bluuebunny/crossref_metadata_2025 huggingface dataset:
Soft Ultra-Orthodoxy: Revival Movement Activists, Synagogue Communities and the Mizrahi-Haredi Teshuva Movement in Israel
Authors: Leon Nissim
Published: January 2023

This article offers an in-depth ethnographic and historical description of how an ethnic-religious revival movement has had an impact on religious life. The article will focus on the story of one of Israeli’s foremost religious revival movements—the Mizrahi-Haredi teshuva movement. We will look at the encounter between the Mizrahi-Haredi teshuva movement activists and Mizrahi synagogue congregations, and at the outcomes of that encounter on religious infrastructures, and on the activists’ religious agenda. The following questions will be addressed: How did the relationship between the activists and the synagogue congregations develop? What tensions arose and how did they turn a strict religious outlook into a soft religious approach? The article is based on many years of fieldwork in congregations exposed to the impact of the Mizrahi-Haredi teshuva movement in Israel. The fieldwork provided both a rich ethnographic inventory and an opportunity to describe a historical trend that illuminates the communal, authoritative, and gender models that originated with the encounter between the Mizrahi-Haredi teshuva movement activists and the local synagogue congregations.
Self-Care for Nurses Who Care for Others: The Effectiveness of Meditation as a Self-Care Strategy
Authors: Kwon Junghyun
Published: January 2023

Self-care is how nurses promote their own physical and mental health. Effective self-care is any strategy practiced on a regular basis to prevent stress and anxiety and to enhance the health and well-being. Self-care ranges from getting more rest to seeking professional help. Meditation practice is known to be an effective self-care strategy. The purpose of this study is to assess the effect of meditation as a self-care strategy among Korean nurses. Two groups of nurses at the university hospital, one with meditation experience and the other without, were selected as study participants, and their depression and resilience were analyzed. The results of the study show that, first, depressive symptoms were more prevalent among the nurses in the non-meditative group (62.2%) than the ones in the meditative group (15.6%), and resilience positivity was higher in the meditative group (4.01 ± 0.44) than in non-meditative group (3.04 ± 0.41). Second, nurses with depressive symptoms demonstrated particularly low resilience in both groups, indicating that resilience is inversely associated with other metal disorders. Third, after one and a half years since the study, the turnover rate of the non-meditative group (17.8%) was twice that of meditative group (8.9%). As stated in the earlier self-care literature that stressed the benefits of meditation, this study confirms that consistent meditation experience on a regular basis has an effect on nurses’ well-being via lower depression and promotes higher psychological well-being via resilience. This study is expected to provide the data collected from the field, including personal narratives, to establish more effective self-care strategies in personal and professional settings.
Long-Term Exercise Assistance: Group and One-on-One Interactions between a Social Robot and Seniors
Authors: Shao Mingyang , Pham-Hung Michael , Alves Silas Franco Dos Reis , Snyder Matt , Eshaghi Kasra , Benhabib Beno , Nejat Goldie
Published: January 2023

For older adults, regular exercises can provide both physical and mental benefits, increase their independence, and reduce the risks of diseases associated with aging. However, only a small portion of older adults regularly engage in physical activity. Therefore, it is important to promote exercise among older adults to help maintain overall health. In this paper, we present the first exploratory long-term human–robot interaction (HRI) study conducted at a local long-term care facility to investigate the benefits of one-on-one and group exercise interactions with an autonomous socially assistive robot and older adults. To provide targeted facilitation, our robot utilizes a unique emotion model that can adapt its assistive behaviors to users’ affect and track their progress towards exercise goals through repeated sessions using the Goal Attainment Scale (GAS), while also monitoring heart rate to prevent overexertion. Results of the study show that users had positive valence and high engagement towards the robot and were able to maintain their exercise performance throughout the study. Questionnaire results showed high robot acceptance for both types of interactions. However, users in the one-on-one sessions perceived the robot as more sociable and intelligent, and had more positive perception of the robot’s appearance and movements.
Analyses of Key Variables to Industrialize a Multi-Camera System to Guide Robotic Arms
Authors: Puerto Pablo , Leizea Ibai , Herrera Imanol , Barrios Asier
Published: January 2023

Robotic arms are widely used in sectors such as automotive or assembly logistics due to their flexibility and cost. Other manufacturing sectors would like to take advantage of this technology, however, higher accuracy is required for their purposes. This paper integrated a multi-camera system to achieve the requirements for milling and drilling tasks in aeronautic parts. A closed-loop framework allows the position of the robot’s end-effector to be corrected with respect to a static reference. This is due to the multi-camera system tracking the position of both elements due to the passive targets on their surface. The challenge is to find an auxiliary system to measure these targets with an uncertainty that allows the desired accuracy to be achieved in high volumes (>3 m3). Firstly, in a reduced scenario, a coordinate measuring machine (CMM), a laser tracker (LT), and portable photogrammetry (PP) have been compared following the guidelines from VDI/VDE 2634-part 1. The conclusions allowed us to jump into an industrial scenario and run a similar test with a higher payload than in the laboratory. The article ends with an application example demonstrating the suitability of the solution.
SAR Image Classification Using Gated Channel Attention Based Convolutional Neural Network
Authors: Zhang Anjun , Jia Lu , Wang Jun , Wang Chuanjian
Published: January 2023

Algorithms combining CNN (Convolutional Neural Network) and super-pixel based smoothing have been proposed in recent years for Synthetic Aperture Radar (SAR) image classification. However, the smoothing may lead to the damage of details. To solve this problem the feature fusion strategy is utilized, and a novel adaptive fusion module named Gated Channel Attention (GCA) is designed in this paper. In this module, the relevance between channels is embedded into the conventional gated attention module to emphasize the variation in contribution on classification results between channels of feature-maps, which is not well considered by the conventional gated attention module. A GCA-CNN network is then constructed for SAR image classification. In this network, feature-maps corresponding to the original image and the smoothed image are extracted, respectively, by feature-extraction layers and adaptively fused. The fused features are used to obtain the results. Classification can be performed by the GCA-CNN in an end-to-end way. By the adaptive feature fusion in GCA-CNN, the smoothing of misclassification and the detail keeping can be realized at the same time. Experiments have been performed on one elaborately designed synthetic image and three real world SAR images. The superiority of the GCA-CNN is demonstrated by comparing with the conventional algorithms and the relative state-of-the-art algorithms.
Assessment of Dynamic Mode Decomposition (DMD) Model for Ionospheric TEC Map Predictions
Authors: Landa Vlad , Reuveni Yuval
Published: January 2023

In this study, we assess the Dynamic Mode Decomposition (DMD) model applied with global ionospheric vertical Total Electron Content (vTEC) maps to construct 24-h global ionospheric vTEC map forecasts using the available International GNSS Service (IGS) 2-h cadence vTEC maps. In addition, we examine the impact of a EUV 121.6 nm time series data source with the DMD control (DMDc) framework, which shows an improvement in the vTEC Root Mean Square Error (RMSE) values compared with the IGS final solution vTEC maps. Both the DMD and DMDc predictions present close RMSE scores compared with the available CODE 1-day predicted ionospheric maps, both for quiet and disturbed solar activity. Finally, we evaluate the predicted global ionospheric vTEC maps with the East-North-Up (ENU) coordinate system errors metric, as an ionospheric correction source for L1 single-frequency GPS/GNSS Single Point Positioning (SPP) solutions. Based on these findings, we argue that the commonly adopted vTEC map comparison RMSE metric fails to correctly reflect an informative impact with L1 single-frequency positioning solutions using dual-frequency ionospheric corrections.
Quantitative Evaluation of Dust and Black Carbon Column Concentration in the MERRA-2 Reanalysis Dataset Using Satellite-Based Component Retrievals
Authors: Li Lei , Che Huizheng , Su Xin , Zhang Xindan , Gui Ke , Zheng Yu , Zhao Hujia , Zhao Hengheng , Liang Yuanxin , Lei Yadong , Zhang Lei , Zhong Junting , Wang Zhili , Zhang Xiaoye
Published: January 2023

The aerosol optical property products of Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis dataset have been extensively investigated on a global or regional scale. However, the understanding of MERRA-2 aerosol component products on an extensive temporal and spatial scale is inadequate. Recently, the aerosol component products have been derived from the observations of Polarization and Directionality of the Earth’s Reflectances/Polarization and Anisotropy of Reflectance for Atmospheric Science coupled with observations from a Lidar (POLDER/PARASOL). This study presents a quantitative evaluation of the MERRA-2 reanalysis dust and black carbon (BC) column concentration using independent satellite-based aerosol component concentration retrievals. Both GRASP/Component and MERRA-2 reanalysis products can capture well the temporal variation in dust column concentration over the dust emission resource and downwind dust-dominated regions with the correlation coefficient (R) varying from 0.80 to 0.98. MERRA-2 reanalysis dust products present higher column concentration than GRASP/Component dust retrievals with relative differences of about 20~70%, except in the Taklamakan Desert and Bay of Bengal, where the relative differences can be negative. The differences in dust column concentration over the African dust regions are larger than that over the Asian dust regions. Similar temporal variations in BC column concentration are characterized by both GRASP/Component BC retrievals and MERRA-2 BC products with R of about 0.70~0.90, except in the North China Plain region. We should pay more caution with the regional applicability of MERRA-2 component products when large differences and high correlation coefficients are obtained simultaneously. The results are favorable for identifying the behavior of MERRA-2 reanalysis component estimation in a new view and demonstrate a practical application of the satellite-based component retrievals, which could make more contributions to the improvement of model estimation in the near future.
A Comparison of Machine Learning and Empirical Approaches for Deriving Bathymetry from Multispectral Imagery
Authors: Zhou Wenneng , Tang Yimin , Jing Wenlong , Li Yong , Yang Ji , Deng Yingbin , Zhang Yumeng
Published: January 2023

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.
Dynamic Loss Reweighting Method Based on Cumulative Classification Scores for Long-Tailed Remote Sensing Image Classification
Authors: Liu Jiahang , Feng Ruilei , Chen Peng , Wang Xiaozhen , Ni Yue
Published: January 2023

Convolutional neural networks have been widely used in remote sensing classification and achieved quite good results. Most of these methods are based on datasets with relatively balanced samples, but such ideal datasets are rare in applications. Long-tailed datasets are very common in practice, and the number of samples among categories in most datasets is often severely uneven and leads to bad results, especially in the category with a small sample number. To address this problem, a novel remote sensing image classification method based on loss reweighting for long-tailed data is proposed in this paper to improve the classification accuracy of samples from the tail categories. Firstly, abandoning the general weighting approach, the cumulative classification scores are proposed to construct category weights instead of the number of samples from each category. The cumulative classification score can effectively combine the number of samples and the difficulty of classification. Then, the imbalanced information of samples from each category contained in the relationships between the rows and columns of the cumulative classification score matrix is effectively extracted and used to construct the required classification weights for samples from different categories. Finally, the traditional cross-entropy loss function is improved and combined with the category weights generated in the previous step to construct a new loss reweighting mechanism for long-tailed data. Extensive experiments with different balance ratios are conducted on several public datasets, such as HistAerial, SIRI-WHU, NWPU-RESISC45, PatternNet, and AID, to verify the effectiveness of the proposed method. Compared with other similar methods, our method achieved higher classification accuracy and stronger robustness.
Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices
Authors: Fang Wenjing , Zhu Hongfen , Li Shuai , Ding Haoxi , Bi Rutian
Published: January 2023

Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.
Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor
Authors: Tarkhov Dmitriy , Lazovskaya Tatiana , Malykhina Galina
Published: January 2023

A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters of the governing equations as trainable parameters. Constructing the network is carried out in three stages. In the first step, a neural network solution to an equation corresponding to a numerical scheme is constructed. It allows for forming an initial low-fidelity neural network solution to the original problem. At the second stage, the network with physics-based architecture (PBA) is further trained to solve the differential equation by minimising the loss function, as is typical in works devoted to physics-informed neural networks (PINNs). In the third stage, the physics-informed neural network with architecture based on physics (PBA-PINN) is trained on high-fidelity sensor data, parameters are identified, or another task of interest is solved. This approach makes it possible to solve insufficiently studied PINN problems: selecting neural network architecture and successfully initialising network weights corresponding to the problem being solved that ensure rapid convergence to the loss function minimum. It is advisable to use the devised PBA-PINNs in the problems of surrogate modelling and modelling real objects with multi-fidelity data. The effectiveness of the approach proposed is demonstrated using the problem of modelling processes in a chemical reactor. Experiments show that subsequent retraining of the initial low-fidelity PBA model based on a few high-accuracy data leads to the achievement of relatively high accuracy.
Design and Experiment of Online Detection System for Water Content of Fresh Tea Leaves after Harvesting Based on Near Infra-Red Spectroscopy
Authors: Wang Shishun , Wu Zhengmin , Cao Chengmao , An Minhui , Luo Kun , Sun Liang , Wang Xiaoran
Published: January 2023

Fresh tea leaves continuously lose water after harvesting, and the level of water content directly affects the configuration of tea processing parameters. To address this problem, this study established an online detection system for the water content of fresh tea leaves after harvesting based on near-infrared spectroscopy. The online acquisition and analysis system of the temperature and humidity sensor signal data was developed based on LabVIEW and Python software platforms. Near-infrared spectral data, environmental temperature, and humidity were collected from fresh leaves after harvesting. Spectral data were combined with PLS (partial least squares) to develop a prediction model for the water content of fresh tea leaves. Simultaneously, data communication between LabVIEW and PLC was established, laying the foundation for establishing a feedback mechanism to send the prediction results to the main platform of the lower computer. This provides a more objective and accurate basis for the detection of fresh leaves before processing and regulation during processing, thereby effectively promoting the standardisation and intelligent development of tea-processing equipment.
Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems
Authors: Liu Peng , Zhou Shuyu , Zhang Peng , Li Mengwei
Published: January 2023

For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.
A Moving Magnetic Grid-Type Long-Range Linear Absolute Displacement Sensor
Authors: Zhang Zhen , Wang Lei , Cao Bingzhang , Zhang Haoze , Liu Jiawei
Published: January 2023

In order to overcome the shortcomings of the traditional magnetic absolute linear displacement sensors in which cables affect the flexibility and measurement range in linear motor transmission systems, this paper proposes a novel cable-free moving magnetic grid-type long-range absolute displacement sensor. The sensor consists of a magnetic grid and a signal acquisition board. The magnetic grid is a moving component that contains two rows of permanent magnet arrays, one for relative displacement measurement and the other for the displacement interval code. The signal acquisition board is a fixed component that uses n groups of two-row Hall sensor arrays for continuous absolute displacement measurement. The principle of the sensor using the 2D magnetic field signal for the relative displacement measurement is analyzed, and a measurement method based on Hall sensor arrays for coding and absolute displacement detection over n cycles is proposed. Finally, a sensor prototype is fabricated and the experiments are performed. The experimental results show that the measurement resolution of the sensor is 5 μm, and the measurement accuracy is ±14.8 μm within the measurement range of 0–98.3 mm. The proposed sensor can realize continuous absolute displacement measurement over multiple cycles without cable binding.
Non-Destructive Direct Pericarp Thickness Measurement of Sorghum Kernels Using Extended-Focus Optical Coherence Microscopy
Authors: Sen Dipankar , Fernández Alma , Crozier Daniel , Henrich Brian , Sokolov Alexei V. , Scully Marlan O. , Rooney William L. , Verhoef Aart J.
Published: January 2023

Non-destructive measurements of internal morphological structures in plant materials such as seeds are of high interest in agricultural research. The estimation of pericarp thickness is important to understand the grain quality and storage stability of seeds and can play a crucial role in improving crop yield. In this study, we demonstrate the applicability of fiber-based Bessel beam Fourier domain (FD) optical coherence microscopy (OCM) with a nearly constant high lateral resolution maintained at over ~400 µm for direct non-invasive measurement of the pericarp thickness of two different sorghum genotypes. Whereas measurements based on axial profiles need additional knowledge of the pericarp refractive index, en-face views allow for direct distance measurements. We directly determine pericarp thickness from lateral sections with a 3 µm resolution by taking the width of the signal corresponding to the pericarp at the 1/e threshold. These measurements enable differentiation of the two genotypes with 100% accuracy. We find that trading image resolution for acquisition speed and view size reduces the classification accuracy. Average pericarp thicknesses of 74 µm (thick phenotype) and 43 µm (thin phenotype) are obtained from high-resolution lateral sections, and are in good agreement with previously reported measurements of the same genotypes. Extracting the morphological features of plant seeds using Bessel beam FD-OCM is expected to provide valuable information to the food processing industry and plant breeding programs.
A Low-Profile Antenna for On-Body and Off-Body Applications in the Lower and Upper ISM and WLAN Bands
Authors: Ali Esraa Mousa , Awan Wahaj Abbas , Naqvi Syeda Iffat , Alzaidi Mohammed S. , Alzahrani Abdullah , Elkamchouchi Dalia H. , Falcone Francisco , Alharbi Turki E. A.
Published: January 2023

The article presents a Co-planar Waveguide (CPW) fed antenna of a low-profile, simple geometry, and compact size operating at the dual band for ISM and WLAN applications for 5G communication devices. The antenna has a small size of 30 mm × 18 mm × 0.79 mm and is realized using Rogers RT/Duroid 5880 substrate. The proposed dual-band antenna contains a CPW feedline along with the triangular patch. Later on, various stubs are loaded to obtain optimal results. The proposed antenna offers a dual band at 2.4 and 5.4 GHz while covering the impedance bandwidths of 2.25–2.8 GHz for ISM and 5.45–5.65 GHz for WLAN applications, respectively. The proposed antenna design is studied and analyzed using the Electromagnetic (EM) High-Frequency Structure Simulator (HFSSv9) tool, and a hardware prototype is fabricated to verify the simulated results. As the antenna is intended for on-body applications, therefore, Specific Absorption Rate (SAR) analysis is carried out to investigate the Electromagnetic effects of the antenna on the human body. Moreover, a comparison between the proposed dual-band antenna and other relevant works in the literature is presented. The results and comparison of the proposed work with other literary works validate that the proposed dual-band antenna is suitable for future 5G devices working in Industrial, Scientific, Medical (ISM), and Wireless Local Area Network (WLAN) bands.
Wideband and Channel Switchable Mode Division Multiplexing (MDM) Optical Power Divider Supporting 7.682 Tbit/s for On-Chip Optical Interconnects
Authors: Hung Tun-Yao , Chen Guan-Hong , Lin Yuan-Zeng , Chow Chi-Wai , Jian Yin-He , Kuo Pin-Cheng , Peng Ching-Wei , Tsai Jui-Feng , Liu Yang , Yeh Chien-Hung
Published: January 2023

Silicon photonics (SiPh) are considered a promising technology for increasing interconnect speed and capacity while decreasing power consumption. Mode division multiplexing (MDM) enables signals to be transmitted in different orthogonal modes in a single waveguide core. Wideband MDM components simultaneously supporting wavelength division multiplexing (WDM) and orthogonal frequency-division multiplexing (OFDM) can significantly increase the transmission capacity for optical interconnects. In this work, we propose, fabricate and demonstrate a wideband and channel switchable MDM optical power divider on an SOI platform, supporting single, dual and triple modes. The switchable MDM power divider consists of two parts. The first part is a cascaded Mach–Zehnder interferometer (MZI) for switching the data from their original TE0, TE1 and TE2 modes to different modes among themselves. After the target modes are identified, mode up-conversion and Y-branch are utilized in the second part for the MDM power division. Here, 48 WDM wavelength channels carrying OFDM data are successfully switched and power divided. An aggregated capacity of 7.682 Tbit/s is achieved, satisfying the pre-forward error correction (pre-FEC) threshold (bit-error-rate, BER = 3.8 × 10−3). Although up to three MDM modes are presented in the proof-of-concept demonstration here, the proposed scheme can be scaled to higher order modes operation.
A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring
Authors: Pereira Leonor , Plácido da Silva Hugo
Published: January 2023

In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65–80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse health effects lead to decreased productivity, increased absenteeism and health care costs. However, lack of regulation targeting these issues has oftentimes left them unattended. This article proposes a smart chair system, with posture and electrocardiography (ECG) monitoring modules, using an “invisible” sensing approach, to optimize working conditions, without hindering everyday tasks. For posture classification, machine learning models were trained and tested with datasets composed by center of mass coordinates in the seat plane, computed from the weight measured by load cells fixed under the seat. Models were trained and evaluated in the classification of five and seven sitting positions, achieving high accuracy results for all five-class models (>97.4%), and good results for some seven-class models, particularly the best performing k-NN model (87.5%). For ECG monitoring, signals were acquired at the armrests covered with conductive nappa, connected to a single-lead sensor. Following signal filtering and segmentation, several outlier detection methods were applied to remove extremely noisy segments with mislabeled R-peaks, but only DBSCAN showed satisfactory results for the ECG segmentation performance (88.21%) and accuracy (90.50%).
A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal
Authors: Wei Lin , Wang Zhenyuan , Xu Jing , Shi Yucheng , Wang Qingxian , Shi Lei , Tao Yongcai , Gao Yufei
Published: January 2023

Sentiment analysis aims to mine polarity features in the text, which can empower intelligent terminals to recognize opinions and further enhance interaction capabilities with customers. Considerable progress has been made using recurrent neural networks or pre-trained models to learn semantic representations. However, recently published models with complex structures require increasing computational resources to reach state-of-the-art (SOTA) performance. It is still a significant challenge to deploy these models to run on micro-intelligent terminals with limited computing power and memory. This paper proposes a lightweight and efficient framework based on hybrid multi-grained embedding on sentiment analysis (MC-GGRU). The gated recurrent unit model is designed to incorporate a global attention structure that allows contextual representations to be learned from unstructured text using word tokens. In addition, a multi-grained feature layer can further enrich sentence representation features with implicit semantics from characters. Through hybrid multi-grained representation, MC-GGRU achieves high inference performance with a shallow structure. The experimental results of five public datasets show that our method achieves SOTA for sentiment classification with a trade-off between accuracy and speed.
DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays
Authors: Malik Hassaan , Naeem Ahmad , Naqvi Rizwan Ali , Loh Woong-Kee
Published: January 2023

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients’ right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model’s accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.
FPGA-Based Smart Sensor to Detect Current Transformer Saturation during Inrush Current Measurement
Authors: Martínez-Figueroa G. de J. , Córcoles-López Felipe , Bogarra Santiago
Published: January 2023

Current transformer saturation affects measurement accuracy and, consequently, protection reliability. One important concern in the case of overcurrent protections is the discrimination between faults and inrush current in power transformers. This paper presents an FPGA-based smart sensor to detect current transformer saturation, especially during inrush current conditions. Several methods have been proposed in the literature, but some are unsuitable for inrush currents due to their particular waveform. The proposed algorithm implemented on the smart sensor uses two time-domain features of the measured secondary current: the second-order difference function and the third-order statistic central moment. The proposed smart sensor presents high effectiveness and immunity against noise with accurate results in different conditions: different residual flux, resistive burdens, sampling frequency, and noise levels. The points at which saturation starts are detected with an accuracy of approximately 100%. Regarding the end of saturation, the proposed method detects the right ending points with a maximum error of a sample. The smart sensor has been tested on experimental online and real-time conditions (including an anti-aliasing filter) with accurate results. Unlike most existing methods, the proposed smart sensor operates efficiently during inrush conditions. The smart sensor presents high-speed processing despite its simplicity and low computational cost.
Determination of Cordycepin Using a Stability-Indicating Greener HPTLC Method
Authors: Alam Prawez , Shakeel Faiyaz , Alqarni Mohammed H. , Foudah Ahmed I. , Aljarba Tariq M. , Alam Aftab , Ghoneim Mohammed M. , Asdaq Syed Mohammed Basheeruddin , Alshehri Sultan , Iqbal Muzaffar
Published: January 2023

A wide range of analytical techniques have been reported to determine cordycepin (CDN) in various sample matrices. Nevertheless, greener analytical approaches for CDN estimation are scarce in the literature. As a result, this study was designed to develop and validate a stability-indicating greener “high-performance thin-layer chromatography (HPTLC)” technique for CDN determination in a laboratory-developed formulation. The greener eluent system for CDN detection was ethanol–water (75:25 v/v). At a wavelength of 262 nm, CDN was measured. The greenness scale of the proposed analytical technology was derived using the “Analytical GREENness (AGREE)” approach. The proposed stability-indicating HPTLC assay was linear for CDN analysis in the 50–1000 ng/band range with a determination coefficient of 0.9978. The proposed analytical technique for CDN analysis was simple, rapid, accurate, precise, robust, selective, stability-indicating, and greener. The AGREE score for the proposed stability-indicating HPTLC technique was calculated to be 0.79 using the AGREE calculator. The current protocol was able to detect CDN degradation products under various stress conditions, indicating its stability-indication characteristics and selectivity. The AGREE quantitative score indicated that the stability-indicating current protocol had outstanding greener characteristics. The amount of CDN in the laboratory-developed formulation was determined to be 98.84%, indicating the suitability of the current protocol in the assay of CDN in the formulations. These results suggested that CDN in a laboratory-developed formulation may be regularly determined using the stability-indicating greener HPTLC strategy.
Development and Validation of a Stability-Indicating Greener HPTLC Method for the Estimation of Flufenamic Acid
Authors: Alam Prawez , Shakeel Faiyaz , Alqarni Mohammed H. , Foudah Ahmed I. , Aljarba Tariq M. , Ghoneim Mohammed M. , Asdaq Syed Mohammed Basheeruddin , Alshehri Sultan
Published: January 2023

The literature on ecofriendly/greener high-performance thin-layer chromatographic (HPTLC) methods for quantifying flufenamic acid (FFA) is scant. In order to develop and validate a stability-indicating greener HPTLC densitometry assay for FFA determination in marketed products, this research was conducted. The ecofriendly eluent system was composed of ethanol–water (70:30 v/v). FFA was measured at 290 nm of wavelength. The greenness scale of suggested analytical assay was derived using “Analytical GREENness (AGREE)” methodology. The suggested stability-indicating HPTLC assay was linear for FFA determination in 25–1400 ng/band range with a determination coefficient of 0.9974. The suggested analytical assay for FFA analysis was simple, rapid, accurate, precise, robust, selective, stability-indicating, and greener. The AGREE scale for the developed stability-indicating HPTLC assay was derived to be 0.77 utilizing AGREE methodology, indicating an outstanding greenness characteristic of the suggested densitometry technique. The ecofriendly HPTLC technique was able to detect FFA degradation product under forced degradation studies, indicating its stability-indication characteristics and selectivity. The amount of FFA in marketed tablets brand A and B was determined to be 101.28 and 99.17%, respectively, indicating the suitability of the suggested analytical technique in the assay of FFA in marketed products. These results indicated that FFA in marketed products may be routinely measured using the stability-indicating greener HPTLC technique.
Sociotechnical Characteristics of Conceptually Related Smart Cities’ Services from an International Perspective
Authors: Kim Nammi , Yang Seungwoo
Published: January 2023

Sustainable smart cities (SSCs) have developed various services and technologies with multi-stakeholderism under multiple names. The characteristics of SSCs are specified by implementing conceptually related smart cities (CRSCs), which are ICT-based transformative cities. Many scholars point out that in-depth empirical studies of CRSCs are necessary to clarify the nature of the sociotechnical transition of SSCs while avoiding the oversimplified narratives of techno-utopia. Utilizing a periodic matrix taxonomy, this study aims to examine empirical characteristics of CRSCs services’ socio-technical transformation from international perspectives. The target cities were sampled using cluster sampling through three screening steps based on four representative documents reflecting the critical aspects of the operational definition of SSCs. The city-level data were collected using Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol and preprocessed with coding and weighting to create a periodic matrix taxonomy. The outcomes are the commonalities and different services’ sociotechnical transitions of sampled European cities from perspectives of multi-stakeholderism. The outcomes have managerial implications demonstrating empirically the sequences of service transformation of European megacities. Theoretical implications for the existing theories also arise through empirical analysis of historical real-city data and specification of stakeholders’ partnerships in conceptually related smart cities.
Treating Preeclampsia in the COVID-19 Era: Is Allopurinol Useful as an Adjuvant Therapy? A Case Report and Review of the Literature
Authors: Mitranovici Melinda-Ildiko , Chiorean Diana Maria , Mureșan Maria Cezara , Buicu Corneliu-Florin , Moraru Raluca , Moraru Liviu , Cotoi Titiana Cornelia , Cotoi Ovidiu Simion , Toru Havva Serap , Apostol Adrian , Turdean Sabin Gligore , Petre Ion , Mărginean Claudiu , Oală Ioan Emilian , Ivan Viviana , Pușcașiu Lucian
Published: January 2023

Acute respiratory syndrome-related coronavirus 2, or SARS-CoV-2, mainly affects the vulnerable population, especially those with comorbidities, such as pregnant women. SARS-CoV-2 has been found to cause multiple manifestations, one of which is preeclampsia. In preeclampsia, uric acid is excessively produced in the ischemic placenta and is released into circulation by placental reperfusion. Another effect of uric acid is oxidative stress with the production of oxygen free radicals associated with severe preeclampsia and fetal hypoxia. In our case report, we present the situation of a 38-year-old pregnant woman who developed preeclampsia after infection with SARS-CoV-2 with rapid evolution and an increased level of uric acid. We discuss the option of Allopurinol treatment in the third trimester of pregnancy instead of premature birth, with excellent benefits for both the mother and newborn. Additional clinical correlations between antioxidant treatment with Allopurinol and placental findings are needed.
Modification of Sympathetic and Hypothalamic Responses to Prevent Complications of COVID-19: “Dam and Wall Concept”
Authors: Hyoju Sanjiv K.
Published: January 2023

We are in the midst of the COVID-19 pandemic. Since December 2019, severe acute respiratory coronavirus (SARS-CoV-2) has infected more than half a billion people, killing nearly 7 million people worldwide. Now various variants of SARS-CoV-2 are causing mayhem and driving the global surge. Epidemiologists are aware of the fact that this virus is capable of escaping immunity and likely to infect the same person multiple times despite adequate vaccination status. Elderly people and those with underlying health conditions who are considered high-risk are likely to suffer complications. While it is tempting to frame complications and mortality from COVID-19 as a simple matter of too much of a virulent virus in too weak of a host, much more is at play here. Framing the pathophysiology of COVID-19 in the context of the Chrousos and Gold model of the stress response system can shed insight into its complex pathogenesis. Understanding the mechanisms of pharmacologic modification of the sympathetic and hypothalamic response system via administration of clonidine and/or dexamethasone may offer an explanation as to why a viral pathogen can be well tolerated and cleared by one host while inflaming and killing another.
Application of All-Ages Lead Model Based on Monte Carlo Simulation of Preschool Children’s Exposure to Lead in Guangdong Province, China
Authors: Hu Jing , Zhang Zhengbao , Lin Senwei , Zhang Qiuhuan , Du Guoxia , Zhou Ruishan , Qu Xiaohan , Xu Guojiang , Yang Ying , Cai Yongming
Published: January 2023

Introduction: Lead (Pb) poisoning in children is a major public health issue worldwide. The physiologically based pharmacokinetic model (PBPK model) has been extensively utilized in Pb exposure risk assessment and can connect external exposure with biological monitoring data. This study aimed to combine a Monte Carlo simulation with the all-ages lead model (ALLM) to quantify the heterogeneity and uncertainty of certain parameters in the population. The parameters of the all-ages lead model based on Monte Carlo simulation (ALLM + MC) were localized in Guangdong Province. Our study discusses the practicability of the application of the localized ALLM + MC in Guangdong Province. Methods: A local sensitivity analysis was used to assess the impact of pharmacokinetic parameters on the prediction of blood lead level (BLL). Environmental Pb concentration, exposure parameters, and sensitive parameters were included in the ALLM + MC, and the differences between the ALLM- and the ALLM + MC-predicted values were compared. Additionally, we localized the exposure parameters in the ALLM + MC and used them to evaluate BLL in preschool children from Guangdong Province. Finally, we compared the predictive values to those observed in the literature. Results: The predictive values of ALLM and ALLM + MC had a significant correlation (r = 0.969, p < 0.001). The predictive value of ALLM was included in the ALLM + MC prediction range. Moreover, there were no significant differences between the predictive and the observed values of preschool children from Guangdong Province (z = −0.319, p = 0.749). Except for children aged 5–6, the difference between the predictive and the observed values was less than 1 μg/dL. The root mean square error (RMSE) and the mean deviation (RMD) of ALLM and ALLM + MC were reduced by 24.73% and 32.83%, respectively. Conclusions: The localized ALLM + MC is more suitable for predicting the BLL of preschool children in Guangdong Province, which can be used to explain the heterogeneity and uncertainty of parameters in the population. The ALLM + MC has fewer time, space, and financial restrictions, making it more appropriate for determining the BLLs in large populations. The use of ALLM + MC would improve the feasibility of regular and long-term blood Pb detection.
Bonding or Indulgence? The Role of Overborrowing on Firms’ Innovation: Evidence from China
Authors: Meng Qiankun , Liu Yupei , Li Wei’an , Yu Mingshan
Published: January 2023

This paper examines the innovation spending gap associated with overborrowing in China’s state-owned enterprises (SOEs). Based on the double agency problem of the banking sector, the authors hypothesize that SOEs are more inclined to a higher level of overborrowing and therefore worsen firms’ debt governance system for innovation. We argue that obtaining excessive bank loans has an indulgence effect and is used by firms’ managers as an entrenchment strategy for underinvestment in innovation. We test our theoretical model under the unique institutional setting of China’s banks, in particular the administrative-economic governance. Using a longitudinal panel dataset that contains a cross section of Chinese listed companies from 2012 to 2018, we confirm overborrowing’s mediating role between state ownership and firm innovation expenditure, implying that enhancing the delegated monitoring of banks is also essential to firm innovativeness in transition economies. Additionally, we further test the role of political connections and managers’ R&D functional experience to leverage the benefits of SOEs’ innovation resource endowment. Our study demonstrates another debt governance channel through which government intervention has a negative impact on firm innovation resource allocation. It expands the understanding of the debt governance role for innovation in transition economies.