Detection System Research Articles

Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital

The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.

Fluorescent Iron-Doped Polymer Dot Nanozyme-Based Cascade System for Dual-Mode Detection of Acetylcholinesterase Activity and Its Inhibitors.

The advancement of acetylcholinesterase (AChE) activity and its inhibitor assays is crucial for clinical diagnosis, drug screening, and environmental monitoring. A nanozyme-mediated cascade reaction system could offer promising prospects for a wide range of applications in such biosensing; however, the creation of nanozyme catalysts with diverse functionalities remains a significant challenge. Herein, we have proposed a multifunctional iron-doped polymer dots (Fe-PDs) nanozyme possessing excellent fluorescence and peroxidase (POD)-mimicking activity. Notably, the Fe-PDs nanozyme is capable of catalyzing H2O2 to produce a series of reactive oxygen species, which can simultaneously quench the fluorescence of Fe-PDs and induce a chromogenic reaction of 3,3',5,5'-tetramethylbenzidine (TMB), enabling the dual-mode detection of H2O2 through both fluorescence turn-off and absorbance turn-on signals. Furthermore, by integrating acetylcholine (ACh) and choline oxidase (ChOx), we have developed a three-enzyme (AChE-ChOx-POD) cascade-based fluorometric and colorimetric dual-mode sensing platform for monitoring AChE activity and its inhibitors. The sensitive and convenient dual-mode sensor has achieved low limits of detection with 0.5 mU/mL (fluorometry) and 0.014 mU/mL (colorimetry) for AChE, respectively, which are superior to the traditional Ellman's assay. More significantly, this sensor can also be extended to detect the reversible and irreversible inhibitors of AChE, such as tacrine (IC50 = 23.3 nM) and carbaryl (LOD = 0.8 nM). We firmly believe that this innovative dual-mode nanozyme-involved multienzyme cascade system-based sensing strategy will stimulate further exploration and serve as a versatile and practical tool for biochemical sensing applications.

Determination of objective taste perception among Iranian medical sciences students during COVID‑19 pandemic in Yazd, Eastern Iran: a case-control pilot study

BackgroundTaste disorders in patients suffering from COVID-19 were popular. Some people even after recovery report residual effects of loss of smell and taste. This study aimed to investigate the taste Perception of Iranian Medical Sciences students during the COVID-19 pandemic.MethodsThe case-control study evaluated 50 Medical Sciences students with a history of COVID-19 infection, as confirmed by the Novel Coronavirus (2019-nCOV) CFX96™ Real-Time PCR Detection System. The other participants consisted of 50 volunteer students with COVID-19 negative. The taste perception was measured with 4 different concentrations of the basic tastes. The stimuli were applied to the protruded tongue. Subjects were asked to identify the researcher when they felt the taste. Data are expressed as frequency distribution and analyzed with the Chi-Square test (P < 0.05).ResultsIn this survey, 54% of participants were male and 46% were female, the mean age of participants was 22.96 ± 5 years. The results showed a significant difference in the sweet and bitter taste perception score according to the history of dietary supplement use. The bitter taste perception score declared a considerable difference since COVID-19 infection. There was no significant difference between the taste perception in the two studied groups for all 4 basic tastes according to gender, COVID-19 infection status, history of taste and smell disorders, and the elapsed time since COVID-19 infection.ConclusionOur findings could provide important insights into taste perception. The history of dietary supplementation may influence how sweetness and saltiness are perceived. There was a noticeable difference in bitter taste perception depending on the time that had elapsed since the most recent COVID-19 infection.

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

The frequent usage of computer networks and the Internet has made computer networks vulnerable to numerous attacks, highlighting the critical need to enhance the precision of security mechanisms. One of the most essential measures to safeguard networking resources and infrastructures is an intrusion detection system (IDS). IDSs are widely used to detect, identify, and track malicious threats. Although various machine learning algorithms have been used successfully in IDSs, they are still suffering from low prediction performances. One reason behind the low accuracy of IDSs is that existing network traffic datasets have high computational complexities that are mainly caused by redundant, incomplete, and irrelevant features. Furthermore, standalone classifiers exhibit restricted classification performance and typically fail to produce satisfactory outcomes when dealing with imbalanced, multi-category traffic data. To address these issues, we propose an efficient intrusion detection model, which is based on hybrid feature selection and stack ensemble learning. Our hybrid feature selection method, called MI-Boruta, combines mutual information (MI) as a filter method and the Boruta algorithm as a wrapper method to determine optimal features from our datasets. Then, we apply stacked ensemble learning by using random forest (RF), Catboost, and XGBoost algorithms as base learners with multilayer perceptron (MLP) as meta-learner. We test our intrusion detection model on two widely recognized benchmark datasets, namely UNSW-NB15 and CICIDS2017. We show that our proposed IDS outperforms existing IDSs in almost all performance criteria, including accuracy, recall, precision, F1-Score, false positive rate, true positive rate, and error rate.

Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and Trends

Advanced persistent threat (APT) attacks present a significant challenge for any organization, as they are difficult to detect due to their elusive nature and characteristics. In this paper, we conduct a comprehensive literature review to investigate the various APT attack detection systems and approaches and classify them based on their threat model and detection method. Our findings reveal common obstacles in APT attack detection, such as correctly attributing anomalous behavior to APT attack activities, limited availability of public datasets and inadequate evaluation methods, challenges with detection procedures, and misinterpretation of requirements. Based on our findings, we propose a reference architecture to enhance the comparability of existing systems and provide a framework for classifying detection systems. In addition, we look in detail at the problems encountered in current evaluations and other scientific gaps, such as a neglected consideration of integrating the systems into existing security architectures and their adaptability and durability. While no one-size-fits-all solution exists for APT attack detection, this review shows that graph-based approaches hold promising potential. However, further research is required for real-world usability, considering the systems’ adaptability and explainability.

Chemiluminescence detection of kanamycin by DNA aptamer regulating peroxidase-like activity of Co3O4 nanoparticles.

Kanamycin (KAN) is widely used as a growth hormone analog and an antibacterial agent. However, abuse of this substance has resulted in the accumulation of excessive residue levels in foods of animal origin, which presents a significant risk to human health. A chemiluminescent aptasensor was constructed for the rapid quantitative detection of KAN by combining the properties of Co3O4 nanoparticles (Co3O4 NPs) nanozyme activity and DNA aptamer with high specificity. The DNA aptamer/Co3O4 NPs nanozyme regulated the chemiluminescence signal by exploiting the chemiluminescent properties of luminol oxidation by H2O2. Specific binding of KAN to the aptamer led to the formation of a steric hindrance block in the solution, which inhibited the activity of nanozyme and reduced signal intensity. The degree of signal reduction is related to the concentration of KAN. Under optimal conditions, there was good linearity between KAN concentration and chemiluminescence signal intensity in the range of 0.5-8.0 μΜ, with a detection limit of 0.26 μΜ. The detection system performed well in the presence of competing antibiotics and was virtually unaffected. The method was also suitable for the detection of KAN in milk samples with sample recoveries of 97.8%-99.1%. The chemiluminescence sensor has the advantages of low cost, specificity, and sensitivity, and does not require an external light source or modification of the nucleic acid aptamer which makes it a promising candidate for applications in the field of food detection.

An Efficient Gradient Based Modified Cuckoo Search Optimization with Dynamic Levy Flight (MCSO-DLF) Algorithm for Breast Cancer Detection

Purpose of Study: This study evaluates the Gradient-Based Dynamic Levy Flight Cuckoo Search Optimization (MCSO-DLF) for breast cancer detection and compares it to six established optimization algorithms. The goal is to determine if MCSO-DLF offers higher accuracy and computational efficiency for optimizing diagnostic models. Methodology: The study compares MCSO-DLF with PSO, GA, ACO, SA, DE, and ABC using the Breast Cancer Wisconsin dataset. The algorithms are evaluated based on accuracy, convergence speed, and efficiency. MCSO-DLF uses dynamic Levy flight and gradient-based optimization to improve solution quality. Main Findings: MCSO-DLF achieved the highest accuracy (0.9912) and fastest computation time (65.94 seconds), outperforming all other algorithms. This demonstrates its effective balance between exploration and local solution refinement. Implications: MCSO-DLF significantly improves accuracy and speed, offering potential advancements in breast cancer detection systems, leading to better patient outcomes and more efficient healthcare. Novelty of Study: This study introduces MCSO-DLF as a novel hybrid optimization method that combines gradient-based optimization with dynamic Levy flight, outperforming traditional algorithms in complex medical diagnostics.

Reproducible, Accurate, and Sensitive Food Toxin On-Site Detection with Carbon Nanotube Transistor Biosensors.

Field-effect transistor (FET) biosensors based on nanomaterials are promising in the areas of food safety and early disease diagnosis due to their ultrahigh sensitivity and rapid response. However, most academically developed FET biosensors lack real-world reproducibility and comprehensive methodological validation to meet the standards of regulatory bodies. Here, highly uniform and well-packaged semiconducting carbon nanotube (CNT) FET biosensor chips were developed and assessed for the plug-and-play sensing for the rapid and highly sensitive detection of aflatoxin B1 (AFB1) in real food samples to meet international standards. In order to meet the requirements for reproducibility and stability, a scalable residual-free passivation and packaging process was developed for CNT FET biosensors. Portable detection systems were then constructed for on-site detection. The resulting packaged chips were functionalized with nucleic aptamers to enable highly selective detection of AFB1 in food samples with a detection limit (LOD) of 0.55 fg/mL (standard) for AFB1 and cross-reactivity coefficients to interferences as low as 1.8 × 10-7 in simulated solutions. Utilizing the portable detection system, on-site real food detection was achieved with a rapid response time less than 60 s, and LOD of 0.25 pg/kg (standard) in complex corn sample matrices. Single-blind tests demonstrated the ability of the chips to detect AFB1-positive food with 100% accuracy, using a set of 30 peanut samples. Validation experiments confirmed that the detection range, stability, and repeatability met international standards. This study showcased the accuracy, reliability, and potential practical applications of CNT FET biosensor chips in areas such as food safety and rapid biomedical testing.

A Study on Network Anomaly Detection Using Fast Persistent Contrastive Divergence

As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.

Enhancing Pap Smear Image Classification: Integrating Transfer Learning and Attention Mechanisms for Improved Detection of Cervical Abnormalities

Abstract Cervical cancer remains a major global health challenge, accounting for significant morbidity and mortality among women. Early detection through screening, such as Pap smear tests, is crucial for effective treatment and improved patient outcomes. However, traditional manual analysis of Pap smear images is labor-intensive, subject to human error, and requires extensive expertise. To address these challenges, automated approaches using deep learning techniques have been increasingly explored, offering the potential for enhanced diagnostic accuracy and efficiency. This research focuses on improving cervical cancer detection from Pap smear images using advanced deep-learning techniques. Specifically, we aim to enhance classification performance by leveraging Transfer Learning (TL) combined with an attention mechanism, supplemented by effective preprocessing techniques. Our preprocessing pipeline includes image normalization, resizing, and the application of Histogram of Oriented Gradients (HOG), all of which contribute to better feature extraction and improved model performance. The dataset used in this study is the Mendeley Liquid-Based Cytology (LBC) dataset, which provides a comprehensive collection of cervical cytology images annotated by expert cytopathologists. Initial experiments with the ResNet model on raw data yielded an accuracy of 63.95%. However, by applying our preprocessing techniques and integrating an attention mechanism, the accuracy of the ResNet model increased dramatically to 96.74%. Further, the Xception model, known for its superior feature extraction capabilities, achieved the best performance with an accuracy of 98.95%, along with high precision (0.97), recall (0.99), and F1-Score (0.98) on preprocessed data with an attention mechanism. These results underscore the effectiveness of combining preprocessing techniques, TL, and attention mechanisms to significantly enhance the performance of automated cervical cancer detection systems. Our findings demonstrate the potential of these advanced techniques to provide reliable, accurate, and efficient diagnostic tools, which could greatly benefit clinical practice and improve patient outcomes in cervical cancer screening.

High-Precision Measurement of Microscales Based on Optoelectronics and Image Integration Method

Currently, there are various types of microscales and the conventional line detection system usually has only one detection method, which is difficult to adapt to the diverse calibration needs of microscales. This article investigates the high-precision measurement method of a microscale based on optoelectronics and the image integration method to solve the diversified calibration needs of microscales. The automatic measurement and processing system integrates two methods: the photoelectric signal measurement method and the photoelectric image measurement method. This article studies the smooth motion method based on ordinary linear guides, investigates the method of reducing the cosine error of a small-range interference length measurement, proposes an image-based line positioning method, and studies the edge and center recognition algorithms of the line. According to the experimental data, the system’s measurement accuracy was analyzed using the photoelectric signal measurement method to measure the 1 mm microscale, the maximum difference from the reference value was 0.105 μm, the standard uncertainty was 0.068 μm, and the absolute value of normalized error was less than 1. The accuracy of the image measurement method to measure the 1 mm microscale was consistent with that of the photoelectric signal method. The results show good consistency in the measurement results between the two methods of the integrated measurement system. The photoelectric signal method has the technical characteristics of high measurement efficiency and high accuracy, while the pixel-based measurement of the image method has two-dimensional measurement characteristics, which can realize measurements that cannot be realized by the photoelectric signal method; therefore, the measurement system of optoelectronics and image integration is characterized by high precision and a wide range of measurement adaptations.

Choosing the machine learning algorithm for detecting intrusions into IoT

Aim. The paper aims to improve the security of IoT devices by applying machine learning algorithms to detect attacks against IoT networks. The relevance of the goal is defined by the ever-growing number of such attacks around the world and the widespread use of IoT systems. The paper provides relevant statistical data. An analysis of the available papers showed that various methods were examined individually and were not compared to each other, so the aim of this paper that consists in identifying the most promising machine learning algorithm for detecting attacks against IoT networks is of relevance. Methods. The paper used the following machine learning methods to detect attacks against IoT networks: logistic regression, SVC, random forest, K-nearest neighbour method, k-means method, naive Bayes classifier, and variants of gradient boosting (XGBoost, AdaBoost, and CatBoost). The novelty consists in the comparison of the outputs of the supervised algorithms with the unsupervised K-means in the context of detection of attacks against IoT networks. The attack detection systems under development were trained using the UNSWNB15 dataset that contains data on nine types of attacks. The number of entries is more than 80 thousand. More than half of the entries deal with attacks. The methods were compared using a number of metrics. Results. An intrusion detection system was structurally defined and implemented. The stages of its operation include the analysis of input data and the output of final statistical data. The results show that the random forest algorithm is the best one out of those examined. The method also performs well in terms of learning speed. That means that the algorithm can be deployed and applied with the greatest success. Conclusions. This paper presents the results of comparing various machine learning algorithms in the context of IoT device intrusion detection. The accuracy and the ROC-AUC curve are used to evaluate the efficiency of the employed models. Having compared the models of the employed algorithms we found that the RandomForestClassifier model has the highest accuracy and a high AUC, which means that this algorithm is the most efficient in terms of IoT network intrusion detection. Further research will be dedicated to distinguishing between the types of attack.

High Sensitivity X-Ray Detectors with Low Degradation Based on Deuterated Halide Perovskite Single Crystals.

Methylammonium lead single crystal (MAPbI3 SC) possesses superior optoelectronic properties and low manufacturing cost, making it an ideal candidate for X-ray detection. However, the ionic migration of the perovskites usually leads to instability, dark current drift, and hysteresis of the detector, limiting their applications in well-established technologies. Here, a series of X-ray detectors of MAPbI3 SCs are reported with different degrees of deuteration (DxMAPbI3, x = 0, 0.15, 0.75, 0.99). By controlling the content of deuterium (D) in organic cations, the sensitivity, detection limits, ion migration, and resistivity of the detector can be controlled, thereby improving its performance. Due to stronger hydrogen bonds (N─D···I), the ion activation energy significantly increases to 886 meV. Consequently, the D0.99MAPbI3 SC detector shows more than five-fold enhancement, achieving a record-high mobility-lifetime (µτ) product of 5.39 × 10-2cm2V-1, with an ultrahigh sensitivity of 2.18 × 106 µC Gy-1 cm-2 under 120keV hard X-ray and a low detection limit of 4.8 nGyair s-1, as well as long-term stability. The study provides a straightforward strategy for constructing ultrasensitive X-ray detection and imaging systems based on perovskite SCs.

A silicon-based functional self-assembled aptasensor for the detection of aflatoxin B1 by SERS sensing.

One of the most harmful contaminants found in corn and its products is aflatoxin B1 (AFB1) and thus developing reliable detection methods is of great significance to consumers and the food industry. In this research, AuMBA@Ag nanoparticles (NPs) and AgNPs deposited on a silicon wafer (Si@AgNPs) were functionalized with an aptamer and its complementary strand, respectively, and self-assembled into a SERS aptasensor, which generated strong SERS signals. AFB1 bound to the aptamer prior to the complementary chain, causing AuMBA@Ag NPs to detach from Si@AgNPs. The complex dissociated, leading to a decrease in signal intensity from the solid-phase substrate. Under optimal conditions, the linear detection range was 0.05-20.0 ng mL-1, and the detection limit was 0.039 ng mL-1. Notably, the aptasensor demonstrated a recovery rate between 92.77% and 110.13% when utilized for the detection of AFB1 in corn flour and oil, indicating its good potential for detecting AFB1 in real sample matrices. In conclusion, a quantitative and reliable specific SERS detection system for AFB1 was developed in this study with significant applicability to food safety.

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

Strengthen Method Attacks Detection in Network using VFDT Algorithm

With the tremendous progress in organizing data over networks, network security has become more important than ever in modern technologies. Hacking represents a serious security risk in the climate of data transmission and reception. Sometimes there are new types of intrusions that are constantly evolving and detection systems have a difficult problem in identifying them. In this research, we proposed Organization based Abnormality Discovery Framework (OADF) system based on training an algorithm VFDT to detect attacks detection (AD).Exploratory outcomes on the kdd cup'99 data mining list show that our system is profoundly material to arrange interruption acknowledgment. It has been shown that the proposed methodology performs better as far as acknowledgment rate, deceiving issue rate, and computational time contrasted with various techniques. To enhance the success of this work, we used other type of previously prepared test groups, it ssenet-2011 dataset, and we trained these two types separately for the purpose of reaching optimal training. The purpose of this work is to discover the largest number of attacks or penetrations that occur on the internet. Exceptionally very fast decision tree (VFDT) algorithm was utilized to build the classifier for attacks. Exceptionally VFDT algorithm is one of data mining algorithms that arrangement with high data streams in an extremely brief time frame. Trial results exhibited that the proposed Organization based Abnormality Discovery Framework (OADF) system is exceptionally effective in detecting known and obscure assaults by 93%.

Self-powered piezoelectric sensing system for rotational speed detection of AUV propellers

To realize high-precision self-supplied speed measurement of Autonomous Underwater Vehicle (AUV) propellers, a self-powered piezoelectric speed sensing and detecting system is developed, which consists of a self-powered piezoelectric detection module and energy management and sensing circuits. The system uses three piezoelectric cantilevers for power and sensing detection. The proposed speed detection system is based on the piezoelectric effect and realizes self-powered sensing by utilizing the kinetic energy generated during the rotation of the propeller into electrical energy. The comprehensive model of the proposed piezoelectric sensing system under periodic toggled excitation is established and analyzed. The optimal output power of the system is investigated through theoretical analysis and simulation. The experimental system of the self-powered speed sensing system is constructed and the effect of speed measurement is tested. Experimental results show that the maximum output power of the piezoelectric self-powered sensing system developed in this work is 218 μW, which is much higher than that of the purely triboelectric sensing system. And the average detection error of the self-powered sensing system is 1.4% and the linearity is 1.09% when the propeller speed ranges from 0 to 300 r/min. Compared with similar self-powered systems, the proposed piezoelectric self-powered system is more advantageous in terms of measurement accuracy and can provide more accurate propeller speed measurement.

Impact of Caries Experience on the Oral Health-Related Quality of Life of Pre-school Children and their Families in an Indian City - An Evaluative Study.

The presence of dental caries may have a negative impact on well-being of children and their parents, causing pain and discomfort, leading to poor oral health-related quality of life (OHRQoL) in them. The objective of this study was to evaluate the impact of different levels of caries experience on the OHRQoL of pre-school children and their families in Odisha, assessed using Odia version of Early Childhood Oral Health Impact Scale (Od-ECOHIS). A cross-sectional survey was carried out on a sample of 369 pre-school children using Od-ECOHIS. Dental caries was recorded using International Caries Detection and Assessment System (ICDAS) index. The total score of ECOHIS ranged from 0 to 42 with a mean of 2.4 (±5.3) and a median score of 0. The items bearing high mean scores in the child impact section were related to "pain", "difficulty in drinking hot or cold beverages", "difficulty in eating some food", and "irritable or frustrated". Though half of children (50.4%) had cavitated carious lesions, the prevalence of negative impact was 30.6%. Compared to the child being caries-free, the expected count of total Od-ECOHIS score increased by 1.54 times if the child had white spot lesion and 1.94 times if the child had a cavitated lesion. Presence of dental carious lesions has a significant impact on OHRQoL of pre-school children and their families measured by using Od-ECOHIS even after adjusting for SES and caregivers' perception of their children's oral health.

A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT)

AbstractThe proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.