bluuebunny/crossref_metadata_2025 huggingface dataset:
Fine Structure of Optical Vortices in Linearly Polarized Laguerre–Gaussian Beams in Oblique Beams Propagating a Uniaxial Crystal
Authors: Egorov Yuriy , Rubass Alexander
Published: June 2023

Traditional ideas about linearly polarized paraxial beam propagation along the optical axis of a uniaxial crystal suggest that at the crystal exit face, after propagation through the polarizer, the beam will form an intensity distribution in the form of a conoscopic pattern. Any violation of axial propagation was considered as a perturbation of the conoscopic pattern and was not taken into account. Nevertheless, this process opens up a wide variety of transformations of polarization singularities caused by weak perturbations. In this article, the behavior of linearly polarized low-order Laguerre–Gauss beams in a uniaxial crystal is considered. The existence of a fine structure of radiation on the output face of a uniaxial crystal and the dependence of this fine structure on the parameters of the crystal and the beam are shown.
The Microbial Connection to Sustainable Agriculture
Authors: Nadarajah Kalaivani , Abdul Rahman Nur Sabrina Natasha
Published: June 2023

Microorganisms are an important element in modeling sustainable agriculture. Their role in soil fertility and health is crucial in maintaining plants’ growth, development, and yield. Further, microorganisms impact agriculture negatively through disease and emerging diseases. Deciphering the extensive functionality and structural diversity within the plant–soil microbiome is necessary to effectively deploy these organisms in sustainable agriculture. Although both the plant and soil microbiome have been studied over the decades, the efficiency of translating the laboratory and greenhouse findings to the field is largely dependent on the ability of the inoculants or beneficial microorganisms to colonize the soil and maintain stability in the ecosystem. Further, the plant and its environment are two variables that influence the plant and soil microbiome’s diversity and structure. Thus, in recent years, researchers have looked into microbiome engineering that would enable them to modify the microbial communities in order to increase the efficiency and effectiveness of the inoculants. The engineering of environments is believed to support resistance to biotic and abiotic stressors, plant fitness, and productivity. Population characterization is crucial in microbiome manipulation, as well as in the identification of potential biofertilizers and biocontrol agents. Next-generation sequencing approaches that identify both culturable and non-culturable microbes associated with the soil and plant microbiome have expanded our knowledge in this area. Additionally, genome editing and multidisciplinary omics methods have provided scientists with a framework to engineer dependable and sustainable microbial communities that support high yield, disease resistance, nutrient cycling, and management of stressors. In this review, we present an overview of the role of beneficial microbes in sustainable agriculture, microbiome engineering, translation of this technology to the field, and the main approaches used by laboratories worldwide to study the plant–soil microbiome. These initiatives are important to the advancement of green technologies in agriculture.
Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation
Authors: Huang Ying
Published: June 2023

Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm’s performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.
Research on Micromechanical Behavior of Current Collector of Lithium-Ion Batteries Battery Cathode during the Calendering Process
Authors: Yang Kaiyue , Xie Xinbing , Du Xiaozhong , Zuo Yang , Zhang Ying
Published: June 2023

Calendering is a crucial process in the manufacturing of lithium-ion battery electrodes. However, this process introduces several challenges to the current collector, including uneven stress distribution, stress concentration, and plastic pits, which ultimately impact electrode consistency and safety. It is important to note that the load exerted on the current collector during calendering cannot be determined solely through experimental means. Moreover, due to the extremely thin nature of the current collector, there is a size effect problem. To address these issues, this study establishes a lithium-ion battery cathode model based on real experimental data and conducts a numerical simulation of the calendering process. By obtaining the load applied to the current collector and incorporating it into the crystal plasticity model, we investigate the mechanical behavior of the current collector at the crystal level during calendering. The results demonstrate that the lithium battery cathode collectors undergo plastic deformation during calendering. Furthermore, current collectors with a smaller number of grains exhibit a more pronounced stress concentration zone, and their stress levels are highly sensitive to the crystal direction. The maximum stress fluctuation range can reach approximately 100 MPa. Conversely, current collectors with a greater number of grains exhibit a more uniform stress distribution during calendering and are less sensitive to the crystal orientation. Their stress levels remain stable within a smaller range, approximately 20 MPa. These findings justify and emphasize the importance of investigating the current collector at the microscopic level, thereby providing valuable research insights for the field of calendering.
The Development and Evaluation of Novel Self-Degrading Loss-Circulation Material for Ultra-Deepwater Drilling in South China Sea
Authors: Liu Zhiqin , Xu Jiafang , Peng Wei , Yu Xiaodong , Chen Jie
Published: June 2023

Focusing on the problem of drilling fluid loss circulation due to fractured granite formation in ultra-deepwater drilling in the research area, a novel self-degrading loss-circulation material is developed with polymer resin as the core material, according to the principles of loss-circulation prevention and reservoir and environmental protection. The relevant properties of the novel self-degrading loss-circulation material are evaluated by using tests or experiments. The pressure-bearing properties are evaluated by using a sealing capability test; the self-degrading properties are evaluated by using a self-degrading performance test; the pressure-bearing capability and reservoir protection properties are tested by measuring pressure sealing and gas permeability. The results of the tests and experiments show that the grinding rate of the loss-circulation material is less than 6% under 25 MPa, indicating that the novel loss-circulation material is of high compressive strength. The degradation process of the novel self-degrading loss-circulation material can be accelerated by increasing temperature and pH, and the degradation of the self-degradable polymer composite can be accomplished within 5 days under 95 °C and pH = 14. After self-degrading, the permeability recovery values of the highly permeable reservoir and the fractured reservoir are more than 96.9% and 99.15%, respectively, which indicates outstanding reservoir protection capability. Therefore, the novel self-degrading loss-circulation material has excellent temporary plugging and reservoir protection performance and can be used for plugging while drilling in ultra-deepwater drilling.
Structural Design and Simulation Analysis of a Dual-Row Pneumatic Vegetable Precision Planter
Authors: Li Yanjun , Bai Yang , Zhang Xunlin , Xie Fuxiang
Published: June 2023

In order to solve the problems of domestic and foreign vegetable precision metering devices that easily damage seeds and have low seeding efficiency and poor qualification rate, this paper designs a one-machine for two-row pneumatic combination seeder, which is mainly composed of a pneumatic seeding mechanism and a honeycomb seeding mechanism. The mechanism and other components and theoretical analysis of the working process of the combined seed metering device clarifies the minimum negative pressure for adsorbing a seed during the suction stage, along with the negative pressure required for the seed-carrying stage. It also establishes the seeding speed and movement during planting with the trajectory parameter equation and uses ADAMS software to conduct virtual experiments on the influence of the number of holes, diameters, and heights of different types of honeycomb seeding disks on the success rate of seeding. Based on research on previous pneumatic seeding mechanisms, single factor tests, orthogonal tests, and high-speed photography tests of the honeycomb seeding disk of the combined seeder were carried out on the number of holes, the diameter of holes, and the height of seeding. The results of tests conducted on okra seeds showed that when the rotating speed of the pneumatic seeding disc was 18 r/min, the diameter of the pneumatic seeding disc hole was 2.4 mm, the vacuum degree was 3.5 kPa, the number of honeycomb seeding disc holes was 24, the hole diameter was 13 mm, and the seed height was 60 mm, the working performance of the combined seed meter reached the optimal level, which provides a reference for the development of a small seed vegetable precision seeder.
The Implications of COVID-19 on Catholic Education at the Catholic University of Zimbabwe: The Case of Bulawayo Campus 2019–2023
Authors: Makwati Luyanduhlobo Bophelo
Published: June 2023

The main aim of this article is to unravel the implications of the COVID-19 pandemic on Catholic education at the Catholic University of Zimbabwe (CUZ), Bulawayo campus. The research, qualitative in nature, has been guided by the importance of Catholic education nationally. It has interrogated the effects of COVID-19 on that educational system. It does emerge quite prominently that Catholic education is key, and it is important in the lives of many citizens. The researcher establishes that the distinctive features of Catholic education have been part of the Bulawayo campus community. It is also shown that the implications of COVID-19 have been at two extremes. The researcher has managed to establish problems that were created by the pandemic such as the disruption in liturgy, learning and living daily life in an environment inspired by Jesus Christ. Furthermore, the history of the Catholic presence in Zimbabwe has played an important role in developing citizens, from the colonial era to the present day. The practices rooted in the principles of the church have been lived and practiced by many. Post-COVID-19 life slowly returned, and the researcher has managed to unravel factors that previously affected the lives of students and members of staff. Respondents highlighted changes and improvements in their well-being following the easing of lockdown (since March 2023) in relation to the values of Catholic education. Life in the “new normal” has brought hope as worship through Mass and other platforms of human contact have led to the cross-fertilisation of ideas amongst members of the university. The Catholic education features that were affected by the pandemic have been restored. In that regard, it can be argued that the restoration of traditional ways of doing things—the post-COVID-19 era—has brought hope to the CUZ community in Bulawayo and that it will produce men and women who will be morally upright citizens. Such citizens will use their knowledge to better the world following the principles acquired from a Catholic Education. Recipients of Catholic education have expressed optimism in the importance of being part of the Catholic community in its multi-faceted dimensions.
Participation “In the Heavenlies” in Christ: Deification in Ephesians
Authors: Kim Kangil
Published: June 2023

Paul’s expression “in the heavenlies” provides an intriguing showcase of the power dynamics of the divine–human relationship (e.g., 1:3, 20; 2:6; 3:10; 6:12). While scholars have identified the theme of union with Christ as an interpretive key for understanding believers’ position in the heavenlies, few have provided adequate attention to “in the heavenlies” according to the significance of theosis. I argue that a patristic idea of theosis offers an interpretive lens in understanding believers’ lives in the heavenlies. Thus, this study aims to situate the discussion on the heavenlies vis-à-vis the conversation around theosis in the New Testament.
Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques
Authors: Behkamal Bahareh , Entezami Alireza , De Michele Carlo , Arslan Ali Nadir
Published: June 2023

Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.
Characterizing the Development of Photovoltaic Power Stations and Their Impacts on Vegetation Conditions from Landsat Time Series during 1990–2022
Authors: Ma Su , Liu Junhui , Zhang Ping , Tu Xingyue , Zhou Jianan , Liu Yang , Zheng Yingjuan
Published: June 2023

To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. Remote sensing technology has been used to map the spatial distribution and development status of PV power stations quickly and accurately in ecologically fragile areas, as well as assess the ecological and environmental impact of their construction. However, current remote sensing monitoring of PV power stations focuses mainly on mapping and time series analysis to measure their development process and assess the environmental conditions on a large scale over a long period of time. Therefore, we constructed a random forest model based on image spectral and texture features and mapped 2022 PV power stations in the junction area of Hobq Desert, Ulan Buh Desert, Tengger Desert, and Mu Us Sands in China. Following that, we identified the construction time of the PV power stations by identifying the turning points of the normalized construction land index (NDBI) time series from 1990–2022 using the LandTrendr method. To assess the ecological impact of PV power stations, we used the NDVI to measure the change in vegetation condition before and after the construction of PV power stations and constructed NDVI changes for PV power stations constructed in different years. The results showed that this mapping method achieved an overall classification accuracy of 96.65% and a Kappa coefficient of 0.92. The root mean square error (RMSE) for construction year identification was less than 0.5, and the number of new PV power stations increased significantly after 2010, reaching a total area of 14.52 km2 by 2016, which is consistent with the trend driven by national and regional development plans. Furthermore, the study found that the vegetation cover level could be restored to the average level before construction within 5–6 years and continued to increase after that. These findings may help government policymakers and practitioners make decisions on PV power station planning and ecological environment protection, thus contributing promptly to the achievement of China’s dual carbon goals.
Identifying Urban and Socio-Environmental Patterns of Brazilian Amazonian Cities by Remote Sensing and Machine Learning
Authors: dos Santos Bruno Dias , de Pinho Carolina Moutinho Duque , Páez Antonio , Amaral Silvana
Published: June 2023

Identifying urban patterns in the cities in the Brazilian Amazon can help to understand the impact of human actions on the environment, to protect local cultures, and secure the cultural heritage of the region. The objective of this study is to produce a classification of intra-urban patterns in Amazonian cities. Concretely, we produce a set of Urban and Socio-Environmental Patterns (USEPs) in the cities of Santarém and Cametá in Pará, Brazilian Amazon. The contributions of this study are as follows: (1) we use a reproducible research framework based on remote sensing data and machine learning techniques; (2) we integrate spatial data from various sources into a cellular grid, separating the variables into environmental, urban morphological, and socioeconomic dimensions; (3) we generate variables specific to the Amazonian context; and (4) we validate these variables by means of a field visit to Cametá and comparison with patterns described in other works. Machine learning-based clustering is useful to identify seven urban patterns in Santarém and eight urban patterns in Cametá. The urban patterns are semantically explainable and are consistent with the existing scientific literature. The paper provides reproducible and open research that uses only open software and publicly available data sources, making the data product and code available for modification and further contributions to spatial data science analysis.
C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR
Authors: Zhu Mingzhe , Cheng Jie , Lei Tao , Feng Zhenpeng , Zhou Xianda , Liu Yuanjing , Chen Zhihan
Published: June 2023

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.
Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach
Authors: Wang Shuaijie , Nguyen Tuan Khang , Bhatt Tanvi
Published: June 2023

Trip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall. Therefore, this study aimed to develop trip-related fall risk prediction models from one’s regular gait pattern using machine-learning approaches. A total of 298 older adults (≥60 years) who experienced a novel obstacle-induced trip perturbation in the laboratory were included in this study. Their trip outcomes were classified into three classes: no-falls (n = 192), falls with lowering strategy (L-fall, n = 84), and falls with elevating strategy (E-fall, n = 22). A total of 40 gait characteristics, which could potentially affect trip outcomes, were calculated in the regular walking trial before the trip trial. The top 50% of features (n = 20) were selected to train the prediction models using a relief-based feature selection algorithm, and an ensemble classification model was selected and trained with different numbers of features (1–20). A ten-times five-fold stratified method was utilized for cross-validation. Our results suggested that the trained models with different feature numbers showed an overall accuracy between 67% and 89% at the default cutoff and between 70% and 94% at the optimal cutoff. The prediction accuracy roughly increased along with the number of features. Among all the models, the one with 17 features could be considered the best model with the highest AUC of 0.96, and the model with 8 features could be considered the optimal model, which had a comparable AUC of 0.93 and fewer features. This study revealed that gait characteristics in regular walking could accurately predict the trip-related fall risk for healthy older adults, and the developed models could be a helpful assessment tool to identify the individuals at risk of trip-falls.
Participatory Action Design and Engineering of Powered Personal Transfer System for Wheelchair Users: Initial Design and Assessment
Authors: Satpute Shantanu A. , Candiotti Jorge Luis , Duvall Jonathan A. , Kulich Hailee , Cooper Rosemarie , Grindle Garrett G. , Gebrosky Benjamin , Brown Josh , Eckstein Ian , Sivakanthan Sivashankar , Deepak Nikitha , Kanode Joshua , Cooper Rory A.
Published: June 2023

Caregivers that assist with wheelchair transfers are susceptible to back pain and occupational injuries. The study describes a prototype of the powered personal transfer system (PPTS) consisting of a novel powered hospital bed and a customized Medicare Group 2 electric powered wheelchair (EPW) working together to provide a no-lift solution for transfers. The study follows a participatory action design and engineering (PADE) process and describes the design, kinematics, and control system of the PPTS and end-users’ perception to provide qualitative guidance and feedback about the PPTS. Thirty-six participants (wheelchair users (n = 18) and caregivers (n = 18)) included in the focus groups reported an overall positive impression of the system. Caregivers reported that the PPTS would reduce the risk of injuries and make transfers easier. Feedback revealed limitations and unmet needs of mobility device users, including a lack of power seat functions in the Group-2 wheelchair, a need for no-caregiver assistance/capability for independent transfers, and a need for a more ergonomic touchscreen. These limitations may be mitigated with design modifications in future prototypes. The PPTS is a promising robotic transfer system that may aid in the higher independence of powered wheelchair users and provide a safer solution for transfers.
Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
Authors: Kim Tae-Kyoung
Published: June 2023

This study considers an improved likelihood probability in multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs). MIMO systems using one-bit ADCs are known to exhibit from performance degradation because of inaccurate likelihood probabilities. To overcome this degradation, the proposed method leverages the detected symbols to estimate the true likelihood probability by combining the initial likelihood probability. An optimization problem is formulated to minimize the mean-squared error between the true and combined likelihood probabilities, and a solution is derived using the least-squares method. Simulation results show that the proposed method obtains a signal-to-noise gain of approximately 0.3 dB to achieve a frame error rate of 10−1 compared to conventional methods. This improvement in performance is attributed to the enhanced reliability of the likelihood probability.
Crack-Based Sensor with Microstructures for Strain and Pressure Sensing
Authors: Kim Nakung , Yun Daegeun , Hwang Injoo , Yoon Gibaek , Kang Seong Min , Choi Yong Whan
Published: June 2023

Recent extensive research on flexible electronics has led to the development of various flexible sensors. In particular, sensors inspired by the slit organs of a spider, which utilize cracks in a metal film to measure strain, have garnered considerable interest. This method exhibited significantly high sensitivity, repeatability, and durability in measuring strain. In this study, a thin-film crack sensor was developed using a microstructure. The results exhibited its ability to simultaneously measure the tensile force and pressure in a thin film, further expanding its applications. Furthermore, the strain and pressure characteristics of the sensor were measured and analyzed using an FEM simulation. The proposed method is expected to contribute to the future development of wearable sensors and artificial electronic skin research.
Polymer Doping as a Novel Approach to Improve the Performance of Plasmonic Plastic Optical Fibers Sensors
Authors: Pitruzzella Rosalba , Rovida Riccardo , Perri Chiara , Chiodi Alessandro , Arcadio Francesco , Cennamo Nunzio , Pasquardini Laura , Vanzetti Lia , Fedrizzi Michele , Zeni Luigi , D’Agostino Girolamo
Published: June 2023

In this work, Fe2O3 was investigated as a doping agent for poly(methyl methacrylate) (PMMA) in order to enhance the plasmonic effect in sensors based on D-shaped plastic optical fibers (POFs). The doping procedure consists of immerging a premanufactured POF sensor chip in an iron (III) solution, avoiding repolymerization and its related disadvantages. After treatment, a sputtering process was used to deposit a gold nanofilm on the doped PMMA in order to obtain the surface plasmon resonance (SPR). More specifically, the doping procedure increases the refractive index of the POF’s PMMA in contact with the gold nanofilm, improving the SPR phenomena. The doping of the PMMA was characterized by different analyses in order to determine the effectiveness of the doping procedure. Moreover, experimental results obtained by exploiting different water–glycerin solutions have been used to test the different SPR responses. The achieved bulk sensitivities confirmed the improvement of the plasmonic phenomenon with respect to a similar sensor configuration based on a not-doped PMMA SPR-POF chip. Finally, doped and non-doped SPR-POF platforms were functionalized with a molecularly imprinted polymer (MIP), specific for the bovine serum albumin (BSA) detection, to obtain dose-response curves. These experimental results confirmed an increase in binding sensitivity for the doped PMMA sensor. Therefore, a lower limit of detection (LOD), equal to 0.04 μM, has been obtained in the case of the doped PMMA sensor when compared to the one calculated for the not-doped sensor configuration equal to about 0.09 μM.
ECG Electrode Localization: 3D DS Camera System for Use in Diverse Clinical Environments
Authors: Bayer Jennifer , Hintermüller Christoph , Blessberger Hermann , Steinwender Clemens
Published: June 2023

Models of the human body representing digital twins of patients have attracted increasing interest in clinical research for the delivery of personalized diagnoses and treatments to patients. For example, noninvasive cardiac imaging models are used to localize the origin of cardiac arrhythmias and myocardial infarctions. The precise knowledge of a few hundred electrocardiogram (ECG) electrode positions is essential for their diagnostic value. Smaller positional errors are obtained when extracting the sensor positions, along with the anatomical information, for example, from X-ray Computed Tomography (CT) slices. Alternatively, the amount of ionizing radiation the patient is exposed to can be reduced by manually pointing a magnetic digitizer probe one by one to each sensor. An experienced user requires at least 15 min. to perform a precise measurement. Therefore, a 3D depth-sensing camera system was developed that can be operated under adverse lighting conditions and limited space, as encountered in clinical settings. The camera was used to record the positions of 67 electrodes attached to a patient’s chest. These deviate, on average, by 2.0 mm ±1.5 mm from manually placed markers on the individual 3D views. This demonstrates that the system provides reasonable positional precision even when operated within clinical environments.
Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm
Authors: Zhang Weipeng , Zhao Bo , Gao Shengbo , Zheng Yuankun , Zhou Liming , Liu Suchun
Published: June 2023

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.
Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes
Authors: Maciura Łukasz , Cieplak Tomasz , Pliszczuk Damian , Maj Michał , Rymarczyk Tomasz
Published: June 2023

This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups.
HGR-ViT: Hand Gesture Recognition with Vision Transformer
Authors: Tan Chun Keat , Lim Kian Ming , Chang Roy Kwang Yang , Lee Chin Poo , Alqahtani Ali
Published: June 2023

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.
Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems
Authors: Lee Seungmin , Kwon Jisu , Park Daejin
Published: June 2023

As the application fields for digital twins have expanded, various studies have been conducted with the objective of optimizing the costs. Among these studies, research on low-power and low-performance embedded devices has been implemented at a low cost by replicating the performance of existing devices. In this study, we attempt to obtain similar particle count results in a single-sensing device replicated from the particle count results in a multi-sensing device without knowledge of the particle count acquisition algorithm of the multi-sensing device. Through filtering, we suppressed the noise and baseline movements of the raw data of the device. In addition, in the process of determining the multi-threshold for obtaining the particle counts, the existing complex particle count determination algorithm was simplified to make it possible to utilize the look-up table. The proposed simplified particle count calculation algorithm reduced the optimal multi-threshold search time by 87% on average and the root mean square error by 58.5% compared to existing method. In addition, it was confirmed that the distribution of particle count from optimal multi-thresholds has a similar shape to that from multi-sensing devices.
Smartphone-Enabled Colorimetry
Authors: Ciaccheri Leonardo , Adinolfi Barbara , Mencaglia Andrea Azelio , Mignani Anna Grazia
Published: June 2023

A smartphone is used as a colorimeter. The performance characterization for colorimetry is presented using both the built-in camera and a clip-on dispersive grating. Certified colorimetric samples provided by Labsphere® are considered as test samples. Color measurements directly performed utilizing the smartphone camera only are obtained using the RGB Detector app, downloaded from the Google Play Store. More precise measurements are achieved using the commercially available GoSpectro grating and related app. In both cases, to quantify the reliability and sensitivity of smartphone-based color measurements, the CIELab color difference ΔE between the certified and smartphone-measured colors is calculated and is reported in this paper. In addition, as an example of a practical application of interest for the textile industry, several samples of cloth fabrics with a palette of the most common colors are measured, and the comparison with the certified color values is presented.
360° Map Establishment and Real-Time Simultaneous Localization and Mapping Based on Equirectangular Projection for Autonomous Driving Vehicles
Authors: Lin Bo-Hong , Shivanna Vinay M. , Chen Jiun-Shiung , Guo Jiun-In
Published: June 2023

This paper proposes the design of a 360° map establishment and real-time simultaneous localization and mapping (SLAM) algorithm based on equirectangular projection. All equirectangular projection images with an aspect ratio of 2:1 are supported for input image types of the proposed system, allowing an unlimited number and arrangement of cameras. Firstly, the proposed system uses dual back-to-back fisheye cameras to capture 360° images, followed by the adoption of the perspective transformation with any yaw degree given to shrink the feature extraction area in order to reduce the computational time, as well as retain the 360° field of view. Secondly, the oriented fast and rotated brief (ORB) feature points extracted from perspective images with a GPU acceleration are used for tracking, mapping, and camera pose estimation in the system. The 360° binary map supports the functions of saving, loading, and online updating to enhance the flexibility, convenience, and stability of the 360° system. The proposed system is also implemented on an nVidia Jetson TX2 embedded platform with 1% accumulated RMS error of 250 m. The average performance of the proposed system achieves 20 frames per second (FPS) in the case with a single-fisheye camera of resolution 1024 × 768, and the system performs panoramic stitching and blending under 1416 × 708 resolution from a dual-fisheye camera at the same time.
Discomfort in Use and Physical Disturbance of FFP2 Masks in a Group of Italian Doctors, Nurses and Nursing Aides during the COVID-19 Pandemic
Authors: Micheletti Cremasco Margherita , Vigoroso Lucia , Solinas Cristina , Caffaro Federica
Published: June 2023

Face masks represent an effective COVID-19 mitigation strategy; this study investigated the quality in use of FFP2 masks in a group of 156 frontline HCWs recruited through a snowball procedure in northwest Italy. Participants filled out an online questionnaire (January 2022) on FFP2 sources of discomfort, physical disturbance at different anatomical points and suggestions for improvement. Most of the participants (69%) reported a feeling of protection and safety, but they also reported episodes of dyspnea (70%). The majority of glasses wearers (62%) reported fogging and displacement of their glasses. Humidity and heat were the main discomfort sources (39%), followed by elastic bands (32%). Physical disturbances were frequent and heavier on the ears, nose and cheekbones. Nursing aides and nurses perceived significantly more discomfort compared to doctors and nursing aides had the highest rate of physical disturbance. To address these issues, following participants’ suggestions, FFP2 masks should be redesigned to be more adjustable, with different sizes and softer fabrics. The investigation pointed out criticalities in the use of FFP2 masks related to different professional roles within the overall group of HCWs and stressed the need for an FFP2 human-centered design that accounts not only for physical needs but also for workload and task variability.
Synthesis and Characterization of Amorphous SiO2−FexOy Materials Starting from Iron Sulfate for Preliminary Studies of CO2 Adsorption
Authors: Ianăşi Cătălin , Pascu Bogdan , Nemeş Nicoleta , Popa Alexandru
Published: June 2023

The goal of this work is to investigate the effectiveness of amorphous SiO2−FexOy loaded by functionalization with Ce(SO4)2, Li2SO4, and 3-aminopropyltriethoxysilane (APTES) for CO2 adsorption. Silica and iron-based materials are gaining popularity due to their wide range of applications, such as catalysis, photocatalysis, imaging, etc.; however, there are very few studies regarding the adsorption of CO2 with the aforementioned materials. In our study, we proposed to test their ability in this direction by adding cerium sulfate and lithium sulfate. Three base materials were obtained and characterized using XRD, FTIR, RAMAN, TG, SEM, and BET followed by their functionalization with amino groups by using of the APTES precursor. The SEM images indicate an increase in size, forming clusters from 100 nm for base materials to 500 nm for functionalized materials. The results indicate a maximum CO2 adsorption of 1.58 mmol/g material for the SiO2−FexOy−Li−APTES sample.
In Vitro and Reactive Metabolites Investigation of Metabolic Profiling of Tyrosine Kinase Inhibitors Dubermatinib in HLMs by LC–MS/MS
Authors: Al-Shakliah Nasser S. , A. Kadi Adnan , Abuelizz Hatem A. , Al-Salahi Rashad
Published: June 2023

Dubermatinib (DMB, TP-0903), a benzenesulfonamide, is an inhibitor of the tyrosine kinase AXL, which is a member of the TAM family and can prevent GAS6-mediated activation of AXL in cancer cells. Patients with previously treated chronic lymphocytic leukemia are being studied in phase I/II clinical trials to determine its antineoplastic potential (CLL). In the current work, the Xenosite web predictor tool was employed to predict the vulnerable sites of metabolism and the reactivity pathways (cyanide and GSH) of DMB. Subsequently, we present the analysis and identification of in vitro and reactive intermediates of DMB using liquid chromatography ion trap mass spectrometry (LC–ITMS). Human liver microsomes (HLMs) were exposed to dimethylbenzene in a laboratory setting, and the resulting metabolites were collected through protein precipitation. Intense reactivity toward nucleophilic macromolecules was seen in the metabolites of the piperazine and pyrimidine rings in DMB, iminium, and 2,5-quinone-imine, respectively. To assess the toxicities of the possibly reactive metabolites, DMB was incubated with HLMs in the presence of 1.0 mM KCN and 1.0 mM glutathione. The DMB metabolites found by LC–MS/MS were seven in vitro phase I metabolites, three cyano adducts, and two GSH conjugates. Phase I in vitro metabolic reactions included N-demethylation, hydroxylation, and dechlorination. DMB and its metabolites have not been investigated for their metabolism in vitro.
Comprehensive Metabolite Fingerprinting of Australian Black and Green Olives and Their Antioxidant and Pharmacokinetics Properties
Authors: Ali Akhtar , Ahmadi Farhad , Cottrell Jeremy J. , Dunshea Frank R.
Published: June 2023

Polyphenols, especially flavonoids, are well-known for their bioactive antioxidant properties. Therefore, this study aimed to analyze Australian black (ripe) and green olives (unripe) for phenolic and non-phenolic metabolites, antioxidant activities, and pharmacokinetic properties. Liquid chromatography–mass spectrometry coupled with quadrupole–time of flight (LC–ESI–QTOF–MS/MS) was applied to elucidate the composition, identification, and characterization of bioactive metabolites from Australian olives. This study identified 110 metabolites, including phenolic acids, flavonoids, stilbenes, lignans, and other compounds (phenolic terpenes, tyrosols, fatty acids, and terpenoids). Luteolin (flavonoid) and verbascoside (hydroxycinnamic acid) are identified with higher concentrations in black olives. Black (ripe) olives were measured at a higher TPC (10.94 ± 0.42 mg GAE/g) and total antioxidant potential than green olives. The pharmacokinetic properties (absorption, distribution, metabolism, excretion, toxicity) of phenolic compounds for human health were evaluated to predict the potential of the most abundant metabolites in olives. Gastrointestinal absorption and Caco-2 cell permeability of metabolites in olives were also predicted. This study will develop into further research to identify the Australian olives’ therapeutic, nutraceutical, and phytopharmaceutical potential.