bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Short-Term High-CO2 Treatments on the Quality of Highbush and Rabbiteye Blueberries During Cold Storage
Authors: Toledo-Guerrero Jose David , Álvarez Maria Dolores , Herranz Beatriz , Escribano M. Isabel , Merodio Carmen , Romero Irene , Sanchez-Ballesta M. Teresa
Published: December 2024

The global demand for blueberries has increased due to their health benefits, but postharvest losses, particularly firmness loss and decay, present significant challenges. This study evaluated the effects of high CO2 concentrations (15% and 20%) applied for 3 d at 1.0 °C on highbush (cv. ‘Duke’) and rabbiteye (cv. ‘Ochlockonee’) blueberries, with a focus on quality maintenance during cold storage. The quality parameters evaluated included titratable acidity, pH, total soluble solids, weight loss, and decay. The effect of gaseous treatments on firmness was analyzed using mechanical parameters and the expression of genes related to cell wall integrity (XTH23, PL8, PG, PM3, EXP4, and VcGH5). Treatment efficacy varied between species. High CO2 levels reduced decay in both cultivars, but only the highbush cultivar (‘Duke’) showed improvements in firmness. In ‘Duke’, CO2 treatments affected the expression of XTH23, PL8, and GH5, while the role of PG and PME in maintaining firmness was minimal, with no significant differences between treatments. In ‘Ochlockonee’, CO2 effectively reduced weight loss but did not improve firmness. In conclusion, these results highlight the need for tailored postharvest strategies for different blueberry cultivars and suggest that short-term high CO2 treatments may effectively prolong the postharvest life of highbush blueberries.
Development of an Analytical Model for Predicting the Tensile Modulus of Complex Polypropylene Compounds
Authors: Seifert Lukas , Leuchtenberger-Engel Lisa , Hopmann Christian
Published: December 2024

The extensive use of polypropylene (PP) in various industries necessitates the development of efficient and reliable methods for predicting the mechanical properties of PP compounds. This study presents the development of an analytical model (AM) designed to predict the tensile modulus for a dataset of 64 PP compounds with various fillers and additives, including chalk, impact strength modifiers, and peroxide additives. The AM, incorporating both logarithmic and linear components, was benchmarked against an artificial neural network (ANN) to evaluate its performance. The results demonstrate that the AM consistently outperforms the ANN, achieving lower mean absolute error (MAE) and higher coefficient of determination (R2) values. A maximum R2 of 0.98 could be achieved in predicting the tensile modulus. The simplicity and robustness of the AM with its 14 fitting parameters compared to the ~1300 parameters of the ANN make it a useful tool for the plastics industry, providing a practical approach to optimising compound formulations with minimal empirical testing.
Effect of Reservoir Heterogeneity on Polymer–Surfactant Binary Chemical Flooding Efficiency in Conglomerate Reservoirs
Authors: Lv Jianrong , Liao Guangzhi , Liu Weidong , Wang Xiaoguang , Jing Yuqian , Liu Hongxian , Jiang Ruihai
Published: December 2024

Reservoir heterogeneity significantly affects reservoir flooding efficiency and the formation and distribution of residual oil. As an effective method for enhancing recovery, polymer–surfactant (SP) flooding has a complex mechanism of action in inhomogeneous reservoirs. In this study, the effect of reservoir heterogeneity on the SP drive was investigated by designing core parallel flooding experiments combined with NMR and CT scanning techniques, taking conglomerate reservoirs in a Xinjiang oilfield as the research object. The experimental results show that inter-layer heterogeneity significantly affects water flooding efficiency and SP driving in low-permeability cores—the larger the permeability difference is, the more obvious the effect is—while it has almost no effect on high-permeability cores. The limited recovery enhancement in low-permeability cores is mainly due to the small percentage of contributing pores. When the permeability difference undergoes an extreme increase, the polymer molecular weight is biased towards higher values; when the polymer molecular weight is fixed, the recovery enhancement of low-permeability cores may be comparable to that of high-permeability cores when the permeability difference is extremely small. However, the recovery enhancement of the former is smaller than that of the latter when the permeability difference is extremely large. Due to intra-layer heterogeneity, there is a serious fingering phenomenon in the flooding stage, while in the SP flooding stage, recovery enhancement is most significant in the 5–20 μm pore range. This study provides an important geological basis for the rational development of a chemical flooding programme.
Synthesis and Engineering of Hyaluronic Acid-Gelatin Hydrogels with Improved Cellular Attachment and Growth
Authors: Banigo Alma Tamunonengiofori , Konings Irene B. M. , Nauta Laura , Zoetebier Bram , Karperien Marcel
Published: December 2024

Injectable hydrogels are promising materials for cartilage regeneration in tissue engineering due to their tunable crosslinking rates, mechanical properties, and biodegradation profiles. This study investigates the chondrogenic potential of hyaluronic acid (HA) hydrogels crosslinked via tyramine (TA) moieties, with and without gelatin modified with TA (Gel-TA). Incorporating Gel-TA improved cell viability, spreading, and cartilage matrix deposition, particularly in medium and high molecular weight (MMW and HMW) HA-TA/Gel-TA hydrogels. Although the hydrogels’ molecular weight did not significantly alter stiffness, MMW and HMW HA-TA/Gel-TA formulations exhibited enhanced functional properties such as slower degradation and superior cartilage matrix deposition. These attributes, coupled with Gel-TA’s effects, underscore the importance of both molecular weight and biofunctional components in hydrogel design for cartilage regeneration. While low molecular weight (LMW) HA-TA hydrogels offered excellent injectability and supported high cell viability, they degraded rapidly and exhibited reduced cartilage matrix formation. Gel-TA enhanced cell adhesion and spreading by providing integrin-binding sites and promoted collagen type II deposition, crucial for cartilage regeneration. Moreover, the increased stiffness of MMW and HMW HA-TA/Gel-TA hydrogels facilitated extracellular matrix production. These findings show the potential of Gel-TA-modified HA-TA hydrogels for cartilage tissue engineering, with the opportunity for further optimization through the incorporation of bioactive components.
Study on the Influence of Heterogeneity of Low-Permeability Reservoirs on Wormhole Morphology and Acidizing Process Parameters
Authors: Yang Guobin , Wu Xinwei , Hou Jingtao , Zhou Fei , Nie Fajian
Published: December 2024

Carbonate rocks typically exhibit strong heterogeneity, which can have a significant impact on the effectiveness of acidification processes. This article developed a non-homogeneous reservoir acidification process program based on the TSC model and open-source software FMOT, and studied the influence of heterogeneity intensity on wormhole morphology and acidizing process parameters. The results indicate that different heterogeneity intensities can produce different wormhole development patterns and wormhole morphology. In the early stage of acidizing, there is competitive development at low heterogeneity intensities and a transition to dominant wormhole development as the intensity increases. The differences in the wormholes morphology are mainly reflected in the branching wormholes. Low intensity forms fewer and wider branches, while high intensity forms more and narrower branches. As the heterogeneity intensity increases, the curve shows a downward trajectory characterized by a progressively diminishing rate of decline. However, this enhanced heterogeneity does not affect the optimal injection rate. The optimal injection rate increases with the increase in the acid injection temperature. Under high-heterogeneity conditions, the optimal injection rate increases more significantly with the increase in the inject temperature. Additionally, although typically there are increases with the rise in the inject temperature, this trend reverses under high injection rates.
DFT and TST Study of the Calcium Cyanamide Process for Synthesizing Cyanamide and Dicyandiamide
Authors: Huang Jiyong , Hong Qin , Zhang Tiantong , Zhang Jinli , Nian Yao , Han You
Published: December 2024

Exploring the microscopic reaction mechanism of dicyandiamide (DCD) synthesis using calcium cyanamide (CaCN2) is highly desirable because of the low conversion of reactants and selectivity of DCD products. DCD synthesis consists of a two-step sequential hydrolysis of CaCN2, followed by dimerization of cyanamide to DCD in an alkaline environment. Density functional theory (DFT) results revealed that the rate-limiting step (RLS) was the formation of a C-N bond between the cyanamide and cyanamide anion in the dimerization of the DCD reaction. Secondary reactions of cyanamide with water, hydrogen sulfide, and DCD were also analyzed. The effects of solvation on the principal and secondary reactions were systematically explored. A single explicit water molecule can significantly lower the free energy barrier of the RLS. Water molecules facilitate the C-N bonding of the reactants in DCD reactions, resulting in a reduction in the free energy barrier of the RLS. The facilitation of double explicit water for the reaction is weaker than that of single explicit water and even yields negative catalysis. The effect of the [OH(H2O)3]− cluster lowering the reaction barrier with the hydrogen-bonding network is the most remarkable, which can alter the reaction path by the direct and indirect involvement of OH− ions. Furthermore, the reaction rate constants were computed by canonical variational theory with the Eckart tunneling correction (CVT/Eckart) and fitted to the Arrhenius expression. The reaction mechanism and kinetics revealed at the microscopic level provide efficient and clean production of DCD with certain theoretical guidance.
Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method
Authors: Wang Er , Huang Tianbao , Liu Zhi , Bao Lei , Guo Binbing , Yu Zhibo , Feng Zihang , Luo Hongbin , Ou Guanglong
Published: December 2024

Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.
Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions
Authors: Yao Lu , Yang Dongxu , Jiang Zhe , Liu Yi , Chen Lixu , Tian Longfei , Hakkarainen Janne , Cai Zhaonan , Wang Jing , Ren Xiaoyu
Published: December 2024

Monitoring and accurately quantifying greenhouse gas (GHG) emissions from point sources via satellite measurements is crucial for validating emission inventories. Numerous studies have applied varied methods to estimate emission intensities from both natural and anthropogenic point sources, highlighting the potential of satellites for point source quantification. To promote the development of the space-based GHG monitoring system, it is pivotal to assess the satellite’s capacity to quantify emissions from distinct sources before its design and launch. However, no universal method currently exists for quantitatively assessing the ability of satellites to quantify point source emissions. This paper presents a parametric conceptual model and database for efficiently evaluating the quantification capabilities of satellites and optimizing their technical characteristics for particular detection missions. Using the model and database, we evaluated how well various satellites can detect and quantify GHG emissions. Our findings indicate that accurate estimation of point source emissions requires both high spatial resolution and measurement precision. The requirement for satellite spatial resolution and measurement precision to achieve unbiased emission estimation gradually decreases with increasing emission intensity. The model and database developed in this study can serve as a reference for harmonious satellite configuration that balances measurement precision and spatial resolution. Furthermore, to progress the evaluation model of satellites for low-intensity emission point sources, it is imperative to implement a more precise simulation model and estimate method with a refined mask-building approach.
Semantic Segmentation of Aerial Laser Point Clouds Based on Deep-Residual Enhanced Coding of Multi-Feature Information
Authors: Luo Xin , Lin Peng , Li Xiaoxi , Wei Zuqi , Li Hai
Published: December 2024

The semantic segmentation of laser point clouds is critical for many applications of aerial point clouds. However, most of the existing deep learning networks do not make full use of point cloud data information. PointNet++ was chosen as the baseline network, and a deep-residual enhanced encoding method of multi-feature information is proposed in this work. Firstly, a more efficient network structure to enhance geometric information encoding is constructed, called the GEO–PointNet layer. Then, a novel structure for feature aggregation, named SEP–PointNet, is introduced to encode the auxiliary and geometric features of points separately. Additionally, the segmentation network is deepened in the way of residual structures, which can effectively restrain network degradation. Meanwhile, ‘Dropout’ operations are applied to the fully connected layer to cope with the problem that the model is prone to overfitting due to many network parameters. Finally, a novel segmentation network, named SGDD–PointNet++, is built, and its effectiveness was evaluated by using four airborne benchmark datasets. The experimental results performed on the DALES dataset indicate that the overall accuracy and average intersection-over-union (mIoU) value of the modified PointNet++ networks are better than the original baseline and the other two state-of-the-art segmentation methods. The overall accuracy of the improved SGDD–PointNet++ network reached 87.88%. For the category IoU, it also outperforms other networks, and it has a maximum accuracy increment of 11.43%. Meanwhile, in terms of the generalization capabilities of the trained models, the proposed network can provide better discrimination effects for three public aerial datasets than other methods.
Msplit Estimation with Local or Global Robustness Against Outliers—Applications and Limitations in LiDAR Data Processing
Authors: Wyszkowska Patrycja , Duchnowski Robert
Published: December 2024

Light Detection and Ranging (LiDAR) systems become more prevalent in remote sensing for modeling buildings, engineering structures, or their deformations and displacements. Processing data from such systems, usually point clouds, can be performed using different methods, including Msplit estimation. The method in question is relatively novel but it has several variants. From a practical point of view, the variants that are globally or locally robust against outliers seem very promising. The paper addresses robustness and the problem of different types of outliers that might disturb LiDAR point cloud processing by Msplit estimation. The basic variants, the squared and the absolute Msplit estimations, are often sensitive to global outliers and cannot always deal with local outliers. The comparative analyses show that the modifications of the basic Msplit estimation variants complement each other. Hence, one can always find an Msplit estimation variant that is appropriate for processing LiDAR data disturbed by different types or share of outliers. The paper points out such variants and their application range. It also gives clues on using the methods in question in practice.
Fiducial Reference Measurements for Air Quality Monitoring Using Ground-Based MAX-DOAS Instruments (FRM4DOAS)
Authors: Van Roozendael Michel , Hendrick Francois , Friedrich Martina M. , Fayt Caroline , Bais Alkis , Beirle Steffen , Bösch Tim , Navarro Comas Monica , Friess Udo , Karagkiozidis Dimitris , Kreher Karin , Merlaud Alexis , Pinardi Gaia , Piters Ankie , Prados-Roman Cristina , Puentedura Olga , Reischmann Lucas , Richter Andreas , Tirpitz Jan-Lukas , Wagner Thomas , Yela Margarita , Ziegler Steffen
Published: December 2024

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO2 vertical profiles and formaldehyde profiles. The ozone and NO2 products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A.
Initial Cross-Validation of the OI 135.6 nm and LBH Radiances from FY3E/TriIPM and TIMED/GUVI
Authors: Ye Qian , Song Qian , Mao Tian , Zhang Xiaoxin
Published: December 2024

Initial comparisons of the OI 135.6 nm and LBH radiances collected by the FY3E/TriIPM and TIMED/GUVI from 1 January 2023 to 15 May 2024 at 0500 LT and 1700 LT are performed in this study. The analysis reveals that the consistencies of the OI 135.6 nm and LBH radiances are better at 1700 LT than those at 0500 LT in the Northern and Southern Hemispheres, and that the consistencies of the LBH radiances are better than those of OI 135.6 nm in both hemispheres at 0500 LT and 1700 LT. Moreover, compared to the equinoxes, the measurements of FY3E/TriIPM and TIMED/GUVI agree well in summer and winter, especially at 1700 LT. Despite the observed consistencies between these two instruments, differences are also existent, especially for the OI 135.6 nm in equinoxes at 1700 LT and at latitudes between 30°S and 60°S at 0500 LT, which may be due to the altitudinal and responsivity differences between these two instruments. This study illustrates the possibility of the fusion of FUV observations from multiple sources for future modeling.
Radar HRRP Feature Fusion Recognition Method Based on ConvLSTM Network with Multi-Input Gate Recurrent Unit
Authors: Yang Wei , Chen Tianqi , Lei Shiwen , Zhao Zhiqin , Hu Haoquan , Hu Jun
Published: December 2024

Recently, the radar high-resolution range profiles (HRRPs) have gained significant attention in the field of radar automatic target recognition due to their advantages of being easy to acquire, having a small data footprint, and providing rich target structural information. However, existing recognition methods typically focus on single-domain features, utilizing either the raw HRRP sequence or the extracted feature sequence independently. To fully exploit the multi-domain information present in HRRP sequences, this paper proposes a novel target feature fusion recognition approach. By combining a convolutional long short-term memory (ConvLSTM) network with a cascaded gated recurrent unit (GRU) structure, the proposed method leverages multi-domain and temporal information to enhance recognition performance. Furthermore, a multi-input framework based on learnable parameters is designed to improve target representation capabilities. Experimental results of 6 ship targets demonstrate that the fusion recognition method achieves superior accuracy and faster convergence compared to methods relying on single-domain sequences. It is also found that the proposed method consistently outperforms the other previous methods. And the recognition accuracy is up to 93.32% and 82.15% for full polarization under the SNRs of 20 dB and 5 dB, respectively. Therefore, the proposed method consistently outperforms the previous methods overall.
STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration
Authors: Zhu Xu , Wang Jing , Wang Guojun , Jiang Yangming , Sun Yi , Zhao Huihui
Published: December 2024

In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the Spatio-Temporal Decomposition Network (STDNet), to predict the daily SIC in the Arctic. The model is based on the Seasonal and Trend decomposition using Loess (STL) decomposition idea to decompose the model into trend and seasonal components. In addition, we have designed the Global Sparse Attention Module (GSAM) to help the model extract global information. STDNet not only extracts seasonal signals and trend information with periodical correspondence from the data but also obtains the spatio-temporal dependence features in the data. The experimental methodology involves predicting the next 10 days based on the first 10 days of data. The prediction results provided the following metrics for the 10-day forecast of STDNet: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination of 1.988%, 3.541%, 5.843%, and 0.979, respectively. The average Binary Accuracy (BACC) at the beginning of September for the period 2018–2022 reached 93.85%. The proposed STDNet model outperforms and is lighter than existing deep-learning-based SIC prediction models.
Tomato Urban Gardening Supported by an IoT-Based System: A Latin American Experience Report on Technology Adoption
Authors: Ibarra-Cabrera Manuel J. , Estrada Torres Irwin , Aquino Cruz Mario , Rentería Ayquipa Ronald A. , Ochoa Sergio F. , Ochoa Juan Manuel
Published: December 2024

When urban agriculture is addressed at a family scale, known as urban gardening, it is assumed as a non-commercial activity where some family members voluntarily take care of the plantation during their free time. If technology is going to be used to support such a process, then the solutions should consider the particularities of these gardeners (e.g., life dynamics and culture) to make them adoptable. The literature reports several urban agriculture experiences in Western countries and Southeast Asia; however, this activity has been poorly explored in South American countries, particularly at a family scale and considering the culture and the affordability of the solutions. This article presents an experience report of urban gardening in Peru, where a prototype of an IoT system and a mobile application were conceived, implemented, and used to support the gardening of vegetables at a family scale, considering the cultural aspects of the gardeners. This experience obtained positive results in terms of tomato production, mainly showing the system’s capability to self-adapt its behavior to consider the cultivation conditions of these urban gardeners. To the best of our knowledge, this is the first IoT system that can be iteratively adjust its behavior to improve the chances of being adopted by a particular end-user population (i.e., gardeners).
Effect of Alkali Source on Crystal Regulation and Ethanol Gas Sensing Properties of Nano-ZnO
Authors: Liao Yinying , Qiu Lu , Ouyang Yunfei , Feng Dayang , Huang Shiyi , Zhang Zhaoyang , Xie Xinyao , Ke Junwei , Liu Tianhao , Chen Xiangxiang , Bi Hongshan , Zuo Weiran
Published: December 2024

This study investigates the ethanol gas-sensing mechanisms of ZnO nanocrystals with distinct morphologies, synthesized via a hydrothermal method using various alkali sources. Significant differences in the gas-sensing performance and morphology of ZnO samples synthesized with ammonium carbonate (Na2CO3), hexamethylenetetramine (HMTA), ammonia solution (NH3·H2O), and sodium hydroxide (NaOH) were observed. ZnO were confirmed to be impurity-free through XRD analysis, and their morphological features were characterized by SEM. TEM, XPS, and FTIR were employed to further analyze the crystal structure and binding energy of ZnO. To elucidate the underlying mechanisms, density functional theory (DFT) calculations combined with electron depletion layer theory were applied to assess charge transfer processes and identify the most sensitive ZnO crystal planes for ethanol detection. Experimental gas-sensing tests, conducted across 5–1000 ppm ethanol concentrations within a 150–350 °C range, showed that ZnO prepared with Na2CO3, HMTA, and NaOH was responsive at high ethanol concentrations as low as 100 °C, while ZnO synthesized with ammonia required 250 °C to exhibit sensitivity. All ZnO samples demonstrated excellent recovery at low concentrations at 250 °C. By integrating experimental findings with theoretical insights, this study provides a comprehensive understanding of ethanol gas-sensing mechanisms in ZnO, highlighting the role of crystal plane engineering and charge transfer dynamics as critical factors influencing gas response.
Radar Sensor Data Fitting for Accurate Linear Sprint Modelling
Authors: Geneau Daniel , Cormier Patrick , Tsai Ming-Chang , Agar-Newman Dana , Lenetsky Seth , Klimstra Marc
Published: December 2024

Background: Accurate linear sprint modelling is essential for evaluating athletes’ performance, particularly in terms of force, power, and velocity capabilities. Radar sensors have emerged as a critical tool in capturing precise velocity data, which is fundamental for generating reliable force-velocity (FV) profiles. This study focuses on the fitting of radar sensor data to various sprint modelling techniques to enhance the accuracy of these profiles. Forty-seven university-level athletes (M = 23, F = 24; 1.75 ± 0.1 m; 79.55 ± 12.64 kg) participated in two 40 m sprint trials, with radar sensors collecting detailed velocity measurements. This study evaluated five different modelling approaches, including three established methods, a third-degree polynomial, and a sigmoid function, assessing their goodness-of-fit through the root mean square error (RMSE) and coefficient of determination (r2). Additionally, FV metrics (Pmax, F0, V0, FVslope, and DRF) were calculated and compared using ANOVA. Results: Significant differences (p < 0.001) were identified across the models in terms of goodness-of-fit and most FV metrics, with the sigmoid and polynomial functions demonstrating superior fit to the radar-collected velocity data. Conclusions: The results suggest that radar sensors, combined with appropriate modelling techniques, can significantly improve the accuracy of sprint performance analysis, offering valuable insights for both researchers and coaches. Care should be taken when comparing results across studies employing different modelling approaches, as variations in model fitting can impact the derived metrics.
Capacitance-Based Untethered Fatigue Driving Recognition Under Various Light Conditions
Authors: Zeng Cheng , Wang Haipeng
Published: December 2024

This study proposes a capacitance-based fatigue driving recognition method. The proposed method encompasses four principal phases: signal acquisition, pre-processing, blink detection, and fatigue driving recognition. A measurement circuit based on the FDC2214 is designed for the purpose of signal acquisition. The acquired signal is initially subjected to pre-processing, whereby noise waves are filtered out. Subsequently, the blink detection algorithm is employed to recognize the characteristics of human blinks. The characteristics of human blink include eye closing time, eye opening time, and idle time. Lastly, the BP neural network is employed to calculate the fatigue driving scale in the fatigue driving recognition stage. Experiments under various working and light conditions are conducted to verify the effectiveness of the proposed method. The results show that high fatigue driving recognition accuracy (92%) can be obtained by the proposed method under various light conditions.
A Hybrid Photoplethysmography (PPG) Sensor System Design for Heart Rate Monitoring
Authors: Jhuma Farjana Akter , Harada Kentaro , Misran Muhamad Affiq Bin , Mo Hin-Wai , Fujimoto Hiroshi , Hattori Reiji
Published: December 2024

A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor.
Real-Time and Ultrasensitive Prostate-Specific Antigen Sensing Using Love-Mode Surface Acoustic Wave Immunosensor Based on MoS2@Cu2O-Au Nanocomposites
Authors: Yu Yan , Xie Haiyu , Zhou Tao , Zhang Haonan , Lu Chenze , Tao Ran , Tang Zhaozhao , Luo Jingting
Published: December 2024

Prostate-specific antigen (PSA) is a well-established tumour marker for prostatic carcinoma. In this study, we present a novel, real-time, and ultrasensitive Love-mode surface acoustic wave (L-SAW) immunosensor for PSA detection enhanced by MoS2@Cu2O-Au nanocomposite conjugation. The MoS2@Cu2O-Au nanocomposites were analyzed by SEM, XRD, and EDS. The experiments show a significant improvement in sensitivity and detection limit compared with the previous detection methods utilizing nanogold alone to detect PSA biomolecules. The experimental results show a good linear relationship when the range of PSA concentrations between 200 pg/mL and 5 ng/mL was tested. The experimental results also show good specificity against alpha 1 fetoprotein and L-tryptophan disruptors.
Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm
Authors: Cheng Shun , Wang Zhiqian , Liu Shaojin , Han Yan , Sun Pengtao , Li Jianrong
Published: December 2024

Underwater object detection is highly complex and requires a high speed and accuracy. In this paper, an underwater target detection model based on YOLOv8 (SPSM-YOLOv8) is proposed. It solves the problems of high computational complexities, slow detection speeds and low accuracies. Firstly, the SPDConv module is utilized in the backbone network to replace the standard convolutional module for feature extraction. This enhances computational efficiency and reduces redundant computations. Secondly, the PSA (Polarized Self-Attention) mechanism is added to filter and enhance the polarization of features in the channel and spatial dimensions to improve the accuracy of pixel-level prediction. The SCDown (spatial–channel decoupled downsampling) downsampling mechanism is then introduced to reduce the computational cost by decoupling the space and channel operations while retaining the information in the downsampling process. Finally, MPDIoU (Minimum Point Distance-based IoU) is used to replace the CIoU (Complete-IOU) loss function to accelerate the convergence speed of the bounding box and improve the bounding box regression accuracy. The experimental results show that compared with the YOLOv8n baseline model, the SPSM-YOLOv8 (SPDConv-PSA-SCDown-MPDIoU-YOLOv8) detection accuracy reaches 87.3% on the ROUD dataset and 76.4% on the UPRC2020 dataset, and the number of parameters and amount of computation decrease by 4.3% and 4.9%, respectively. The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.
An Integration of Deep Neural Network-Based Extended Kalman Filter (DNN-EKF) Method in Ultra-Wideband (UWB) Localization for Distance Loss Optimization
Authors: Eang Chanthol , Lee Seungjae
Published: December 2024

This paper examines the critical role of indoor positioning for robots, with a particular focus on small and confined spaces such as homes, warehouses, and similar environments. We develop an algorithm by integrating deep neural networks (DNNs) with the extended Kalman filter (EKF) method, which is known as DNN-EKF, to obtain an accurate indoor localization for ensuring precise and reliable robot movements within the use of Ultra-Wideband (UWB) technology. The study introduces a novel methodology that combines advanced technology, including DNN, filtering techniques, specifically the EKF and UWB technology, with the objective of enhancing the accuracy of indoor localization systems. The objective of integrating these technologies is to develop a more robust and dependable solution for robot navigation in challenging indoor environments. The proposed approach combines a DNN with the EKF to significantly improve indoor localization accuracy for mobile robots. The results clearly show that the proposed model outperforms existing methods, including NN-EKF, LPF-EKF, and other traditional approaches. In particular, the DNN-EKF method achieves optimal performance with the least distance loss compared to NN-EKF and LPF-EKF. These results highlight the superior effectiveness of the DNN-EKF method in providing precise localization in indoor environments, especially when utilizing UWB technology. This makes the model highly suitable for real-time robotic applications, particularly in dynamic and noisy environments.
The Role of LEM in Mine Slope Safety: A Pre- and Post-Blast Perspective
Authors: Nata Refky Adi , Ren Gaofeng , Ge Yongxiang , Fadhly Ahmad , Muzer Fadhilah , Ramadhan M. Fajar , Syahmer Verra
Published: December 2024

Slopes are formed as a result of mining operations. These slopes are classified as artificial slopes. Improper planning of slopes can lead to instability and potentially trigger landslides. PT. Allied Indo Coal Jaya employs the open-pit mining method in its coal mining operations. Slopes are naturally formed in open-pit mines. Additionally, PT. Allied Indo Coal Jaya utilizes blasting for rock demolition. Therefore, it is crucial to assess the impact of blasting activities on slope stability. This study investigates the influence of blasting on slope stability in coal mines using the limit equilibrium method (LEM). The study evaluates the effects of factors such as ground vibration, blast distance, and blast hole count on the factor of safety (FoS) of slopes. The limit equilibrium method (Fellenius, Bishop, Janbu, Spencer, and Morgenstern-Price) is employed to determine the factor of safety. The factor of safety is modeled using RocScience SLIDE version 6.0 in this study. The factor of safety (FoS) is defined as the ratio of the stabilizing force to the destabilizing force acting on the slope. This study also models the influence of ground vibration, distance, and total number of blast holes on the factor-of-safety (FoS) value. The results indicate that the slope remains stable both pre- and post-blasting, with an overall FoS value greater than 1 for the five slopes examined using various limit equilibrium method (LEM) techniques. However, the FoS value decreased prior to blasting due to the impact of ground vibration and blast distance. It is evident that the ground vibration (PPA) increases with the number of blast holes. The amount of ground vibration decreases as the number of blast holes increases. An increased number of blast holes leads to a decrease in the FoS value. The observed decline in slope FoS values and the increase in PPAs is attributable to the growing number of blast holes. The type of explosive, along with its power and rate of detonation, influences the amount of energy produced, which in turn affects the degree of ground vibration. The findings indicate that the slopes remain stable (FoS > 1) both before and after blasting, although blasting slightly reduces the FoS. The study reveals that as the number of blast holes increases, both ground vibration (PPA) and the reduction in FoS increase, underscoring the effects of explosive power and detonation rate on slope stability.
Does Supply Chain Transaction Volatility Affect Corporate Sustainability? Evidence from Corporate Tax Burden
Authors: Li Xingchen , Liu Guochao , Qi Wen , Wang Yifan , Sun Yanhan
Published: December 2024

The tax burden significantly influences corporate sustainability, making the study of the impact of supply chain transaction volatility on corporate tax burden crucial for alleviating tax pressures and promoting healthy corporate development. Using a sample of A-share listed companies in China from 2013 to 2019, we find the following: (1) Overall, supplier transaction volatility significantly increases corporate tax burden, while customer transaction volatility does not have an effect, a finding that remains robust under various tests for endogeneity. (2) Mechanism analysis reveals that supplier transaction volatility raises the level of corporate tax burden by increasing operational risk and relationship maintenance costs. (3) Further analysis indicates that the volatility of major supplier transactions exacerbates corporate income tax burdens, while the impact of customer transaction fluctuations on the tax burden is more pronounced for non-state-owned enterprises, and state-owned enterprises experience a suppressive effect on value-added tax from customer transaction volatility. This study clarifies the tax burden dynamics among supply chain firms, expands the literature on the determinants of corporate tax burdens and the economic consequences of transaction volatility, and provides insights for promoting corporate sustainability.
Coupling-Coordination Analysis of Water Resources–Social Economy–Ecological Environment in the Yellow River Golden Triangle Area
Authors: Kou Zhao , Xu Linjuan , Cao Yongtao , Wang Zhanqiao , Wan Qiang , Gao Xiangyu
Published: December 2024

Water resources, the social economy, and the ecological environment are interrelated and interacting complex systems, and the relationship among them affects the sustainable development of a region. To explore the interactive relationship and driving factors between water resources, the social economy, and the ecological environment, the Yellow River Golden Triangle region is taken as the research object in this paper. By constructing a coupling-coordination evaluation index system of water resources, the social economy, and the ecological environment system, the coupling-coordination development of this region from 2011 to 2021 is studied using the coupling-coordination degree model, and the influencing factors of coupling-coordination development are identified by gray relational analysis. The results show that from 2011 to 2021, the comprehensive evaluation index of the water resources, social economy, and ecological environment in the Yellow River Golden Triangle region shows a trend of steady development followed by a gradual increase. The water-resources subsystem restricts the development of the coupling system. The coupling-coordination degree increased from a barely coordinated stage in 2011 to a well-coordinated stage in 2021. The social economy subsystem and water-resources subsystem are the main factors affecting the coordinated development of the coupling system.
Research on Decision-Making and Coordination of Closed-Loop Supply Chain Under Green and Low-Carbon Perspective
Authors: Wang Jianhua , Cai Xinyan
Published: December 2024

This study explores low-carbon tech investment decisions in a closed-loop supply chain and their impact on supply chain optimization and relationships, using non-cooperative and cooperative game theories. This study examines the behavior patterns and potential gains of suppliers and manufacturers in independent vs. cooperative circumstances using model simulations. This study reveals that the following: (1) Optimal pricing, technology investment, profit, and cost-sharing ratios for low-carbon technologies can be determined under certain conditions to maximize stakeholder profits. (2) Suppliers and manufacturers can mutually benefit in supplier-driven markets. (3) Manufacturers have greater investment opportunities in low-carbon technology when suppliers have market power. (4) Provider utility decreases as consumer preference for low-carbon products rises. (5) The dominant market structure for low-carbon products evolves with the market stage. These findings provide strategic guidance for green supply chain management. The research presented in this paper offers a theoretical foundation and strategic direction for low-carbon technology investment in CLSCs.
Spatial Distribution, Risk Assessment and Source Analysis of Heavy Metals in the Sediments of Jinmucuo Lake, Southern Tibetan Plateau
Authors: Li Yurong , Wang Yang , Ye Chun , Xie Zijian , Li Chunhua
Published: December 2024

Sediments serve as a crucial accumulation site for pollutants, and the source and sink effects of sediment pollutants significantly influence the overlying water. In this research, the concentrations of heavy metals in the sediments of Jinmucuo Lake were investigated. The pollution levels and ecological risks of the heavy metals were assessed via the geoaccumulation index and potential ecological risk index, and the potential sources were identified via multivariate statistical methods. Results revealed that the mean concentrations of cadmium, chromium, copper, zinc, nickel, arsenic and mercury exceeded the soil background values in Tibet, and each element generally tended to increase with decreasing depth in the sediment cores. Heavy metals were mainly distributed in the estuaries of the western part of the lake. Moreover, the geoaccumulation index revealed that mercury pollution was severe, followed by nickel and copper. The potential ecological risk index revealed that Jinmucuo Lake was a highly strong ecological risk and that mercury was the most important ecological risk factor. Multivariate statistical analysis indicated that chromium and nickel pollution was influenced by natural factors and anthropogenic activities, while geothermal activity, river inputs and atmospheric transmission were the main sources of mercury and arsenic. Moreover, cadmium, lead, copper and zinc pollution was mainly affected by transportation and high background values. The results provide an important reference for the ecological and environmental protection of Jinmucuo Lake.