bluuebunny/crossref_metadata_2025 huggingface dataset:
Red Beetroot and Banana Peels as Value-Added Ingredients: Assessment of Biological Activity and Preparation of Functional Edible Films
Authors: Šeremet Danijela , Durgo Ksenija , Komljenović Anamaria , Antolić Mihaela , Mandura Jarić Ana , Huđek Turković Ana , Komes Draženka , Šantek Božidar
Published: November 2022

In the present study, water extracts from banana and red beetroot peels were evaluated as a potential source of biologically active compounds for the formulation of edible films. Using spectrophotometric and HPLC-DAD methodologies, banana peel extract was found to be a valuable source of dopamine (156.08 mg L−1), while red beetroot peel extract was abundant in red-violet pigments betacyanins (90.1 mg betanin L−1). The biological activity of the extracts was studied by determining their effects on macromolecular models, including DNA (plasmid phiX RF1 DNA), protein (bovine serum albumin), and lipid (linoleic acid) models, as well as on continuous human cell lines of colon cancer Caco-2 and hepatocellular liver cancer Hep G2 at concentrations of 0.2 and 1 mg mL−1. Results showed that the extracts had no adverse effects and both were further used for the formulation of edible films using alginate in combination with three types of plant proteins—rice, peanut, and pumpkin. In general, edible films based on banana peel extract were characterized by better bioactive properties compared with the films based on red beetroot peel extract. The addition of peanut proteins into the formulations resulted in the most desirable bioactive profile of the formulated edible films, including total phenolic content and antioxidant capacity. Aside from the control sample prepared only with the alginate, the highest dopamine content was determined in the film with incorporated pumpkin proteins (10.72 mg g−1 dw), while the sample prepared with peanut proteins was richest in betacyanins (175.58 mg betanin g−1 dw).
A Critical Review on Hygrothermal and Sound Absorption Behavior of Natural-Fiber-Reinforced Polymer Composites
Authors: Bhuvaneswari V. , Devarajan Balaji , Arulmurugan B. , Mahendran R. , Rajkumar S. , Sharma Shubham , Mausam Kuwar , Li Changhe , Eldin Elsayed Tag
Published: November 2022

Increasing global environmental problems and awareness towards the utilization of eco-friendly resources enhanced the progress of research towards the development of next-generation biodegradable and environmentally friendly material. The development of natural-based composite material has led to various advantages such as a reduction in greenhouse gases and carbon footprints. In spite of the various advantages obtained from green materials, there are also a few disadvantages, such as poor interfacial compatibility between the polymer matrix and natural reinforcements and the high hydrophilicity of composites due to the reinforcement of hydrophilic natural fibers. This review focuses on various moisture-absorbing and sound-absorbing natural fiber polymer composites along with the synopsis of preparation methods of natural fiber polymer composites. It was stated in various studies that natural fibers are durable with a long life but their moisture absorption behavior depends on various factors. Such natural fibers possess different moisture absorption behavior rates and different moisture absorption behavior. The conversion of hydrophilic fibers into hydrophobic is deemed very important in improving the mechanical, thermal, and physical properties of the natural-fiber-reinforced polymer composites. One more physical property that requires the involvement of natural fibers in place of synthetic fibers is the sound absorption behavior. Various researchers have made experiments using natural-fiber-reinforced polymer composites as sound-absorbing materials. It was found from various studies that composites with higher thickness, porosity, and density behaved as better sound-absorbing materials.
Is the Study of Hebrew Useless for Orthodox Theologians? A Response to Some Recent Assertions Put Forward by Jean-Claude Larchet
Authors: Oancea Constantin Horia
Published: November 2022

In one of his recent books, Jean-Claude Larchet argued that the study of Hebrew is useless for those doing research in the field of Orthodox theology, since the Church fathers used the Greek version of the Old Testament (Septuagint). There are at least two reasons that might be advanced in order to prove the invalidity of Larchet’s thesis. First, fostering an attitude of ignorance as regards the Hebrew Bible might nurture anti-Judaic beliefs among Orthodox students, considering that such attitudes could be noticed in Romania during the interwar period. Second, the Fathers of the Church had a favorable attitude towards Hebrew, despite the fact that few of them actually knew that language. They held the belief that Hebrew was the primordial language through which God spoke to mankind in the beginning and through which the Old Testament was revealed. Both the Hebrew language and the Hebrew alphabet played a significant role in their theoretical considerations on the canon of the Holy Scripture. Since Larchet published his work in Romanian, his theses and their implications are discussed from the perspective and in the context of Romanian Orthodox theology.
The Voice of Peace: Philosophical Musicality as a Promoter of Peace in Confucianism
Authors: Patt-Shamir Galia
Published: November 2022

The main focus of this article is the explanatory power that music has in Confucianism according to the Analects (Lunyu 論語) and The Classic of Rites (Liji 禮記), which is reaffirmed in the Song Dynasty by Zhou Dunyi in the chapters on music in Tongshu (通書, The Penetrating Book). The article suggests that Tongshu’s chapters on music demonstrate the non-linear and non-metaphysical musical nature of Confucianism. According to this suggestion, the chapters introduce a dynamic, living model for the Confucian Way, on its own terms. This musical model supports the early Confucian vision of a multifaceted person, progressing in a multi-dimensional Way within a multi-layered polysemic world. Progressing along the Way, self-cultivation appears as one’s task to develop the various musical potentialities inherent in her or himself. The article opens with the epistemological idea of “musical knowledge” acquired by attuned hearing that winds up in a creative, peacemaking heart. Next, it introduces the ontological idea of a government that models cosmic harmony, depicting the leader as having an orchestral conductor’s aptitude; last, it presents a pragmatic perspective on the idea of musical education through the rules of propriety, depicting the practitioner as a skillful music player.
Eco-Preservation through the Lens of Igbo Beliefs and Practices: A Re-Imagination
Authors: Nkama Chinyere Lilian , Okoro Kingsley Nwannennaya , Egbule Emmanuel
Published: November 2022

This research was carried out to investigate the various cultural practices of the Igbo people of Eastern Nigeria that were/are useful in saving the ecosystem from capricious human activities in traditional society. This is with the aim of finding how they could be adopted to checkmate the modern practices that degrade and violate the environment. The researchers adopted a qualitative approach for data collection. This is because the research is a social survey and addresses social issues. As such, data were collected using surveys and oral/personal communication. The study discovered that there is a nexus between indigenous cultural/cosmological knowledge and ecosystem preservation/sustainability and as such notes that the current earth devastation within modern Igbo society is a result of neglect of the indigenous knowledge system. The work observes that, if this knowledge system is incorporated into current ethics of eco-preservation, the present eco-risk would diminish. The work therefore recommends that cultural/indigenous environmental education, advocacy and ecosystem activism and locally managed ecotourism be incorporated into both formal and informal education of the modern Igbo knowledge system.
Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
Authors: Pang Yongqi , Li Yudong , Feng Zhongke , Feng Zemin , Zhao Ziyu , Chen Shilin , Zhang Hanyue
Published: November 2022

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.
CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record
Authors: Tzallas Vasileios , Hünerbein Anja , Stengel Martin , Meirink Jan Fokke , Benas Nikos , Trentmann Jörg , Macke Andreas
Published: November 2022

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).
Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification
Authors: Jiang Nan , Shi Haowen , Geng Jie
Published: November 2022

Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote sensing image scene classification is how to extract effective features from insufficient labeled data. To solve these issues, a multi-scale graph-based feature fusion (MGFF) model is proposed for few-shot remote sensing image scene classification. In the MGFF model, a graph-based feature construction model is developed to transform traditional image features into graph-based features, which aims to effectively represent the spatial relations among images. Then, a graph-based feature fusion model is proposed to integrate graph-based features of multiple scales, which aims to enhance sample discrimination based on different scale information. Experimental results on two public remote sensing datasets prove that the MGFF model can achieve superior accuracy than other few-shot scene classification approaches.
Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series
Authors: Ge Shaojia , Su Weimin , Gu Hong , Rauste Yrjö , Praks Jaan , Antropov Oleg
Published: November 2022

Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.
Supervised Classifications of Optical Water Types in Spanish Inland Waters
Authors: Pereira-Sandoval Marcela , Ruescas Ana B. , García-Jimenez Jorge , Blix Katalin , Delegido Jesús , Moreno José
Published: November 2022

Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality algorithm easier on each occasion is by knowing ahead of time the type of water being handled. This approach is used, for instance, in the Lake Water Quality production chain of the Copernicus Global Land Service. The objective of this work is to determine which supervised classification approach might give the most accurate results. We use a dataset of manually labeled pixels on lakes and reservoirs in Eastern Spain. High-resolution images from the Multispectral Instrument sensor on board the ESA Sentinel-2 satellite, atmospherically corrected with the Case 2 Regional Coast Colour algorithm, are used as the basis for extracting the pixels for the dataset. Three families of different supervised classifiers have been implemented and compared: the K-nearest neighbor, decision trees, and support vector machine. Based on the results, the most appropriate for our study area is the random forest classifier, which was selected and applied on a series of images to derive the temporal series of the optical water types per lake. An evaluation of the results is presented, and an analysis is made using expert knowledge.
Incremental Learning of Human Activities in Smart Homes
Authors: Chua Sook-Ling , Foo Lee Kien , Guesgen Hans W. , Marsland Stephen
Published: November 2022

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.
An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology
Authors: Liu Juan , Luo Yang , Ge Liang , Zeng Wen , Rao Ziyang , Xiao Xiaoting
Published: November 2022

Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in a low degree of detection accuracy, erroneous judgments, and car locking. In order to solve the problem, this study firstly designed a sensor array based on the gas diffusion model and the characteristics of a car steering wheel. Secondly, the data fusion algorithm is proposed according to the data characteristics of the sensor array on the steering wheel. The support matrix is used to improve the data consistency of the single sensor data, and then the adaptive weighted fusion algorithm is used for multiple sensors. Finally, in order to verify the reliability of the system, an online intelligent detection device for drunk driving based on multi-sensor fusion was developed, and three people using different combinations of drunk driving simulation experiments were conducted. According to the test results, a drunk person in the passenger seat will not cause the system to make a drunk driving determination. When more than 50 mL of alcohol is consumed and the driver is seated in the driver’s seat, the online intelligent detection of drunk driving can accurately identify drunk driving, and the car will lock itself as soon as a real-time online voice prompt is heard. This study enhances and complements theories relating to data fusion for online automobile drunk driving detection, allowing for the online identification of drivers who have been drinking and the locking of their vehicles to prevent drunk driving. It provides technical support for enhancing the accuracy of online systems that detect drunk driving in automobiles.
Method for Improving Range Resolution of Indoor FMCW Radar Systems Using DNN
Authors: Park Hwesoo , Kim Minji , Jung Yunho , Lee Seongjoo
Published: November 2022

Various studies on object detection are being conducted, and in this regard, research on frequency-modulated continuous wave (FMCW) RADAR is being actively conducted. FMCW RADAR requires high-distance resolution to accurately detect objects. However, if the distance resolution is high, a high-modulation bandwidth is required, which has a prohibitively high cost. To address this issue, we propose a two-step algorithm to detect the location of an object through DNN using many low-cost FMCW RADARs. The algorithm first infers the sector by measuring the distance to the object for each FMCW RADAR and then measures the position through the grid according to the inferred sector. This improves the distance resolution beyond the modulation bandwidth. Additionally, to detect multiple targets, we propose a Gaussian filter. Multiple targets are detected through an ordered-statistic constant false-alarm rate (OS-CFAR), and there is an 11% probability that multiple targets cannot be detected. In the lattice structure proposed in this paper, the performance of the proposed algorithm compared to those in existing works was confirmed with respect to the cost function. The difference in performance versus complexity was also confirmed when the proposed algorithm had the same complexity and the same performance, and it was confirmed that there was a performance improvement of up to five-fold compared to those in previous papers. In addition, multi-target detection was shown in this paper. Through MATLAB simulation and actual measurement on a single target, RMSEs were 0.3542 and 0.41002 m, respectively, and through MATLAB simulation and actual measurement on multiple targets, RMSEs were confirmed to be 0.548265 and 0.762542 m, respectively. Through this, it was confirmed that this algorithm works in real RADAR.
Radio Astronomical Antennas in the Central African Region to Improve the Sampling Function of the VLBI Network in the SKA Era?
Authors: Atemkeng Marcellin , Okouma Patrice , Maina Eric , Ianjamasimanana Roger , Zambou Serges
Published: November 2022

On the African continent, South Africa has world-class astronomical facilities for advanced radio astronomy research. With the advent of the Square Kilometre Array project in South Africa (SA SKA), six countries in Africa (SA SKA partner countries) have joined South Africa to contribute towards the African Very Long Baseline Interferometry (VLBI) Network (AVN). Each of the AVN countries aims to construct a single-dish radio telescope that will be part of the AVN, the European VLBI Network, and the global VLBI network. The SKA and the AVN will enable very high sensitivity VLBI in the southern hemisphere. In the current AVN, there is a gap in the coverage in the central African region. This work analyses the increased scientific impact of having additional antennas in each of the six countries in central Africa, i.e., Cameroon, Gabon, Congo, Equatorial Guinea, Chad, and the Central African Republic. A number of economic human capital impacts of having a radio interferometer in central Africa are also discussed. This work also discusses the recent progress on the AVN project and shares a few lessons from some past successes in ground stations retrofitting.
M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity
Authors: Akter Sumya , Prodhan Rumman Ahmed , Pias Tanmoy Sarkar , Eisenberg David , Fresneda Fernandez Jorge
Published: November 2022

Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility.
Innovative Bidirectional Isolated High-Power Density On-Board Charge for Vehicle-to-Grid
Authors: Hrbac Roman , Hrdina Libor , Kolar Vaclav , Slanina Zdenek , Blazek Vojtech , Vantuch Tomas , Bartłomiejczyk Mikołaj , Misak Stanislav
Published: November 2022

This paper deals with developing and implementing a bidirectional galvanically isolated on-board charger of a high-power density. The power density of the new charger was 4 kW/kg and 2.46 kW/dm3, and the maximum efficiency was 96.4% at 3.4 kW. Due to the requirement to achieve a high-power density, a single-stage inverter topology was used. Regarding switching losses, due to the topology of the circuit with so-called hard switching, the switching frequency was set to 150 kHz. A laboratory prototype was built to verify the properties and operating principles of the described charger topology. The on-board charger has been tested in a microgrid test platform. Due to the parasitic properties of the transformer and other electronic components, overvoltage with subsequent oscillations occurred on the primary side of the transformer and damped resonance on its secondary side. These parasitic properties caused interference and especially voltage stress on the semiconductor elements. These undesirable phenomena have been eliminated by adding an active element to the charger topology and a new transistor control strategy. This new switching control strategy of transistors has been patented.
New Generation Federated Learning
Authors: Li Boyuan , Chen Shengbo , Peng Zihao
Published: November 2022

With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup in which the task size is fixed and the storage space is unlimited, which is impossible in the real world. In fact, new classes of these participating clients always emerge over time, and some samples are overwritten or discarded due to storage limitations. We urgently need a new framework to adapt to the dynamic task sequences and strict storage constraints in the real world. Continuous learning or incremental learning is the ultimate goal of deep learning, and we introduce incremental learning into FL to describe a new federated learning framework. New generation federated learning (NGFL) is probably the most desirable framework for FL, in which, in addition to the basic task of training the server, each client needs to learn its private tasks, which arrive continuously independent of communication with the server. We give a rigorous mathematical representation of this framework, detail several major challenges faced under this framework, and address the main challenges of combining incremental learning with federated learning (aggregation of heterogeneous output layers and the task transformation mutual knowledge problem), and show the lower and upper baselines of the framework.
Transformer-Based Maneuvering Target Tracking
Authors: Zhao Guanghui , Wang Zelin , Huang Yixiong , Zhang Huirong , Ma Xiaojing
Published: November 2022

When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of the network to trajectories outside the dataset. In this study, we propose a transformer-based network (TBN) that consists of an encoder part (transformer layers) and a decoder part (one-dimensional convolutional layers), to track maneuvering targets. Assisted by the attention mechanism of the transformer network, the TBN can capture the long short-term dependencies of target states from a global perspective. Moreover, we propose a center–max normalization to reduce the complexity of TBN training and improve its generalization. The experimental results show that our proposed methods outperform the LSTM-based tracking network.
A Real-Time FPGA Implementation of Infrared and Visible Image Fusion Using Guided Filter and Saliency Detection
Authors: Zhang Ling , Yang Xuefei , Wan Zhenlong , Cao Dingxin , Lin Yingcheng
Published: November 2022

Taking advantage of the functional complementarity between infrared and visible light sensors imaging, pixel-level real-time image fusion based on infrared and visible light images of different resolutions is a promising strategy for visual enhancement, which has demonstrated tremendous potential for autonomous driving, military reconnaissance, video surveillance, etc. Great progress has been made in this field in recent years, but the fusion speed and quality of visual enhancement are still not satisfactory. Herein, we propose a multi-scale FPGA-based image fusion technology with substantially enhanced visual enhancement capability and fusion speed. Specifically, the source images are first decomposed into three distinct layers using guided filter and saliency detection, which are the detail layer, saliency layer and background layer. Fusion weight map of the saliency layer is subsequently constructed using attention mechanism. Afterwards weight fusion strategy is used for saliency layer fusion and detail layer fusion, while weight average fusion strategy is used for the background layer fusion, followed by the incorporation of image enhancement technology to improve the fused image contrast. Finally, high-level synthesis tool is used to design the hardware circuit. The method in the present study is thoroughly tested on XCZU15EG board, which could not only effectively improve the image enhancement capability in glare and smoke environments, but also achieve fast real-time image fusion with 55FPS for infrared and visible images with a resolution of 640 × 470.
SF6 High-Voltage Circuit Breaker Contact Status Detection at Different Currents
Authors: Guo Ze , Li Linjing , Han Weimeng , Guo Zixuan
Published: November 2022

Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF6 gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF6 HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs.
Building a Real-Time Testing Platform for Unmanned Ground Vehicles with UDP Bridge
Authors: Sobczak Łukasz , Filus Katarzyna , Domańska Joanna , Domański Adam
Published: November 2022

Perception and vehicle control remain major challenges in the autonomous driving domain. To find a proper system configuration, thorough testing is needed. Recent advances in graphics and physics simulation allow researchers to build highly realistic simulations that can be used for testing in safety-critical domains and inaccessible environments. Despite the high complexity of urban environments, it is the non-urban areas that are more challenging. Nevertheless, the existing simulators focus mainly on urban driving. Therefore, in this work, we describe our approach to building a flexible real-time testing platform for unmanned ground vehicles for indoor and off-road environments. Our platform consists of our original simulator, robotic operating system (ROS), and a bridge between them. To enable compatibility and real-time communication with ROS, we generate data interchangeable with real-life readings and propose our original communication solution, UDP Bridge, that enables up to 9.5 times faster communication than the existing solution, ROS#. As a result, all of the autonomy algorithms can be run in real-time directly in ROS, which is how we obtained our experimental results. We provide detailed descriptions of the components used to build our integrated platform.
Accuracy Verification of Surface Models of Architectural Objects from the iPad LiDAR in the Context of Photogrammetry Methods
Authors: Łabędź Piotr , Skabek Krzysztof , Ozimek Paweł , Rola Dominika , Ozimek Agnieszka , Ostrowska Ksenia
Published: November 2022

The creation of accurate three-dimensional models has been radically simplified in recent years by developing photogrammetric methods. However, the photogrammetric procedure requires complex data processing and does not provide an immediate 3D model, so its use during field (in situ) surveys is infeasible. This paper presents the mapping of fragments of built structures at different scales (finest detail, garden sculpture, architectural interior, building facade) by using a LiDAR sensor from the Apple iPad Pro mobile device. The resulting iPad LiDAR and photogrammetric models were compared with reference models derived from laser scanning and point measurements. For small objects with complex geometries acquired by iPad LiDAR, up to 50% of points were unaligned with the reference models, which is much more than for photogrammetric models. This was primarily due to much less frequent sampling and, consequently, a sparser grid. This simplification of object surfaces is highly beneficial in the case of walls and building facades as it smooths out their surfaces. The application potential of the IPad LiDAR Pro is severely constrained by its range cap being 5 m, which greatly limits the size of objects that can be recorded, and excludes most buildings.
Carbon Dioxide Chemical Absorption Using Diamines with Different Types of Active Centers
Authors: Gómez-Díaz Diego , Navaza José Manuel , Rumbo Antonio
Published: November 2022

The present research analyzes chemical solvents based on the use of diamines (Ethylenediamine-EDA, 1,2-Dimethylethylenediamine-DMEDA and Tetramethylethylenediamine-TMEDA) for carbon dioxide absorption, taking into account the type of amino centers in the molecules. The presence and type of radicals can affect amine solubility in water, reaction mechanism, reaction kinetics, etc. Diamines have been considered interesting candidates for carbon dioxide chemical absorption, observing a high influence of the molecule structure. The present work analyzes a series of solvents based on diamines with the same chain length between amino centers, but different types of radicals. This study shows an important variability in the behavior of these solvents. EDA-based solvents have shown high absorption rates and stability, but carbamate hydrolysis is relatively low, avoiding an increase in carbon dioxide loading.
The Comparison of Biotreatment and Chemical Treatment for Odor Control during Kitchen Waste Aerobic Composting
Authors: Wei Wei , Wang Ningjie , Zhang Zhaobin , Zhang Xiaolei
Published: November 2022

Odor ΨΩγemission has become mathvariant="normal" mathvariant="sans-serif-bold-italic" an important issue in kitchen waste management. Ammonia and hydrogen sulfide are the two most important odor sources as they contribute malodor and can cause health problems. As biotreatment and chemical treatment are two majorly applied technologies for odor control, in this study, they were used to remove ammonia and hydrogen sulfide and the performance of each process was compared. It was found that chemical absorption could efficiently eliminate both ammonia and hydrogenmathvariant="script" sulfide, and the removal efficiencies of ammonia and hydrogen sulfide highly depended on the pH of the adsorbent, contacting time, and gas and solution ratio (G/S). The ammonia-removal efficiency reached 100% within less than 2 s at G/S 600 and pH 0.1. The complete removal of hydrogen sulfide was achieved within 2 s at G/S 4000 and pH 13. Biotrickling filter showed better ability for hydrogen sulfide removal and the removal efficiency was 91.9%; however, the ammonia removal was only 73.5%. It suggests that chemical adsorption is more efficient compared to biotreatment for removing ammonia and hydrogen sulfide. In the combination of the two processes, biotrickling filter followed by chemical adsorption, the final concentrations of ammonia and hydrogen sulfide could meet the Level 1 standard of Emission Standards for Odor Pollution (China). The study provides a potential approach for odor control during kitchen waste aerobic composting.
Incorporating Survey Perceptions of Public Safety and Security Variables in Crime Rate Analyses for the Visegrád Group (V4) Countries of Central Europe
Authors: Ghani Usman , Toth Peter , Fekete Dávid
Published: November 2022

Public governance has evolved in terms of safety and security management, incorporating digital innovation and smart-analytics-based tools to visualize abundant data collections. Urban safety and security are vital social problems that have many branches to be solved, simplified, and improved. Currently, we can see that data-driven insights have often been incorporated into planning, forecasting, and fighting such challenges. The literature has extensively indicated several aspects of solving urban safety problems, i.e., social, technological, administrative, urban, and societal. We have a keen interest in the data analysis and smart analytics options that can be deployed to enhance the presentation, promotional analysis, planning, forecasting, and fighting of these problems. For this, we chose to focus on crime statistics and public surveys regarding victimization and perceptions of crime. As we found through a review, many studies have indicated the vitality of crime rates but not public perceptions in decision-making and planning regarding security. There is always a need for the integration of widespread data insights into unified analyses. This study aimed to answer (1) how effectively we can utilize the crime rates and statistics, and incorporate community perceptions and (2) how promising these two ways of seeing the same phenomena are. For the data analysis, we chose four neighboring countries in Central Europe. We selected CECs, i.e., Hungary, Poland, Czech Republic, and Slovakia, known collectively as the Visegrád Group or V4. The data resources were administrative police statistics and ESS (European Social Survey) statistical datasets. The choice of this region helped us reduce variability in regional dynamics, regime changes, and social control practices.
The Affective Dimension of Social Protection: A Case Study of Migrant-Led Organizations and Associations in Germany
Authors: Barglowski Karolina , Bonfert Lisa
Published: November 2022

This article follows the recent ‘affective turn’ in social sciences and migration scholarship by analyzing the role of emotions in the handling of social risks by people with different migration biographies. The study is based on large-scale research with migrant organizations in Germany, which are important, though often neglected, sources of social protection, identity development, and community building. Interviews and egocentric network diagrams with people using services in various organizations demonstrate the impact of emotions on social protection practices. Contrasting these practices among adult movers, the German-born, and the 1.5 generation with different migration biographies, we shed light on the processual, material, and relational nature and the emotional dimensions of dealing with social risks. In doing so, this work aims to engage in discussions on emotions in migration and settlement processes and to increase the understanding of their impact on social protection.
Child Citizenship Status in Immigrant Families and Differential Parental Time Investments in Siblings
Authors: Wikle Jocelyn , Ackert Elizabeth
Published: November 2022

This study describes how parental time investments in children in immigrant families vary according to children’s citizenship status. In families with multiple children, parents make allocation decisions about how to invest in each child. In immigrant households, a child’s citizenship status may shape parental time allocations because of how this status relates to a child’s prospects for socioeconomic mobility. It is unclear whether parents reinforce citizenship differences among siblings, compensate for these differences, or treat all siblings equally regardless of citizenship status. Moreover, past empirical research has not investigated differences in parental time investments in siblings with different citizenship statuses. To evaluate differential time investments in children based on citizenship, we conduct a quantitative analysis using data from the American Time Use Survey from 2003–2019 and focus on children in immigrant households with at least two children (N = 13,012). Our research shows that parents spend more time with children who have citizenship, but this result is primarily explained by a child’s age and birth order. Our study provides a basis for further inquiry on how legal contexts shaping socioeconomic mobility may influence micro-level family processes in immigrant households.
Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
Authors: Wang Cheng-Gang , Chen Bor-Sen
Published: November 2022

The coronavirus disease 2019 (COVID-19) pandemic has claimed many lives since it was first reported in late December 2019. However, there is still no drug proven to be effective against the virus. In this study, a candidate host–pathogen–interactive (HPI) genome-wide genetic and epigenetic network (HPI-GWGEN) was constructed via big data mining. The reverse engineering method was applied to investigate the pathogenesis of SARS-CoV-2 infection by pruning the false positives in candidate HPI-GWGEN through the HPI RNA-seq time profile data. Subsequently, using the principal network projection (PNP) method and the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, we identified the significant biomarkers usable as drug targets for destroying favorable environments for the replication of SARS-CoV-2 or enhancing the defense of host cells against it. To discover multiple-molecule drugs that target the significant biomarkers (as drug targets), a deep neural network (DNN)-based drug–target interaction (DTI) model was trained by DTI databases to predict candidate molecular drugs for these drug targets. Using the DNN-based DTI model, we predicted the candidate drugs targeting the significant biomarkers (drug targets). After screening candidate drugs with drug design specifications, we finally proposed the combination of bosutinib, erlotinib, and 17-beta-estradiol as a multiple-molecule drug for the treatment of the amplification stage of SARS-CoV-2 infection and the combination of erlotinib, 17-beta-estradiol, and sertraline as a multiple-molecule drug for the treatment of saturation stage of mild-to-moderate SARS-CoV-2 infection.
Sustainable Proposal for Plant-Based Cementitious Composites, Evaluation of Their Mechanical, Durability and Comfort Properties
Authors: Juárez-Alvarado César A. , Magniont Camille , Escadeillas Gilles , Terán-Torres Bernardo T. , Rosas-Diaz Felipe , Valdez-Tamez Pedro L.
Published: November 2022

This research evaluates four sustainable cementitious composites with sustainable plant fibers and bio-aggregates: (1) cementitious matrix composite with lechuguilla fibers (LFC) and (2) with flax fibers (FFC); and (3) cementitious matrix composite with wood shavings (WSC) and (4) with hemp shavings (HSC). The fibers are for reinforcement and the shavings act as bio-aggregates as a total replacement for limestone aggregates. The lechuguilla (LF) and flax (FF) fibers were treated; wood (WS) and hemp (HS) bio-aggregates were also processed. Nineteen mixtures were manufactured, and five were used as controls, and the hygrothermal, mechanical, and durability properties were evaluated. The results for LFC and FFC showed that fiber treatment negatively affected flexural–compressive strength; untreated LFC with accelerated deterioration had better mechanical behavior, higher density, and lower porosity than FFC. Strength and density decreased, but porosity increased with increasing fiber volume (Vf). Regarding WSC and HSC, the microstructure of WS and HS had a significant effect on the physical and mechanical properties. The high porosity influenced the results obtained, since it decreased compressive strength and bulk density; however, thermal conductivity, hygroscopicity, and vapor resistance showed better behavior in most cases than the control specimens, i.e., without bio-aggregates.