bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical Analysis of the Essential Oils from Three Populations of Lippia dulcis Trevir. Grown at Different Locations in Southern Ecuador
Authors: Castillo Leydy Nathaly , Calva James , Ramírez Jorge , Vidari Giovanni , Armijos Chabaco
Published: January 2024

In this investigation, we have analyzed for the first time the essential oils (EOs) isolated by steam distillation of the leaves and flowers of Lippia dulcis Trevir., grown at three different locations in southern Ecuador: the Catacocha canton (Ca), the Vilcabamba parish (Vi), and the Chuquiribamba parish (Ch). Around 98.5% of the oils’ constituents were identified by Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Flame Ionization Detector (GC-FID) analysis using a DB-5ms capillary column. Sesquiterpene hydrocarbons were predominant in the EOs (79.77, 78.22, and 76.51%, respectively). The most representative constituents of the sample from the Ca canton were β-cedrene (16.75%), δ-selinene (11.04%), and β-cubebene (12.09%), while the sample from the Vi parish was characterized by the abundant presence of β-cedrene (17.9%), δ-selinene (12.52%), and bicyclogermacrene (11.34%). β-Cedrene (18.89%), δ-selinene (11.78%), and δ-cadinene (11.07%) were the main constituents of the essential oil (EO) from the Ch parish. The likely occurrence of low amounts of thermolabile hernandulcin in the volatile oils was indicated by the presence of the fragmentation products 6-methyl-5-hepten-2-one and 3-methyl-2-ciclohexen-1-one. In summary, the study gave us a clue to the variability of Lippia dulcis chemotypes depending on the collection sites.
Transcriptome Profiling of a Soybean Mutant with Salt Tolerance Induced by Gamma-ray Irradiation
Authors: Kang Byeong Hee , Chowdhury Sreeparna , Kang Se-Hee , Shin Seo-Young , Lee Won-Ho , Lee Hyeon-Seok , Ha Bo-Keun
Published: January 2024

Salt stress is a significant abiotic stress that reduces crop yield and quality globally. In this study, we utilized RNA sequencing (RNA-Seq) to identify differentially expressed genes (DEGs) in response to salt stress induced by gamma-ray irradiation in a salt-tolerant soybean mutant. The total RNA library samples were obtained from the salt-sensitive soybean cultivar Kwangan and the salt-tolerant mutant KA-1285. Samples were taken at three time points (0, 24, and 72 h) from two tissues (leaves and roots) under 200 mM NaCl. A total of 967,719,358 clean reads were generated using the Illumina NovaSeq 6000 platform, and 94.48% of these reads were mapped to 56,044 gene models of the soybean reference genome (Glycine_max_Wm82.a2.v1). The DEGs with expression values were compared at each time point within each tissue between the two soybeans. As a result, 296 DEGs were identified in the leaves, while 170 DEGs were identified in the roots. In the case of the leaves, eight DEGs were related to the phenylpropanoid biosynthesis pathway; however, in the roots, Glyma.03G171700 within GmSalt3, a major QTL associated with salt tolerance in soybean plants, was differentially expressed. Overall, these differences may explain the mechanisms through which mutants exhibit enhanced tolerance to salt stress, and they may provide a basic understanding of salt tolerance in soybean plants.
Physiological Mechanisms of Citrus Fruit Cracking: Study on Cell Wall Components, Osmoregulatory Substances, and Antioxidant Enzyme Activities
Authors: Huang Shengjia , Yang Xinxia , Wang Tie , Li Hang , Deng Lijun , Bi Xiaoyi , Hu Juan , Gong Yan , Li Yunjie , Qin Zeyu , Yao Yuan , Sun Guochao , Liao Ling , Zhang Mingfei , He Siya , Jiang Lingping , Wang Zhihui
Published: January 2024

Fruit cracking affects both the yield and economic efficiency of citrus; however, the underlying mechanism remains unclear. Therefore, this study focused on resistant and susceptible cultivars to identify the mechanisms underlying fruit cracking. The results showed that in ‘Mingrijian’, pectin morphological transformation and hemicellulose and lignin degradation in the pericarp were important contributing factors. During the critical fruit-cracking period (115–150 days after flowering), the water-soluble pectin, protopectin, and lignin contents in the pericarp of ‘Daya’ presented inverse changes relative to those in ‘Mingrijian’, thus enhancing the mechanical properties and resistance of pericarp. From 115 to 150 days after flowering, the soluble sugar content in the pulp of ‘Mingrijian’ increased rapidly by 97.35%, aiding in pulp water absorption and expansion. Moreover, the soluble protein content in the pericarp of ‘Mingrijian’ exhibited a declining trend and was lower than that of ‘Daya’, thus affecting the overall metabolism. The superoxide dismutase (SOD) activity in the pericarp of ‘Mingrijian’ gradually decreased from 115 to 180 days after flowering, while the peroxidase (POD) activity remained at a low level, resulting in weaker antioxidant capacity and lower environmental resistance. This study provides valuable insights into the mechanisms of citrus fruit cracking, laying the foundation for preventive and control strategies.
Nitrogen Addition and Heterotroph Exclusion Affected Plant Species Diversity–Biomass Relationship by Affecting Plant Functional Traits
Authors: Xu Xile , Yang Luping , Shen Kai , Cao Huijuan , Lin Yishi , Liu Jinliang , Han Wenjuan
Published: January 2024

(1) Background: Heterotrophs can affect plant biomass and alter species diversity–productivity relationships. However, these studies were conducted in systems with a low nitrogen (N) availability, and it is unclear how heterotroph removal affects the relationship between plant species diversity and productivity in different N habitats. (2) Methods: Three typical understory herbaceous plants were selected to assemble the plant species diversity (three plant species richness levels (1, 2, and 3) and seven plant species compositions), and the control, insecticide, fungicide, and all removal treatments were performed at each plant species diversity level in systems with or without N addition treatments. (3) Results: In systems without N addition, the insecticide treatment increased the plant aboveground biomass, total biomass, and leaf area, while the fungicide treatment reduced the plant belowground biomass, root length, and root tip number; the presence of Bidens pilosa increased the plant aboveground biomass. Similarly, the presence of Bletilla striata increased the plant belowground biomass and root diameter under each heterotroph removal treatment. In systems with N addition, all removal treatments reduced the plant belowground biomass and increased the plant leaf area; the presence of B. pilosa significantly increased the plant aboveground biomass, total biomass, and root length under each heterotroph removal treatment. The presence of B. striata significantly increased the plant belowground biomass and leaf area under insecticide and fungicide treatments. (4) Conclusions: Heterotroph removal alters the plant species diversity–biomass relationship by affecting the plant functional traits in systems with different N availabilities. The impact of biodiversity at different trophic levels on ecosystem functioning should be considered under the background of global change.
Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System
Authors: Pengphorm Panuwat , Thongrom Sukrit , Daengngam Chalongrat , Duangpan Saowapa , Hussain Tajamul , Boonrat Pawita
Published: January 2024

Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand’s unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm−2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.
Development of a Silicone Rubber Mold with an Innovative Waterfall Cooling Channel
Authors: Kuo Chil-Chyuan , Lin Pin-Han , Xu Jing-Yan , Lin Zhe-Xhi , Wang Zi-Huan , Lai Zhi-Jun , Huang Song-Hua
Published: January 2024

A conformal cooling channel (CCC) follows the mold core or cavity profile to carry out uniform cooling in the cooling stage. However, the significant pressure drop along the cooling channels is a distinct disadvantage of the CCC. In this study, an innovative waterfall cooling channel (WCC) was proposed and implemented. The WCC cools the injected products via surface contact, replacing the conventional line contact to cool the injected products. The WCC was optimized using numerical simulation software. Silicone rubber molds with two kinds of cooling channels were designed and implemented. The cooling time of the molded part was evaluated using a low-pressure wax injection molding machine. The experimental results of the cooling time of the molded part were compared with the simulation results from numerical simulation software. The results showed that the optimal mesh element count was about 1,550,000 with a mesh size of 1 mm. The simulation software predicted the filling time of the water cup injection-molded product to be approximately 2.008 s. The cooling efficiency for a silicone rubber mold with a WCC is better than that of the silicone rubber mold with a CCC since the core and cavity cooling efficiency is close to 50%. The pressure drop of the WCC is smaller than that of the CCC, which reduces the pressure drop by about 56%. Taking a water cup with a mouth diameter of 70 mm, a height of 60 mm, and a thickness of 2 mm as an example, the experimental results confirmed that the use of the WCC can save the cooling time of the product by about 265 s compared with the CCC. This shows how a WCC can increase cooling efficiency by approximately 17.47%.
Energy Recovery Efficiency of Integrating Anaerobic Co-Digestion of Pig Slurry and Feedlot Cattle Manure and Hydrothermal Carbonization of Anaerobic Sludge Cake
Authors: Lee Jun-Hyeong , Yoon Young-Man
Published: January 2024

Hydrothermal carbonization (HTC) is a technology designed to improve the efficiency of bioenergy recovery by subjecting biomass to high-temperature and high-pressure conditions. By integrating this technical feature with anaerobic digestion (AD), enhanced energy recovery efficiency is achieved in treating anaerobic digestate (AD-T). The study investigates enhancing bioenergy recovery efficiency through an integrated process, combining AD of livestock manure and HTC. The primary objective is to improve the energy conversion efficiency of biomass characterized by varying solid contents and chemical compositions. Shortening the hydraulic retention time (HRT) in AD of livestock manure resulted in decreased degradation rate efficiency within the AD-T. This led to increased solid material accumulation, which was crucial for the subsequent HTC reaction. The HTC reaction exhibited its maximum bioenergy recovery at 160 °C. The input energy of the livestock manure, obtained by mixing pig slurry and feedlot cattle manure in a 1:1 (w/w) ratio, was 171,167 MJ/day. Under different HRT conditions (40, 30, and 20 days), recoverable energy from AD of livestock manure ranged from 60,336 to 68,517 MJ/ton. Integration of HTC increased net bioenergy recovery to 106,493 to 130,491 MJ/day under corresponding HRT conditions, highlighting the potential of integrating HTC with AD from livestock manure for enhanced bioenergy recovery efficiency.
‘When Faith Is Not Enough’: Encounters between African Indigenous Religious Practices and Prophetic Pentecostal Movements in Zimbabwe
Authors: Manyonganise Molly
Published: January 2024

African Pentecostalism remains the fastest growing form of Christianity on the African continent. Scholarship on Zimbabwean Pentecostalism has noted how the emergence of New Pentecostal Movements (NPMs), specifically Prophetic Pentecostalism (PP), has increased this growth. Apart from other attracting factors, such as the Holy Spirit, claims of faith healing, deliverance and prophecy, among others, African Pentecostalism is known for its emphasis on faith as a major anchor of any Pentecostal Christian. Hebrews 11, with its emphasis on faith, is, therefore, a central scripture in this Christian tradition. However, the emergence of NPMs at the height of the Zimbabwean crisis from the year 2008 to the present, has challenged Zimbabwean Pentecostal Christians from their sole dependency on faith. The crisis called for much more than faith could stand on its own. Hence, NPMs responded to this need by infusing indigenous religious practices with biblical ones as a way of strengthening believers through the crisis. Prophetic Pentecostal Movements (PPMs) in Zimbabwe introduced touchable objects such as anointed towels, handkerchiefs, wrist bands, stickers, oils and even condoms. While this appears to be sophisticated syncretism, a critical analysis of the practices shows how steeped they are in the African indigenous religious worldview. This article, therefore, seeks to examine the religious encounters between indigenous African religious practices and Pentecostal practices as practiced in the NPMs in Zimbabwe. The focus of this paper is to establish the resilience of indigenous religious practices within a Christian tradition that claims to have totally broken from the past. It further argues that the fast growth of PPMs depends on the ‘Christianization’ of indigenous religious practices, which are presented to believers as ‘purely biblical’. This is largely a desktop research project in which secondary sources were used as sources of data.
A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data
Authors: Zhou Fangcheng , Han Xiuzhen , Tang Shihao , Cao Guangzhen , Song Xiaoning , Wang Binqian
Published: January 2024

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).
A Novel Vision- and Radar-Based Line Tracking Assistance System for Drone Transmission Line Inspection
Authors: Wang Wei , Shen Zhening , Zhou Zhengran
Published: January 2024

This paper introduces a position controller for drone transmission line inspection (TLI) utilizing the integral sliding mode control (SMC) method. The controller, leveraging GNSS and visual deviation data, exhibits high accuracy and robust anti-interference capabilities. A deviation correction strategy is proposed to capture high-voltage transmission line information more robustly and accurately. Lateral position deviation is calculated using microwave radar data, attitude angle data, and deviation pixels derived from transmission line recognition via MobileNetV3. This approach enables accurate and stable tracking of transmission lines in diverse and complex environments. The proposed inspection scheme is validated in settings with 10-kilovolt and 110-kilovolt transmission lines using a drone with a diagonal wheelbase of 0.275 m. The experimental process is available in the YouTube link provided. The validation results affirm the effectiveness and feasibility of the proposed scheme. Notably, the absence of a high-precision positioning system in the validation platform highlights the scheme’s versatility, indicating applicability to various outdoor visual-based tracking scenarios using drones.
Quantifying the Spatiotemporal Changes in Evapotranspiration and Its Components Driven by Vegetation Greening and Climate Change in the Northern Foot of Yinshan Mountain
Authors: Wang Zijun , Liu Yangyang , Wang Zhenqian , Zhang Hong , Chen Xu , Wen Zhongming , Lin Ziqi , Han Peidong , Xue Tingyi
Published: January 2024

Evapotranspiration (E), a pivotal phenomenon inherent to hydrological and thermal dynamics, assumes a position of utmost importance within the intricate framework of the water–energy nexus. However, the quantitative study of E on a large scale for the “Grain for Green” projects under the backdrop of climate change is still lacking. Consequently, this study examined the interannual variations and spatial distribution patterns of E, transpiration (Et), and soil evaporation (Eb) in the Northern Foot of Yinshan Mountain (NFYM) between 2000 and 2020 and quantified the contributions of climate change and vegetation greening to the changes in E, Et, and Eb. Results showed that E (2.47 mm/a, p < 0.01), Et (1.30 mm/a, p < 0.01), and Eb (1.06 mm/a, p < 0.01) all exhibited a significant increasing trend during 2000–2020. Notably, vegetation greening emerged as the predominant impetus underpinning the augmentation of both E and Eb, augmenting their rates by 0.49 mm/a and 0.57 mm/a, respectively. In terms of Et, meteorological factors emerged as the primary catalysts, with temperature (Temp) assuming a predominant role by augmenting Et at a rate of 0.35 mm/a. Temp, Precipitation (Pre), and leaf area index (LAI) collectively dominated the proportional distribution of E, accounting for shares of 32.75%, 28.43%, and 25.01%, respectively. Within the spectrum of predominant drivers influencing Et, Temp exerted the most substantial influence, commanding the largest proportion at 33.83%. For Eb, the preeminent determinants were recognized as LAI and Temp, collectively constituting a substantial portion of the study area, accounting for 32.10% and 29.50%, respectively. The LAI exerted a pronounced direct influence on the Et, with no significant effects on E and bare Eb. Wind speed (WS) had a substantial direct impact on both E and Et. Pre exhibited a strong direct influence on E, Et, and Eb. Relative humidity (RH) significantly affected E directly. Temp primarily influenced Eb indirectly through radiation (Rad). Rad exerted a significant direct inhibitory effect on Eb. These findings significantly advanced our mechanistic understanding of how E and its components in the NFYM respond to climate change and vegetation greening, thus providing a robust basis for formulating strategies related to regional ecological conservation and water resources management, as well as supplying theoretical underpinnings for constructing sustainable vegetation restoration strategies involving water resources in the region.
Detection of the Infection Stage of Pine Wilt Disease and Spread Distance Using Monthly UAV-Based Imagery and a Deep Learning Approach
Authors: Tan Cheng , Lin Qinan , Du Huaqiang , Chen Chao , Hu Mengchen , Chen Jinjin , Huang Zihao , Xu Yanxin
Published: January 2024

Pine wood nematode (PWN) is an invasive species which causes pine wilt disease (PWD), posing a significant threat to coniferous forests globally. Despite its destructive nature, strategies for the management of PWD spread lack a comprehensive understanding of the occurrence pattern of PWNs. This study investigates the outbreak timing and spread distances of PWD on a monthly scale. Two regions (A and B) in southeastern China, characterized by varying mixed ratios of coniferous and broadleaf trees, were examined. Infected trees were classified into early, middle, late, and dead stages. Monthly unmanned aerial vehicle (UAV) RGB data covering one year and three deep learning algorithms (i.e., Faster R-CNN, YOLOv5, and YOLOv8) were employed to identify the stress stages and positions of the trees. Further, each month, newly infected trees were recorded to calculate spread distances from the location of surrounding trees. The results indicate that the YOLOv5 model achieved the highest accuracy (mean average precision (mAP) = 0.58, F1 = 0.63), followed by Faster R-CNN (mAP = 0.55, F1 = 0.58) and YOLOv8 (mAP = 0.57, F1 = 0.61). Two PWD outbreak periods occurred between September–October and February of the following year, with early and middle-stage outbreaks in August and September and late and dead-tree outbreaks occurring between October and February of the following year. Over one year, the nearest spread distance for PWD-infected trees averaged 12.54 m (median: 9.24 m) for region A in September and 13.14 m (median: 10.26 m) for region B in October. This study concludes that February through August represents the optimal period for PWD control. Additionally, mixed conifer–broadleaf forests with a higher proportion of broadleaf trees prove beneficial in mitigating PWD outbreaks and reducing the number of infected trees. This work demonstrates the effectiveness of integrating monthly UAV-based imagery and deep learning algorithms for monitoring PWD outbreak times and spread distances, offering technical support for forest pest prevention and management.
Measuring Dam Deformation of Long-Distance Water Transfer Using Multi-Temporal Synthetic Aperture Radar Interferometry: A Case Study in South-to-North Water Diversion Project, China
Authors: Xiao Ruya , Gao Xiaoyuan , Wang Xun , Yuan Shanshui , Wu Zhou , He Xiufeng
Published: January 2024

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam’s substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage.
Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network
Authors: Fard Fariba , Sadeghi Naieni Fard Fereshteh
Published: January 2024

Accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, the efficacy of Random Forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) in predicting the condition rating of the nation’s bridge decks has remained unexplored. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level. To achieve this, the study collected bridge data, which includes National Bridge Inventory (NBI), traffic, and climate regions gathered using Geospatial Information Science (GIS) and remote sensing techniques. Two datasets were collected: bridge data for a single year of 2020 and historical bridge data covering a five-year period from 2016 to 2020. Three models were trained using 319,404 and 1,246,261 bridge decks in the single-year bridge data and the five-year historical bridge data, respectively. Results show that the use of historical bridge data significantly improves the performance of the models compared to the single-year bridge data. Specifically, the Random Forest model achieved an overall accuracy of 83.4% and an average F1 score of 79.7%. In contrast, the XGBoost model achieved an overall accuracy of 79.4% and an average F1 score of 77.5%, while the ANN model obtained an overall accuracy of 79.7% and an average F1 score of 78.4%. Permutation-based variable importance reveals that NBI, traffic, and climate regions significantly contribute to model development. In conclusion, the Random Forest, XGBoost, and ANN models, trained using updated historical bridge data, provide useful tools for accurately predicting the condition rating of bridge decks in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.
Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery
Authors: Xu Jie , Su Minbin , Sun Yuxuan , Pan Wenbin , Cui Hongchuan , Jin Shuo , Zhang Li , Wang Pei
Published: January 2024

The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method’s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm’s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes.
Anti-CD20-Triggered Crohn’s-Like Disease with Severe Perianal Involvement in a Patient with Multiple Sclerosis: Case Report, Review of the Literature, and Potential Therapeutic Approach
Authors: Quesada-Simó Adrià , Giner Francisco , Barea-Moya Lucas , Garrido-Marin Alejandro , Mínguez Alejandro , Nos Pilar , Gil-Perotín Sara
Published: January 2024

This case report describes a 38-year-old female patient with a 3-year history of multiple sclerosis who developed rituximab-induced pancolitis, possibly representing a new onset of inflammatory bowel disease. The patient presented with bloody diarrhea, epigastric pain, fever, and general malaise. Laboratory testing revealed elevated acute inflammation markers, and endoscopy showed deep ulcerations and severe perianal disease. The patient was treated effectively with corticosteroids. Monthly doses of ustekinumab have been administered during follow-up due to perianal disease that has remitted. Rituximab was discontinued and ozanimod was initiated with clinical and analytical stability to date.
Towards Sustainable Management of Beach-Cast Seagrass in Mediterranean Coastal Areas
Authors: Manfra Loredana , Chiesa Stefania , Simeone Simone , Borrello Patrizia , Piermarini Raffaella , Agaoglou Chara , Elbour Monia , Zaaboub Noureddine , Vandarakis Dimitris , Kourliaftis Ioannis , Scarpato Alfonso , Rotini Alice
Published: January 2024

Marine environmental conservation and tourist exploitation are often in conflict, particularly where anthropogenic pressure is greatest, such as along the coasts of the Mediterranean Sea. A case in point is the accumulation of beach-cast seagrass, a typical feature of the Mediterranean Sea that is nowadays perceived as an “obstacle” to tourist activities and thus treated and removed as waste, leading to environmental impacts. In this paper, we analyzed the legislative context at the Mediterranean basin level and the contribution of twenty virtuous research projects related to the topic. In our opinion, the main benefits for the beach–dune ecosystem would be generated by the use of seagrass banquettes directly within the beach system (temporary displacement, creating beach walkways, dune consolidation), while their uses outside the beach system (agricultural and farm solutions—compost and bedding) should be considered as alternative solutions for a circular economy, in case of consistent biomass deposits along the shores. In this perspective, we support the “Ecological Beach” model which integrates most of the science-derived solutions addressed in this study. This model provides good practices that can be usefully spread and shared along Mediterranean coasts: to achieve this result, it is necessary to create a regional or Mediterranean network involving local communities and stakeholders.
Natural and Sociocultural Values of a Tourism Destination in the Function of Sustainable Tourism Development—An Example of a Protected Area
Authors: Stojanović Tijana , Trišić Igor , Brđanin Eldin , Štetić Snežana , Nechita Florin , Candrea Adina Nicoleta
Published: January 2024

The Special Nature Reserve “Kraljevac” (RK) is located in the Autonomous Province of Vojvodina, in southern Banat. This first-category reserve covers an area of 264.3 ha and represents a natural item of exceptional importance. The reserve is characterized by significant geological heritage such as loess sections and dune relief, a lake with famous floating peat islands that form the physiology of this wetland, and very rare representatives of flora and fauna. The RK is characterized by the rich cultural heritage of the population living in South Banat. The different ethnic composition of the population and multiculturalism are the main sociocultural tourist motives of this protected area. The Deliblato Sands, known in Europe for their rare geological features, flora and fauna, are located in the immediate vicinity. This significantly increases the value of the RK. Quantitative methodology was used in the paper, which included interviewing respondents as a research technique. The research was conceived according to the Prism of Sustainability (PoS) model. For this sustainable tourism research, a total of 750 respondents (470 residents and 280 visitors) were surveyed. A written and online questionnaire was used in the survey of respondents. By using a questionnaire, respondents expressed their views on certain statements that were grouped into two categories. The claims refer to the natural and sociocultural factors of tourism development in this protected area. Both categories of tourism destination factors can significantly influence sustainable tourism. In addition, we examined the impact of sociocultural and natural factors on respondents’ satisfaction with sustainable tourism. The research results indicate that there is a significant influence of these factors on the satisfaction of residents and visitors with sustainable tourism. The results of this research can provide important information when planning the development of tourism in this protected area. Also, the results can help in the evolution of tourism development at the local, national and regional levels. The forms of tourism that could be central in the RK are scientific research, sports–recreational, ecotourism, bird-watching, events, cultural, gastronomic, and other tourism forms. These forms of tourism can provide environmental, economic and social benefits for this protected area as a tourism destination.
A Feasibility Assessment of Heat Energy Productivity of Geothermal Wells Converted from Oil/Gas Wells
Authors: Zhang Peng , Guo Boyun
Published: January 2024

The mitigation of greenhouse gas emissions necessitates a shift from fossil fuel to environmentally friendly energy, such as geothermal energy. It is advantageous to retrofit end-of-life oil/gas wells for geothermal energy extraction. Prior to repurposing depleted wells into geothermal wells, it is imperative to conduct the heat-energy potential assessment. In this work, an analytical model was developed for this purpose. A case study was conducted using the model and the data from a well in North-west Louisiana for a feasibility assessment. A sensitivity study was performed with the model to identify major factors affecting well productivity. The result of the case study shows that reverse circulation is 35% more efficient than direct circulation for improving the heat-energy productivity of geothermal wells converted from oil/gas wells. The sensitivity analysis revealed that well productivity increases with higher injection rates and greater horizontal wellbore lengths. Additionally, well productivity rises in correspondence with reservoir temperature as well as the temperature of the injected water. However, well productivity decreases as the thermal conductivity of the tubing insulation increases. Counteracting this trend, well productivity increases with thicker tubing insulation layers. This study furnishes engineers with an easy-to-use tool for predicting the heat-energy deliverability of wells converted from end-of-life oil/gas wells.
Sustainable Energy Safety Management Utilizing an Industry-Relative Assessment of Enterprise Equipment Technical Condition
Authors: Hrinchenko Hanna , Prokopenko Olha , Shmygol Nadiia , Koval Viktor , Filipishyna Liliya , Palii Svitlana , Cioca Lucian-Ionel
Published: January 2024

The study considers approaches to ensuring energy management for the safe operation of facilities and their equipment and ways to improve it. It has been established that to ensure effective safety management of industrial enterprises, one of the critical areas is the technical diagnostics of power equipment during operation. An assessment of the actual technical condition of power equipment of VVER-1000 power units is proposed based on establishing the aging mechanisms and determining the relative evaluation coefficients for the characteristics of individual equipment elements. The results of the calculations allowed us to conclude that the obtained results correspond to the coefficients of relative assessment Ki of the technical characteristics of the power equipment that determine its degradation. Studies indicates that when assessing the state of power equipment, it is necessary to consider the presence and impact of the following operational factors that are not considered in the design calculations: loads, high levels of mechanical stress, fatigue damage, and metal defects, which primarily indicate the presence of degradation changes. To assess the technical condition of the equipment, considering the degree of mechanical wear, 17 technical characteristics were selected to determine the aging mechanisms by signs of degradation. A mathematical model of the dependence of the relative evaluation coefficient K on changes in the operating parameters is presented, and it is noted that the most significant influence on the value of the coefficient is the temperature of the coolant at the inlet (K = 0.56). The developed approach makes it possible to improve the safety management system of power facilities by introducing the proposed model to assess the technical conditions of power equipment by defining the parameters in the overall safety management system.
The Effect of the Flipped Learning Approach on Engineering Students’ Technology Acceptance and Self-Directed Learning Perception
Authors: Etemi Blerta Prevalla , Uzunboylu Huseyin , Latifi Shpetim , Abdigapbarova Ulzharkyn
Published: January 2024

The contemporary landscape of education is witnessing a paradigm shift toward innovative instructional methods, with the flipped learning approach gaining considerable attention. The purpose of this research was to investigate the impact of the flipped learning approach on students’ perception and acceptance throughout an entire semester in the ‘Introduction to Programming with Java’ course. The research utilized a research design with a quantitative approach, ultimately aiming to inform educational practice and advance our knowledge of innovative teaching methods in higher education. This study was conducted at a university with 174 students involved, divided into two groups: 87 students in the experimental group and 87 students in the control group. The data collected through the scales were analyzed by using descriptive and inferential statistical analysis techniques in statistical software. At the end of the measurements, the technology acceptance level and self-directed learning perceptions of engineering students who received education with flipped learning were high. The results suggest that educators should consider students’ readiness for self-directed learning when implementing the flipped learning approach and focus on creating an environment that supports their autonomy and engagement. This research offers valuable guidance for instructors, curriculum designers, and educational policymakers seeking to enhance the effectiveness of flipped learning in higher education courses.
A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China
Authors: Zhang Siqi , Sang Xuefeng , Liu Pan , Li Ziheng , He Sheng , Chang Jiaxuan
Published: January 2024

Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m3. At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m3 per year, and 36 million m3 every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m3/a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.
Financial Aspects of Sustainable Rainwater Management in Small-Scale Urban Housing Communities
Authors: Musz-Pomorska Anna , Widomski Marcin K. , Gołębiowska Justyna
Published: January 2024

Sustainable rainwater management may counteract the effects of climate change and significantly improve the distorted water balance in urbanized catchments. However, despite the hydrologic advantages of rainwater management, acceptance and willingness-to-pay in the local society are required. This paper presents an assessment of the financial aspects, i.e., the cost-efficiency and economic feasibility, of various designs of rainwater management for a small-scale urban housing community in Lublin, Poland. The research was performed for a housing community covering approx. 1.38 ha and five multi-family residential buildings. The proposed designs covered rainwater harvesting systems, supported with extensive green roofs, with rainwater retained in underground or above-ground reservoirs, used with variable demand for watering the green areas and for underground parking lot flushing. For each designed variant, the investment as well as operation and maintenance costs were estimated. The assessment of the cost-efficiency and profitability of the proposed rainwater management systems was based on three indicators: Dynamic Generation Costs, Payback Period and Benefits–Costs Ratio. The performed calculations showed that only two designs, utilizing above-ground rainwater reservoirs, could be assessed as economically profitable. Thus, local communities may be unable to financially sustain investments in sustainable rainwater management, so it seems that financial support is required.
Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams
Authors: Starzec Mariusz , Kordana-Obuch Sabina
Published: January 2024

The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.