bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Aging Methods and Ultrasonication Treatment on the Sensory Profile of Beef Longissimus lumborum Muscle
Authors: Garcia-Galicia Ivan A. , Suleman Raheel , Hussain Ujala , Huerta-Jimenez Mariana , Carrillo-Lopez Luis M. , Alarcon-Rojo Alma D.
Published: May 2023

Tenderness is one of the most appreciated quality characteristics in beef by consumers. Meat aging is the most recognized natural methodology to improve tenderness in beef. The current study was designed to evaluate if ultrasonication was able to simulate (ultrasonication alone) or to grant an additional effect (aging plus ultrasonication) to two different aging methods (dry and wet) on the sensory profile of the beef Longissimus lumborum muscle. The two aging methods (dry and wet), or ultrasonication for 40 min (US), had no effect (p > 0.05) in overall consumer acceptability. However, in terms of sensory attribute liking, the highest values (p < 0.05) were observed in the ultrasonicated and wet-aged meat for 10 d and the dry-aged meat for 10 d, without difference between them (p > 0.05). It is concluded that ultrasound offers the possibility to obtain tender meat without the cost and contamination risks implicated in the dry-aging method.
Research on Low-Carbon Strategies of Supply Chains, Considering Livestreaming Marketing Modes and Power Structures
Authors: Gong Yonghua , He Guangqiang
Published: May 2023

A livestreaming supply chain composed of a single manufacturer and a single streamer in the low-carbon market is examined. Motivated by the actual production and operation, both the manufacturer and the streamer have a chance to dominate the supply chain. Low-carbon strategies and livestreaming marketing modes of the supply chain are studied. The impacts of the consumer’s price sensitivity coefficient, low-carbon preference, and streamer’s promotion sensitivity coefficient on the equilibrium results are further studied. The results show that: the streamer achieves the optimal level of promotion effort in the resale mode under both power structures. The manufacturer achieves the optimal low-carbon level in the commission mode when the promotion sensitivity coefficient is smaller under both of two power structures. The streamer’s profit is optimal in the resale mode, while the manufacturer’s profit is optimal in the commission mode when under the streamer-led structure. Two parties’ profits are optimal in the commission mode when the promotion sensitivity coefficient is smaller under the manufacturer-led structure. The low-carbon level, streamer promotion effort and selling price in two livestreaming marketing modes will increase when the streamer promotion sensitivity coefficient and consumer low-carbon preference increase and will decrease when consumer price sensitivity increases under two power structures. Lastly, the selling price in resale mode is always higher than that in commission mode under two power structures.
Assessment of Mass and Energy Balance of Waste Printed Circuit Board Recycling through Hydrogen Reduction in Black Copper Smelting Process
Authors: Mairizal Aulia Qisthi , Sembada Agung Yoga , Tse Kwong Ming , Haque Nawshad , Rhamdhani M. Akbar
Published: May 2023

Due to growing concern over environmental impacts and the pressure to lower carbon footprints in the metals industry, hydrogen (H2) has gained attention as a promising alternative for the replacement of carbon as a reductant and fuel. This paper evaluates the potential use of hydrogen as an energy source and reducing agent during the processing of waste printed circuit boards (waste PCBs) from e-waste through black copper smelting. The effect of the use of carbon and hydrogen during the reduction–oxidation process was analysed and compared from the perspective of thermodynamics and heat balance. The thermodynamic analyses of waste-PCB processing were carried out using the FactSage thermochemical package for the smelting process at temperatures from 1473 K to 1673 K (1200–1400 °C). The results show that the CO2 emissions can be reduced by 73% when hydrogen is used as the reducing agent. A minimum of 10 wt% of waste PCBs in the feed material can be used to replace the necessary carbon to supply heat for the reduction process. The addition of waste PCBs can increase the volume of slag and affect the composition of the off gas.
Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults
Authors: Hadi Russul H. , Hady Haider N. , Hasan Ahmed M. , Al-Jodah Ammar , Humaidi Amjad J.
Published: May 2023

The growing complexity of data derived from Industrial Internet of Things (IIoT) systems presents substantial challenges for traditional machine-learning techniques, which struggle to effectively manage the needs of predictive maintenance applications. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process, reducing the necessity for manual hyperparameter tuning and computational resources, thereby positioning themselves as a potentially transformative innovation in the Industry 4.0 era. This research introduces two distinct models: AutoML, employing PyCaret, and Auto Deep Neural Network (AutoDNN), utilizing AutoKeras, both aimed at accurately identifying various types of faults in ball bearings. The proposed models were evaluated using the Case Western Reserve University (CWRU) bearing faults dataset, and the results showed a notable performance in terms of achieving high accuracy, recall, precision, and F1 score on the testing and validation sets. Compared to recent studies, the proposed AutoML models demonstrated superior performance, surpassing alternative approaches even when they utilized a larger number of features, thus highlighting the effectiveness of the proposed methodology. This research offers valuable insights for those interested in harnessing the potential of AutoML techniques in IIoT applications, with implications for industries such as manufacturing and energy. By automating the machine-learning process, AutoML models can help decrease the time and cost related to predictive maintenance, which is crucial for industries where unplanned downtime can lead to substantial financial losses.
A Dynamic Partition Model for Multi-Energy Power Grid Energy Balance Considering Electric Vehicle Response Willingness
Authors: Qiu Shi , Zhang Kun , Chen Zhuo , Ma Yiling , Chen Zhe
Published: May 2023

In multi-energy power grids in which electric vehicles (EVs) participate in response, there are significant differences in the power balance between multi-energy supply and load at different time scales and spatial scales. To optimize the energy balance demand of each region, this paper proposes a dynamic partition coordination model for power grid energy regulation demand that considers the willingness of electric vehicles to respond and the uncertainties related to sources, loads, and storage. Firstly, the charging and discharging characteristics of multi-energy conversion devices in power grids, as well as the response uncertainties of these devices, are studied, and a source, load, and storage uncertainty model is established. Then, based on the Markov random field theory and the energy prior model, the dynamic partition model and its solution algorithm for the multi-energy power grid are proposed. Finally, a simulation system is established based on the actual operating data of a multi-energy power grid, and the proposed method is simulated and analyzed. The results indicate that the energy balance partition optimization method proposed in this article is effective. The application of the method proposed in this article can fully leverage the regulatory potential of energy conversion equipment, effectively reduce the proportion of traditional energy supply and peak shaving capacity, and improve the utilization rate of renewable energy.
NiCoAl-Based Monolithic Catalysts for the N2O Intensified Decomposition: A New Path towards the Microwave-Assisted Catalysis
Authors: Muccioli Olga , Meloni Eugenio , Renda Simona , Martino Marco , Brandani Federico , Pullumbi Pluton , Palma Vincenzo
Published: May 2023

Nitrous oxide (N2O) is considered the primary source of NOx in the atmosphere, and among several abatement processes, catalytic decomposition is the most promising. The thermal energy necessary for this reaction is generally provided from the external side of the reactor by burning fossil fuels. In the present work, in order to overcome the limits related to greenhouse gas emissions, high heat transfer resistance, and energy losses, a microwave-assisted N2O decomposition was studied, taking advantages of the microwave’s (MW) properties of assuring direct and selective heating. To this end, two microwave-susceptible silicon carbide (SiC) monoliths were layered with different nickel–cobalt–aluminum mixed oxides. Based on the results of several characterization analyses (SEM/EDX, BET, ultrasound washcoat adherence tests, Hg penetration technique, and TPR), the sample showing the most suitable characteristics for this process was reproduced in the appropriate size to perform specific MW-assisted catalytic activity tests. The results demonstrated that, by coupling this catalytic system with an opportunely designed microwave heated reactor, it is possible to reach total N2O conversion and selectivity of a highly concentrated N2O stream (50 vol%) at T = 550 °C, the same required in the conventionally heated process to remove N2O from a less concentrated gas stream (20 vol%).
Ceramics for Building Structures Made from Contaminated Soils: A Fuzzy Logic Intelligence Approach to Circular Mining
Authors: Terrones-Saeta Juan María , Portela Daniel Camazón , Luís Ana Teresa , Suárez-Macías Jorge , Grande José Antonio
Published: May 2023

Soils contaminated by mining activities are a major environmental concern, and to avoid this type of environmental impact, carrying out high-cost processes is necessary. For this reason, a solution is proposed in this study in order to eliminate the soils contaminated by mining activities and, in turn, prevent the soil’s contaminating elements from causing harm. All this is achieved by using contaminated soils as raw materials for the production of ceramics for bricks. For this purpose, the materials were initially characterized physically and chemically, and different ceramic test pieces were manufactured with different percentages of clay and contaminated soil, subsequently determining the physical properties and the leaching of toxic elements. In this way, it was possible to evaluate, via innovative data mining and fuzzy logic techniques, the influence of the contaminated soil's contribution on the properties of ceramics. Based on this, it was possible to affirm that the contaminated soil incorporation negatively affects the physical properties of ceramics as well as the leaching of polluting elements. The ceramic formed by contaminated soil and clay has a lower compressive strength, and it is associated with lower linear shrinkage and lower density, as well as higher porosity and cold-water absorption. However, the addition of different percentages of contaminated soil (up to 70%) to clay created a ceramic that complied with regulation restrictions. Therefore, it was possible to obtain a sustainable material that eliminates environmental problems at a lower cost and that fits within the new circular mining concept thanks to fuzzy logic techniques.
Performance Evaluation of Multi-Typed Precipitation Products for Agricultural Research in the Amur River Basin over the Sino–Russian Border Region
Authors: Zhou Yezhi , Wang Juanle , Grigorieva Elena , Li Kai , Xu Huanyu
Published: May 2023

Precipitation data are crucial for research on agricultural production, vegetation growth, and other topics related to environmental resources and ecology. With an increasing number of multi-typed gridded precipitation products (PPs), it is important to validate the applicability of PPs and improve their subsequent monitoring capabilities to ensure accurate precipitation-based research. This study evaluates the performance of four mainstream PPs—European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ERA5), ERA5-Land, Multi-Source Weighted-Ensemble Precipitation (MSWEP), and integrated multi-satellite retrievals for the Global Precipitation Mission (GPM)—in capturing the characteristics of precipitation intensity and derived agricultural drought in the crop-enrichment area over the Sino–Russian border region. The results show that, overall, GPM has the most balanced capability among the different experimental scenarios, with well-identified seasonal precipitation intensities. ERA5-Land had strong abilities in depicting annual distribution from spatial/stationary outcomes and obtained advantages in daily multi-parameter consistency verification. When evaluating monthly data in different agroclimatic areas, MSWEP and GPM had outstanding performances in the regions of Russia and China, respectively. For evaluating precipitation intensities and agricultural drought based on daily and monthly precipitation, MSWEP and GPM demonstrated finer performances based on combined agricultural thematic areas (ATAs). However, seasonal effects and affiliated material features were found to be the main factors in exhibiting identification capabilities under different scenarios. Despite good handling of intensity recognition in the eastern Chinese area, ERA5′s capabilities need to be improved by extending sources for calibrating gauged data and information on dry–wet conditions. Overall, this study provides insight into the characterization of PP performances and supports optimal product selection for different applications.
Vicarious Radiometric Calibration of the Multispectral Imager Onboard SDGSAT-1 over the Dunhuang Calibration Site, China
Authors: Cui Zhenzhen , Ma Chao , Zhang Hao , Hu Yonghong , Yan Lin , Dou Changyong , Li Xiao-Ming
Published: May 2023

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world’s first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data.
Robust Feature-Guided Generative Adversarial Network for Aerial Image Semantic Segmentation against Backdoor Attacks
Authors: Wang Zhen , Wang Buhong , Zhang Chuanlei , Liu Yaohui , Guo Jianxin
Published: May 2023

Profiting from the powerful feature extraction and representation capabilities of deep learning (DL), aerial image semantic segmentation based on deep neural networks (DNNs) has achieved remarkable success in recent years. Nevertheless, the security and robustness of DNNs deserve attention when dealing with safety-critical earth observation tasks. As a typical attack pattern in adversarial machine learning (AML), backdoor attacks intend to embed hidden triggers in DNNs by poisoning training data. The attacked DNNs behave normally on benign samples, but when the hidden trigger is activated, its prediction is modified to a specified target label. In this article, we systematically assess the threat of backdoor attacks to aerial image semantic segmentation tasks. To defend against backdoor attacks and maintain better semantic segmentation accuracy, we construct a novel robust generative adversarial network (RFGAN). Motivated by the sensitivity of human visual systems to global and edge information in images, RFGAN designs the robust global feature extractor (RobGF) and the robust edge feature extractor (RobEF) that force DNNs to learn global and edge features. Then, RFGAN uses robust global and edge features as guidance to obtain benign samples by the constructed generator, and the discriminator to obtain semantic segmentation results. Our method is the first attempt to address the backdoor threat to aerial image semantic segmentation by constructing the robust DNNs model architecture. Extensive experiments on real-world scenes aerial image benchmark datasets demonstrate that the constructed RFGAN can effectively defend against backdoor attacks and achieve better semantic segmentation results compared with the existing state-of-the-art methods.
EGMStream, a Desktop App for EGMS Data Downstream
Authors: Festa Davide , Del Soldato Matteo
Published: May 2023

The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding services and tools for optimal dissemination of radar data from the Copernicus Sentinel-1 satellite mission urges the scientific community to find efficient solutions. A desktop R-based application with a user-friendly interface capable of automatically downloading and transforming EGMS products delivered as large .csv tiles, equivalent to a radar burst into geospatial databases, is presented here. EGMStream is a self-contained desktop app that enables users to systematically store, customize, and convert ground movement data into geospatial databases, burst per burst or for an area of interest directly selectable on the app interface.
Spatiotemporal Variation Characteristics of Reference Evapotranspiration and Relative Moisture Index in Heilongjiang Investigated through Remote Sensing Tools
Authors: Wen Siyi , Liu Zihan , Han Yu , Chen Yuyan , Xu Liangsi , Li Qiongsa
Published: May 2023

Reference evapotranspiration (ET0) is one of the significant parameters in agricultural irrigation, especially in Heilongjiang, a big agricultural province in China. In this research, the spatiotemporal variation characteristics of evapotranspiration (ET), relative moisture index (MI) and influencing factors of ET0 in Heilongjiang, which was divided into six ecology districts according to landforms, were analyzed with meteorological data observed over 40 years from 1980 and MOD16 products from 2000 to 2017 using Morlet wavelet analysis and partial correlation analysis. The results indicated that (1) the spatial distribution of ET and PET in Heilongjiang in humid, normal and arid years showed a distribution of being higher in the southwest and lower in the northwest, and higher in the south and lower in the north. The PET was higher than ET from 2002 to 2017, and the difference was small, indicating that the overall moisture in Heilongjiang was sufficient in these years. (2) In the last 40 years, the ET0 increased while the annual MI decreased. The annual minimum of MI in the six regions of Heilongjiang was −0.25, showing that all six regions were drought free. (3) The importance of the meteorological factors affecting ET0 was ranked as average relative humidity > average wind speed > sunshine duration. This research provides scientific guidance for the study of using remote sensing to reverse ET.
A Novel Staggered Double-Segmented Grating Slow-Wave Structure for 340 GHz Traveling-Wave Tube
Authors: Wang Zechuan , Zhu Junwan , Lu Zhigang , Duan Jingrui , Chen Haifeng , Wang Shaomeng , Wang Zhanliang , Gong Huarong , Gong Yubin
Published: May 2023

In this paper, a novel staggered double-segmented grating slow-wave structure (SDSG-SWS) is developed for wide-band high-power submillimeter wave traveling-wave tubes (TWTs). The SDSG-SWS can be considered as a combination of the sine waveguide (SW) SWS and the staggered double-grating (SDG) SWS; that is, it is obtained by introducing the rectangular geometric ridges of the SDG-SWS into the SW-SWS. Thus, the SDSG-SWS has the advantages of the wide operating band, high interaction impedance, low ohmic loss, low reflection, and ease of fabrication. The analysis for high-frequency characteristics shows that, compared with the SW-SWS, the SDSG-SWS has higher interaction impedance when their dispersions are at the same level, while the ohmic loss for the two SWSs remains basically unchanged. Furthermore, the calculation results of beam–wave interaction show that the output power is above 16.4 W for the TWT using the SDSG-SWS in the range of 316 GHz–405 GHz with a maximum power of 32.8 W occurring at 340 GHz, whose corresponding maximum electron efficiency is 2.84%, when the operating voltage is 19.2 kV and the current is 60 mA.
Efficient Autonomous Exploration and Mapping in Unknown Environments
Authors: Feng Ao , Xie Yuyang , Sun Yankang , Wang Xuanzhi , Jiang Bin , Xiao Jian
Published: May 2023

Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the regional legacy issues, i.e., the great impact of smaller unexplored regions on the whole exploration process, which results in a dramatic reduction in their later exploration efficiency. To this end, this paper proposes a Local-and-Global Strategy (LAGS) algorithm that combines a local exploration strategy with a global perception strategy, which considers and solves the regional legacy issues in the autonomous exploration process to improve exploration efficiency. Additionally, we further integrate Gaussian process regression (GPR), Bayesian optimization (BO) sampling, and deep reinforcement learning (DRL) models to efficiently explore unknown environments while ensuring the robot’s safety. Extensive experiments show that the proposed method could explore unknown environments with shorter paths, higher efficiencies, and stronger adaptability on different unknown maps with different layouts and sizes.
Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach
Authors: Weerasinghe Shakthi , Zaslavsky Arkady , Loke Seng Wai , Hassani Alireza , Medvedev Alexey , Abken Amin
Published: May 2023

Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.
Plant Disease Detection and Classification: A Systematic Literature Review
Authors: Ramanjot  , Mittal Usha , Wadhawan Ankita , Singla Jimmy , Jhanjhi N.Z , Ghoniem Rania M. , Ray Sayan Kumar , Abdelmaboud Abdelzahir
Published: May 2023

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM
Authors: Haider Irfan , Yang Hyung-Jeong , Lee Guee-Sang , Kim Soo-Hyung
Published: May 2023

Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.
Single Position ECG Detection System Based on Charge Induction
Authors: Yang Yi , Xu Kun , Li Yu , Zhang Yahui , Zhang Limin
Published: May 2023

With the growing incidence of cardiovascular disease (CVD) in recent decades, the demand for out-of-hospital real-time ECG monitoring is increasing day by day, which promotes the research and development of portable ECG monitoring equipment. At present, two main categories of ECG monitoring devices are “limb lead ECG recording devices” and “chest lead ECG recording devices”, which both require at least two electrodes. The former needs to complete the detection by means of a two-hand lap joint. This will seriously affect the normal activities of users. The electrodes used by the latter also need to be kept at a certain distance, usually more than 10 cm, to ensure the accuracy of the detection results. Decreasing the electrode spacing of the existing ECG detection equipment or reducing the area required for detection will be more conducive to improving the integration of the out-of-hospital portable ECG technologies. Therefore, a single-position ECG system based on charge induction is proposed to realize ECG detection on the surface of the human body with only one electrode with a diameter of less than 2 cm. Firstly, the ECG waveform detected in a single location is simulated by analyzing the electrophysiological activities of the human heart on the human body surface with COMSOL Multiphysics 5.4 software. Then, the hardware circuit design of the system and the host computer are developed and the test is performed. Finally, experiments for static and dynamic ECG monitoring are carried out and the heart rate correlation coefficients are 0.9698 and 0.9802, respectively, which proves the reliability and data accuracy of the system.
Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, and GEMTEC Ionospheric Models: A Comparison in Total Electron Content and Positioning Domains
Authors: Yasyukevich Yury V. , Zatolokin Dmitry , Padokhin Artem , Wang Ningbo , Nava Bruno , Li Zishen , Yuan Yunbin , Yasyukevich Anna , Chen Chuanfu , Vesnin Artem
Published: May 2023

Global navigation satellite systems (GNSS) provide a great data source about the ionosphere state. These data can be used for testing ionosphere models. We studied the performance of nine ionospheric models (Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, and GEMTEC) both in the total electron content (TEC) domain—i.e., how precise the models calculate TEC—and in the positioning error domain—i.e., how the models improve single frequency positioning. The whole data set covers 20 years (2000–2020) from 13 GNSS stations, but the main analysis involves data during 2014–2020 when calculations are available from all the models. We used single-frequency positioning without ionospheric correction and with correction via global ionospheric maps (IGSG) data as expected limits for errors. Improvements against noncorrected solution were as follows: GIM IGSG—22.0%, BDGIM—15.3%, NeQuick2—13.8%, GEMTEC—13.3%, NeQuickG and IRI-2016—13.3%, Klobuchar—13.2%, IRI-2012—11.6%, IRI-Plas—8.0%, GLONASS—7.3%. TEC bias and mean absolute TEC errors for the models are as follows: GEMTEC—−0.3 and 2.4 TECU, BDGIM—−0.7 and 2.9 TECU, NeQuick2—−1.2 and 3.5 TECU, IRI-2012—−1.5 and 3.2 TECU, NeQuickG—−1.5 and 3.5 TECU, IRI-2016—−1.8 and 3.2 TECU, Klobuchar—1.2 and 4.9 TECU, GLONASS—−1.9 and 4.8 TECU, and IRI-Plas—3.1 and 4.2 TECU. While TEC and positioning domains differ, new-generation operational models (BDGIM and NeQuickG) could overperform or at least be at the same level as classical empirical models.
ACG-EmoCluster: A Novel Framework to Capture Spatial and Temporal Information from Emotional Speech Enhanced by DeepCluster
Authors: Zhao Huan , Li Lixuan , Zha Xupeng , Wang Yujiang , Xie Zhaoxin , Zhang Zixing
Published: May 2023

Speech emotion recognition (SER) is a task that tailors a matching function between the speech features and the emotion labels. Speech data have higher information saturation than images and stronger temporal coherence than text. This makes entirely and effectively learning speech features challenging when using feature extractors designed for images or texts. In this paper, we propose a novel semi-supervised framework for extracting spatial and temporal features from speech, called the ACG-EmoCluster. This framework is equipped with a feature extractor for simultaneously extracting the spatial and temporal features, as well as a clustering classifier for enhancing the speech representations through unsupervised learning. Specifically, the feature extractor combines an Attn–Convolution neural network and a Bidirectional Gated Recurrent Unit (BiGRU). The Attn–Convolution network enjoys a global spatial receptive field and can be generalized to the convolution block of any neural networks according to the data scale. The BiGRU is conducive to learning temporal information on a small-scale dataset, thereby alleviating data dependence. The experimental results on the MSP-Podcast demonstrate that our ACG-EmoCluster can capture effective speech representation and outperform all baselines in both supervised and semi-supervised SER tasks.
Research on a Precision Calibration Model of a Flexible Strain Sensor Based on a Variable Section Cantilever Beam
Authors: Wang Qi , Cui Jianjun , Tang Yanhong , Pang Liang , Chen Kai , Zhang Baowu
Published: May 2023

The flexible strain sensor’s measuring range is usually over 5000 με, while the conventional variable section cantilever calibration model has a measuring range within 1000 με. In order to satisfy the calibration requirements of flexible strain sensors, a new measurement model was proposed to solve the inaccurate calculation problem of the theoretical strain value when the linear model of a variable section cantilever beam was applied to a large range. The nonlinear relationship between deflection and strain was established. The finite element analysis of a variable section cantilever beam with ANSYS shows that the linear model’s relative deviation is as high as 6% at 5000 με, while the relative deviation of the nonlinear model is only 0.2%. The relative expansion uncertainty of the flexible resistance strain sensor is 0.365% (k = 2). Simulation and experimental results show that this method solves the imprecision of the theoretical model effectively and realizes the accurate calibration of a large range of strain sensors. The research results enrich the measurement models and calibration models for flexible strain sensors and contribute to the development of strain metering.
Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals
Authors: Li Chao , Chen Jie , Yang Cheng , Yang Jingjian , Liu Zhigang , Davari Pooya
Published: May 2023

Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods.
Improving Hazardous Gas Detection Behavior with Palladium Decorated SnO2 Nanobelts Networks
Authors: Araújo Estácio P. de , Paiva Murilo P. , Moisés Lucas A. , Santo Gabriel S. do Espírito , Blanco Kate C. , Chiquito Adenilson J. , Amorim Cleber A.
Published: May 2023

Transparent Conductive Oxides (TCOs) have been widely used as sensors for various hazardous gases. Among the most studied TCOs is SnO2, due to tin being an abundant material in nature, and therefore being accessible for moldable-like nanobelts. Sensors based on SnO2 nanobelts are generally quantified according to the interaction of the atmosphere with its surface, changing its conductance. The present study reports on the fabrication of a nanobelt-based SnO2 gas sensor, in which electrical contacts to nanobelts are self-assembled, and thus the sensors do not need any expensive and complicated fabrication processes. The nanobelts were grown using the vapor–solid–liquid (VLS) growth mechanism with gold as the catalytic site. The electrical contacts were defined using testing probes, thus the device is considered ready after the growth process. The sensorial characteristics of the devices were tested for the detection of CO and CO2 gases at temperatures from 25 to 75 °C, with and without palladium nanoparticle deposition in a wide concentration range of 40–1360 ppm. The results showed an improvement in the relative response, response time, and recovery, both with increasing temperature and with surface decoration using Pd nanoparticles. These features make this class of sensors important candidates for CO and CO2 detection for human health.
Effect of Grape Pomace Varieties and Soil Characteristics on the Leaching Potential of Total Carbon, Nitrogen and Polyphenols
Authors: Korz Sven , Sadzik Sullivan , More Camilla , Buchmann Christian , Richling Elke , Munoz Katherine
Published: May 2023

Grape pomace (GP) has an added value because of its contribution to carbon (C) and nitrogen (N) in soils when applied as an organic fertilizer. Macronutrients from GP are translocated into the soil after amendment, but little is known about the factors that may influence the mobility of C, N and bioactive molecules, i.e., polyphenols, in the soil column. We investigated the mobility of the macronutrient content of GP, derived from two red (Dornfelder and Pinot noir) and two white grape varieties (Riesling and Pinot blanc). For that, three different soils (loamy sand RefeSol01A, silt loam RefeSol02A and a vineyard soil) were evaluated in a column model using a GP application rate of 30 t ha−1. The three-step lab-scale approach included the analysis of total C, N and polyphenols expressed as total polyphenolic content (TPC) in: (a) the fresh GP, representing the total amount of C, N and TPC; (b) the mobility with rainwater, representing the aqueous extractable fraction and (c) the mobility in the soil column and leaching potential. Our results showed that total C/N ratios were wider in the white GP varieties compared with the red ones. Higher TPC values were measured in Dornfelder and Pinot noir compared with white varieties. Analysis of the water-extractable fraction showed that the C recovery may reach up to 48% in Pinot blanc, which also corresponds to the highest N contribution. Extractable polyphenols were higher in the red compared with the white varieties by a factor of 2.4. C and N were mobilized with rainwater from the GP through the soil column. However, the application rate used in the experiment was not indicative of an accumulation in the soil. Compared with the control (no GP application), C values were significantly higher in the leachates from GP-treated soils, in contrast to N values. Up to 10% of the TPC of the pomace was leached into the soil. The TPC recovery in the soils strongly depended on the combination of soil type and GP variety. Generally, the sandy and more acidic soil showed an even distribution of phenolics with a high recovery rate (up to 92%) compared with more neutral and silty soil. Most of the polyphenol content could accumulate in the upper soil layer (0–10 cm). These results provide the first insights on the mobility of macronutrients in the soil using a column model and point out the need to relate those experiments to soil and GP properties in order to avoid the accumulation of nutrients in soil or mobility to adjacent ecosystems.
Single and Combined Effects of Preferred Music and Endpoint Knowledge on Jump Performance in Basketball Players
Authors: Jebabli Nidhal , Khlifi Mariem , Ouerghi Nejmeddine , Boujabli Manar , Bouassida Anissa , Abderrahman Abderraouf Ben , van den Tillaar Roland
Published: May 2023

Both music and endpoint knowledge of exercise have been shown to independently influence exercise performance. However, whether these factors work as synergists or counteract one another during exercise is unknown. The purpose of this study was to determine the single and combined effect of listening to preferred music and types of endpoint knowledge on repeated countermovement jump (CMJ) test performance. Twenty-four (n = 24) current or previously competitive basketball players underwent CMJ testing under the following endpoint knowledge conditions: (1) unknown/no knowledge, (2) knowledge of the number of jumps, and (3) knowledge of exercise duration. For each of these, participants listened to either their preferred music or no music during the duration of testing. For the exercise portion, participants completed repeated CMJs where participants were encouraged to jump as high as possible with jump height, contact time, and flight time as outcomes. Rate of perceived exertion (RPE) and feeling scale were measured before and after exercise. The results showed that, regardless of knowledge type, preferred music resulted in a significant decrease in both contact time and flight time (F ≥ 10.4, p ≤ 0.004, and ηp2 ≥ 0.35), and a significant improvement of jump height (F = 11.36, p = 0.001, and ηp2 = 0.09) and feeling scale ratings (F = 36.9, p < 0.001, and ηp2 = 0.66) compared to no-music condition, while RPE was not significantly affected. Regardless of the presence of music, knowledge of the number of jumps and duration resulted in lower contact time (p < 0.001, 0.9 < d < 1.56) versus unknown condition during CMJs. Moreover, a significant decrease in RPE values was found during prior endpoint knowledge of number (p = 0.005; d = 0.72) and duration (p = 0.045; d = 0.63) compared to unknown condition. However, feeling scale ratings were not significantly affected. Moreover, no interactions with significance findings were found for any parameters. Overall, data suggest that listening to music and endpoint knowledge alter exercise responses in basketball players, but they do not interact with one another.
Smart Elderly Care: An Intelligent e-Procurement System for Elderly Supplier Selecting
Authors: Qin Simeng , Zhang Mingli , Hu Haiju , Wang Yanan
Published: May 2023

(1) Objective: to accelerate the digitalization of the elderly care service industry and the construction of the smart elderly care industry, this paper designs an intelligent e-procurement system for elderly suppliers selecting from the perspective of smart elderly care, which can enhance the efficiency of elderly care supply chains and assist manufacturers of elderly products in choosing a reliable, high-quality supplier during trades. (2) Methods: the e-procurement system, including six modules, is built with an improved dynamic Markov Decision Process selection model combined with an Analytic Network Process, bringing dynamic evolution of both inventory cost and purchasing cost into long-term reward calculation, and taking into account 15 common indexes and 7 specific indexes when evaluating suppliers’ competitiveness. (3) Results: a real sample shows that when facing 50 suppliers with 50 different quotations, the e-procurement system selects a stable and reliable supplier that brings the best long-term profits for demand enterprises in ten purchase periods, and it makes the selecting process more efficient and more prompt. (4) Conclusions: the model can be used in the circumstance where an elderly product producer is forced to decide on a long-term strategy or reselect a new stable supplier since it is focused on choosing long-term and high-quality suppliers over numerous periods.
Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction
Authors: Bona Rossana , Marini Piergiorgio , Turilli Davide , Masala Salvatore , Scaglione Mariano
Published: May 2023

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuses on the optimization of the prospectively ECG-triggered scan technique, which delivers the radiation efficiently only during a fraction of the R–R interval, matching the aim of reducing radiation dose in this increasingly used radiological examination. In this work, we analyzed how the median DLP (Dose-Length Product) values for CCTA of our Center decreased significantly in recent times mainly due to a notable change in the technology used. We passed from a median DLP value of 1158 mGy·cm to 221 mGy·cm for the whole exam and from a value of 1140 mGy·cm to 204 mGy·cm if considering CCTA scanning only. The result was obtained through the association of important factors during the dose imaging optimization: technological improvement, acquisition technique, and image reconstruction algorithm intervention. The combination of these three factors allows us to perform a faster and more accurate prospective CCTA with a lower radiation dose. Our future aim is to tune the image quality through a detectability-based study, combining algorithm strength with automatic dose settings.
Quantitative Assessment of Intervertebral Disc Composition by MRI: Sensitivity to Diurnal Variation
Authors: Hamaguchi Hiroyuki , Kitagawa Maho , Sakamoto Daiki , Katscher Ulrich , Sudo Hideki , Yamada Katsuhisa , Kudo Kohsuke , Tha Khin Khin
Published: May 2023

Whether diurnal variation exists in quantitative MRI indices such as the T1rho relaxation time (T1ρ) of the intervertebral disc (IVD) is yet to be explored. This prospective study aimed to evaluate the diurnal variation in T1ρ, apparent diffusion coefficient (ADC), and electrical conductivity (σ) of lumbar IVD and its relationship with other MRI or clinical indices. Lumbar spine MRI, including T1ρ imaging, diffusion-weighted imaging (DWI), and electric properties tomography (EPT), was conducted on 17 sedentary workers twice (morning and evening) on the same day. The T1ρ, ADC, and σ of IVD were compared between the time points. Their diurnal variation, if any, was tested for correlation with age, body mass index (BMI), IVD level, Pfirrmann grade, scan interval, and diurnal variation in IVD height index. The results showed a significant decrease in T1ρ and ADC and a significant increase in the σ of IVD in the evening. T1ρ variation had a weak correlation with age and scan interval, and ADC variation with scan interval. Diurnal variation exists for the T1ρ, ADC, and σ of lumbar IVD, which should be accounted for in image interpretation. This variation is thought to be due to diurnal variations in intradiscal water, proteoglycan, and sodium ion concentration.