggered inference also lowers mean wall-clock time per episode; Transformer inference is well suited to modern hardware acceleration. Ablations support $CL=50$ and show that finer action grids improve stability and final accuracy. Full article

Cyclodextrin-based nanosponges (CDNSs) are novel polymers composed of cross-linked cyclodextrin (CD) macrocyclic units, whose characteristics make them great candidates for drug delivery systems with adjustable properties for the drug release process. Examination of the molecular structure and dynamics of CDNSs is a necessary starting point in the first step toward their broad application. Spectroscopic methods are effective analytical tools for probing the structure–property relationships of polymer structures. Infrared (IR) and Raman spectroscopies provide insight into the behavior of hydrogen bond (H-bond) networks influencing the properties of CDNS polymeric networks. Scattering techniques such as inelastic neutron scattering (INS) and Brillouin light scattering (BLS) probe elastic properties, while small-angle neutron scattering (SANS) examines the structural inhomogeneities and water sorption abilities of CDNS materials. Complete evaluation is possible using nuclear magnetic resonance (NMR), which can provide data on CDNS network dynamics. This article summarizes the results of a wide examination of CDNSs with the use of spectroscopic methods and reveals the links between the microscopic behavior and macroscopic properties of CDNSs, enabling the customization of their properties for various biomedical purposes. Full article

Illegal broadcasting is one of the primary challenges for Over the Top (OTT) service providers. Watermarking is a method used to trace illegal redistribution of video content. However, watermarking introduces processing overhead due to the embedding of unique patterns into the video content, which results in additional latency. End-to-end network latency, caused by network congestion or heavy load on the origin server, can slow data transmission, impacting the time it takes for the segment to reach the client. This paper addresses 5xx errors (e.g., 503, 504) at the Content Delivery Network (CDN) in real-world video streaming platforms, which can negatively impact Quality of Experience (QoE), particularly when watermarking techniques are employed. To address the performance issues caused by the integration of watermarking technology, we enhanced the system architecture by introducing and optimizing a shield cache in front of the packager at the origin server and fine-tuning the CDN configuration. These optimizations significantly reduced the processing load on the packager, minimized latency, and improved overall content delivery. As a result, we achieved a 6% improvement in the Key Performance Indicator (KPI), reflecting enhanced system stability and video quality. Full article

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) and blockchain individually address aspects of these limitations, their combined potential for comprehensive optimization remains underexplored. This study proposes a distributed SDN (DSDN) architecture enhanced with blockchain support to provide secure, scalable, and reliable P2P video streaming. We identified research gaps through critical analysis of the literature. We systematically compared traditional P2P, SDN-enhanced, and hybrid architectures across six performance metrics: latency, throughput, packet loss, authentication accuracy, packet delivery ratio, and control overhead. Simulations with 200 peers demonstrate that the proposed hybrid SDN–blockchain framework achieves a latency of 140 ms, a throughput of 340 Mbps, an authentication accuracy of 98%, a packet delivery ratio of 97.8%, a packet loss ratio of 2.2%, and a control overhead of 9.3%, outperforming state-of-the-art solutions such as NodeMaps, the reinforcement learning-based routing framework (RL-RF), and content delivery networks-P2P networks (CDN-P2P). This work establishes a scalable and attack-resilient foundation for next-generation P2P streaming. Full article

Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive feedback loop. The framework is evaluated using 18 months of real traffic traces from a production multi-tier CDN, capturing realistic workload seasonality, cache–tier interactions, and propagation delays. Unlike generic cloud-edge predictors, our design incorporates CDN-specific features and model-switching mechanisms to balance prediction accuracy with computational cost. Seasonal ARIMA (S-ARIMA), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Online Sequential Extreme Learning Machine (OS-ELM) are combined to support both short-horizon scaling and longer-term capacity planning. The predictions drive a queue-based resource-estimation model, enabling proactive cache–server scaling with low rejection rates. Experimental results demonstrate that the framework maintains high accuracy while reducing computational overhead through adaptive model selection. The proposed approach offers a practical, production-tested solution for predictive autoscaling in CDNs and can be extended to other latency-sensitive edge-cloud services with hierarchical architectures. Full article

Multi-person pose estimation is the task of detecting and regressing the keypoint coordinates of multiple people in a single image. Significant progress has been achieved in recent years, especially with the introduction of transformer-based end-to-end methods. In this paper, we present DualPose, a novel framework that enhances multi-person pose estimation by leveraging a dual-block transformer decoding architecture. Class prediction and keypoint estimation are split into parallel blocks so each sub-task can be separately improved and the risk of interference is reduced. This architecture improves the precision of keypoint localization and the model’s capacity to accurately classify individuals. To improve model performance, the Keypoint-Block uses parallel processing of self-attentions, providing a novel strategy that improves keypoint localization accuracy and precision. Additionally, DualPose incorporates a contrastive denoising (CDN) mechanism, leveraging positive and negative samples to stabilize training and improve robustness. Thanks to CDN, a variety of training samples are created by introducing controlled noise into the ground truth, improving the model’s ability to discern between valid and incorrect keypoints. DualPose achieves state-of-the-art results outperforming recent end-to-end methods, as shown by extensive experiments on the MS COCO and CrowdPose datasets. The code and pretrained models are publicly available. Full article

The phosphodiester linkage is found in DNA, RNA and many signaling molecules, such as cyclic mononucleotide, cyclic dinucleotides (CDNs) and cyclic oligonucleotides (cONs). Enzymes that cleave the phosphodiester linkage (nucleases and phosphodiesterases) play important roles in cell persistence and fitness and have therefore become targets for various diseased states. While various inhibitors have been developed for nucleases and cyclic mononucleotide phosphodiesterases, and some have become clinical successes, there is a paucity of inhibitors of the recently discovered phosphodiesterases or ring nucleases that cleave CDNs and cONs. Inhibitors of bacterial c-di-GMP or c-di-AMP phosphodiesterases have the potential to be used as anti-virulence compounds, while compounds that inhibit the degradation of 3′,3′-cGAMP, cA₃, cA₄, cA₆ could serve as antibiotic adjuvants as the accumulation of these second messengers leads to bacterial abortive infection. In humans, 2′3′-cGAMP plays critical roles in antiviral and antitumor responses. ENPP1 (the 2′3′-cGAMP phosphodiesterase) or virally encoded cyclic dinucleotide phosphodiesterases, such as poxin, however, blunt this response. Inhibitors of ENPP1 or poxin-like enzymes have the potential to be used as anticancer and antiviral agents, respectively. This review summarizes efforts made towards the discovery and development of compounds that inhibit CDN phosphodiesterases and cON ring nucleases. Full article

This paper addresses the problem of fixed-time consensus tracking for a class of nonlinear multiagent systems (MASs) with disturbances. We establish a novel fixed-time consensus tracking protocol with adaptive disturbance rejection capabilities, leveraging adaptive selective disturbance elimination (ASDE) technology. This protocol consists of a distributed fixed-time observer (DFTO), a fixed-time disturbance observer (FTDO), and an adaptive selective disturbance elimination backstepping controller (ASDE) with adaptive lumped disturbance compensation abilities. The DFTO estimates the leader’s output using the communication network topology of each follower, while the FTDO rapidly observes the lumped disturbances and their derivatives. By adding disturbance indicator terms and disturbance observation attenuation terms to the control law, the beneficial and harmful effects of disturbance are distinguished. Under favorable disturbance conditions, lumped disturbances can be used to accelerate tracking speed. If disturbances are harmful, they are adaptively compensated to improve tracking accuracy. Furthermore, the fixed-time stability of each part of the protocol is analyzed using Lyapunov theory. Simulation results show that, under different initial states and command inputs, the proposed method achieves faster convergence and smaller tracking errors compared to the adaptive conditional disturbance negaton backstepping controller (ACDN), conditional disturbance negation backstepping controller (CDN), and non-smooth backstepping controller (NBCDC), verifying the effectiveness of the proposed method. The research outcomes serve as a reference for future multiagent adaptive anti-disturbance cooperative control technology. Full article

Bisphenol A is a widely recognized endocrine disruptor that persists in ecosystems, harms aquatic organisms, and contributes to ecological degradation, raising global environmental concerns. Numerous studies have explored β-cyclodextrin–based adsorbents for Bisphenol A removal; however, their regeneration remains a major challenge, often relying on energy-intensive processes and excessive use of organic solvents. In this study, Bisphenol A was selected as a model pollutant, and its adsorption onto β-cyclodextrin nanosponges was investigated. After adsorption, Bisphenol A was efficiently recovered from the saturated β-cyclodextrin nanosponges using an innovative and sustainable supercritical CO₂-based green process, which simultaneously regenerated the adsorbent. The adsorption process achieved an efficiency of 95.51 ± 0.82% under optimized conditions (C₀ = 150 mg/L, m_β-CDNS = 0.15 g, T = 25 °C, and N = 200 rpm), with a maximum adsorption capacity of 47.75 ± 0.28 mg/g. The regeneration process achieved over 99% efficiency at 60 °C and 300 bar, with 10% (v/v) ethanol as a co-solvent, nearly fully restoring the adsorbent’s performance. Unlike conventional regeneration techniques, this green approach eliminates the need for environmentally harmful organic solvents while preserving the adsorbent’s structural integrity, making it a highly efficient and sustainable alternative. This study is the first to demonstrate the effective application of supercritical CO₂-based regeneration for β-cyclodextrin nanosponges in Bisphenol A removal, providing a scalable and environmentally sustainable solution for wastewater treatment. Furthermore, characterization analyses confirmed that the adsorbent retained its chemical and morphological stability after adsorption and regeneration. Full article

In this study, cyclodextrin-based nanostructures (CDNSs) were synthesized through the cross-linking of cyclodextrin (CD) with epichlorohydrin (ECH) as a cross-linker. Two types of CDNSs, α-CDNS and β-CDNS, were prepared to systematically investigate the influence of reaction parameters—such as the solubilization time of α-CD and β-CD, the molar ratio of ECH to CD, and NaOH concentration—on the physicochemical properties of the final product. Naproxen (NAP), a poorly water-soluble drug, was selected as a model compound to assess the drug-loading capacity of the synthesized CDNSs. The effect of each reaction parameter on NAP integration into the CDNSs was examined at varying weight ratios. The optimal reaction conditions were determined to be a solubilization time of 6 h, an ECH/CD molar ratio of 8/1, and an NaOH concentration of 33%. Under these conditions, the NAP loading efficiency of α-CDNSs was calculated as 67.12%. Comparative analysis revealed that α-CDNSs outperformed β-CDNSs in terms of drug-loading capacity. Additionally, the synthesized CDNSs and NAP-loaded CDNSs were characterized using FTIR, DSC, XRD, SEM, and Zetasizer analyses, while the NAP concentration was determined by HPLC. Full article

Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains. Full article

mRNA-based vaccines against the COVID-19 pandemic have propelled the use of nucleic acids for drug delivery. Conventional lipid-based carriers, such as liposomes and nanolipogels, effectively encapsulate and deliver RNA but are hindered by issues such as premature burst release and immunogenicity. To address these challenges, cell membrane-coated nanoparticles offer a promising alternative. We developed a novel nanoparticle system using chitosan methacrylate-tripolyphosphate (CMATPP), which capitalizes on interactions involving membrane proteins at biointerfaces. Ionic crosslinking between chitosan methacrylate and tripolyphosphate facilitates the formation of nanoparticles amenable to coating with red blood cell (RBC) membranes, extracellular vesicles (EVs), and cell-derived nanovesicles (CDNs). Coating CMATPP nanoparticles with RBC membranes effectively mitigated the initial burst release of encapsulated small interfering RNA (siRNA), sustaining controlled release while preserving membrane proteins. This concept was extended to EVs, where CMATPP nanoparticles and CDNs were incorporated into a microfluidic device and subjected to electroporation to create hybrid CDN-CMATPP nanoparticles. Our findings demonstrate that CMATPP nanoparticles are a robust siRNA delivery system with suppressed burst release and enhanced membrane properties conferred by cell or vesicle membranes. Furthermore, the adaptation of the CDN-CMATPP nanoparticle formation in a microfluidic device suggests its potential for personalized therapies using diverse cell sources and increased throughput via automation. This study underscores the versatility and efficacy of CMATPP nanoparticles in RNA delivery, offering a pathway towards advanced therapeutic strategies that utilize biomimetic principles and microfluidic technologies. Full article

The allosteric SERCA (Sarcoplasmic/Endoplasmic Reticulum Ca²⁺-ATPase) activator CDN1163 has been recently added to the group of pharmacological tools for probing SERCA function. We chose to investigate the effects of the compound on T lymphocyte Ca²⁺ stores, using the well-described Jurkat T lymphocyte as a reliable cell system for Ca²⁺ signaling pathways. Our study identified the lowest concentrations of the SERCA inhibitors thapsigargin (TG) and 2,5-di-(tert butyl)-1,4-benzohydroquinone (tBHQ) capable of releasing Ca²⁺, permitting the differentiation of the TG-sensitive SERCA 2b Ca²⁺ store from the tBHQ-sensitive SERCA 3 Ca²⁺ store. We proceeded to test the effects of CDN1163 on Ca²⁺ stores, examining specific actions on the SERCA 2b and SERCA 3 Ca²⁺ pools using our low-dose SERCA blocker regimen. In contrast to previous work, we find CDN1163 exerts complex time-sensitive and SERCA isoform-specific actions on Ca²⁺ stores. Surprisingly, short-term exposure (0–30 min) to CDN1163 perturbs T cell Ca²⁺ stores by suppressing Ca²⁺ uptake with diminished Ca²⁺ release from the SERCA 2b-controlled store. Concomitantly, we find evidence for a SERCA-activating effect of CDN1163 on the SERCA-3 regulated store, given the observation of increased Ca²⁺ release inducible by low-dose tBHQ. Intriguingly, longer-term (>12 h) CDN1163 exposure reversed this pattern, with increased Ca²⁺ release from SERCA 2b-regulated pools yet decreased Ca²⁺ release responses from the tBHQ-sensitive SERCA 3 pool. Indeed, this remodeling of SERCA 2b Ca²⁺ stores with longer-term CDN1163 exposure also translated into the compound’s ability to protect Jurkat T lymphocytes from TG but not tBHQ-induced growth suppression. Full article

The search for new heterometallic metal pseudohalides will be significant for the development of novel functional materials. In this work, a new silver/cadmium heterometallic thiocyanate templated by benzyl viologen has been synthesized and structurally determined, i.e., {(BV)[Ag₂Cd(SCN)₆]}_n (BV²⁺ = benzyl viologen). The interesting 1-D double chain [Ag₂Cd(SCN)₆]_n²ⁿ⁻ was constructed from the CdN₆ octahedron and Ag₂SCN₆ dimers via μ₂-SCN and μ₃-S,S N SCN bridge, in which the Ag···Ag interaction can be found. Inter-molecular C-H···S/N hydrogen bonds between BV²⁺ cations and [Ag₂Cd(SCN)₆]_n²ⁿ⁻ chains contribute to the formation of a stable 3-D network. The short S···N distance implies the strong charge transfer (CT) interactions between the electron-rich silver/cadmium thiocyanate donor and BV²⁺ acceptor. This hybrid can exhibit a photo-generated current performance with an intensity of 1.75 × 10⁻⁸ A. Interestingly, this hybrid can present good photocatalytic degradation performance on rhodamine B in artificial seawater with a degradation ratio of 86.5% in 240 min. This work provides a new catalyst way for the organic dye-type ocean pollutant treatments. Full article