Pattern Recognition Research Articles

Toward Quantum Noses: Quantum Chemosensing Based on Molecular Qubits in Metal-Organic Frameworks.

ConspectusQuantum sensing leverages quantum properties to enhance the sensitivity and resolution of sensors beyond their classical sensing limits. Quantum sensors, such as diamond defect centers, have been developed to detect various physical properties, including magnetic fields and temperature. However, the spins of defects are buried within dense solids, making it difficult for them to strongly interact with molecular analytes. Therefore, nanoporous materials have been implemented in combination with electron spin center of molecules (molecular qubits) to produce quantum chemosensors that can distinguish various chemical substances. Molecular qubits have a uniform structure, and their properties can be precisely controlled by changing their chemical structure. Metal-organic frameworks (MOFs) are suitable for supporting molecular qubits because of their high porosity, structural regularity, and designability. Molecular qubits can be inserted in the MOF structures or adsorbed as guest molecules. The qubits in the MOF can interact with analytes upon exposure, providing an effective and tunable sensing platform.In this Account, we review the recent progress in qubit-MOF hybrids toward the realization of room-temperature quantum chemosensing. Molecular qubits can be introduced in controlled concentrations at targeted positions by exploiting metal ions, ligands, or guests that compose the MOF. Heavy metal-free organic chromophores have several outstanding features as molecular qubits; namely, they can be initialized by light irradiation and exhibit relatively long coherence times of submicroseconds to microseconds, even at room temperature. One detection method involves monitoring the hyperfine interaction between the electron spins of the molecular qubits and the nuclear spins of the analyte incorporated in the pore. There is also an indirect detection method that relies on the motional change in molecular qubits. If the motion of the molecular qubit changes with the adsorption of the analyte, it can be detected as a change in the spin relaxation process. This mechanism is unique to qubits exposed in nanopores, not observed in conventional qubits embedded in dense solids.By maximizing the guest recognition ability of MOFs and the environmental sensitivity of qubits, quantum chemosensing that recognizes specific chemical species in a highly selective and sensitive manner may be possible. It is difficult to distinguish between diverse chemical species by employing only one combination of MOF and qubit, but by creating arrays of different qubit-MOF hybrids, it would become possible to distinguish between various analytes based on pattern recognition. Inspired by the human olfactory mechanism, we propose the use of multiple qubit-MOF hybrids and pattern recognition to identify specific molecules. This system represents a quantum version of olfaction, and thus we propose the concept of a "quantum nose." Quantum noses may be used to recognize biometabolites and biomarkers and enable new medical diagnostic technologies and olfactory digitization.

CCL20 chemokine and other proinflammatory markers after Ad26.COV2.S vaccination.

In highly stressed circumstances, such as COVID-19 pandemic, biomarkers of the vaccine-induced immunity could be especially convenient. The main aim of our study was to determine C-C motif ligand 20 (CCL20) concentration after Ad26.COV2.S vaccination in regard to more common proinflammatory molecules and its correlation with anti-SARS-CoV-2 antibody concentration. Secondly, we investigated inflammatory and immunologic profile differences between patients with and without arterial hypertension. The study included 84 subjects vaccinated with Ad26.COV2.S vaccine. Concentration of CCL20, interleukin (IL) 6, C-reactive protein (CRP) was investigated before, 7 and 14 days after vaccination and concentration of anti-SARS-CoV-2 IgG antibody 7 and 14 days after vaccination. All the markers were measured by well-established laboratory methods. There were no statistically significant changes of CCL20 and IL-6 concentration after the vaccination. The obtained results have shown statistically significant differences for CRP (P = 0.006) concentrations between 3 time points and SARS-CoV-2 IgG antibody (P < 0.001) concentrations between 2 time points. CCL20 did not correlate with IL-6, CRP or anti-SARS-CoV-2 IgG antibody concentration. Statistically significant difference for CRP (P = 0.025) concentration between 3 time points was observed in the subgroup of subjects with arterial hypertension. Although our results did not show changes in CCL20 concentration after the vaccination, possibly due to the study timeframe, further investigations on chemokine profile post SARS-CoV-2 immunization could facilitate the recognition of specific patterns of response (supra- or sub-optimal) to the vaccine.

Calretinin Staining Pattern is Variable in Hirschsprung Disease Occurring in Patients with Down Syndrome

Abstract Introduction/Objective Hirschsprung disease (HD), a congenital absence of enteric ganglion cells, affects 2-10% of patients with Down syndrome (DS). Submucosal nerve hypertrophy (SNH) and abnormal calretinin immunohistochemistry (IHC) support a diagnosis of HD, but the concordance of these findings can be variable in DS. Recognizing differences in HD occuring in DS can provide insight into the disease and help to improve outcomes and reduce delays in diagnosis for these patients. We sought to describe the histologic and calretinin staining patterns in HD occurring in patients with DS. Methods/Case Report From 2017-2023, 22 rectal biopsies from 20 patients with DS were retrospectively collected and reviewed. Subsequent pull-through (PT) resections were evaluated from 8 patients. Biopsies were evaluated for ganglion cells and SNH. Calretinin IHC was qualitatively evaluated for intensity (strong, weak) and density (many, partial, rare). Quantitative analysis used a calculation of the percent area above threshold (%AAT) function of the NIH ImageJ software version 2.1.0. Representative areas of the PT resections were also evaluated by ImageJ %AAT analysis. Results (if a Case Study enter NA) HD biopsies (12/22, 54.5%) were from male patients (age 3 days-6 weeks) and showed SNH in 11/12 (91.7%) with a mean nerve diameter of 57.3 microns (range 25-89 microns). Ganglionated biopsies (10/22, 45.5%) were collected from patients 3 days to 12 years old (M:F ratio 1:1.5) and showed SNH in 6/10 (60%) with a mean nerve diameter of 44.4 microns (range 10-70 microns). Strong normal calretinin staining was only seen in ganglionated biopsies (7/10 non-HD versus 0/12 HD). Negative calretinin staining was only seen in HD (0/10 non-HD vs 7/12 HD). Abnormal staining was seen in both non-HD (3/10, 30%) and HD (5/12, 41.7%) as partial strong positivity and rare weak positivity, respectively. ImageJ %AAT quantitative analysis revealed an average of 1.71 in biopsies with strong calretinin expression, 0.130 with weak staining, and 0.033 with negative staining. Evaluation of 8 PT resections (mean length of aganglionosis 7.1 cm) showed a mean nerve diameter in the aganglionic segment of 88.3 microns, 49.6 microns in the TZ, and 32.7 microns in the ganglionated segment. ImageJ %AAT quantitative analysis of calretinin revealed a mean of 0.044 in the aganglionic area, 0.66 in the TZ, and 1.56 in the ganglionated segment. Conclusion Recognition of the variable calretinin patterns seen in rectal biopsies from patients with DS can assist in accurate and timely diagnosis of HD.

Olfactory Visualization Sensing Array Made with CelluMOFs to Predict Fruit Ripeness Using Deep Learning.

Developing a colorimetry-based artificial scent screening system (i.e., an olfactory visual sensing system) with high sensitivity and accurate pattern recognition for detecting fruit ripeness remains challenging. In this work, we construct a flexible dye/CelluMOFs-based sensor array with improved sensitivity for on-site detection of characteristic gases of fruits and integrate a densely connected convolutional network (DenseNet) into the sensor array, enabling it to recognize unique scent fingerprints and categorize the ripeness of fruits. In the system, CelluMOFs are synthesized through in situ growth of γ-cyclodextrin metal-organic frameworks (γ-CD-MOFs) on flexible fiber filter paper to fabricate a uniform, flexible and porous dye/CelluMOFs sensitive membrane. Compared to the pristine filter paper, the CelluMOFs exhibit increased porosity with a 62 times higher specific surface area and a 3-fold increase in dye loading capacity after 12 h of adsorption. The prepared dye/CelluMOFs sensing film shows outstanding mechanical and detection stability with negligible deviation after 100 cycles of rubbing. The colorimetric visualization arrays with multiple colorimetric dye/CelluMOFs chips, enable the sensitive recognition and detection of nine kinds of characteristic fruit odors and achieve a high response at 8-1500 ppm of trans-2-hexenal, showcasing remarkably low gas detection thresholds. On the basis of the ppm-level limit of detection with high sensitivity, the fabricated colorimetric sensor arrays are typically used for in situ assessment of fruit ripeness by integrating DenseNet. This approach achieves a satisfactory classification accuracy of 99.09% on the validation set, enabling high-precision prediction of fruit ripeness levels.

Optimizing Muzzle Pattern Identification through Improved Pattern Recognition Techniques

Optimizing Muzzle Pattern Identification through Improved Pattern Recognition Techniques

Travel pattern recognition of urban rail passengers based on spatiotemporal sequence similarity

Understanding passenger travel patterns is helpful for the allocation of passenger resources in urban rail transit. Based on rail transit smart card data, this paper proposes a method to identify travel patterns by modeling individual spatiotemporal sequences. First, all the stations visited by individual passengers are extracted, and the similarity of the stations is calculated by the frequency of inter-station travel, the distance between stations and the activity duration of the stations. The main spatial activity area of the individual is divided using a hierarchical clustering algorithm. Then, the spatiotemporal sequence is inferred based on the individual's travel order. The sequence is a set of discrete values that characterize the spatiotemporal state. PCA-KL and K-Means++ are used to extract the similarity sequence structure to identify the passenger travel pattern. Finally, the rail transit smart card data of Xi'an in a certain month is taken as an example to identify its passenger travel pattern. The results show that complex passenger flow has 5 travel modes, of which 3 typical modes are commuting travel in a macro sense, accounting for 79% of the passenger flow. It can be seen that the pattern recognition based on the similarity of individual spatiotemporal sequences in this paper fully reflects the particularity and versatility of the research method, and is highly operational for different cities.

Detection of virulence factors and antimicrobial susceptibility pattern of clinically significant Klebsiella pneumoniae isolates in a tertiary care hospital

Introduction: Klebsiella pneumoniae is associated with a variety of infections across all age groups, both in community and hospital settings. Many strains of K. pneumoniae produce virulence factors such as a capsule, siderophores, biofilm, and hypermucoviscosity, which facilitate adhesion, invasion, and colonization. Additionally, the rising antibiotic resistance in K. pneumoniae strains poses a significant challenge, limiting treatment options. This study aims to identify the virulence factors and assess the antimicrobial susceptibility of clinically significant K. pneumoniae isolates. Materials and Methods: K. pneumoniae isolates were obtained from urine, sputum, blood, cerebrospinal fluid, pus swabs/aspirates, and tissue samples. Standard biochemical tests were performed for identification. Capsule formation, hypermucoviscosity, siderophore production, and biofilm formation were detected phenotypically. Antibiotic susceptibility testing was carried out using the disk diffusion method, and primary and confirmatory tests were conducted for extended-spectrum beta-lactamases (ESBL) and carbapenemase production. Results: Capsule formation, hypermucoviscosity, siderophore production, and biofilm formation were detected in 100%, 15%, 40%, and 15% of the isolates, respectively. Of the isolates, 40% were multidrug-resistant, 55% produced ESBL, 8% expressed AmpC beta-lactamase, and 10% produced carbapenemase. Conclusion: Capsule formation was the most prevalent virulence factor, followed by siderophore production. The majority of K. pneumoniae isolates demonstrated susceptibility to amikacin, piperacillin-tazobactam, and carbapenems. However, 40% of the isolates were multidrug-resistant, with ESBL production being the most common resistance mechanism.

Self‐supervised vessel trajectory segmentation via learning spatio‐temporal semantics

AbstractThe study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods.

Reducing phantom limb pain using phantom motor execution enabled by augmented and virtual reality: participants’ perspectives on an innovative treatment

Purpose Phantom limb pain (PLP) is a condition that greatly diminishes quality of life. Phantom motor execution (PME), enabled by myoelectric pattern recognition combined with virtual and augmented reality, is a novel treatment for PLP. Here we, aimed to describe patients’ experiences of this treatment. Material and Methods Qualitative, using semi-structured interviews one month after completed PME treatment. Twenty-one Swedish and Dutch patients (mean age 57, 16 males, 16 lower limb amputations) participated. The analysis followed the framework approach. Results The main themes were 1) treatment effects on the perception of the phantom limb, 2) living with PLP before and after treatment, and 3) facilitators and barriers to treatment. Most participants learned to control their phantom limb, perceived it more positively and as more complete. This control over the phantom limb became a tool for managing PLP. Most participants’ outlook on life and energy levels improved after treatment. Being mentally focused during treatment was important. Therapists were pivotal to the success of the treatment. Conclusion Controlling the phantom limb improved their perception of it and pain management, self-agency, and quality of life. The therapists’ role was invaluable. We suggest training for patients in phantom limb control before and after amputation.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

PGLYRP-1 mediated intracellular peptidoglycan detection promotes mucosal protection.

Peptidoglycan recognition proteins (PGRPs or PGLYRPs) are implicated in the control of the intestinal microbiota; however, molecular requirements for peptidoglycan (PGN) binding and receptor signaling mechanisms remain poorly understood. We identified PGLYRP-1 as a receptor for the disaccharide motif of lysine N-acetylglucosamine N-acetylmuramic tripeptide (GMTriP-K) with a newly constructed PGN microarray. Surprisingly, PGLYRP-1 was required for innate immune activation of macrophages by GMTriP-K but not N-acetylglucosamine N-acetylmuramic dipeptide (GMDiP) or muramyl dipeptide (MDP). In macrophages, intracellular PGLYRP-1 complexed with NOD2 and GEF-H1, both of which were required for GMTriP-K-regulated gene expression. PGLYRP-1 localized to the endoplasmic reticulum and interacted at the Golgi with NOD2 upon GMTriP-K stimulation. PGLYRP-1 upregulation and its dependent gene expression signatures were induced in both mouse intestinal inflammation and human ulcerative colitis. Importantly, PGLYRP-1 activation by GMTriP-K resulted in innate immune activation and protection of mice from colitis. Our results show that PGLYRPs can function as intracellular PGN pattern recognition receptors for the control of host defense responses in the intestine.

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Brief Study on Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and pattern recognition, offering state-of-the-art performance in tasks such as image classification, object detection, and segmentation. This study explores the architecture, training strategies, and applications of CNNs, focusing on their ability to automatically learn spatial hierarchies of features through layers of convolutional filters. The research evaluates various CNN architectures, including Lent, Alex Net, and ResNet, highlighting their improvements in accuracy and efficiency. Additionally, the study investigates advanced techniques such as data augmentation, transfer learning, and regularization methods (e. g, dropout and batch normalization), which enhance the model's generalization capabilities. Through empirical experiments, we demonstrate the effectiveness of CNNs in real-world scenarios, including medical image analysis, autonomous driving, and facial recognition. The study concludes with a discussion of the future trends of CNNs, particularly in the context of deep learning optimization, hardware acceleration, and integration with emerging technologies like edge computing and quantum machine learning.

RIPK2 is crucial for the microglial inflammatory response to bacterial muramyl dipeptide but not to lipopolysaccharide.

Receptor-interacting serine/threonine protein kinase 2 (RIPK2) is a kinase that plays an essential role in the modulation of innate and adaptive immune responses. As a downstream signaling molecule for nucleotide-binding oligomerization domain 1 (NOD1), NOD2, and Toll-like receptors (TLRs), it is implicated in the signaling triggered by recognition of microbe-associated molecular patterns by NOD1/2 and TLRs. Upon activation of these innate immune receptors, RIPK2 mediates the release of pro-inflammatory factors by activating mitogen-activated protein kinases (MAPKs) and nuclear factor-kappa B (NF-κB). However, whether RIPK2 is essential for downstream inflammatory signaling following the activation of NOD1/2, TLRs, or both remains controversial. In this study, we examined the role of RIPK2 in NOD2- and TLR4-dependent signaling cascades following stimulation of microglial cells with bacterial muramyl dipeptide (MDP), a NOD2 agonist, or lipopolysaccharide (LPS), a TLR4 agonist. We utilized a highly specific proteolysis targeting chimera (PROTAC) molecule, GSK3728857A, and found dramatic degradation of RIPK2 in a concentration- and time-dependent manner. Importantly, the PROTAC completely abolished MDP-induced increases in iNOS and COX-2 protein levels and pro-inflammatory gene transcription of Nos2 , Ptgs2 , Il-1β , Tnfα , Il6 , Ccl2 , and Mmp9 . However, increases in iNOS and COX-2 proteins and pro-inflammatory gene transcription induced by the TLR4 agonist, LPS, were only slightly attenuated with the GSK3728857A pretreatment. Further findings revealed that the RIPK2 PROTAC completely blocked the phosphorylation and activation of p65 NF-κB and p38 MAPK induced by MDP, but it had no effects on the phosphorylation of these two mediators triggered by LPS. Collectively, our findings strongly suggest that RIPK2 plays an essential role in the inflammatory responses of microglia to bacterial MDP but not to LPS.

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Breaking the barriers: Overcoming cancer resistance by targeting the NLRP3 inflammasome.

Inflammation has a pivotal role in the initiation and progression of various cancers, contributing to crucial processes such as metastasis, angiogenesis, cell proliferation and invasion. Moreover, the release of cytokines mediated by inflammation within the tumour microenvironment (TME) has a crucial role in orchestrating these events. The activation of inflammatory caspases, facilitated by the recruitment of caspase-1, is initiated by the activation of pattern recognition receptors on the immune cell membrane. This activation results in the production of proinflammatory cytokines, including IL-1β and IL-18, and participates in diverse biological processes with significant implications. The NOD-Like Receptor Protein 3 (NLRP3) inflammasome holds a central role in innate immunity and regulates inflammation through releasing IL-1β and IL-18. Moreover, it interacts with various cellular compartments. Recently, the mechanisms underlying NLRP3 inflammasome activation have garnered considerable attention. Disruption in NLRP3 inflammasome activation has been associated with a spectrum of inflammatory diseases, encompassing diabetes, enteritis, neurodegenerative diseases, obesity and tumours. The NLRP3 impact on tumorigenesis varies across different cancer types, with contrasting roles observed. For example, colorectal cancer associated with colitis can be suppressed by NLRP3, whereas gastric and skin cancers may be promoted by its activity. This review provides comprehensive insights into the structure, biological characteristics and mechanisms of the NLRP3 inflammasome, with a specific focus on the relationship between NLRP3 and tumour-related immune responses, and TME. Furthermore, the review explores potential strategies for targeting cancers via NLRP3 inflammasome modulation. This encompasses innovative approaches, including NLRP3-based nanoparticles, gene-targeted therapy and immune checkpoint inhibitors.

An Effective Yak Behavior Classification Model with Improved YOLO-Pose Network Using Yak Skeleton Key Points Images

Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.

Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks

Aims and background: Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection. Objectives and Methods: Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks(CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization(GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules. Results: The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data. Conclusions: In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

Multivariate Modelling Based on Isotopic, Elemental, and Fatty Acid Profiles to Distinguish the Backyard and Barn Eggs.

The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student's t-test) showing significant differences in δ18O, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens' diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and multilayer perceptron artificial neural networks (MLP-ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP-ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system.

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models

Background and Objective:Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals. Methods:A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used. Results:The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods. Conclusions:The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.