Detection Mechanism Research Articles

Composite Anti-Disturbance Control for Nonlinear Hidden Markov Jump Systems Under Replay Attacks: A Dynamic Output-Feedback Method.

This article is dedicated to researching the problem of composite anti-disturbance control for nonlinear hidden Markov jump systems, described by the interval type-2 Takagi-Sugeno fuzzy method, under replay attacks in the continuous-time domain. Based on dynamic output feedback control and disturbance observer, a fuzzy composite controller, following the core idea of nonparallel distribution compensation, is designed to compensate for the impact of multiple disturbances. For replay attacks, a multisensor scheme with the detection mechanism is introduced, enabling the studied systems to remain stable even under attacks. Afterward, with the aid of the Lyapunov stability theory, sufficient conditions for ensuring the stability of the resulting systems are deduced, and then the gains of the desired controller and disturbance observer are obtained. Finally, the rationality and effectiveness of the established method are demonstrated through simulation, as well as its superiority over the traditional Hinfty control method.

Design and synthesis of naphthalimide-based CO fluorescent probes realizing tricolor detection and turn-on/off response

Carbon monoxide (CO) fluorescent probes have attracted tremendous attention attributed to their low detection limit, high specificity, simple operation and good biocompatibility. However, the relationship between the molecular structure and the fluorescence response signal still needs to be systematically elucidated for different applications. Herein, three different CO fluorescent probes based on naphthalimide derivative with blue emission (B-NIA), green emission (G-NIA), and near-infrared emission (R-NIA) were designed and synthesized. Three-primary colors and opposite turn-on or turn-off fluorescent responses to CO were achieved by regulating the conjugation system of fluorophores and the position of fluorescent recognition group. The change of fluorescence response and sensing mechanism for CO detection was studied by theory calculation and mass spectrometry analysis. All the B/G/R-NIA possessed high sensitivity for CO detection. Notably, the R-NIA emerged with a noticeable NIR fluorescence response to CO with a low detection limit (0.61 µM) and high selectivity and relative pH stability. In addition, the R-NIA showed particularly low cytotoxicity and has been successfully used to detect CO in living cells. These studies provided the theoretical reference and technical route to synthesize fluorescent probes with different emission wavelengths and opposite fluorescent responses to CO for various application scenarios

Ankle torque variance is a better indicator of balance control performance than plantar perceptual sensitivity threshold.

We explored whether ankle torque variability or plantar perceptual threshold explains human balance control more effectively. We hypothesized that ankle torque variance is a better indicator of center of pressure (COP) velocity variance than plantar perceptual sensitivity. Two conditions were tested: loaded (23-kg vest added) and unloaded, as loading should diminish plantar sensitivity and increase COP velocity variability. We created a linear feedback model to assess the noise change in the sensorimotor loop induced by loading. Plantar sensitivity was quantified using a psychophysical approach while participants stood barefoot. A linear motor applied a force impulse on the participant's heel. A "yes-no" method of limits was selected to identify plantar sole sensory thresholds in both conditions. We observed reduced plantar sensitivity in loaded compared with unloaded conditions. In the loaded condition, participants exhibited greater COP velocity variance, with significant positive Pearson's correlations confirming a substantial association between ankle torque and COP velocity variances for both loaded [variance accounted for (VAF): r2 = 44.56%, P = 0.018] and unloaded conditions (VAF: r2 = 58.83%, P = 0.004). No significant correlation existed between COP velocity variance and plantar sensitivity threshold for both loaded (VAF: r2 = 0.002%, P = 0.99) and unloaded conditions (VAF: r2 = 21.81%, P = 0.35). The model confirmed an ∼88% rise in sensorimotor loop noise in the loaded condition. Ankle torque variance assesses the precision of nonperceptual and perceptual detection mechanisms in evaluating whole body motions and the accuracy in converting sensory cues into ankle torque.NEW & NOTEWORTHY Plantar cutaneous information contributes to balance control by modulating motor commands, but plantar perceptual sensitivity is a suboptimal indicator of balance performance. Multiple sensory cues encode whole body dynamics, guiding sensorimotor mechanisms to minimize body sway variability. Ankle torque variance is proposed as a superior measure for explaining balance control performance and evaluating the sensorimotor loop's functioning in balance control.

Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation

Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets. Despite these advancements, segmentation failures remain a significant concern for real-world clinical applications, necessitating reliable detection mechanisms. This paper introduces a comprehensive benchmarking framework aimed at evaluating failure detection methodologies within medical image segmentation. Through our analysis, we identify the strengths and limitations of current failure detection metrics, advocating for the risk-coverage analysis as a holistic evaluation approach. Utilizing a collective dataset comprising five public 3D medical image collections, we assess the efficacy of various failure detection strategies under realistic test-time distribution shifts. Our findings highlight the importance of pixel confidence aggregation and we observe superior performance of the pairwise Dice score (Roy et al., 2019) between ensemble predictions, positioning it as a simple and robust baseline for failure detection in medical image segmentation. To promote ongoing research, we make the benchmarking framework available to the community.

A single phosphorylatable amino acid residue is essential forthe recognition of multiple potyviral HCPro effectors by potato Nytbr.

Potato virus Y (PVY, Potyviridae) is among the most important viral pathogens of potato. The potato resistance gene Nytbr confers hypersensitive resistance to the ordinary strain of PVY (PVYO), but not the necrotic strain (PVYN). Here, we unveil that residue 247 of PVY helper component proteinase (HCPro) acts as a central player controlling Nytbr strain-specific activation. We found that substituting the serine at 247 in the HCPro of PVYO (HCProO) with an alanine as in PVYN HCPro (HCProN) disrupts Nytbr recognition. Conversely, an HCProN mutant carrying a serine at position 247 triggers defence. Moreover, we demonstrate that plant defences are induced against HCProO mutants with a phosphomimetic or another phosphorylatable residue at 247, but not with a phosphoablative residue, suggesting that phosphorylation could modulate Nytbr resistance. Extending beyond PVY, we establish that the same response elicited by the PVYO HCPro is also induced by HCPro proteins from other members of the Potyviridae family that have a serine at position 247, but not by those with an alanine. Together, our results provide further insights in the strain-specific PVY resistance in potato and infer a broad-spectrum detection mechanism of plant potyvirus effectors contingent on a single amino acid residue.

Detection and Mitigation of DDoS Attacks : A Review of Robust and Scalable Solutions

Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network security, causing significant disruptions by overwhelming systems with malicious traffic. The motivation behind this review is the growing sophistication and frequency of DDoS attacks, which demand more robust and scalable detection and mitigation techniques. While numerous methods have been proposed, limitations such as high false positive rates, resource constraints, and the evolving nature of attacks continue to challenge existing solutions. This review aims to analyze and evaluate various robust detection mechanisms, including machine learning, anomaly detection, and hybrid models, with a focus on scalability and adaptability in real-world applications. The objective is to identify key strengths and weaknesses in current approaches, highlighting future research directions for building more resilient DDoS defense systems capable of operating efficiently under high-traffic conditions.

Designing a Novel Network Anomaly Detection Framework using Multi-Serial Stacked Network with Optimal Feature Selection Procedures over DDOS Attacks

- Distributed denial-of-service (DDoS) attacks are the major threat that disrupts the services in the computer system and networks using traffic and targeted sources. So, real-world attack detection techniques are considered an important element in executing cybersecurity tasks. The present DDoS techniques are prone to False Positive Rates (FPR) and also it didn’t acquire the complicated patterns presented in the attack traffic. Internet of Things (IoT) is a complicated network with resource-constrained devices and networks that are prone to different security threats like DDoS attacks. Later, the Software Defined Networking (SDN) with IoT models is used to enhance the access control techniques and security models. DDoS attacks are considered as an important threat in the IoT networks. Hence, it is important to construct a novel network anomaly detection model with a deep learning mechanism to resolve the limitations of the existing techniques. Initially, essential data required for the validation are gathered from the IDS ISCX 2012 dataset. The optimal features are selected from input data using the Predefined-Mud Ring Algorithm (P-MRA). The optimally selected features are provided to the Multi-Serial Stacked Networks (Multi-SSN), which is the fusion of Convolutional Autoencoder (CAE), Gated Recurrent Unit (GRU), and Bayesian Learning (BL) networks. Here, the essential features for the validation are acquired from the CAE and GRU. Then, these features are stacked and given to the BL mechanism for detecting the anomalies in the network. Further, several experimental validations are performed in the developed framework over traditional network anomaly detection mechanism.

Correlation-Based Attention Weights Mechanism in Multimodal Depression Detection: A Two-Stage Approach

Correlation-Based Attention Weights Mechanism in Multimodal Depression Detection: A Two-Stage Approach

Role of TRPV channels in texture discrimination during Bactrocera dorsalis egg-laying behaviour.

Deciding where to lay an egg is critical for the survival of insects' offspring. Compared with our understanding of the chemosensory assessment of egg-laying sites, the mechanisms of texture detection are largely unknown. Here, we show that Bactrocera dorsalis, a notoriously agricultural pest laying its eggs within ripening fruits, can discriminate substrate texture during the egg-laying process. Exposure to drugs targeting transient receptor potential vanilloid (TRPV) mechanosensory channels abolished their oviposition preference for hard textures. BdorNan and BdorIav are two members of the TRPV subfamily, and their transcripts were detected in the labellum, the foreleg tarsi and the ovipositor. Then, we successfully obtained knockout strains of each gene using the CRISPR/Cas9 technique. The results showed that BdorNan is required for the discrimination of stiffness difference. BdorIav knockout had no significant effect on the ability of B. dorsalis to choose harder substrates. Our study thus reveals that BdorNan plays a substantial role in the texture assessment of egg-laying behaviour in B. dorsalis.

Synthesis and structure of an aqueous stable Cu(II)-based coordination polymer with multifunctional fluorescence sensing property

Advanced methods for both biological and environmental hazardous pollutants are of sustained interest. Herein, a new coordination polymer, namely, [Cu2(OH)(1,2,4-bca)(bmoe)]·H2O (Cu-CP) has been hydrothermally constructed with a mix-ligand strategy, employing 1,2,4-benzenetricarboxylic acid (1,2,4-bca) and 1,1′-bis(1H-benzimidazolyl) oxydiethane (bmoe) as organic ligands. The intrinsic strong blue-light emission and the great stability in water solutions with the broad range of pH values (pH = 4 − 12) of Cu-CP provide a platform for practical fluorescence sensing application in water samples. Fluorescence measurements reveal that Cu-CP displays interesting multi-responsive dection activities toward toxic heavy-metal ions Cr2O72- / CrO42− / MnO4−, nitrofuran antibiotics nitrofurantoin (NFT) / nitrofurazone (NFZ) and acetylacetone (ACAC) in aqueous solutions achieving both high sensitivity and low detection limits (micromolar for Cr(VI) / Mn(VII) / ACAC and nanomolar for NFT / NFZ). Furthermore, the interference experiments have verified its excellent selectivity. A synergetic contribution of internal filtration effect (IFE) and Förster resonance energy transfer (FRET) between Cr2O72- / CrO42− / MnO4− and the framework of Cu-CP realize the fluorescence quenching behavior for Cr(VI) / Mn(VII) detection, while FRET, IFE and photoinduced electron transfer (PET) mechanisms are synergistically responsible in the case of NFT / NFZ, ACAC sensing supported by the molecular simulations and UV−vis spectral overlap experiments. This present work affords precious guidance for the effective and facile design and preparation of multifunctional luminescent coordination polymers with promising performance.

Advanced Algorithms for Offline Signature Recognition and Enhanced Forgery Detection Mechanisms

In contemporary society, signatures hold significant importance in various critical documents such as bank cheques, passports, and driver's licenses. Unfortunately, they can be counterfeited through various means, giving rise to issues like fraudulent identifications, identity theft, and cyber-attacks. To mitigate this problem, our project is centered around the development of a system that discerns the authenticity of a signature, distinguishing between genuine and forged signatures within a dataset. We have employed Convolutional Neural Networks (CNN) and deep learning for this purpose. This choice is driven by the understanding that signatures evolve over time due to a range of behavioral factors like age, mental state, and physical well-being. Consequently, our system is designed to adapt and learn from diverse training datasets, enhancing its accuracy in detection. While online and offline signature verification methods exist, our project primarily focuses on the latter, specifically targeting the detectionof forged offline signatures.

DNA‐Origami‐Vesikel‐Sensoren für gesteuerten Einzelmolekül‐Frachttransfer

AbstractVoraussetzung für Interaktionen mit lebenden Organismen ist in der Regel der Zugang zur Lipidmembran eines zellulären Systems. Für einen Nanoroboter, der mit einer Zelle interagiert, ist es deshalb zunächst wichtig eine Bindung an die Lipidmembran zu erkennen. Wir stellen in dieser Arbeit einen DNA‐Origami‐Biosensor vor, der Einzelmolekül‐Fluoreszenz‐Resonanz‐Energie‐Transfer (smFRET) als Transduktionsmechanismus nutzt, um Lipidvesikel präzise zu erkennen und der in der Lage ist Biomoleküle zu bewegen. Das System basiert auf einzelsträngiger DNA (ssDNA), die aus einer DNA‐Origami‐Nanostruktur herausragt und mit dem hydrophoben Farbstoff ATTO647N modifiziert ist. In einer Umgebung ohne Vesikel rollt sich die ssDNA zusammen, was zu einer hohen FRET‐Effizienz führt. Die Bindung eines Vesikels an Cholesterinanker auf dem DNA‐Origami bewirkt, dass durch das hydrophobe ATTO647N die ssDNA in Richtung der Lipiddoppelschicht verdrängt wird und sich dabei ausdehnt. Die größere Distanz zum FRET‐Donor im Origami resultiert in geringerer FRET‐Effizienz. Der Sensorstrang dient außerdem als molekulare Fracht, die durch eine ausgelöste Strangverschiebungsreaktion auf das Vesikel übertragen werden kann. Je nachdem, mit wie vielen Cholesterin‐Molekülen die Verdrängungsstränge funktionalisiert sind, wird die fluoreszierende Fracht entweder über Diffusion in benachbarte Vesikel übertragen oder eine stöchiometrische Anzahl an einzelnen Frachtgütern in das Vesikel auf dem Nanosensor übertragen. Unsere multifunktionale Plattform für Liposom‐Interaktion und Detektion eröffnet somit neue Möglichkeiten für innovative Anwendungen von Biosensoren und die gezielte, stöchiometrische Beladung von Vesikeln mit Einzelmolekülkontrolle.

DNA Origami Vesicle Sensors with Triggered Single-Molecule Cargo Transfer.

Interacting with living systems typically involves the ability to address lipid membranes of cellular systems. The first step of interaction of a nanorobot with a cell will thus be the detection of binding to a lipid membrane. Utilizing DNA origami, we engineered a biosensor with single-molecule Fluorescence Resonance Energy Transfer (smFRET) as transduction mechanism for precise lipid vesicle detection and cargo delivery. The system hinges on a hydrophobic ATTO647N modified single-stranded DNA (ssDNA) leash, protruding from a DNA origami nanostructure. In a vesicle-free environment, the ssDNA coils, ensuring high FRET efficiency. Upon vesicle binding to cholesterol anchors on the DNA origami, hydrophobic ATTO647N induces the ssDNA to stretch towards the lipid bilayer, reducing FRET efficiency. As the next step, the sensing strand serves as molecular cargo that can be transferred to the vesicle through a triggered strand displacement reaction. Depending on the number of cholesterols on the displacer strands, we either induce a diffusive release of the fluorescent load towards neighboring vesicles or a stoichiometric release of a single cargo-unit to the vesicle on the nanosensor. Ultimately, our multi-functional liposome interaction and detection platform opens up pathways for innovative biosensing applications and stoichiometric loading of vesicles with single-molecule control.

Observer‐Based Attack Detection and Data‐Driven Resilient Control for Heterogeneous Nonlinear Nonaffine Mass Under FDI Attacks

ABSTRACTThe attack detection and data‐driven resilient control problem for heterogeneous nonlinear nonaffine multi‐agent systems (MASs) in the presence of intermittent false data injection (FDI) attacks is investigated in this article. First, an attack detection and isolation mechanism based on the distributed observer are designed to determine the attack signals and isolate the attacked communication links in time. Then, in order to solve the problem of the unknown system model, the dynamic linearization method is used to convert the nonlinear nonaffine MASs into an equivalent linear data model. Further, based on the linear data model, the data‐driven resilient controller with the attack isolation mechanism is designed to achieve the leader‐following tracking control objective. Finally, the simulation result illustrates the performance of the proposed scheme with comparisons.

Federated Learning's Dynamic Defense Against Byzantine Attacks: Integrating SIFT-Wavelet and Differential Privacy for Byzantine Grade Levels Detection

Federated learning, which enables decentralized training across multiple devices while maintaining data privacy, is susceptible to Byzantine poisoning attacks. This paradigm reduces the need for centralized data storage and transmission, thereby mitigating privacy risks associated with traditional data aggregation methods. However, FL introduces new challenges, notably susceptibility to Byzantine poisoning attacks, where rogue participants can tamper with model updates, threatening the consistency and security of the aggregated model. Our approach addresses this vulnerability by implementing robust aggregation methods, sophisticated pre-processing techniques, and a novel Byzantine grade-level detection mechanism. We introduce a federated aggregation operator designed to mitigate the impact of malicious clients. Our pre-processing includes data loading and transformation, data augmentation, and feature extraction using SIFT and wavelet transforms. Additionally, we employ differential privacy and model compression to improve the robustness and performance of the federated learning framework. Our approach is assessed using a tailored neural network model applied to the MNIST dataset, achieving 97% accuracy in detecting Byzantine attacks. Our results demonstrate that robust aggregation significantly improves the resilience and performance. This comprehensive approach ensures the integrity of the federated learning process, effectively filtering out adversarial influences and sustaining high accuracy even when faced with adversarial Byzantine clients.

Ratiometric detection of lethal oxalyl and thionyl chloride by a thiophene-appended phenanthridine sensor

As fatal and potentially accessible reagents, oxalyl and thionyl chloride have become severe concerns for public safety. Therefore, it is essential to develop effective methods to discriminate them. Herein, a new fluorescent sensor, 2-(7,8,13,14-tetrahydrodibenzo[a, i]phenanthridin-5-yl)-4,6-di(thiophen-2-yl)aniline (TPTA) has been designed with the incorporation of classical phenanthridine fluorophore and o-aniline functionality to explore the versatile sensing capabilities. Due to the electrophilic character of chlorocarbonyls, the detection process occurs between TPTA and SOCl2, (COCl)2 via nucleophilic addition and subsequent cyclization, which produces new, green emissive cyclic adducts. An inhibited excited-state intramolecular proton transfer (ESIPT) followed by the ICT process endows sensitive ratiometric detection of oxalyl and thionyl chloride with a low limit of detection (4.15 and 2.93 nM). The limit of quantification (LOQ) was calculated to be (9.81 and 13.8 nM for SOCl2 and (COCl)2, which is far lower than the LD50 values. Because of its reaction-based sensing mechanism, robust selectivity with rapid response time has been achieved towards SOCl2 and (COCl)2 (<5 sec) with good stability. The anti-interference properties reveal that the sensor TPTA can efficiently detect SOCl2 and (COCl)2 even in the presence of other analytes. Further, the detection mechanism of the TPTA induced by oxychlorides was studied and well supported by NMR, HRMS, and DFT theoretical calculations. Remarkably, smartphone-assisted concentration-dependent test kit experiments enhance the practical utility of TPTA and facilitate the real-time detection of oxychlorides. Hence, TPTA has proven to be an inevitable and promising tool for the sensitive recognition oxychloride species.

A dataset of drone-captured, segmented images for oil spill detection in port environments

The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhance polluter accountability, potentially deterring future incidents. Currently, there’s a scarcity of datasets employing RGB images for oil spill detection in maritime settings. This paper presents a unique, annotated dataset aimed at addressing this gap, leveraging a neural network for analysis on both desktop and edge computing platforms. The dataset, captured via drone, comprises 1268 images categorized into oil, water, and other, with a convolutional neural network trained using an Unet model architecture achieving an F1 score of 0.71 for oil detection. This underscores the dataset’s practicality for real-world applications, offering crucial resources for environmental conservation in port environments.

Electrochemical Strips Modified with Zeolites Embedding Silver Clusters for Versatile (Bio)Systems.

Disposable biosensors provide high portability and advantageous response time, which are crucial in the next generation of analytical platforms. Herein, we report on zeolites embedding silver clusters as an electroactive material that enhances the electrode-analyte interaction in disposable electrochemical strips made of plastic or paper. The resulting analytical platform effectively immobilizes analytes and facilitates electron exchange transfer. Two detection mechanisms were explored in the electrodes decorated with zeolites embedding silver clusters. The oxidative-reductive character of zeolites embedding silver clusters was applied for chloride ion detection, which is suitable for environmental analysis. Additionally, the electrocatalytic activity of the composite was applied in glucose sensing in the presence of glucose oxidase, demonstrating the versatility of the composites for detecting molecules of a different nature (clinically or environmentally relevant) without further modification. Our approach contributes to the development of simple and low-cost modification procedures for integrating electroactive materials into miniaturized electrochemical sensors for portable platforms and point-of-care devices.

Development of a Web-Based Document Repository with Plagiarism Checker

In the digital age, managing vast volumes of documents and ensuring the originality of content has become a significant challenge. This paper presents the development of a web-based document repository integrated with a plagiarism checker, aimed at providing a comprehensive solution for storing, retrieving, and verifying the uniqueness of documents. The system allows users to upload, organise, and search documents efficiently while employing a robust plagiarism detection mechanism to ensure the integrity of content. By leveraging web technologies and plagiarism detection algorithms, this platform serves as a valuable tool for educational institutions, businesses, and content creators. The system enhances document management practices by offering a centralised, secure repository and reducing the risk of intellectual property infringement. This paper discusses the architecture, features, and implementation challenges of the system, along with its potential applications in various domains.

A Novel Carbon Dot-Bromothymol Blue System for Ratiometric Colorimetric-Fluorometric Sensing of Glutathione in Urine: A Smartphone-Compatible Approach.

This study presents a novel dual-modal approach for glutathione (GSH) detection using blue and yellow dual-emission carbon dots (BY-CDs) and bromothymol blue (BTB) at pH 8.0. The method employs both colorimetric and fluorometric detection modes, offering a new perspective on GSH quantification. BTB's blue coloration induces selective fluorescence quenching of the CDs' 610nm emission peak, with minimal effect on the 445nm peak. Upon GSH addition, the quinonoid structure (blue color) of BTB transforms to its benzenoid form (yellow color). This transformation triggers fluorescence restoration at 610nm and significant quenching at 445nm, enabling ratiometric fluorescence analysis (F610/F445). Concurrently, colorimetric detection is also ratiometric, based on measuring the ratio between the emerging yellow color peak (435nm) and the decreasing blue color peak (618nm) (A435/A618). The state-of-the-art aspect of this detection method lies in the strategic choice of dual-emission CDs and a dye with distinct absorption spectra that closely match the emission spectra of the CDs. This unique combination allows for dual detection with opposite responses in the two detection modes, enhancing selectivity and reliability. The probe was thoroughly characterized, and its detection mechanism was elucidated using various spectroscopic techniques. The method shows excellent linearity, a broad detection range, and low detection limits for both fluorometry (0.02 - 10.0μM, 5.88nM) and colorimetry (1.0 - 35.0μM, 301.25nM). Additionally, a smartphone-based platform was developed for colorimetric GSH determination, enhancing the method's potential for on-site analysis. The assay's practicality was validated through successful application to human urine samples, yielding excellent recovery values (97.33% to 99.13%).