bluuebunny/crossref_metadata_2025 huggingface dataset:
The Biosynthesis Pattern and Transcriptome Analysis of Sapindus saponaria Oil
Authors: Zhou Xiao , Jiang Lijuan , Li Peiwang , Chen Jingzhen , Chen Yunzhu , Yang Yan , Zhang Luhong , Ji Yuena , Xiao Zhihong , Sheng Kezhai , Sheng Xiaoqian , Yao Hui , Liu Qiang , Li Changzhu
Published: June 2024

The Sapindus saponaria (soapberry) kernel is rich in oil that has antibacterial, anti-inflammatory, and antioxidant properties, promotes cell proliferation, cell migration, and stimulates skin wound-healing effects. S. saponaria oil has excellent lubricating properties and is a high-quality raw material for biodiesel and premium lubricants, showing great potential in industrial and medical applications. Metabolite and transcriptome analysis revealed patterns of oil accumulation and composition and differentially expressed genes (DEGs) during seed development. Morphological observations of soapberry fruits at different developmental stages were conducted, and the oil content and fatty acid composition of the kernels were determined. Transcriptome sequencing was performed on kernels at 70, 100, and 130 days after flowering (DAF). The oil content of soapberry kernels was lowest at 60 DAF (5%) and peaked at 130 DAF (31%). Following soapberry fruit-ripening, the primary fatty acids in the kernels were C18:1 (oleic acid) and C18:3 (linolenic acid), accounting for an average proportion of 62% and 18%, respectively. The average contents of unsaturated fatty acids and saturated fatty acids in the kernel were 86% and 14%, respectively. Through the dynamic changes in fatty acid composition and DEGs analysis of soapberry kernels, FATA, KCR1, ECR, FAD2 and FAD3 were identified as candidate genes contributing to a high proportion of C18:1 and C18:3, while DGAT3 emerged as a key candidate gene for TAG biosynthesis. The combined analysis of transcriptome and metabolism unveiled the molecular mechanism of oil accumulation, leading to the creation of a metabolic pathway pattern diagram for oil biosynthesis in S. saponaria kernels. The study of soapberry fruit development, kernel oil accumulation, and the molecular mechanism of oil biosynthesis holds great significance in increasing oil yield and improving oil quality.
Effects of Soil Conditioner (Volcanic Ash) on Yield Quality and Rhizosphere Soil Characteristics of Melon
Authors: Xue Dongxu , Wang Yangyang , Sun Hong , Fu Lina , Zhu Lihe , Liu Jiaqi , Zhi Zhenyi , He Jiayi , Wang Wei , Wu Chunyan
Published: June 2024

In this study, the effects of soil conditioners on the growth and development of melons and the rhizosphere soil environment were explored. The optimal amount of added soil conditioner was screened to solve the practical production problems of high-quality and high-yield thin-skinned melon. The melon variety “Da Shetou” was used as the material. Under the conditions of conventional fertilization and cultivation technology management, different soil conditioners were set up for potted melons. The effects of Pastoral soil (CK), 95% Pastoral soil + 5% volcanic ash soil conditioner (KT1), 85% Pastoral soil + 15% volcanic ash soil conditioner (KT2), 75% Pastoral soil + 25% volcanic ash soil conditioner (KT3), 65% Pastoral soil + 35% volcanic ash soil conditioner (KT4), and 55% Pastoral soil + 45% volcanic ash soil conditioner (KT5) on melon yield, quality, and rhizosphere soil characteristics were investigated. The soil microbial community was analyzed using Illumina MiSeq technology. Compared to CK, KT1, KT3, KT4, and KT5, the KT2 treatment could improve the single fruit yield of melon, increasing it by 4.35%, 2.48%, 2.31%, 5.92%, and 2.92%. Meanwhile, the highest contents of soluble protein, soluble solid, and soluble sugar in the KT2 treatment were 1.89 mg·100 g−1, 16.35%, and 46.44 mg·g−1, which were significantly higher than those in the control treatment. The contents of organic matter, total nitrogen, alkali-soluble nitrogen, nitrate nitrogen, ammonium nitrogen, available potassium, and available phosphorus in melon rhizosphere soil were the highest in the KT2 treatment. Through Alpha diversity analysis, it was found that the Chao1 index, Shannon index, and ACE index were significantly higher in the KT1 treatment than in the control, while, among all groups, the Simpson index and coverage were not significantly different. The dominant bacteria in the six treated samples were mainly Actinobacteriota, Proteobacteria, Cyanobacteria, Chloroflexi, Acidobacteria, Bacteroidetes, Myxomycota, Firmicutes, Gemmatimonadota, Verrucomicrobia, and Planctomycetes, which accounted for 96.59~97.63% of the relative abundance of all bacterial groups. Through redundancy analysis (RDA), it was found that the organic matter, electrical conductivity, available phosphorus, and nitrate nitrogen of melon rhizosphere soil were the dominant factors of bacterial community change at the dominant genus level. In summary, 15% ash soil conditioner applied on melon was the selected treatment to provide a theoretical reference for the application of soil conditioner in facility cultivation.
Effect of Support on Steam Reforming of Ethanol for H2 Production with Copper-Based Catalysts
Authors: Nippes Ramiro Picoli , Macruz Paula Derksen , Domingues Gomes Aline , de Souza Marcos , Ferreira Bruna Rodrigues , Rizzo-Domingues Roberta Carolina Pelissari , Pereira Ramos Luiz
Published: June 2024

Catalytic studies hydrogen production via steam reforming of ethanol (SRE) are essential for process optimization. Likewise, selecting the ideal support for the active phase can be critical to achieve high conversion rates during the catalytic steam reforming process. In this work, copper-based catalysts were synthesized using two different supports, NaY zeolite and Nb2O5/Al2O3 mixed oxides. The materials were prepared using wet impregnation and characterized for their physicochemical properties using different analytical techniques. Differences in the catalyst morphologies were readily attributed to the characteristics of the support. The Cu/NaY catalyst exhibited a higher specific surface area (210.40 m2 g−1) compared to the Cu/Nb2O5/Al2O3 catalyst (26.00 m2 g−1), resulting in a homogeneous metal dispersion over the support surface. The obtained results showed that, at 300 °C, both the Cu/Nb2O5/Al2O3 and Cu/NaY catalysts produced approximately 50% hydrogen and 40% acetaldehyde, but with significant differences in conversion (6% and 56%, respectively). At 450 °C, a greater product distribution and a 10% higher conversion were observed when the catalyst was supported on NaY compared to Nb2O5/Al2O3. Hence, the performance of copper-based catalysts was influenced significantly by the textural properties of the support.
Experimental Investigation on Active Heat Transfer Improvement in Double-Pipe Heat Exchangers
Authors: Jalali A. , Amiri Delouei A. , Zaertaraghi M. R. , Amiri Tavasoli S.
Published: June 2024

In this research, the effect of ultrasonic waves (UWs) on the heat transfer rate of a water-to-water double-pipe heat exchanger (DPHX) was investigated. To conduct the experiments, four ultrasonic transducers with similar sound frequencies of 40 kHz and a maximum power of 60 W were utilized. All the transducers were placed on the outer shell of the DPHX. The effects of the hot water flow rate and the temperature level of the hot water inlet, ranging from 40 to 60 °C in the central pipe, both in the absence and presence of UWs, were measured under UWs at different powers from 0 to 240 W. The performed experiments show that UWs increase the heat transfer rate, while the highest heat transfer rate improvement of 104% occurs at an inlet temperature of 60 °C and ultrasonic power level of 240 W. Given the scarcity of information regarding heat transfer behavior in ultrasonic-assisted DPHXs, these findings could illuminate the path for designing such heat exchangers.
Experimental and Numerical Studies of Modified Polyurethane Diffusion Behavior in Vertical Cracks Based on Line Source Grouting
Authors: Fan Bingsen , Li Xiaolong , Xu Shengjie , Zhong Yanhui , Zhang Bei
Published: June 2024

The diffusion behavior of polyurethane slurry in vertical cracks, especially rough cracks, is not clear and needs to be studied to provide an effective reference for grouting design. In this study, the diffusion morphology and characteristics of modified polyurethane slurry in vertical cracks were investigated through modeling tests using the line source grouting method. Based on the viscous time-varying characteristics of the slurry, a numerical model of slurry diffusion was established using the joint FVM-VOF method. The numerical model was found to be accurate and reliable compared to the test results. Finally, building upon the basic theory of three-dimensional structure, a rough surface model with Gaussian distribution, more consistent with reality, was established. A numerical simulation system was then employed to study the diffusion morphology and characteristics of slurry in different rough cracks. The results indicate that the diffusion of modified polyurethane slurry within vertical cracks under line source grouting is roughly divided into three stages. Despite uniform crack opening, rougher roughness only increases the length of the crack, thereby reducing the straight-line distance of slurry diffusion. However, it has no significant effect on the flow and total distance of the slurry. Based on these findings, optimization of the grouting point arrangement is proposed.
Paraquat Removal from Water by Magnetic Nanoparticles Coated with Waste-Sourced Biobased Substances
Authors: Ocampo Santiago , Parolo María Eugenia , Carlos Luciano
Published: June 2024

The use of biobased substances derived from industrial and household waste as renewable raw materials for environmental applications is gaining prominence due to its sustainable and cost-effective approach to waste valorisation. Herein, we report the uptake of paraquat, a widely used pesticide, by magnetite nanoparticles coated with composted urban biowaste-derived substances (MNP-BBS). The magnetic nanoparticles were prepared using a modified co-precipitation method, and were characterized through various physicochemical techniques. They were tested as an adsorbent for paraquat removal under diverse experimental conditions, exploring the influence of pH (3–10), MNP-BBS dosages (200–1000 mg L−1), ionic strength (0–0.01 M), and presence of organic matter. The kinetic study revealed that the adsorption of paraquat onto MNP-BBS follows the pseudo-second-order model, reaching the adsorption equilibrium after 2 h of contact and 90% of paraquat removal in the best condition tested (1000 mg L−1). The equilibrium experimental data showed a high adsorption performance with a good fitting to the Freundlich isotherm model. Also, from Langmuir model a maximum adsorption capacity of 0.085 mmol g−1 was estimated. The results indicated that electrostatic interaction between the negative functional groups of the adsorbent and the paraquat play a major role in the adsorption mechanism, although the contribution of π-π and hydrophobic interactions cannot be completely ruled out. This research underscores the potential of utilizing MNP-BBS as an effective adsorbent for the removal of paraquat, shedding light on its application in sustainable water purification processes.
Global-Local Collaborative Learning Network for Optical Remote Sensing Image Change Detection
Authors: Li Jinghui , Shao Feng , Liu Qiang , Meng Xiangchao
Published: June 2024

Due to the widespread applications of change detection technology in urban change analysis, environmental monitoring, agricultural surveillance, disaster detection, and other domains, the task of change detection has become one of the primary applications of Earth orbit satellite remote sensing data. However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in remote sensing images, such as intricate textures, seasonal variations in imaging time, climatic differences, and significant differences in the sizes of various objects. In this paper, we propose a novel U-shaped architecture for change detection. In the encoding stage, a multi-branch feature extraction module is employed by combining CNN and transformer networks to enhance the network’s perception capability for objects of varying sizes. Furthermore, a multi-branch aggregation module is utilized to aggregate features from different branches, providing the network with global attention while preserving detailed information. For dual-temporal features, we introduce a spatiotemporal discrepancy perception module to model the context of dual-temporal images. Particularly noteworthy is the construction of channel attention and token attention modules based on the transformer attention mechanism to facilitate information interaction between multi-level features, thereby enhancing the network’s contextual awareness. The effectiveness of the proposed network is validated on three public datasets, demonstrating its superior performance over other state-of-the-art methods through qualitative and quantitative experiments.
Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method
Authors: Zhang Zhao , Luo Yuchuan , Han Jichong , Xu Jialu , Tao Fulu
Published: June 2024

Reliable and spatially explicit information on global crop yield has paramount implications for food security and agricultural sustainability. However, most previous yield estimates are either coarse-resolution in both space and time or are based on limited studied areas. Here, we developed a transferable approach to estimate 4 km global wheat yields and provide the related product from 1982 to 2020 (GlobalWheatYield4km). A spectra–phenology integration method was firstly proposed to identify spatial distributions of spring and winter wheat, followed by choosing the optimal yield prediction model at 4 km grid scale, with openly accessible data, including subnational-level census data covering ~11,000 political units. Finally, the optimal models were transferred at both spatial and temporal scales to obtain a consistent yield dataset product. The results showed that GlobalWheatYield4km captured 82% of yield variations with an RMSE of 619.8 kg/ha, indicating good temporal consistency (r and nRMSE ranging from 0.4 to 0.8 and 13.7% to 37.9%) with the observed yields across all subnational regions covering 40 years. In addition, our dataset generally had a higher accuracy (R2 = 0.71) as compared with the Spatial Production Allocation Model (SPAM) (R2 = 0.49). The method proposed for the global yield estimate would be applicable to other crops and other areas during other years, and our GlobalWheatYield4km dataset will play important roles in agro-ecosystem modeling and climate impact and adaptation assessment over larger spatial extents.
Leveraging Deep Learning as a New Approach to Layer Detection and Cloud–Aerosol Classification Using ICESat-2 Atmospheric Data
Authors: Oladipo Bolaji , Gomes Joseph , McGill Matthew , Selmer Patrick
Published: June 2024

NASA’s Ice, Cloud, and land Elevation Satellite (ICESat-2), designed for surface altimetry, plays a pivotal role in providing precise ice sheet elevation measurements. While its primary focus is altimetry, ICESat-2 also offers valuable atmospheric data. Current conventional processing methods for producing atmospheric data products encounter challenges, particularly in conditions with low signal or high background noise. The thresholding technique traditionally used for atmospheric feature detection in lidar data uses a threshold value to accept signals while rejecting noise, which may result in signal loss or false detection in the presence of excessive noise. Traditional approaches for improving feature detection, such as averaging, lead to a trade-off between detection resolution and accuracy. In addition, the discrimination of cloud from aerosol in the identified features is difficult given ICESat-2’s single wavelength and lack of depolarization measurement capability. To address these challenges, we demonstrate atmospheric feature detection and cloud–aerosol discrimination using deep learning-based semantic segmentation by a convolutional neural network (CNN). The key findings from our research are the effectiveness of a deep learning model for feature detection and cloud–aerosol classification in ICESat-2 atmospheric data and the model’s surprising capability to detect complex atmospheric features at a finer resolution than is currently possible with traditional processing techniques. We identify several examples where the traditional feature detection and cloud–aerosol discrimination algorithms struggle, like in scenarios with several layers of vertically stacked clouds, or in the presence of clouds embedded within aerosol, and demonstrate the ability of the CNN model to detect such features, resolving the boundaries between adjacent layers and detecting clouds hidden within aerosol layers at a fine resolution.
Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML
Authors: Sim Seongmun , Im Jungho , Jung Sihun , Han Daehyeon
Published: June 2024

Ocean fog, a meteorological phenomenon characterized by reduced visibility due to tiny water droplets or ice particles, poses significant safety risks for maritime activities and coastal regions. Accurate prediction of ocean fog is crucial but challenging due to its complex formation mechanisms and variability. This study proposes an advanced ocean fog prediction model for the Yellow Sea region, leveraging satellite-based detection and high-performance data-driven methods. We used Himawari-8 satellite data to obtain a lot of spatiotemporal ocean fog references and employed AutoML to integrate numerical weather prediction (NWP) outputs and sea surface temperature (SST)-related variables. The model demonstrated superior performance compared to traditional NWP-based methods, achieving high performance in both quantitative—probability of detection of 81.6%, false alarm ratio of 24.4%, f1 score of 75%, and proportion correct of 79.8%—and qualitative evaluations for 1 to 6 h lead times. Key contributing variables included relative humidity, accumulated shortwave radiation, and atmospheric pressure, indicating the importance of integrating diverse data sources. The study emphasizes the potential of using satellite-derived data to improve ocean fog prediction, while also addressing the challenges of overfitting and the need for more comprehensive reference data.
SSA-LHCD: A Singular Spectrum Analysis-Driven Lightweight Network with 2-D Self-Attention for Hyperspectral Change Detection
Authors: Li Yinhe , Ren Jinchang , Yan Yijun , Sun Genyun , Ma Ping
Published: June 2024

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth’s surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models.
Study on the Degradation Pattern of Impact Crater Populations in Yutu-2′s Rovering Area
Authors: Ma Xinyu , Chen Meixi , Hu Teng , Kang Zhizhong , Xiao Meng
Published: June 2024

A detailed analysis of the panoramic camera data from the 27th to 33rd lunar days was conducted on the high-resolution scenes captured by the Yutu-2 rover stations. This analysis aimed to determine the detailed morphological parameters of the 2015 impact craters within the inspection area. The levels of degradation observed in the impact craters were determined alongside the surface features. Subsequently, the degradation patterns of the impact craters located within the Yutu-2’s roving area and the distribution patterns of the morphological parameters were analysed and investigated. The results of the analysis indicate that 94% of the impact craters exhibited severe degradation, 80% had depth-to-diameter ratios (DDRs) ranging from 0.07 to 0.17, and the remaining craters were moderately degraded. The DDRs of the impact craters exhibited a declining trend with an increase in the dimensions of the impact craters. Additionally, the degree of degradation of impact crater populations demonstrated a decreasing trend. In general, the impact craters along the rover’s route exhibited severe degradation, with the population of degradation degrees gradually decreasing with increasing diameter.
Observation and Projection of Marine Heatwaves in the Caribbean Sea from CMIP6 Models
Authors: Bustos Usta David Francisco , Torres Parra Rafael Ricardo , Rodríguez-López Lien , Castillo Alvarez Maibelin , Bourrel Luc
Published: June 2024

In recent decades, climate change has led to ocean warming, causing more frequent extreme events such as marine heatwaves (MHWs), which have been understudied in the Caribbean Sea. This study addresses this gap using 30 years of daily sea surface temperature (SST) data, complemented by projections for the 21st century from nineteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In the 1983–2012 period, significant trends were observed in the spatially averaged MHWs frequency (1.32 annual events per decade and node) and mean duration (1.47 ± 0.29 days per decade) but not in mean intensity. In addition, MHWs show large monthly variations in these metrics, modulated by interannual and seasonal changes. MHWs seasonality is different in the three used metrics, being more intense and frequent in warm and rainy months (intensity between 1.01 to 1.11 °C, duration 6.79 to 7.13 days) and longer lasting in late boreal winter (intensity between 0.82 to 1.00 °C, duration 7.50 to 8.31 days). The MHWs behavior from two extreme months show that these events can occur in both small and large areas in the Caribbean. Overall, models tend to underestimate the annually averaged MHWs frequency and intensity, while they overestimate duration when compared to observations. MHWs projections are more extreme under SSP585, as they are sensible to the radiational scenario. However, an increase in MHWs intensity and duration (events lasting as much as 154 days by 2100) is expected, driving a decrease in frequency (–37.39 events per decade under SSP585 by 2100). These projections imply that MHWs conditions at the beginning of the century will be nearly permanent in the Caribbean’s future. Nonetheless, caution is advised in interpreting these projections due to differences between models’ simulations and observed data. While advancements in oceanic models within CMIP6 demonstrate progress compared to previous CMIP initiatives, challenges persist in accurately simulating extreme events such as marine heatwaves.
The Effect of Stress on a Personal Identification System Based on Electroencephalographic Signals
Authors: Abdel-Ghaffar Eman A. , Salama May
Published: June 2024

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals’ stability. Stress is a major emotional state that affects individuals’ capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals were used as an input for the deep learning approaches. Different types of mental and emotional stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved that performing enrollment in a relaxed or calm state and identification in a stressed state have a negative effect on the identification system’s performance. The best achieved accuracy for the DEAP dataset was 99.67% in the calm state and 96.67% in the stressed state. For the SAM 40 dataset, the best accuracy was 99.67%, 93.33%, 92.5%, and 91.67% for the relaxed state and stress caused by identifying mirror images, the Stroop color-word test, and solving arithmetic operations, respectively.
Performance Studies of Aluminium-Based Gas Electron Multiplier Detector
Authors: Mindur Bartosz , Fiutowski Tomasz , Wiącek Piotr
Published: June 2024

In this paper, we report on a systematic study of a soft X-ray Gas Electron Multiplier (GEM) detector built with aluminium-clad kapton GEM foils. The primary objective of this research is to comprehend the performance of this type of detector when irradiated with soft energy photons. The results are analysed and discussed with a particular focus on the long-term detector stability, as well as its gas gain and energy resolution uniformity across the detector area. Presented results lead us to the conclusion that the aluminium based GEM detector is a promising device to suppress the X-ray Fluorescence (XRF) background, simultaneously providing very good stability during long-term measurement campaigns.
A Compact Dual-Polarized Vivaldi Antenna with High Gain for Tree Radar Applications
Authors: Cheng Kaixuan , Lee Yee Hui , Qian Jiwei , Lee Daryl , Yusof Mohamed Lokman Mohd , Yucel Abdulkadir C.
Published: June 2024

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna.
A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life
Authors: Ribeiro Gonçalo , Monge João , Postolache Octavian , Pereira José Miguel Dias
Published: June 2024

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users’ stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study.
Inversion Method for Chlorophyll-a Concentration in High-Salinity Water Based on Hyperspectral Remote Sensing Data
Authors: Wang Nan , Wang Zhiguo , Huang Pingping , Zhai Yongguang , Yang Xiangli , Su Jianyu
Published: June 2024

As one of the important lakes in the “One Lake and Two Seas” of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.
Extraction and Validation of Biomechanical Gait Parameters with Contactless FMCW Radar
Authors: Wang Linyu , Ni Zhongfei , Huang Binke
Published: June 2024

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range–velocity–time (RVT) data cube encompassing the subjects’ information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland–Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home.
Adaptive Weighted Data Fusion for Line Structured Light and Photometric Stereo Measurement System
Authors: Shi Jianxin , Li Yuehua , Zhang Ziheng , Li Tiejun , Zhou Jingbo
Published: June 2024

Line structured light (LSL) measurement systems can obtain high accuracy profiles, but the overall clarity relies greatly on the sampling interval of the scanning process. Photometric stereo (PS), on the other hand, is sensitive to tiny features but has poor geometrical accuracy. Cooperative measurement with these two methods is an effective way to ensure precision and clarity results. In this paper, an LSL-PS cooperative measurement system is brought out. The calibration methods used in the LSL and PS measurement system are given. Then, a data fusion algorithm with adaptive weights is proposed, where an error function that contains the 3D point cloud matching error and normal vector error is established. The weights, which are based on the angles of adjacent normal vectors, are also added to the error function. Afterward, the fusion results can be obtained by solving linear equations. From the experimental results, it can be seen that the proposed method has the advantages of both the LSL and PS methods. The 3D reconstruction results have the merits of high accuracy and high clarity.
NIR-Based Electronic Platform for Glucose Monitoring for the Prevention and Control of Diabetes Mellitus
Authors: Oñate William , Ramos-Zurita Edwin , Pallo Juan-Pablo , Manzano Santiago , Ayala Paulina , Garcia Marcelo V.
Published: June 2024

The glucose level in the blood is measured through invasive methods, causing discomfort in the patient, loss of sensitivity in the area where the sample is obtained, and healing problems. This article deals with the design, implementation, and evaluation of a device with an ESP-WROOM-32D microcontroller with the application of near-infrared photospectroscopy technology that uses a diode array that transmits between 830 nm and 940 nm to measure glucose levels in the blood. In addition, the system provides a webpage for the monitoring and control of diabetes mellitus for each patient; the webpage is hosted on a local Linux server with a MySQL database. The tests are conducted on 120 people with an age range of 35 to 85 years; each person undergoes two sample collections with the traditional method and two with the non-invasive method. The developed device complies with the ranges established by the American Diabetes Association: presenting a measurement error margin of close to 3% in relation to traditional blood glucose measurement devices. The purpose of the study is to design and evaluate a device that uses non-invasive technology to measure blood glucose levels. This involves constructing a non-invasive glucometer prototype that is then evaluated in a group of participants with diabetes.
Piezoelectric Active Sensing-Based Pipeline Corrosion Monitoring Using Singular Spectrum Analysis
Authors: Yang Dan , Wang Hu , Wang Tao , Lu Guangtao
Published: June 2024

Pipelines are an important transportation form in industry. However, pipeline corrosion, particularly that occurring internally, poses a significant threat to safe operations. To detect the internal corrosion of a pipeline, a method utilizing piezoelectric sensors alongside singular spectrum analysis is proposed. Two piezoelectric patches are affixed to the exterior surface of the pipeline, serving the roles of an actuator and a sensor, respectively. During the detection, the signals excited by the actuator are transmitted through the pipeline’s wall and are received by PZT2 through different paths, and the corresponding piezoelectric sensor captures the signals. Then, the response signals are denoised by singular spectrum analysis, and the first several wave packets in the response signals are selected to establish a feature for pipeline corrosion detection. At last, the envelope area of the selected packets is calculated as a feature to detect corrosion. To validate the proposed method, corrosion monitoring experiments are performed. The experimental results indicate that the envelope area of the first several wave packets from the response signals, following singular spectrum analysis, can serve as a feature to assess the degree of pipeline corrosion, and the index has a monotonic relationship with the corrosion depth of the pipeline. This method provides an effective way for pipeline corrosion monitoring.
Effects of Flexibility on Digital Platform-Mediated Work in Five Ibero-American Countries
Authors: Gondim Sonia Maria Guedes , Carneiro Laila , Viego Valentina , Rentería-Pérez Erico , Cifuentes-Leiton Diana , Moscon Daniela , Ansoleaga Elisa , Agulló-Tomás Esteban
Published: June 2024

This study aimed to characterize digital platform-based work arrangements in regard to their degrees of flexibility and their effects on professional achievement, balance between pros and cons, and perception of risk. A survey was conducted with platform-based workers in Argentina, Brazil, Chile, Colombia, and Spain (e.g., transport, care and cleaning, content production, and house renting). Two thousand and forty-eight workers contacted through social networks and unions participated. Based on the factorial analysis, we propose a flexibility index covering five dimensions (contractual agreement, working hours, workplace, remuneration, and union ties). After controlling for covariates, the flexibility index was used in a regression to measure its effects on professional achievement, the balance of pros and cons, and risk perception. The digital workers with the highest flexibility scores worked in transportation and the trade of goods and services. Flexibility reduces professional achievement when the worker accesses platform jobs due to a lack of alternatives.
Research Landscape on Hidden Workers in Aging Populations: Bibliometric Review
Authors: Lee Sora , Kang Woojin
Published: June 2024

In this study, we employ ‘hidden workers’ as a key concept to integrate the three vulnerable subgroups of aging workers: underemployed, unemployed, and discouraged workers. (1) Background: The challenges faced by underemployed, unemployed, and discouraged workers in the older population are complex. It would be beneficial to visualize the intellectual landscape of these three distinct groups in aging populations to understand which aspects have been highlighted by various disciplines and where gaps exist. (2) Method: Through a scientometric analysis of more than 50 years of research, this study identified the size, scope, and structure of knowledge on hidden workers in an aging population using 2831 articles collected from the Web of Science database in January 2024. (3) Results: Indeed, the multidisciplinary nature of hidden workers goes beyond welfare and labor economics and involves issues such as health, occupational science, behavior change, policy interventions, and circles around the keyword of unemployment. Keyword co-occurrence and co-citation analysis confirm that the spectrum of research on hidden workers is being carried out distinctly within distinct disciplines across the broader aging research horizon. (4) Conclusions: The relatively scattered and uneven intellectual, conceptual, and social landscape of research on hidden workers in aging populations evidently falls short of providing concerted policy recommendations for the population group. This study provides a conceptual understanding of hidden workers in different research clusters and identifies gaps and opportunities for future research.
Benefits of Judo Practice for Individuals with Neurodevelopmental Disorders: A Systematic Literature Review
Authors: Descamps Gaston , Campos Maria João , Rizzo Terry , Pečnikar Oblak Viktorija , Massart Alain Guy
Published: June 2024

This systematic literature review evaluates the benefits of judo practice designed for individuals with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), Intellectual Developmental Disorders (IDDs), and attention-deficit/hyperactivity disorder (ADHD). This review adheres to the PRISMA 2020 guidelines, focusing on the physical, social, emotional, and cognitive benefits of judo. A comprehensive search across databases, such as PubMed, Google Scholar, ResearchGate, B-On, and Scopus, was conducted, and relevant studies were selected based on explicit inclusion and exclusion criteria. Sixteen intervention studies were included, which contributed to a detailed understanding of the impact of judo. The results indicated significant benefits in terms of physical activity, social interactions, emotional well-being, and cognitive functions among participants. A synthesis of results is presented, showing the overall positive effect of judo practice. This review highlights the potential of judo as supportive therapy for individuals with NDDs, advocating its inclusion in therapeutic and educational settings. Limitations due to study heterogeneity and the need for more randomized controlled trials are also discussed.
Cooling Effects and Human Comfort of Constructed Wetlands in Desert Cities: A Case Study of Avondale, Arizona
Authors: Brazel Anthony , Ruiz-Aviles Victor , Hagen Bjoern , Davis Jonathan M. , Pijawka David
Published: June 2024

Heat continues to be a hazard in the desert southwestern USA. This study presents the results of a preliminary microclimate field survey in two Avondale, Arizona, neighborhoods developed with artificial wastewater-treatment wetlands and one adjacent desert neighborhood. The preliminary field study reported here measured morning, near-noon, and afternoon air temperatures and, together with other observed variables, calculated mean radiant temperatures (critical to human comfort) at 28 locations across three neighborhoods on a sample day in September of 2018. The aim was to determine cooling effects of blue/green environments and identify benefits for residents. Overall results for September indicate 1–3 °C cooling, which is understandable for this time of year at summer’s end. Mean radiant temperature results are substantially different at lake sites versus dry neighborhood sites (by some 5–20 °C), likely due to the presence of fewer lateral radiant fluxes and cooler exposures at lake sites compared with dry neighborhoods. Cooling benefits likely provide year-round outdoor comfort compared to desert-landscaped communities. The authors reinforce the conclusion that recycled water and treatment systems can reduce local heat island conditions and aid in combating extreme heat in the desert southwest. This study also shows that constructed wastewater-treatment wetlands in desert cities support sustainable residential developments.
The “Education, Entrepreneurship and Cultural Heritage” Initiative: Eleusis 2023 European Capital of Culture
Authors: Brinia Vasiliki , Belloyiani Maria , Manolopoulou Georgia , Tziros Georgios , Kasiola Eirini , Georgogala Sofia , Marinopoulou Stavroula
Published: June 2024

The purpose of the present study is to underline the importance of our cultural heritage and how potential future teachers can connect education, culture, and the entrepreneurial field. The reason behind this study is the “Education, Entrepreneurship and Cultural Heritage” Initiative of the Teacher Education Program of Athens University of Economics and Business. The research questions refer to the investigation of (1) the impressions and opinions of the Cultural Heritage Entrepreneurship program participants about their experience in the respective program; (2) the business sectors that they had suggested to develop a Cultural Heritage Business as teacher trainees; and (3) the innovative educational methods which they suggest for a respective initiative. For this reason, we conducted interviews with a sample of 45 Teacher Education Program graduates from Athens University of Economics and Business. We used the qualitative type of research. Key findings of our study are the positive assessment of the experience of the “Education, Entrepreneurship and Cultural Heritage” initiative and the plethora of innovative business sectors that could be used to develop the region of Elefsina, a Greek region with a deep culture and history. Finally, we drew some opinions concerning introducing a respective program in high schools. Through this study, we emphasize the importance of Cultural Heritage Entrepreneurship and bring to the forefront future teachers’ positive beliefs about introducing similar initiatives at schools.
Changes of the Northern Zhejiang Canal: Renaissance and Cultural Tourism Development of Ancient Towns
Authors: Jin Wu , Fukuda Hiroatsu
Published: June 2024

The Grand Canal was an important transportation facility in ancient China, and the ancient towns along the canal were once very prosperous commercial centers. With the development of industrialization and urbanization, the ancient towns along the canal gradually declined. This paper took the canal and ancient towns in northern Zhejiang as the research object to reveal the concurrent relationship between canal changes and the rise and fall of ancient towns. This study summarized the development revelations of ancient canal towns and put forward the current development strategy of cultural tourism in ancient towns. Effective government policies protect the cultural heritage of the ancient town. Reasonable industry layout promotes the development of the cultural tourism industry in the ancient town. Public participation is needed to build a harmonious living community.
Construction of a Symbiotic Traffic Network of Traditional Villages in the Western Hunan Region of China Based on Circuit Theory
Authors: Peng Jiayun , Tang Meng , Li Qin , Yang Lin , Qiao Lin , Xie Wenhai , Zhou Chunshan
Published: June 2024

The concentrated and contiguous development of traditional Chinese villages is imminent, and the realization of their symbiotic and coordinated development has become both a priority and a challenge. Taking 370 traditional villages in Western Hunan as the research object, a GIS spatial analysis method was used to identify and extract the sources of traditional villages in Western Hunan, construct traffic resistance surfaces, identify traffic corridors and traffic nodes based on circuit theory, and construct and optimize the symbiotic traffic network of traditional villages in Western Hunan. The results show that the symbiotic traffic network of traditional villages in Western Hunan is composed of 47 traditional village sources, 77 traffic corridors, and 68 key traffic nodes, which are concentrated in the northwest and southeast of Western Hunan, showing a spider-like structure. The distribution of traditional village sources shows the characteristics of a high distribution in the north and a low distribution in the middle, and the traffic resistance surface shows spatial characteristics of being high in the north and low in the south. Four traffic corridor optimization principles and five key traffic node restoration strategies are proposed. This study provides a new concept underlying, and method for, the planning and construction of the symbiotic transport network of traditional villages in Western Hunan, promoting the centralized protection and utilization of traditional villages and the coordinated symbiotic development of regions and assisting in the implementation of the rural revitalization strategy.