bluuebunny/crossref_metadata_2025 huggingface dataset:
Melatonin-Mediated Enhancement of Photosynthetic Capacity and Photoprotection Improves Salt Tolerance in Wheat
Authors: Yan Di , Wang Jiajie , Lu Zhenzong , Liu Rui , Hong Yue , Su Baocai , Wang Ye , Peng Zhen , Yu Chunxin , Gao Yuerong , Liu Ziyan , Xu Zhaoshi , Duan Liusheng , Li Runzhi
Published: November 2023

The role of melatonin in plant growth and response to environmental stress has been widely demonstrated. However, the physiological and molecular regulation of salt tolerance in wheat seedlings by melatonin remains unclear. In this study, we investigated changes in phenotype, physiology, photosynthetic parameters, and transcript levels in wheat seedlings to reveal the role of melatonin in the regulation of salt tolerance in wheat. The results indicate that the application of exogenous melatonin significantly alleviates growth inhibition, reactive oxygen species accumulation, and membrane oxidative damage induced by salt stress in wheat. Additionally, exogenous melatonin increased antioxidant enzyme activity and regulated photosynthetic gas exchange. Transcriptomic data showed a significant up-regulation of genes encoding light-harvesting chlorophyll protein complex proteins in photosynthesis and genes related to chlorophyll and carotenoid biosynthesis under the influence of melatonin. These results suggest that exogenous melatonin improves salt tolerance in wheat seedlings by enhancing the antioxidant, photoprotective, and photosynthesis activities.
Correction: Liu et al. LeGRXS14 Reduces Salt Stress Tolerance in Arabidopsis thaliana. Plants 2023, 12, 2320
Authors: Liu Lulu , Li Xiaofei , Su Mengke , Shi Jiaping , Zhang Qing , Liu Xunyan
Published: November 2023

The authors wish to correct the following error in the original paper [...]
Population Genomics of Domesticated Cucurbita ficifolia Reveals a Recent Bottleneck and Low Gene Flow with Wild Relatives
Authors: Aguirre-Dugua Xitlali , Barrera-Redondo Josué , Gasca-Pineda Jaime , Vázquez-Lobo Alejandra , López-Camacho Andrea , Sánchez-de la Vega Guillermo , Castellanos-Morales Gabriela , Scheinvar Enrique , Aguirre-Planter Erika , Lira-Saade Rafael , Eguiarte Luis E.
Published: November 2023

Cucurbita ficifolia is a squash grown from Mexico to Bolivia. Its ancestor is unknown, but it has limited compatibility with wild xerophytic Cucurbita from Mexico’s highlands. We assembled the reference genome of C. ficifolia and assessed the genetic diversity and historical demography of the crop in Mexico with 2524 nuclear single nucleotide polymorphisms (SNPs). We also evaluated the gene flow between C. ficifolia and xerophytic taxa with 6292 nuclear and 440 plastome SNPs from 142 individuals sampled in 58 sites across their area of sympatry. Demographic modelling of C. ficifolia supports an eight-fold decrease in effective population size at about 2409 generations ago (95% CI = 464–12,393), whereas plastome SNPs support the expansion of maternal lineages ca. 1906–3635 years ago. Our results suggest a recent spread of C. ficifolia in Mexico, with high genetic diversity (π = 0.225, FST = 0.074) and inbreeding (FIS = 0.233). Coalescent models suggest low rates of gene flow with C. radicans and C. pedatifolia, whereas ABBA-BABA tests did not detect significant gene flow with wild taxa. Despite the ecogeographic proximity of C. ficifolia and its relatives, this crop persists as a highly isolated lineage of puzzling origin.
Acclimation of the Grapevine Vitis vinifera L. cv. Assyrtiko to Water Deficit: Coordination of Structural and Functional Leaf Traits and the Dynamic of Calcium Oxalate Crystals
Authors: Kolyva Foteini , Nikolopoulos Dimosthenis , Bresta Panagiota , Liakopoulos Georgios , Karabourniotis George , Rhizopoulou Sophia
Published: November 2023

Grapevine leaves contain abundant CaOx crystals located either within the mesophyll in the form of raphides, or in the bundle sheaths as druses. CaOx crystals function as internal carbon pools providing CO2 for a baseline level of photosynthesis, named “alarm photosynthesis”, despite closed stomata; thus, preventing the photoinhibition and the oxidative risk due to carbon starvation under adverse conditions. Structural and functional leaf traits of acclimated grapevine plants (Vitis vinifera L. cv. Assyrtiko) were investigated in response to water availability, in order to evaluate the dynamic functionality of CaOx. Leaf water potential, leaf area, leaf mass per area, stomatal properties, gas exchange parameters and performance index (PI) were decreased in leaves of vines acclimated to water deficit in comparison to the leaves of well-irrigated vines, although the chlorophyll fluorescence parameters showed that the operational efficiency of the photosystem II (PSII) photochemistry (Fv/Fm) did not change, indicating that the photosynthetic apparatus was not subjected to water stress. During the afternoon, more than half of the morning’s existing druses disappeared in the drought-acclimated leaves. Also, the raphides’ area of the drought-acclimated leaves was reduced more than that of the well-watered leaves. The substantial decomposition of druses under water deficit conditions compared to that of the raphides may have important implications for the maintenance of their different though overlapping roles. According to the results, it seems likely that, under water deficit conditions, a mechanism of “alarm photosynthesis” provides an additional tolerance trait in the leaves of Vitis vinifera cv. Assyrtiko; hence, leaf structure relates to function.
Performance Evaluation of Chiller Fault Detection and Diagnosis Using Only Field-Installed Sensors
Authors: Wang Zhanwei , Guo Jingjing , Zhou Sai , Xia Penghua
Published: November 2023

Owing to the rapid expansion of data science, data-driven methods have emerged as a dominant trend in chiller fault detection and diagnosis (FDD). Most of these methods prioritize feature selection to achieve optimal diagnostic performance. However, on-site research indicates a common installation of a limited number of sensors, coupled with a necessity to minimize diagnostic costs. This discrepancy between existing research’s feature selection principles and the current on-site sensor installation status presents a significant challenge. To facilitate the practical implementation of data-driven methods in real chiller units, this study addresses a critical question: under the constraint of limited on-site sensor installations, what is the optimal performance achievable by data-driven methods and their improved versions? To answer this, only features derived from commonly installed sensors on field chillers are chosen as indicators for typical chiller faults. The FDD performance of six frequently used data-driven methods, namely, back-propagation neural network, convolutional neural network, support vector machine, support vector data description, Bayesian network, and random forest, along with their improved versions, is comprehensively evaluated and validated using experimental data, considering four evaluation metrics. The conclusions drawn in this paper provide valuable insights for users/manufacturers with limited or no budget, detailing the best achievable diagnostic performance for each typical fault and offering guidance for those aiming to further enhance FDD performance.
Ultrasonic Vibration-assisted Electrochemical Discharge Machining of Quartz Wafer Micro-Hole Arrays
Authors: Yang Chun-Hao , Wang Tai-Ching , Hung Jung-Chou , Tsui Hai-Ping
Published: November 2023

The micro-hole machining of quartz wafers depends on photolithography techniques akin to those used in semiconductor fabrication. These methods present challenges due to high equipment setup costs, large space requirements, and environmental pollution risks. This research applies ultrasonic vibration assistance in electrochemical discharge machining to create an array of micro-holes on quartz wafers. In the experiments, a self-prepared tungsten carbide micro-electrode array served as the tool electrode. This electrode was a 2 × 2 square array, with needles measuring 30 × 30 μm. A series of experiments was conducted to investigate the effects of various machining parameters, including working voltage, feed rate, duration time, duty factor, and ultrasonic power level, on the characteristics of the micro-hole array. The characteristics included average hole diameter and through-hole surface morphology. The experimental objective was to achieve a through-hole diameter of 80 μm with an accuracy of ±8 μm. During the electrochemical discharge machining, suitable ultrasonic vibrations can thin the insulating gas film coating on the electrode surface, resulting in a more uniform gas film. As the insulating gas film’s thickness decreased, so did the critical voltage needed for the electrochemical discharge machining, reducing the hole’s diameter expansion. The ultrasonic vibration assistance can enable the satisfaction of the dimensional accuracy requirement. The experimental results indicate that ultrasonic vibration assistance can effectively improve the processing capacity and reduce sample fragmentation. A working voltage of 44 V, feed rate of 1 μm/6 s, duration time of 30 μs, duty factor of 30%, and ultrasonic power level of 1 resulted in better inlet and outlet surface morphology without outlet fragmentation. Moreover, the average diameters of the inlet and outlet were roughly 80 μm while meeting the through-hole diameter of 80 μm with accuracy of ±8 μm.
Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review
Authors: Borkar Kailash Kumar , Aljrees Turki , Pandey Saroj Kumar , Kumar Ankit , Singh Mukesh Kumar , Sinha Anurag , Singh Kamred Udham , Sharma Vandana
Published: November 2023

Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.
PM2.5 Collection Enhancement in a Smart Hybrid Wet Scrubber Tower
Authors: Wansom Akasit , Maneechot Pisit , Jiteurtragool Nattagit , Vitidsant Tharapong
Published: November 2023

The removal efficiency of particulate matter of less than 2.5 microns (PM2.5) using an innovative wet scrubber tower with an IoT system for PM2.5 real-time monitoring was investigated. The PM2.5 used in this experiment was obtained from vehicle exhaust, specifically from running the diesel engine of a pickup truck with a range of PM2.5 with a concentration ranging from 50 µg/m3 to 500 µg/m3. Focused parameters related to PM2.5 were analyzed, such as the liquid-to-air ratio (it uses air because this device purifies PM2.5 for the airflow from the polluted ambient air), turbulence techniques enabled by the installation of a deflector and a baffle at the airflow inlet, water level fluctuation above the nozzle, spray nozzle size, and the type of packing material. The average PM2.5 removal efficiency was determined for each parameter relevant to the experiment. The results showed that increasing the liquid-to-air ratio increased the average PM2.5 removal efficiency, while the smaller droplet spraying water resulted in higher efficiency. The spray section achieved its highest efficiency at 58.63%, with a liquid-to-air ratio of 13.21 L/m3 and droplet size of 270 µm. The turbulence technique showed a higher potential for the removal of PM2.5, with an efficiency level of 71.56% at a water level of 150 mm. Moreover, the operation incorporates water spraying and turbulence induction, promoting higher removal efficiency, from 71.56% to 87.59%, at a water level of 150 mm and a liquid-to-air ratio of 9.03 L/m3. This condition resulted in an output concentration of PM2.5 less than 15 µg/m3, which meets the WHO’s guidelines for PM2.5 intensity. This cleverly designed wet scrubber tower can clean up to 13,320 m3 of air daily or remove up to 2,464 g of PM2.5 per day. No enhancement of PM2.5 removal efficiency was observed when two types of packing materials were used due to the formation of bigger droplets as the packing materials were passed through.
Process Scheduling Analysis and Dynamic Optimization Maintaining the Operation Margin for the Acetylene Hydrogenation Fixed-Bed Reactor
Authors: Xie Fuming , Luo Xionglin
Published: November 2023

The full-cycle operation optimization of the acetylene hydrogenation reactor should strictly adhere to the operation optimization scheme within the operation cycle, regardless of scheduling changes. However, in actual industrial processes, in order to meet temporary process scheduling requirements, the acetylene hydrogenation reactor needs to adjust its operation strategy temporarily within the remaining operation cycle based on the results of dynamic optimization for a certain period. It brings additional challenges and a research gap to the operational optimization problem. To make up for this research gap, this paper focuses on researching a type of full-cycle dynamic optimization problem where the operation optimization scheme is temporarily adjusted during the operation cycle. The methods employed for changing the operation optimization scheme include modifying the operation cycle, maximizing economic benefits, and altering the optimization goal to maximize the operation cycle. A novelty full-cycle scheduling optimization framework based on surplus margin estimate is proposed to build a platform for these methods. The paper analyzes the impact of process scheduling changes on full-cycle optimization using a dynamic optimization model that maintains the operation margin. It establishes a full-cycle scheduling optimization model and obtains the optimal scheduling strategy by a novelty method NSGBD (non-convex sensitivity-based generalized Benders decomposition). In this process, an adaptive CVP (control vector parameterization) based on a decomposition optimization algorithm is proposed, which tackles the challenge of optimizing complex acetylene hydrogenation reactor models on a large time scale. Scheduling optimization can be realized as an annualized benefit of 1.56 × 106 and 1.57 × 106 ¥ separately within two scheduling optimization constraints, and the computational time required is much less than previous operational optimizations.
Exploring the Plastic Collection and Recycling Trends in Sri Lanka
Authors: Jayasinghe Rohantha Rukshan , Abeyrathna Wasudha Prabodhani , Jayasingha Krishani Rukmali , Hendawitharana Manuja Promodya , Bandara Thusitha Saman , Liyanage Champika L. , Williams Karl S.
Published: November 2023

This study examined plastic waste management in Sri Lanka, concentrating on the contributing factors to plastic waste generation and collection. Data were gathered through a questionnaire survey from registered plastic recycling and management centres. The analysis revealed a positive correlation between tourist arrivals and plastic waste generation, indicating increased plastic waste production due to tourism spanning from 2014 to 2022. However, plastic waste collection negatively correlated with precipitation levels, suggesting heavy rainfall hampers collection efforts. This study found HDPE, PVC, LDPE, and PP to be the most collected and recycled plastic types, whereas PS and PET recycling were less prevalent. The results demonstrate the need for enhanced recycling infrastructure and highlight the necessity for government participation in plastic waste management initiatives. This study recommends increasing waste collection and recycling during the tourist season. In addition to enhancing public and private awareness, there should be additional investment in recycling facilities. The findings offer crucial insights into plastic waste management in Sri Lanka, emphasizing the importance of collaborative efforts and comprehensive strategies to tackle plastic pollution.
Ecumenical Formation in Theological Education: A Precondition for Ecumenical Encounter and a Tool for Ecumenical Reception
Authors: Oeldemann Johannes
Published: November 2023

For many decades, Christian churches have engaged in ecumenical dialogues addressing church-dividing issues and trying to overcome them. Many valuable texts have emerged from these dialogues, but they are hardly known in the churches. One reason for this is that “ecumenism” is rarely a topic in theological education. The article asks why this is so and argues for including ecumenical formation in theological studies. It highlights various initiatives that have pursued this goal and provides examples of cross-denominational theological education. Cultivating awareness and sensitivity among the younger generation of theologians towards ecumenical concerns is a precondition for promoting ecumenical encounter and applying the results of ecumenical dialogues to the life and the teaching of the churches.
Turning the Theological Turn on Its Head—The Levinasian Secularization of Heidegger’s Theology
Authors: Rolfsen Theodor Sandal
Published: November 2023

In this article, I attempt to contribute to further the understanding of what has happened with the so-called ‘theological turn in French phenomenology’ through juxtaposing two sets of discourses: the theological in Heidegger and the secular in Levinas. While Levinas was identified by Janicaud as the first mover in the theological turn, much attention has recently been given to the theological themes in Heidegger’s writings. Less attention has been given, however, to the way in which Levinas’ philosophical and Talmudic writings often seek to be a secular critique of Heidegger’s philosophy. Through showing how a Levinasian secularization of Heidegger’s theology can make sense, I hope to shake up the debate surrounding the theological turn by placing it on its head.
Yuan Buddhist Centers as the Hub of Monastic Certification: Travels by Korean Monks to China and Some Underlying Reasons
Authors: Kim Sung-Eun Thomas
Published: November 2023

Notably during the Yuan period of Chinese history, Korean Buddhists had a curious custom of making arduous trips to Buddhist centers in mainland China, by sea or overland. To the extent that monks made this trip despite the possible dangers of this long journey, Yuan Buddhism in the practice of Korean Buddhism was conceived as an important hub of monastic certification and the source of new Buddhist developments. In addition, the Chinese masters were seen as essential figures in the monastic careers of the Korean monks. Although there would have been qualified masters in Korea to lead the practice of kanhua chan and to verify the enlightened states of the Korean monks, traveling to China continued up to the end of the Koryŏ period. This continued because the Korean monks obtained obvious benefits after having traveled to China and received their certification of enlightenment 印可. On their return, these monks were given recognition for their spiritual attainment and assigned to high positions in the saṃgha bureaucracy, in many cases, as either a royal or state preceptor. This custom of visiting China was all the more heightened due to Yuan’s domination over Koryŏ from the late-13th to the mid-14th centuries.
Memory-Enhanced Twin Delayed Deep Deterministic Policy Gradient (ME-TD3)-Based Unmanned Combat Aerial Vehicle Trajectory Planning for Avoiding Radar Detection Threats in Dynamic and Unknown Environments
Authors: Li Jiantao , Zhang Tianxian , Liu Kai
Published: November 2023

Unmanned combat aerial vehicle (UCAV) trajectory planning to avoid radar detection threats is a complicated optimization problem that has been widely studied. The rapid changes in Radar Cross Sections (RCSs), the unknown cruise trajectory of airborne radar, and the uncertain distribution of radars exacerbate the complexity of this problem. In this paper, we propose a novel UCAV trajectory planning method based on deep reinforcement learning (DRL) technology to overcome the adverse impacts caused by the dynamics and randomness of environments. A predictive control model is constructed to describe the dynamic characteristics of the UCAV trajectory planning problem in detail. To improve the UCAV’s predictive ability, we propose a memory-enhanced twin delayed deep deterministic policy gradient (ME-TD3) algorithm that uses an attention mechanism to effectively extract environmental patterns from historical information. The simulation results show that the proposed method can successfully train UCAVs to carry out trajectory planning tasks in dynamic and unknown environments. Furthermore, the ME-TD3 algorithm outperforms other classical DRL algorithms in UCAV trajectory planning, exhibiting superior performance and adaptability.
Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
Authors: Liu Rui , Yang Zhiwei , Chen Qidong , Liao Guisheng , Zhu Qinglin
Published: November 2023

GNSS spoofing interference utilizes falsified navigation signals to launch attacks on GNSS systems, posing a significant threat to applications that rely on GNSS signals for positioning, navigation, and time services. Therefore, achieving effective localization of the sources causing spoofing interference is crucial in ensuring the secure operation of GNSS. This article proposes a method for locating GNSS spoofing interference sources using a moving array antenna. Firstly, the proposed method utilizes the inherent characteristics of the double-differenced carrier phase from the deception signals received by the array antenna to effectively extract the spoofing signals. Subsequently, by moving the antenna array, the original carrier phase single-difference data of multiple observation points for deception signals are fused to provide a cost function for direct localization of spoofing interference, and a solution method for the cost function is designed. The proposed method addresses the challenge of extracting and localizing GNSS spoofing interference weak signals, effectively avoiding the data correlation of traditional two-step methods for DOA estimation parameters and ensuring the location accuracy of spoofing interference and the robustness of the method. The effectiveness of the proposed method has been validated through simulation experiments, and its adaptability to factors such as errors in carrier phase measurements has been examined. The method exhibits strong applicability and is well-suited for the hardware platform of the GNSS nulling antenna, thereby enabling it to possess simultaneous capabilities in both anti-interference and spoofing interference localization.
A Novel Approach for Predicting Large Wildfires Using Machine Learning towards Environmental Justice via Environmental Remote Sensing and Atmospheric Reanalysis Data across the United States
Authors: Agrawal Nikita , Nelson Peder V. , Low Russanne D.
Published: November 2023

Large wildfires (>125 hectares) in the United States account for over 95% of the burned area each year. Predicting large wildfires is imperative; however, current wildfire predictive models are region-based and computationally intensive. Using a scalable model based on easily available environmental and atmospheric data, this research aims to accurately predict whether large wildfires will develop across the United States. The data used in this study include 2109 wildfires over 20 years, representing 14 million hectares burned. Remote sensing environmental data (Normalized Difference Vegetation Index—NDVI; Enhanced Vegetation Index—EVI; Leaf Area Index—LAI; Fraction of Photosynthetically Active Radiation—FPAR; Land Surface Temperature during the Day—LST Day; and Land Surface Temperature during the Night—LST Night) consisting of 1.3 billion satellite observations was used. Atmospheric reanalysis data (u component of wind, v component of wind, relative humidity, temperature, and geopotential) at four pressure levels (300, 500, 700, and 850 Ha) were also factored in. Six machine learning classification models (Logistic Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine) were created and tested on the resulting dataset to determine their accuracy in predicting large wildfires. Model validation tests and variable importance analysis were performed. The eXtreme Gradient Boosting (XGBoost) classification model performed best in predicting large wildfires, with 90.44% accuracy, a true positive rate of 0.92, and a true negative rate of 0.88. Furthermore, towards environmental justice, an analysis was performed to identify disadvantaged communities that are also vulnerable to wildfires. This model can be used by wildfire safety organizations to predict large wildfires with high accuracy and prioritize resource allocation to employ protective safeguards for impacted socioeconomically disadvantaged communities.
Physically Based Thermal Infrared Snow/Ice Surface Emissivity for Fast Radiative Transfer Models
Authors: Nalli Nicholas R. , Dang Cheng , Jung James A. , Knuteson Robert O. , Borbas E. Eva , Johnson Benjamin T. , Pryor Ken , Zhou Lihang
Published: November 2023

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.
Mapping Main Grain Crops and Change Analysis in the West Liaohe River Basin with Limited Samples Based on Google Earth Engine
Authors: Wang Zhenxing , Liu Dong , Wang Min
Published: November 2023

It is an important issue to explore achieving high accuracy long-term crop classification with limited historical samples. The West Liaohe River Basin (WLRB) serves as a vital agro-pastoral ecotone of Northern China, which experiences significant changes in crop planting structure due to a range of policy. Taking WLRB as a case study, this study constructed multidimensional features for crop classification suitable for Google Earth Engine cloud platform and proposed a method to extract main grain crops using sample augmentation and model migration in case of limited samples. With limited samples in 2017, the method was employed to train and classify crops (maize, soybean, and rice) in other years, and the spatiotemporal changes in the crop planting structure in WLRB from 2014 to 2020 were analyzed. The following conclusions were drawn: (1) Integrating multidimensional features could discriminate subtle differences, and feature optimization could ensure the accuracy and efficiency of classification. (2) By augmenting the original sample size by calculating the similarity of the time series NDVI (normalized difference vegetation index) curves, migrating the random forest model, and reselecting the samples for other years based on the model accuracy scores, it was possible to achieve a high crop classification accuracy with limited samples. (3) The main grain crops in the WLRB were primarily distributed in the northeastern and southern plains with lower elevations. Maize was the most predominant crop type with a wide distribution. The planting area of main grain crops in the WLRB exhibited an increasing trend, and national policies primarily influenced the variations of planting structure in maize and soybean. This study provides a scheme for extracting crop types from limited samples with high accuracy and can be applied for long-term crop monitoring and change analysis to support crop structure adjustment and food security.
Adequacy Analysis Using UAV of Heavy Rainfall Disaster Reduction Facilities According to Urban Development in Republic of Korea
Authors: Song Youngseok , Park Moojong , Joo Jingul
Published: November 2023

Urban development is a project that enhances human life, but its hydrological impact increases runoff by expanding impervious areas. Furthermore, localized and intense rainfall resulting from the effects of climate change is exacerbating damage to urban areas. The Republic of Korea has established detention ponds as reduction facilities for heavy rainfall disasters, and the law stipulates that the impact of disasters that increase due to the development projects will remain unchanged from before. However, ensuring precision in millimeter or centimeter units during the design of reduction facilities is challenging. In this study, our aim is to assess the suitability of using unmanned aerial vehicles (UAVs) for the detention pond, a facility that reduces runoff resulting from urban development. The target area is a detention pond located in Innovation City, Ulsan Metropolitan City, Republic of Korea. We compared and analyzed design drawings and topographical data. The increased runoff due to the development project was 0.59 m3/s, and the effectiveness of disaster reduction was evaluated by the installation of a detention pond. The detention pond’s reduction effect was analyzed to be 1.16 m3/s for the design drawing and 1.57 m3/s for the topographical analysis. The detention pond currently in place in the target area was found to provide additional disaster reduction benefits compared to the original design plan. The findings of this study can be utilized in relevant laws and guidelines as a method to evaluate the suitability of future reduction facilities.
Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis
Authors: Matyukira Charles , Mhangara Paidamwoyo
Published: November 2023

Land use and land cover change constitute a significant driver of land degradation worldwide, and machine-learning algorithms are providing new opportunities for effectively classifying land use and land cover changes over time. The aims of this study are threefold: Firstly, we aim to compare the accuracies of the parametric classifier Naïve Bayes with the non-parametric classifier Extreme Gradient Boosting Random Forest algorithm on the 2020 LULC dataset. Secondly, we quantify land use and land cover changes in the Cradle of Humankind from 1990 to 2020 using the Extreme Gradient Boosting Random Forest algorithm and post-classification change detection. Thirdly, the study uses landscape metrics to examine landscape structural changes occurring in the same area due to fragmentation. The classification results show that while Naïve Bayers and XGB Random Forest produce classification results of high accuracy, the XGB Random Forest Classifier produced superior results compared to the Naïve Bayers Classifier. From 1990 to 2020, bare ground/rock outcrop significantly increased by 39%, and open bush by 32%. Indigenous forests and natural grasslands lost area (26% and 12%, respectively). The results from this study indicate increasing land cover fragmentation and attest to land degradation, as shown by increases in bare ground and a reduction in indigenous forest and natural grassland. The decline in indigenous forests and natural grassland indicates the degradation of native vegetation, considered as prehistoric plant food sources. The high classification results also attest to the efficacy of the XGBRFClassifier executed in GEE. Land degradation evident in the nature reserve has long-term ecological consequences, such as loss of habitat, biodiversity decline, soil erosion, and alteration of local ecosystems, which together diminish the aesthetic value of the heritage site and negatively impact its tourism value. Consequently, it destroys crucial local economies and threatens sustainable tourism.
Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques
Authors: Selmy Salman A. H. , Kucher Dmitry E. , Mozgeris Gintautas , Moursy Ali R. A. , Jimenez-Ballesta Raimundo , Kucher Olga D. , Fadl Mohamed E. , Mustafa Abdel-rahman A.
Published: November 2023

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.
Characterization of Shallow Ground in Railway Embankments Using Surface Waves Measured by Dark Fiber Optics Sensors: A Case Study
Authors: Obando Hernandez Edwin , Hölscher Paul , Doornenbal Pieter , Mas Cees-jan , van ‘t Schip Joost , van Uitert Agnes
Published: November 2023

For the maintenance of railways on soft soils, accurate knowledge of the subsoil conditions is essential. Soft soils at shallow depths have high variability; thus, high spatial resolution is required. Spare telecommunication fiber-optic cables, known as dark fiber, can be used as an array of sensors to measure waves generated by running trains, which offers a unique opportunity to characterize shallow soils at high spatial resolution. We used dark fiber to measure seismic waves generated by running trains and implemented a seismic interferometry technique to retrieve surface waves. We evaluated the reliability of selected parts of the recorded signals split as bow waves (the train approaching the fiber), train waves (the train passing alongside the fiber), and tail waves (the train leaving the fiber) to retrieve broad-band surface waves. The analysis was performed in two distinctive zones. Zone I consists of a thick–soft (2.0–6.0 m thickness) layer, and Zone II consists of a thin–soft (less than 2.0 m thickness) layer, both overlaying a “stiffer” sand layer. At Zone I, train waves yielded the best results in revealing the thick–soft layer. At Zone II, the bow waves yielded clear high-frequency energy, revealing the overall soil structure but without identifying the shallow thin–soft layer.
Research on the Design and Alignment Method of the Optic-Mechanical System of an Ultra-Compact Fully Freeform Space Camera
Authors: Li Yunfeng , Li Zongxuan , Wang Tiancong , Tao Shuping , Zhang Defu , Ren Shuhui , Ma Bin , Zhang Changhao
Published: November 2023

As space resources become increasingly constrained, the major space-faring nations are establishing large space target monitoring systems. There is a demand for both the number and the detection capability of space-based optical monitoring equipment. The detection range (i.e., field of view) and parasitic capability (lightweight and small size) of a single optical payload will largely reduce the scale and cost of the monitoring system. Therefore, in this paper, the optic-mechanical system of an ultra-lightweight and ultra-compact space camera and the optical alignment method are investigated around a fully freeform off-axis triple-reversal large field of view (FOV) optical system. The optic-mechanical system optimisation design is completed by adopting the optic-mechanical integration analysis method, and the weight of the whole camera is less than 10 kg. In addition, to address the mounting problems caused by the special characteristics of the freeform surface optical system, a dual CGH coreference alignment method is innovatively proposed. The feasibility of the method is verified by the mounting and testing test, and the test results show that the system wavefront difference is better than 1/10 λ. The imaging test of the space camera and the magnitude test results meet the design requirements of the optical system. The optic-mechanical system design method and alignment method proposed in this paper are instructive for the design and engineering of large field of view full freeform optical loads.
A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform
Authors: Mao Wanting , Yang Xiaonan , Wang Chaoran , Hu Yaoguang , Gao Tianxin
Published: November 2023

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.
Reliable Denoising Strategy to Enhance the Accuracy of Arrival Time Picking of Noisy Microseismic Recordings
Authors: Zhang Xiaohui , Li Huailiang , Rong Wenzheng
Published: November 2023

We propose a method to enhance the accuracy of arrival time picking of noisy microseismic recordings. A series of intrinsic mode functions (IMFs) of the microseismic signal are initially decomposed by employing the ensemble empirical mode decomposition. Subsequently, the sample entropy values of the obtained IMFs are calculated and applied to set an appropriate threshold for selecting IMFs. These are then reconstructed to distinguish between noise and useful signals. Ultimately, the Akaike information criterion picker is used to determine the arrival time of the denoised signal. Test results using synthetic noisy microseismic recordings demonstrate that the proposed approach can significantly reduce picking errors, with errors within the range of 1–3 sample intervals. The proposed method can also give a more stable picking result when applied to different microseismic recordings with different signal-to-noise ratios. Further application in real microseismic recordings confirms that the developed method can estimate an accurate arrival time of noisy microseismic recordings.
Whispering-Gallery Mode Micro-Ring Resonator Integrated with a Single-Core Fiber Tip for Refractive Index Sensing
Authors: Halendy Monika , Ertman Sławomir
Published: November 2023

A micro-ring resonator structure was fabricated via the two-photon polymerization technique directly on a single-mode fiber tip and tested for refractive index sensing application. The micro-ring structure was used to excite whispering-gallery modes, and observations of the changes in the resonance spectrum introduced by changes in the refractive index of the environment served as the sensing principle. The proposed structure has the advantages of a very simple design, allowing for measurements in reflection mode, relatively easy and fast fabrication and integration with a single tip of a standard single-mode fiber, which allowed for quick and convenient measurements in the optical setup. The performance of the structure was characterized, and the resonant spectrum giving high potential for refractive index sensing was measured. Future perspectives of the research are addressed.
Improved YOLOv7-Based Algorithm for Detecting Foreign Objects on the Roof of a Subway Vehicle
Authors: Wang Weijun , Chen Jinyuan , Huang Zucheng , Yuan Hai , Li Peng , Jiang Xuyao , Wang Xintong , Zhong Cheng , Lin Qunxu
Published: November 2023

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.