bluuebunny/crossref_metadata_2025 huggingface dataset:
Anti-SARS-CoV-2 IgG ELISA: Replacing the Absorbance Plate Reader by a Regular Scanner with Open-Source Software
Authors: Soares Felipo D. Rojas , Marcellos Caio F. C. , Nogueira Júlia N. P. , de Abreu Daniel P. B. , Castilho Leda R. , de Souza Jr. Maurício B. , Secchi Argimiro R.
Published: January 2023

The COVID-19 global pandemic is still affecting the world, even considering vaccine applications in most countries, especially due to new variant outbreaks and the possibility that they may present immunological escape. Therefore, mass testing is relevant in infection monitoring and restriction policy evaluations, making low-cost and easy-to-use tests essential. Serological tests might also be useful in monitoring immune response after vaccination. The present work proposes a less-expensive ELISA test route, using a scanner instead of a spectrophotometer and using the saturation of the image as a surrogate for the absorbance of each sample. Images from multiple experiments were selected and correlated with their spectrophotometric absorbance. ELISA plate images were digitized by a simple table scanner and, then, preprocessed using Hue, Saturation, Value (HSV) transformation, aiming to determine which correlates best with the obtained absorbance. Saturation correlated better with absorbance, and the experiments presented R2 consistently above 90% between absorbance and the square of saturation. The new methodology showed similar accuracy, sensitivity, and specificity to the original method, all metrics ranging between 90% and 100% in most cases. An open-source software was also designed to analyze the images, perform the diagnosis, and generate reports.
Carbon Material-Based Flow-Electrode Capacitive Deionization for Continuous Water Desalination
Authors: Alsaikhan Khaled , Alsultan Abdullah , Alkhaldi Abdulrahman , Bentalib Abdulaziz , Abutalib Ahmed , Wu Dezhen , Li Jialu , Xie Rongxuan , Peng Zhenmeng
Published: January 2023

Flow-electrode capacitive deionization (FCDI) offers an electrochemical, energy-efficient technique for water desalination. In this work, we report the study of carbon-based FCDI, which consists of one desalination chamber and one salination chamber and applies a carbon nanomaterials-based flow electrode that circulates between the cell anode and cathode, to achieve a fast, continuous desalination process. Five different carbon nanomaterials were used for preparing the flow electrode and were studied for the desalination performance, with properties including average salt removal rate (ASRR), salt removal efficiency (SRE), energy consumption (EC) and charge efficiency (CE) being quantitatively determined for comparation. Different FCDI parameters, including carbon concentration and flow rate of the flow electrode and cell voltage, were investigated to examine the influences on the desalination. Long-term operation of the carbon-based FCDI was evaluated using the optimal results found in the conditions of 1.5 M concentration, 1.5 V cell voltage, and 20 mL min−1 flow rate of electrode and water streams. The results showed an ASRR of 63.7 µg cm−2 min−1, EC of 162 kJ mol−1, and CE of 89.3%. The research findings validate a good efficiency of this new carbon-based FCDI technology in continuous water desalination and suggest its good potential for real, long-term application.
Comparison of Volatile Compositions among Four Related Ligusticum chuanxiong Herbs by HS-SPME-GC-MS
Authors: Huang Shiwei , Chen Jiamei , Li Wan , Song Shanghong , Li Xiaoxue , Yu Han , Long Fei , Chen Rong , Bao Xiaoming , Chan Kelvin , Lu Guanghua
Published: January 2023

Chuanxiong (CX, Ligusticum chuanxiong), Japanese Chuanxiong (JCX, Cnidium officinale), Fuxiong (FX, Ligusticum sinense ‘Fuxiong’), and Jinxiong (JX, Ligusticum sinense ‘Jinxiong’) are aromatic herbs used in China, Japan, and other regions. Their morphology and aromatic odor are similar, resulting in confused and mixed uses. This study compares the volatile compositions of these herbs for defining their medical uses. Headspace solid-phase microextraction–gas chromatography–triple quadrupole–mass spectrometry was employed to separate, identify, and quantify the compounds in the volatile gas of the four herbs. A total of 128 volatile compounds were identified and quantified in 23 these herbal samples. The sums of 106, 115, 116, and 120 compounds were detected in the volatile gas of CX, JCX, FX, and JX, with the mean contents of 4.80, 7.12, 7.67, and 12.0 μg/g, respectively. Types and contents of the main compounds were found to be different in the volatile gas of these herbs. The orthogonal partial least squares discriminant analysis and hierarchical clustering analysis showed the four herbs located in different confined areas or clusters. It is concluded that the volatile compositions in the four herbs are generally similar, but the contents of main volatile compounds are different. These herbs should be clearly differentiated in medical use.
Special Issue on “Modelling, Monitoring, Control and Optimization for Complex Industrial Processes”
Authors: Gao Zhiwei
Published: January 2023

Industrial automation systems, such as chemical processes, manufacturing processes, power networks, transportation systems, sustainable energy systems, wireless sensor networks, robotic systems, and biomedical systems, are becoming more complex [...]
A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO2 Emissions
Authors: Marín-Rodríguez Nini Johana , González-Ruiz Juan David , Botero Sergio
Published: January 2023

Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO2 emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility at low and medium frequencies, particularly in 2014, 2019–2020, and 2022. Also, the Green Bond Index presents significant volatility at the end of 2019–2020 and the beginning of 2022 at low, medium, and high frequencies. Additionally, CO2 futures’ returns present high volatility at low and medium frequencies, expressly in 2015–2016, 2018, the end of 2019–2020, and 2022. WCA’s empirical findings reveal (i) that oil returns have a negative impact on the Green Bond Index in the medium term. (ii) There is a strong interdependence between oil prices and CO2 futures’ returns, in short, medium, and long terms, as inferred from the time–frequency analysis. (iii) There also is evidence of strong short, medium, and long terms co-movements between the Green Bond Index and CO2 futures’ returns, with the Green Bond Index leading.
Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification
Authors: Islam Md Rashedul , Ahmed Boshir , Hossain Md Ali , Uddin Md Palash
Published: January 2023

A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods.
Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale
Authors: Greenberg Isabel , Vohland Michael , Seidel Michael , Hutengs Christopher , Bezard Rachel , Ludwig Bernard
Published: January 2023

Previous studies investigating multi-sensor fusion for the collection of soil information have shown variable improvements, and the underlying prediction mechanisms are not sufficiently understood for spectrally-active and -inactive properties. Our objective was to study prediction mechanisms and benefits of model fusion by measuring mid-infrared (MIR) and X-ray fluorescence (XRF) spectra, texture, total and labile organic carbon (OC) and nitrogen (N) content, pH, and cation exchange capacity (CEC) for n = 117 soils from an arable field in Germany. Partial least squares regression models underwent a three-fold training/testing procedure using MIR spectra or elemental concentrations derived from XRF spectra. Additionally, two sequential hybrid and two high-level fusion approaches were tested. For the studied field, MIR was superior for organic properties (ratio of prediction to interquartile distance of validation (RPIQV) for total OC = 7.7 and N = 5.0)), while XRF was superior for inorganic properties (RPIQV for clay = 3.4, silt = 3.0, and sand = 1.8). Even the optimal fusion approach brought little to no accuracy improvement for these properties. The high XRF accuracy for clay and silt is explained by the large number of elements with variable importance in the projection scores >1 (Fe ≈ Ni > Si ≈ Al ≈ Mg > Mn ≈ K ≈ Pb (clay only) ≈ Cr) with strong spearman correlations (±0.57 < rs < ±0.90) with clay and silt. For spectrally-inactive properties relying on indirect prediction mechanisms, the relative improvements from the optimal fusion approach compared to the best single spectrometer were marginal for pH (3.2% increase in RPIQV versus MIR alone) but more pronounced for labile OC (9.3% versus MIR) and CEC (12% versus XRF). Dominance of a suboptimal spectrometer in a fusion approach worsened performance compared to the best single spectrometer. Granger-Ramanathan averaging, which weights predictions according to accuracy in training, is therefore recommended as a robust approach to capturing the potential benefits of multiple sensors.
SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks
Authors: Hussain Md. Muzakkir , Azar Ahmad Taher , Ahmed Rafeeq , Umar Amin Syed , Qureshi Basit , Dinesh Reddy V. , Alam Irfan , Khan Zafar Iqbal
Published: January 2023

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.
Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review
Authors: Munekata Paulo E. S. , Finardi Sarah , de Souza Carolina Krebs , Meinert Caroline , Pateiro Mirian , Hoffmann Tuany Gabriela , Domínguez Rubén , Bertoli Sávio Leandro , Kumar Manoj , Lorenzo José M.
Published: January 2023

The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.
Micromachined Thermal Gas Sensors—A Review
Authors: Gardner Ethan L. W. , Gardner Julian W. , Udrea Florin
Published: January 2023

In recent years, there has been a growing desire to monitor and control harmful substances arising from industrial processes that impact upon our health and quality of life. This has led to a large market demand for gas sensors, which are commonly based on sensors that rely upon a chemical reaction with the target analyte. In contrast, thermal conductivity detectors are physical sensors that detect gases through a change in their thermal conductivity. Thermal conductivity gas sensors offer several advantages over their chemical (reactive) counterparts that include higher reproducibility, better stability, lower cost, lower power consumption, simpler construction, faster response time, longer lifetime, wide dynamic range, and smaller footprint. It is for these reasons, despite a poor selectivity, that they are gaining renewed interest after recent developments in MEMS-based silicon sensors allowing CMOS integration and smart application within the emerging Internet of Things (IoT). This timely review focuses on the state-of-the-art in thermal conductivity sensors; it contains a general introduction, theory of operation, interface electronics, use in commercial applications, and recent research developments. In addition, both steady-state and transient methods of operation are discussed with their relative advantages and disadvantages presented. Finally, some of recent innovations in thermal conductivity gas sensors are explored.
Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
Authors: Thapa Keshav , Seo Yousung , Yang Sung-Hyun , Kim Kyong
Published: January 2023

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform.
Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks
Authors: Wan Qingzhu , Li Yimeng , Yuan Runjiao , Meng Qinghai , Li Xiaoxue
Published: January 2023

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.
A Numerical Method of Aligning the Optical Stacks for All Pixels
Authors: Hwang Jae-Hyeok , Kim Yunkyung
Published: January 2023

Reducing performance verification time is significant in product launch and production costs. This is especially true because aligning the optical stacks of off-axis pixels is a time-consuming task, but it is important to maintain sensitivity. In this paper, a numerical method to align the optical stacks of off-axis pixels is suggested in order to reduce performance test time. The components of the numerical method are the optical stack height, refractive index, and chief ray angle in order to calculate the optical stacks’ optimal shift distance. The proposed method was investigated to confirm effectiveness by using optical simulation. The sub-micron backside illumination (BSI) pixels were simulated, having 2 × 2 microlens, quad-color filter array, and in-pixel deep trench isolation (DTI). Moreover, the proposed method was evaluated for various pixel pitches, microlens shapes, and CRAs. As a result, the optical stacks were optimized by using the numerical method and validated via optical simulation. Therefore, the proposed numerical method is expected to help reduce the time and cost.
Influence of Differences in the Density of Seawater on the Measurement of the Underwater Gravity Gradient
Authors: Xian Pengfei , Ji Bing , Bian Shaofeng , Zong Jingwen , Zhang Tao
Published: January 2023

In preparing gravity gradient reference maps for navigation purposes, researchers have tended to use a constant value for the density of seawater. However, the actual seawater density at a particular location may vary due to the effects of longitude, latitude and bathymetry. In this study, the right rectangular prism method was used to calculate the disturbing gravity gradient caused by the mass deficiency of seawater for three different seawater profiles in an area east of Taiwan. For this purpose, two seawater density models were used as alternatives to the constant seawater density model, and the alteration in the gravity gradient was calculated to quantify the error in the gravity gradient as a result of using a constant seawater density. The results demonstrated that the error in the gravity gradient can reach 1E for water at large depths. Moreover, the difference between the amplitude of the error of the corrected thermocline and that for the uncorrected seawater density model was found to be quite small. If a gravity gradient reference map with accuracy better than 1E is to be realized, the seawater density cannot be taken as constant during forward modeling.
Mixing of Non-Collinear Lamb Wave Pulses in Plates with Material Nonlinearity
Authors: Pineda Allen Juan Carlos , Ng Ching Tai
Published: January 2023

Guided waves have been extensively studied in the past few years, and more recently nonlinear guided waves have attracted significant research interest for their potential for early damage detection and material state characterization. Combined harmonic generation due to wave mixing can offer some advantages over second harmonic generation. However, studies focused on Lamb wave mixing are still very limited, and have mainly focused on collinear wave mixing and used plane wave assumption. In this paper, numerical simulations and experiments are conducted to understand the interaction of mixing non-collinear Lamb wave pulses with non-planar wavefronts. The results demonstrate that the generated secondary wave is cumulative under internal resonance conditions and the sum-frequency component of the combined harmonics is useful for characterizing material nonlinearities.
UAV’s Status Is Worth Considering: A Fusion Representations Matching Method for Geo-Localization
Authors: Zhu Runzhe , Yang Mingze , Yin Ling , Wu Fei , Yang Yuncheng
Published: January 2023

Visual geo-localization plays a crucial role in positioning and navigation for unmanned aerial vehicles, whose goal is to match the same geographic target from different views. This is a challenging task due to the drastic variations in different viewpoints and appearances. Previous methods have been focused on mining features inside the images. However, they underestimated the influence of external elements and the interaction of various representations. Inspired by multimodal and bilinear pooling, we proposed a pioneering feature fusion network (MBF) to address these inherent differences between drone and satellite views. We observe that UAV’s status, such as flight height, leads to changes in the size of image field of view. In addition, local parts of the target scene act a role of importance in extracting discriminative features. Therefore, we present two approaches to exploit those priors. The first module is to add status information to network by transforming them into word embeddings. Note that they concatenate with image embeddings in Transformer block to learn status-aware features. Then, global and local part feature maps from the same viewpoint are correlated and reinforced by hierarchical bilinear pooling (HBP) to improve the robustness of feature representation. By the above approaches, we achieve more discriminative deep representations facilitating the geo-localization more effectively. Our experiments on existing benchmark datasets show significant performance boosting, reaching the new state-of-the-art result. Remarkably, the recall@1 accuracy achieves 89.05% in drone localization task and 93.15% in drone navigation task in University-1652, and shows strong robustness at different flight heights in the SUES-200 dataset.
A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism
Authors: Wang Jiandong , Dong Yahui , Zhao Shuangrui , Zhang Zhiwei
Published: January 2023

Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method.
Shedding Light on Gas-Dynamic Effects in Laser Beam Fusion Cutting: The Potential of Background-Oriented Schlieren Imaging (BOS)
Authors: Burger Silvana , Schwarzkopf Karen , Klämpfl Florian , Schmidt Michael
Published: January 2023

In laser beam fusion cutting of metals, the interaction of the gas jet with the melt determines the dynamics of the melt extrusion and the quality of the resulting cutting kerf. The gas-dynamic phenomena occurring during laser beam cutting are not fully known, especially regarding temporal fluctuations in the gas jet. The observation of gas and melt dynamics is difficult because the gas flow is not directly visible in video recordings and access to the process zone for observation is limited. In this study, the problem of imaging the gas jet from the cutting nozzle is addressed in a novel way by utilizing the striation pattern formed at the cutting kerf as a background pattern for background-oriented Schlieren imaging (BOS). In this first feasibility study, jets of different gas nozzles were observed in front of a solidified cutting kerf, which served as a background pattern for imaging. The results show that imaging of the characteristic shock diamonds of cutting nozzles is possible. Furthermore, the resulting shock fronts from an interaction of the gas jet with a model of a cutting front can be observed. The possibility of high-speed BOS with the proposed method is shown, which could be suitable to extend the knowledge of gas-dynamic phenomena in laser beam fusion cutting.
Absolute IOP/EOP Estimation Models without Initial Information of Various Smart City Sensors
Authors: Kim Namhoon , Baek Sangho , Kim Gihong
Published: January 2023

In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems.
Human Gait Activity Recognition Machine Learning Methods
Authors: Slemenšek Jan , Fister Iztok , Geršak Jelka , Bratina Božidar , van Midden Vesna Marija , Pirtošek Zvezdan , Šafarič Riko
Published: January 2023

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject’s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm’s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson’s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.
Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network
Authors: Dev Jayashree , Mishra Jibitesh
Published: January 2023

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.
TSR-YOLO: A Chinese Traffic Sign Recognition Algorithm for Intelligent Vehicles in Complex Scenes
Authors: Song Weizhen , Suandi Shahrel Azmin
Published: January 2023

Recognizing traffic signs is an essential component of intelligent driving systems’ environment perception technology. In real-world applications, traffic sign recognition is easily influenced by variables such as light intensity, extreme weather, and distance, which increase the safety risks associated with intelligent vehicles. A Chinese traffic sign detection algorithm based on YOLOv4-tiny is proposed to overcome these challenges. An improved lightweight BECA attention mechanism module was added to the backbone feature extraction network, and an improved dense SPP network was added to the enhanced feature extraction network. A yolo detection layer was added to the detection layer, and k-means++ clustering was used to obtain prior boxes that were better suited for traffic sign detection. The improved algorithm, TSR-YOLO, was tested and assessed with the CCTSDB2021 dataset and showed a detection accuracy of 96.62%, a recall rate of 79.73%, an F-1 Score of 87.37%, and a mAP value of 92.77%, which outperformed the original YOLOv4-tiny network, and its FPS value remained around 81 f/s. Therefore, the proposed method can improve the accuracy of recognizing traffic signs in complex scenarios and can meet the real-time requirements of intelligent vehicles for traffic sign recognition tasks.
Investigation of the Information Possibilities of the Parameters of Vibroacoustic Signals Accompanying the Processing of Materials by Concentrated Energy Flows
Authors: Grigoriev Sergey N. , Kozochkin Mikhail P. , Porvatov Artur N. , Fedorov Sergey V. , Malakhinsky Alexander P. , Melnik Yury A.
Published: January 2023

Creating systems for monitoring technology processes based on concentrated energy flows is an urgent and challenging task for automated production. Similar processes accompany such processing technologies: intensive thermal energy transfer to the substance, heating, development of the melting and evaporation or sublimation, ionization, and expansion of the released substance. It is accompanied by structural and phase rearrangements, local changes in volumes, chemical reactions that cause perturbations of the elastic medium, and the propagation of longitudinal and transverse waves in a wide frequency range. Vibrational energy propagates through the machine’s elastic system, making it possible to register vibrations on surfaces remotely. Vibration parameters can be used in monitoring systems to prevent negative phenomena during processing and to be a tool for understanding the processes’ kinetics. In some cases, it is the only source of information about the progress in the processing zone.
The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems
Authors: Balla Asaad , Habaebi Mohamed Hadi , Elsheikh Elfatih A. A. , Islam Md. Rafiqul , Suliman F. M.
Published: January 2023

Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system’s effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.
Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM
Authors: You Haotian , Lu Yufang , Tang Haihua
Published: January 2023

Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is important to detect the robustness of DNNs through adversarial attacks. The paper firstly improves the EfficientNet by adding the SimAM attention module. The SimAM-EfficientNet is proposed in this paper. The experimental results show that the accuracy of the improved model on PlantVillage reaches 99.31%. The accuracy of ResNet50 is 98.33%. The accuracy of ResNet18 is 98.31%. The accuracy of DenseNet is 98.90%. In addition, the GP-MI-FGSM adversarial attack algorithm improved by gamma correction and image pyramid in this paper can increase the success rate of attack. The model proposed in this paper has an error rate of 87.6% whenattacked by the GP-MI-FGSM adversarial attack algorithm. The success rate of GP-MI-FGSM proposed in this paper is higher than other adversarial attack algorithms, including FGSM, I-FGSM, and MI-FGSM.
Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model
Authors: Panwar Kirtee , Singh Akansha , Kukreja Sonal , Singh Krishna Kant , Shakhovska Nataliya , Boichuk Andrii
Published: January 2023

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.
Blockchain Application in Healthcare Systems: A Review
Authors: Ghosh Pranto Kumar , Chakraborty Arindom , Hasan Mehedi , Rashid Khalid , Siddique Abdul Hasib
Published: January 2023

In the recent years, blockchain technology has gained significant attention in the healthcare sector. It has the potential to alleviate a wide variety of major difficulties in electronic health record systems. This study presents an elaborate overview of the existing research works on blockchain applications in the healthcare industry. This paper evaluates 144 articles that discuss the importance and limits of using blockchain technologies to improve healthcare operations. The objective is to demonstrate the technology’s potential uses and highlight the difficulties and possible sectors for future blockchain research in the healthcare domain. The paper starts with an extensive background study of blockchain and its features. Then, the paper focuses on providing an extensive literature review of the selected articles to highlight the current research themes in blockchain-based healthcare systems. After that, major application areas along with the solutions provided by blockchain in healthcare systems are pointed out. Finally, a discussion section provides insight into the limitations, challenges and future research directions.
Taking a Shot: The Impact of Information Frames and Channels on Vaccination Willingness in a Pandemic
Authors: Ademu Lilian O. , Gao Jingjing , de Assis Janine Rangel , Uduebor Aanuoluwapo , Atawodi Ojonoka
Published: January 2023

The reluctance of people to receive safe and recommended available vaccines is a well-documented public health challenge. As information and communication technologies evolve, this challenge gets more complex and even harder to manage during complex public health situations. In this experimental study, we examine the relationship between vaccine information frames (with scientific information vs. without scientific information) and channels (through government vs. religious organizations) and vaccination willingness in the U.S. in the context of a pandemic. Additionally, we evaluate the interaction between vaccine skepticism, vaccine information frames, and vaccine information channels on vaccination willingness. This experimental study uses data from Amazon Mechanical Turk (MTURK) to evaluate the relationships between vaccine skepticism, vaccine information frames, and channels on vaccination willingness. We find that contrary to our hypothesis, a vaccine advisory framed with scientific information decreases people’s vaccination willingness compared to one framed without scientific information. Additionally, the impact of framing on vaccination willingness is conditioned on participants’ skepticism—participants who hold skepticism toward the vaccine but received information framed with scientific information score significantly higher in vaccination willingness compared to participants who do not hold skepticism toward a vaccine. The results suggest that the factors impacting vaccination willingness are complex and nuanced. Thus, policymakers should be more strategic with the delivery of vaccination information, especially during complex health crises.
Malaysian Women’s Viewpoint on HPV Screening and Vaccination: A Study on Barriers
Authors: Prisha Palpunavam , Tan Khong Sin , Lee Chin Poo
Published: January 2023

Cervical cancer is the second most common cancer in low-income countries and the third most common cancer in Malaysia among women aged 15 to 44. This is a huge concern because of the high mortality rate compared to other countries. Cervical cancer is caused by a common sexually transmitted human papillomavirus (HPV). Of cervical cancer cases, 80% are attributed to serotypes 16 and 18; therefore, early detection of premalignant lesions and infections from these viruses is important. Diagnosis can be carried out by polymerase chain reaction (PCR)-based HPV DNA analysis and Pap smear, which act as a viable preventive strategy. (1) Background: This study determined the adoption of the human papillomavirus (HPV) vaccine and the willingness to get vaccinated in Malaysian women. (2) Methods: An online survey was conducted with women from across Malaysia to gather their views on the barriers that prevent them from accessing HPV services. Sentiment analysis was performed to detect and classify the comments into three groups (positive, neutral, and negative). (3) Results: A total of 449 opinions were received, and the findings revealed that 41.3% were not afraid to be diagnosed early, and were prepared to accept positive or negative screening results. In addition, 18.6% of those surveyed indicated that they feared a Pap smear and were very concerned that they would not get good results. Of the respondents, 36% believed in vaccination and preferred to know more about it; 43.24% claimed that their family members were very supportive towards screening and vaccination; and 21.3% felt embarrassed and were afraid to undergo the screening procedure, as they had no prior experience and were unsure of how the procedure was conducted. In addition, 40.5% indicated that they had no concerns about HPV testing and related procedures, as this information is widely available. Only a few respondents (8.1%) talked about the time constraints and busy work schedules that prevented them from going to medical appointments. The survey also revealed that women are prevented from participating in cervical cancer screening and vaccination programs due to a lack of knowledge, shyness, personal rumors, privacy issues, financial issues, a lack of access to medical services, and ignorance and beliefs about rumors spreading online. (4) Conclusion: Results indicate that awareness of HPV and related prevention measures among women is vague and that negative perceptions continue to exist. It is strongly advised to develop a well-designed and knowledge-based application on the efficacy of screening and vaccination among Malaysian women.
How to Finance New Government Spendings: Possible Mechanisms in the Environment of Sanctions
Authors:   Financial University under the Government of the Russian Federation, Ershov Mikhail V. ,   Institute for Energy and Finance.
Published: January 2023

Sanctions against the Russian Federation have created fundamentally new conditions for the functioning of the Russian economy. This poses new challenges, which require government spendings increase. It appears that Russian regulators have unused mechanisms, the use of which will provide the economy with new money, without complicating the situation of other sectors. The article suggests mechanisms that highlights internal sources of liquidity formation for the economy. In the environment of sanctions, such mechanisms also increase the independence of monetary policy from external constraints.