bluuebunny/crossref_metadata_2025 huggingface dataset:
Wild Species from the Family Apiaceae, Traditionally Used as Food in Some Mediterranean Countries
Authors: Kozuharova Ekaterina , Malfa Giuseppe Antonio , Acquaviva Rosaria , Valdes Benito , Aleksanyan Alla , Batovska Daniela , Stoycheva Christina , Rejdali Moh , Al-Tawaha Abdel Rahman , Marino Pasquale , Spadaro Vivienne
Published: August 2024

Mediterranean countries are a cauldron of cultural exchange, with a strong emphasis on wild plants in cuisine traditions. Many of these plants belong to the family Apiaceae. The common climate determines the common range of distribution. While many plants have wide distribution, the range of distribution of others is restricted to Western Mediterranean or North Africa. This review investigates wild plants from the family Apiaceae traditionally used as food in 13 study sites—11 countries in the Mediterranean and adjacent territories—the mainland and 3 islands. The aim is to trace patterns of native distribution versus consumption. As a result, 81 wild umbellifers are listed, traditionally used as food. Their consumption and distribution patterns are described and discussed. Interestingly in 8 of the 13 study sites (61.5%) are recorded 50 plant taxa (66.7% of all wild umbellifers, traditionally used as food) which are consumed in only one particular country. These are as follows: 8 taxa in Morocco, 9 taxa in Spain, 2 taxa in Sicily, 3 taxa in Bulgaria 3 taxa in Crete, 8 taxa in Armenia, 14 taxa in Turkey, and 3 taxa in Jordan. However, these 50 restrictedly consumed plants are distributed in more than one country (except 15 taxa, which are endemics). Many of the plants that are used in certain countries are not consumed by the neighboring people. The results of the two statistical tests, namely Jaccard index and heatmap clustering (double dendrogram), are discussed. The presence of an outlier, such as Bulgaria, which shares borders, history, and culture with Greece and Turkey, emphasizes the importance of local climate for plant distribution and consumption over cultural interactions. The same was observed for some pairs of countries, such as Spain and Morrocco and Turkey and Armenia, although they had the highest number of common plants that are both distributed and consumed as food.
Halotolerant Microorganism-Based Soil Conditioner Application Improved the Soil Properties, Yield, Quality and Starch Characteristics of Hybrid Rice under Higher Saline Conditions
Authors: Jin Wenyu , Li Lin , Ma Guohui , Wei Zhongwei
Published: August 2024

Soil salinity represents a significant factor affecting agricultural productivity and crop quality. The present study was conducted to investigate the effects of soil conditioner (SC) comprising halotolerant microorganisms on the soil fertility, yield, rice quality, and the physicochemical and structural properties of starch in hybrid rice under saline conditions. The experimental treatments were composed of two high-quality hybrid rice varieties, i.e., ‘Y Liangyou 957’ (YLY957) and Jing Liangyou 534 (JLY534), and two soil amendment treatments, i.e., the application of SC at control levels and 2250 kg hm−2, or ‘CK and SC’, respectively. The crop was subjected to a mixture of fresh and sea water (EC 11 dS/m). The results demonstrated that the application of SC significantly enhanced the rice yield under salt stress conditions owing to an increase in the number of grains per panicle. Furthermore, SC was found to be effective in improving the organic matter and soil nutrient content. Furthermore, the application of SC resulted in an improvement in antioxidant defense, higher leaf SPAD values, and greater crop biomass, as well as the translocation of photo-assimilates at the heading stage. The application of SC not only improved the milling and appearance quality but also enhanced the taste value of rice by increasing the amylose and reducing the protein content. Furthermore, the application of SC also decreased the indentations on the surfaces of starch granules and cracks on the edges of the granules. The rice varieties subjected to SC exhibited excellent pasting properties, characterized by reduced proportions of amylopectin short chains and a lower gelatinization temperature and enthalpy of gelatinization. Overall, these findings serve to reinforce the efficacy of soil conditioner as a valuable tool to improve rice productivity and sustainability with improved rice grain quality under saline conditions.
Validation of SNP Markers for Diversity Analysis, Quality Control, and Trait Selection in a Biofortified Cassava Population
Authors: Mbanjo Edwige Gaby Nkouaya , Ogungbesan Adebukola , Agbona Afolabi , Akpotuzor Patrick , Toyinbo Seyi , Iluebbey Peter , Rabbi Ismail Yusuf , Peteti Prasad , Wages Sharon A. , Norton Joanna , Zhang Xiaofei , Bohórquez-Chaux Adriana , Mushoriwa Hapson , Egesi Chiedozie , Kulakow Peter , Parkes Elizabeth
Published: August 2024

A validated marker system is crucial to running an effective genomics-assisted breeding program. We used 36 Kompetitive Allele-Specific PCR (KASP) markers to genotype 376 clones from the biofortified cassava pipeline, and fingerprinted 93 of these clones with DArTseq markers to characterize breeding materials and evaluate their relationships. The discriminating ability of the 36-quality control (QC) KASP and 6602 DArTseq markers was assessed using 92 clones genotyped in both assays. In addition, trait-specific markers were used to determine the presence or absence of target genomic regions. Hierarchical clustering identified two major groups, and the clusters were consistent with the breeding program origins. There was moderate genetic differentiation and a low degree of variation between the identified groups. The general structure of the population was similar using both assays. Nevertheless, KASP markers had poor resolution when it came to differentiating the genotypes by seed sources and overestimated the prevalence of duplicates. The trait-linked markers did not achieve optimal performance as all markers displayed variable levels of false positive and/or false negative. These findings represent the initial step in the application of genomics-assisted breeding for the biofortified cassava pipeline, and will guide the use of genomic selection in the future.
Contribution of Litter and Root to Soil Nutrients in Different Rocky Desertification Grasslands in a Karst Area
Authors: Wang Yuefeng , Wang Jigao , Wang Yini , Wang Xiaojing , Jin Baocheng , Chen Chao , Zhao Xuechun
Published: August 2024

Litter and root decomposition is an important source of soil organic matter and nutrients. To ascertain the contribution of litter and root to natural grassland nutrients in rocky desertification areas, from March 2017 to January 2018, the continuous soil column method, collector method, and litter decomposition method were used to study the soil nutrients, litter and root biomass, decomposition, and nutrient release of potential, moderate, and severe rocky desertification grasslands, as well as their responses to rocky desertification. The results showed that the litter and root decomposition rate showed a trend of being first fast and then slow, and the decomposition rate of litter and root was greater than 50% after 300 days. The annual litter decomposition rates of potential, moderate, and severe rocky desertification grasslands were 69.98%, 62.14%, and 49.79%, respectively, and the annual decomposition rates of root were 73.64%, 67.61%, and 64.09%, respectively. With a deepening degree of rocky desertification, the litter and root decomposition rate decreased. The decomposition coefficients, k, of litter in potential, moderate, and severe rocky desertification grasslands were 1.128, 0.896, and 0.668, respectively, and the decomposition coefficients, k, of root were 1.152, 1.018, and 0.987, respectively. The nutrient release processes of litter and root were different, and the release mode ultimately manifests as “release”. In rocky desertification grasslands, the organic carbon (OC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK) released by litter and root decomposition were 18.93–263.03 g·m−2·yr−1, 1.79–5.59 g·m−2·yr−1, 0.18–0.47 g·m−2·yr−1, and 0.66–3.70 g·m−2·yr−1, respectively. The contribution of root to soil nutrients was greater than that of litter. The degree of rocky desertification was negatively correlated with the biomass, decomposition rate, and nutrient return amount of litter and root. The results of this study provide direct field evidence and illustrate the contribution of litter and root decomposition in rocky desertification grasslands to soil nutrients.
Aphid-Resistant Plant Secondary Metabolites: Types, Insecticidal Mechanisms, and Prospects for Utilization
Authors: Farhan Muhammad , Pan Jilong , Hussain Hammad , Zhao Jun , Yang Hanjing , Ahmad Ishtiaq , Zhang Shuai
Published: August 2024

Aphids pose a significant threat to global agricultural crop production, leading to widespread pesticide use and resistance. This necessitates the use of alternative substances, like plant secondary metabolites (PSMs). Plants have developed protective compounds known as alkaloids, terpenoids, phenolics, sulfur- and nitrogen-containing metabolites. These compounds exhibit promising characteristics against aphids, such as antifeedant, aphicidal, and disrupting survival fitness. This review highlights the importance and application of secondary metabolites in combating aphid populations. Different insect-resistant substances have different mechanisms for managing aphids and other pests, including defensive signaling, inhibiting growth, and attracting natural predators by releasing herbivore-induced volatiles (HIPV). The application of plant secondary metabolites as biopesticides has proven to be an effective, economical, and eco-friendly alternative to synthetic pesticide chemicals. Furthermore, this review comprehensively discusses the principle role of plant secondary metabolites, encouraging sustainable agricultural practices and emphasizing the integrated management of the aphid population.
Impact of Exogenous dsRNA on miRNA Composition in Arabidopsis thaliana
Authors: Nityagovsky Nikolay N. , Kiselev Konstantin V. , Suprun Andrey R. , Dubrovina Alexandra S.
Published: August 2024

The application of double-stranded RNAs (dsRNAs) to plant surfaces has emerged as a promising tool for manipulating gene expression in plants and pathogens, offering new opportunities for crop improvement. While research has shown the capability of exogenous dsRNAs to silence genes, the full spectrum of their impact, particularly on the intricate network of microRNAs (miRNAs), remains largely unexplored. Here, we show that the exogenous application of chalcone synthase (CHS)-encoding dsRNA to the rosette leaves of Arabidopsis thaliana induced extensive alterations in the miRNA profile, while non-specific bacterial neomycin phosphotransferase II (NPTII) dsRNA had a minimal effect. Two days after treatment, we detected 60 differentially expressed miRNAs among the 428 miRNAs found in the A. thaliana genome. A total of 59 miRNAs were significantly changed after AtCHS-dsRNA treatment compared with water and NPTII-dsRNA, and 1 miRNA was significantly changed after AtCHS-dsRNA and NPTII-dsRNA compared with the water control. A comprehensive functional enrichment analysis revealed 17 major GO categories enriched among the genes potentially targeted by the up- and downregulated miRNAs. These categories included processes such as aromatic compound biosynthesis (a pathway directly related to CHS activity), heterocycle biosynthesis, RNA metabolism and biosynthesis, DNA transcription, and plant development. Several predicted targets of upregulated and downregulated miRNAs, including APETALA2, SCL27, SOD1, GRF1, AGO2, PHB, and PHV, were verified by qRT-PCR. The analysis showed a negative correlation between the expression of miRNAs and the expression of their predicted targets. Thus, exogenous plant gene-specific dsRNAs induce substantial changes in the plant miRNA composition, ultimately affecting the expression of a wide range of genes. These findings have profound implications for our understanding of the effects of exogenously induced RNA interference, which can have broader effects beyond targeted mRNA degradation, affecting the expression of other genes through miRNA regulation.
Sugarcane/Soybean Intercropping with Reduced Nitrogen Application Synergistically Increases Plant Carbon Fixation and Soil Organic Carbon Sequestration
Authors: Zhang Tantan , Liu Yali , Li Lin
Published: August 2024

Sugarcane/soybean intercropping and reduced nitrogen (N) application as an important sustainable agricultural pattern can increase crop primary productivity and improve soil ecological functions, thereby affecting soil organic carbon (SOC) input and turnover. To explore the potential mechanism of sugarcane/soybean intercropping affecting SOC sequestration, a two-factor long-term field experiment was carried out, which included planting pattern (sugarcane monocropping (MS), sugarcane/soybean 1:1 intercropping (SB1), and sugarcane/soybean 1:2 intercropping (SB2)) and nitrogen addition levels (reduced N application (N1: 300 kg·hm−2) and conventional N application (N2: 525 kg·hm−2)). The results showed that the shoot and root C fixation in the sugarcane/soybean intercropping system were significantly higher than those in the sugarcane monocropping system during the whole growth period of sugarcane, and the N application level had no significant effect on the C fixation of plants in the intercropping system. Sugarcane/soybean intercropping also increased the contents of total organic C (TOC), labile organic C fraction [microbial biomass C (MBC) and dissolved organic C (DOC)] in the soil during the growth period of sugarcane, and this effect was more obvious at the N1 level. We further analyzed the relationship between plant C sequestration and SOC fraction content using regression equations and found that both plant shoot and root C sequestration were significantly correlated with TOC, MBC, and DOC content. This suggests that sugarcane/soybean intercropping increases the amount of C input to the soil by improving crop shoot and root C sequestration, which then promotes the content of each SOC fraction. The results of this study indicate that sugarcane/soybean intercropping and reduced N application patterns can synergistically improve plant and soil C fixation, which is of great significance for improving crop yields, increasing soil fertility, and reducing greenhouse gas emissions from agricultural fields.
Modified PI Controller for Robustness Improvement of Quasi-Resonant Converters
Authors: Rodríguez-Benítez Oscar Miguel , Ortega-Velázquez Isaac , Sánchez-Contreras Agustín , Espinosa-Pérez Gerardo
Published: August 2024

The challenge regarding the output voltage regulation control of quasi-resonant converters while concurrently fulfilling zero-current switching is addressed in this study. In particular, an alternative to the usual practice of considering fixed duty cycle operation is presented to deal with the narrow robustness margin against load variations exhibited by this condition. The main contribution was the introduction of an additional block in the control loop that implements a new linear relationship between the duty cycle and the switching frequency in terms of the load current. This block proportionally modifies the duty cycle with the switching frequency that, as usual, is used to regulate the output voltage. The structure of the contribution was obtained by exploiting the knowledge of the differential equations that describe the dynamical behavior of the topology. Although it was shown that this modification could be used regardless of the control scheme implemented for the operation of the converter, its usefulness was illustrated by presenting a modified implementation of a classical PI control scheme. It was shown via numerical evaluations that the robustness of the converter under classical PI control was drastically improved for both increases and decreases in the load value. From the implementation perspective, this contribution is attractive since it exhibits a simple structure and neither requires the use of auxiliary switches nor increases the cost of current solutions.
Recovery of Fennel Non-Polar Bioactives via Supercritical Carbon Dioxide Extraction
Authors: Marčac Duraković Nina , Cegledi Ena , Elez Garofulić Ivona , Balbino Sandra , Pedisić Sandra , Jokić Stela , Dragović-Uzelac Verica , Repajić Maja
Published: August 2024

This study aimed to determine the optimal pressure and temperature for the maximum extraction yield and recovery of lipophilic bioactive compounds (BACs) during the supercritical carbon dioxide extraction (SCO2) of fennel seeds. For this purpose, the SCO2 pressure (78.6–361.4 bar) and temperature (35.9–64.1 °C) were varied and optimized, and all of the extracts obtained were analyzed for the volatiles, fatty acids, sterols, tocochromanols and carotenoids. The results showed that the maximum extract yield and content of all of the compounds analyzed favored a higher pressure (320 bar) and lower temperature (40 °C), except for the volatiles, which were the highest at 120 bar and 42 °C. However, the optimal SCO2 conditions for obtaining the highest overall total lipophilic fraction were 320 bar and 40 °C, respectively. The fennel SCO2 extract obtained under these conditions contained 18 volatiles (trans-anethole as the major component), 12 fatty acids (oleic and petroselinic as the major compounds), 12 sterols (β-sitosterol and stigmasterol as the major compounds), two pentacyclic triterpenoids (α-, β-amyrin), one tocopherol (α-tocopherol), two tocotrienols (γ-, δ-tocotrienol) as well as two carotenoids (lutein and β-carotene). The SCO2 proved to be very efficient for the isolation of various lipophilic BACs from fennel, and the results of this study may be of interest to academia and industry.
Study on the Combined Effects of Bromelain (Ananas comosus) Enzyme Treatment and Bacteria Cultures on the Physicochemical Properties and Oxidative Stability of Horse Meat
Authors: Orynbekov Duman , Amirkhanov Kumarbek , Kalibekkyzy Zhanar , Smolnikova Farida , Assenova Bakhytkul , Nurgazezova Almagul , Nurymkhan Gulnur , Kassenov Amirzhan , Baytukenova Sholpan , Yessimbekov Zhanibek
Published: August 2024

This study investigates the impact of bromelain, a plant enzyme, on the physicochemical and sensory properties of horse meat, as well as the effects of different bacterial cultures (Lactococcus lactis, Lactococcus lactis subsp. lactis biovar diacetylactis, Lactobacillus acidophilus, and Bifidobacterium longum) on the inhibition of lipid oxidation and control of pH during chilled storage. Horse meat (longissimus dorsi) samples (n = 14) were treated with bromelain in two forms (powder and aqueous solution) and with three methods: immersion in enzyme solution, spreading enzyme powder on meat, and syringing enzyme solution into the meat. After fermentation, a part of the meat samples (n = 6) was treated with different bacteria compositions at a 5% weight ratio and stored at 0–2 °C for 6 days. Injecting 3–5% bromelain solutions was most effective at tenderizing the meat, reducing shear force by up to 56% after 8 h. This injection also maximized the water-holding capacity (78–81%) and minimized cooking losses (21–26%), compared to 38% for the control meat sample without treatment. Syringing with 3% bromelain yielded the highest sensory scores across the tenderness, flavor, and overall palatability parameters. The combination of L. acidophilus, Lc. lactis, and B. longum at a ratio of 1.5:1.5:2 was highly effective in reducing oxidative spoilage and optimizing pH levels, thereby ensuring extended meat storability. This study demonstrates that bromelain treatment is an effective method for improving the tenderness, WHC, and sensory properties of horse meat. The LAB combination showed efficient acid formation, crucial for enhancing meat preservation.
Numerical Simulation of Fracture Propagation Induced by Water Injection in Tight Oil Reservoirs
Authors: Shi Dengke , Cheng Shiqing , Bai Wenpeng , Liu Xiuwei , Cai Dingning
Published: August 2024

Dynamic fracture propagation significantly affects water flooding efficiency in tight oil reservoirs. This phenomenon, where moderate fracture openings can enhance water flooding volume and alleviate injection challenges, has been underexplored in current literature. Understanding dynamic fracture behavior poses a challenge due to the difficulty in characterizing them within traditional reservoir numerical simulators. In this study, we propose a numerical simulation method that integrates the KGD dynamic fracture model with a two-phase flow model. This approach enables detailed exploration of dynamic fracture evolution in reservoir scenarios featuring one injector and one production well. Our findings reveal that fractures extend from the water injection well to the oil production well, exhibiting rapid initial growth followed by a slower rate. Fluctuations in fracture tip pressure correspond to cycles of opening and closure. We observe that cumulative oil production increases more rapidly when injection pressure exceeds the fracture opening pressure. However, this growth rate diminishes beyond a certain threshold, highlighting the critical role of injection parameters in dynamic fracture efficacy. Optimal water flooding performance is achieved when injecting water slightly above the fracture opening pressure. Furthermore, we compare water cut curves generated by conventional commercial simulators with our fracture propagation model. Our model’s water cut curve aligns better with on-site data, indicating improved historical fitting accuracy. In conclusion, our study underscores the importance of dynamic fractures in enhancing water flooding efficiency in tight oil reservoirs and presents a robust numerical simulation framework for better understanding and management of reservoir dynamics.
Experimental Study of the Characteristics of HI Distillation in the Thermochemical Iodine–Sulfur Cycle for Hydrogen Production
Authors: Zhang Jinxu , Ling Bo , He Yong , Zhu Yanqun , Wang Zhihua
Published: August 2024

Hydrogen energy, as a clean, renewable, and high-calorific energy carrier, has garnered significant attention globally. Among various hydrogen production methods, the thermochemical iodine–sulfur (I-S) cycle is considered the most promising due to its high efficiency and adaptability for large-scale industrial applications. This study focuses on the distillation characteristics of the HIx (HI–I2–H2O) solution within the I-S cycle, which is crucial for achieving the high-concentration HI necessary for efficient hydrogen production. Previous methods, including phosphoric acid extraction–distillation and reactive distillation, have addressed azeotrope issues but introduced complexities and equipment demands. This research constructs a hypo-azeotropic HIx solution distillation experimental system and uses the Aspen Plus v14 software to optimize distillation parameters. By analyzing the effects of feed stage, reflux ratio, and feed temperature, the study provides essential data for improving distillation efficiency and supports the scale-up of I-S cycle technology. The findings indicate that optimal distillation is achieved with a feed position at 1/3 column height, a reflux ratio of 1.4, and a feed temperature near the boiling point, enhancing the feasibility of industrial hydrogen production via the I-S cycle.
Research for Flow Behavior of Heavy Oil by N2 Foam-Assisted Steam (NFAS) Flooding: Microscopic Displacement Experiment Study
Authors: Fu Qiang , Yang Zhihao , Liu Yongfei , Chen Mingqiang
Published: August 2024

Steam flooding is easily induced to transverse flow, with a limited swept area and low displacement efficiency. Therefore, chemical agents have been used to assist in steam flooding for heavy oil extraction. However, research into the driving mechanisms and modes of occurrence of residual oil is insufficient. In this work, a flooding simulation was conducted to understand the occurrence mechanism of residual oil during the flooding process in heavy oil reservoirs. First, the foam properties of a novel DES (Deep Eutectic Solvent) and CTAB (Cetyltrimethyl Ammonium Bromide) composite system with ultra-low interfacial tension were tested. The optimal concentration and gas–liquid ratio of the foam agent solution were determined. Secondly, the NFAS (N2 foam-assisted steam flooding) was carried out after steam flooding, and the flow behavior of crude oil at different flow pathway zones was researched. In the end, the remaining oil morphology and distribution characteristics under different displacement times were analyzed, determining the mode of remaining oil occurrence during NFAS flooding. The results show that (1) the novel DES and CTAB system has good foam properties. The best concentration is 0.5 wt%, and the optimal ratio of gas to liquid is 1:1. (2) In the steam flooding stage, the columnar remaining oil in the narrow hole near the flow pathway increases, and the cluster remaining oil in the far flow channel changes into film and columnar. (3) During NFAS displacement, the residual oil primarily presents a state of fully mixing the O/W emulsion formed after blending and dispersing with oil, gas, and water. (4) After the NFAS flooding stage, the remaining oil was distributed in each throat. The remaining oil in the near passage is mostly blocked by foam in large holes with a pore coordination number of 4 and 5. The residual oil in the distant runner is distributed in the thick and middle throats. (5) NFAS flooding outperforms steam flooding by significantly decreasing residual oil in narrow passages of the main flow channel and near flow channels, resulting in a substantial 44.9% increase in overall recovery rate.
Runoff Prediction for Hydrological Applications Using an INFO-Optimized Deep Learning Model
Authors: Wang Weisheng , Hao Yongkang , Zheng Xiaozhen , Mu Tong , Zhang Jie , Zhang Xiaoyuan , Cui Zhenhao
Published: August 2024

Runoff prediction is essential in water resource management, environmental protection, and agricultural development. Due to the large randomness, high non-stationarity, and low prediction accuracy of nonlinear effects of the traditional model, this study proposes a runoff prediction model based on the improved vector weighted average algorithm (INFO) to optimize the convolutional neural network (CNN)-bidirectional long short-term memory (Bi-LSTM)-Attention mechanism. First, the historical data are analyzed and normalized. Secondly, CNN combined with Attention is used to extract the depth local features of the input data and optimize the input weights of Bi-LSTM. Then, Bi-LSTM is used to study the time series feature depth analysis data from both positive and negative directions simultaneously. The INFO parameters are optimized to provide the optimal parameter guarantee for the CNN-Bi-LSTM-Attention model. Based on a hydrology station’s water level and flow data, the influence of three main models and two optimization algorithms on the prediction accuracy of the CNN-Bi-LSTM-Attention model is compared and analyzed. The results show that the fitting coefficient, R2, of the proposed model is 0.948, which is 7.91% and 3.38% higher than that of Bi-LSTM and CNN-Bi-LSTM, respectively. The R2 of the vector-weighted average optimization algorithm (INFO) optimization model is 0.993, which is 0.61% higher than that of the Bayesian optimization algorithm (BOA), indicating that the method adopted in this paper has more significant forecasting ability and can be used as a reliable tool for long-term runoff prediction.
The Influence of the Geometric Parameters of an Impeller on the Transport Capability of Long Flexible Fiber in a Non-Clogging Pump
Authors: Liu Rongsheng , Zhang Qiang , Zhuang Suguo , Wang Kai
Published: August 2024

The influence of a different blade number, blade wrapping angle and blade outlet angle on flexible fiber passing performance is analyzed numerically with CFD-DEM coupling. The results demonstrate that a non-clogging pump with two blades exhibits superior passing performance compared to the non-clogging pump with three blades. Specifically, when the fiber length L is 150 mm, the passing performance of the pump with different wrapping angles is 270° > 240° > 300°, from highest to lowest, and the transport time T0 is 0.27 s, 0.34 s, 0.46 s, respectively. When the length L is 200 mm, the passing performance is 240° > 270° ≈ 300°, and the transport time T0 is 0.48 s, 0.55 s, and 0.55 s, respectively. When the fiber length L is 250 mm, the passing performance is 240° ≈ 270° > 300°. When the fiber length L is 150 mm, the passing performance of the pump with different outlet angles is 15° > 25° > 20°, and the transport time T0 is 0.17 s, 0.27 s, and 0.34 s, respectively. When the fiber length L is 200 mm, the passing performance is 25° > 15° ≈ 20° and the transport time T0 is 0.26 s, 0.50 s, and 0.48 s, respectively. When the fiber length L is 250 mm, the passing performance is 25° > 15° > 20°, and the transport time T0 is 0.31 s, 0.54 s and 0.96 s, respectively.
Digital Risk and Financial Inclusion: Balance between Auxiliary Innovation and Protecting Digital Banking Customers
Authors: Ahmed Faraz , Hussain Arsalan , Khan Sajjad Nawaz , Malik Arsalan Haneef , Asim Muhammad , Ahmad Sadique , El-Affendi Mohammed
Published: August 2024

The digital economy’s rise has fueled the growth of digital banking, but concerns linger about customer protection. While offering advantages like financial inclusion, this shift disrupts traditional banking experiences and introduces potential risks. Customer safety in this new landscape is paramount, as dissatisfied users may switch providers and institutions risk reputational damage. To remain competitive, financial institutions must prioritize a secure experience that aligns with customer expectations. This study investigates five key factors influencing customer protection in Pakistan’s digital financial services. Analysis reveals all factors positively impact customer protection, with information security holding the most weight. These findings highlight the need for robust information security measures as a critical driver for the Pakistani digital banking industry’s success.
An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments
Authors: Stavrinidis Stavros , Zacharia Paraskevi
Published: August 2024

Autonomous navigation in dynamic environments is a significant challenge in robotics. The primary goals are to ensure smooth and safe movement. This study introduces a control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It enhances autonomous robot navigation in dynamic environments with a focus on collision-free path planning. The strategy uses a path-planning technique to develop a trajectory that allows the robot to navigate smoothly while avoiding both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experimental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model’s capability in ensuring collision-free navigation, employing a path-planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, particularly in terms of computational efficiency and navigational smoothness.
Online Estimation of the Mounting Angle and the Lever Arm for a Low-Cost Embedded Integrated Navigation Module
Authors: Wang Qinghai , Yan Peihui , Jiang Jinguang , Xie Dongpeng , Li Yuying , Zheng Qiyuan , Tan Hongbin , Wu Jiaji
Published: August 2024

Multi-source fusion constitutes a research focus in the navigation domain. This article focuses on the online estimation of the mounting angles between the body frame and vehicle frame within low-cost embedded vehicle navigation modules and the lever arm between the global satellite navigation system (GNSS) antenna/odometer and the inertial measurement unit (IMU). An online mounting angle error estimation algorithm, using odometers and IMU speeds, has been developed to estimate the angle errors while vehicles are in motion. At the same time, an online estimation algorithm model for the GNSS antenna lever arm and odometer lever arm was constructed. These two types of lever arms are used as the estimated states, and then Kalman filters are used to estimate them. The algorithm can simultaneously estimate the IMU mounting angle error, GNSS antenna arm, and odometer arm online. The experimental outcomes demonstrate that the lever arm estimation algorithm presented herein is effective for tactical and MEMS-level inertial navigation, with an estimation error of less than 2 cm. Meanwhile, the proposed online estimation of the mounting angle algorithm has an accuracy comparable to that of the post-processing algorithm. After making up the mounting angle and lever arm, we found that the position and speed precision of the multi-source fusion navigation systems were significantly improved. The results indicate that the proposed online estimation of mounting angle error and lever arm algorithm are effective and may promote the practical and widespread application of integrated navigation systems in vehicles. It solves the shortcomings of traditional methods, including the cumbersome and inaccurate manual measurement of the lever arm. It provides a technical solution for developing a more accurate and convenient low-cost vehicle navigation module.
Visual Localization Method for Unmanned Aerial Vehicles in Urban Scenes Based on Shape and Spatial Relationship Matching of Buildings
Authors: Liu Yu , Bai Jing , Sun Fangde
Published: August 2024

In urban scenes, buildings are usually dense and exhibit similar shapes. Thus, existing autonomous unmanned aerial vehicle (UAV) localization schemes based on map matching, especially the semantic shape matching (SSM) method, cannot capture the uniqueness of buildings and may result in matching failure. To solve this problem, we propose a new method to locate UAVs via shape and spatial relationship matching (SSRM) of buildings in urban scenes as an alternative to UAV localization via image matching. SSRM first extracts individual buildings from UAV images using the SOLOv2 instance segmentation algorithm. Then, these individual buildings are subsequently matched with vector e-map data (stored in .shp format) based on their shape and spatial relationship to determine their actual latitude and longitude. Control points are generated according to the matched buildings, and finally, the UAV position is determined. SSRM can efficiently realize high-precision UAV localization in urban scenes. Under the verification of actual data, SSRM achieves localization errors of 7.38 m and 11.92 m in downtown and suburb areas, respectively, with better localization performance than the radiation-variation insensitive feature transform (RIFT), channel features of the oriented gradient (CFOG), and SSM algorithms. Moreover, the SSRM algorithm exhibits a smaller localization error in areas with higher building density.
Rapid Prediction of the Lithium Content in Plants by Combining Fractional-Order Derivative Spectroscopy and Wavelet Transform Analysis
Authors: Cui Shichao , Jiang Guo , Bai Yong
Published: August 2024

Quickly determining the metal content in plants and subsequently identifying geochemical anomalies can provide clues and guidance for predicting the location and scale of concealed ore bodies in vegetation-covered areas. Although visible, near-infrared and shortwave infrared (VNIR–SWIR) reflectance spectroscopy at wavelengths ranging from 400 to 2500 nm has been proven by many researchers to be a fast, accurate and nondestructive approach for estimating the contents of copper (Cu), lead (Pb), zinc (Zn) and other metal elements in plants, relatively few studies have been conducted on the estimation of lithium (Li) in plants. Therefore, the potential of applying VNIR–SWIR spectroscopy techniques for estimating the Li content in plants was explored in this study. The Jingerquan Li mining area in Hami, Xinjiang, China, was chosen. Three sampling lines were established near a pegmatite deposit and in a background region, canopy reflectance spectra were obtained for desert plants and Li contents were determined in the laboratory; then, quantitative relationships were established between nine different transformed spectra (including both integer and fractional orders) and the Li content was estimated using partial least squares regression (PLSR). The results showed that models constructed using high-order derivative spectra (with an order greater than or equal to 1) significantly outperformed those based on original and low-order derivative spectra (with an order less than 1). Notably, the model based on a 1.1-order derivative spectrum displayed the best performance. Furthermore, the performance of the model based on the two-layer wavelet coefficients of the 1.1-order derivative spectrum was further improved compared with that of the model based on only the 1.1-order derivative spectrum. The coefficient of determination (Rpre2) and the ratio of performance to deviation (RPD) for the validation set increased from 0.6977 and 1.7656 to 0.7044 and 1.8446, respectively, and the root mean square error (RMSEpre) decreased from 2.5735 to 2.4633 mg/kg. These results indicate that quickly and accurately estimating the Li content in plants via the proposed spectroscopic analysis technique is feasible and effective; however, appropriate spectral preprocessing methods should be selected before hyperspectral estimation models are constructed. Overall, the developed hybrid spectral transformation approach, which combines wavelet coefficients and derivative spectra, displayed excellent application potential for estimating the Li content in plants.
A Deep-Learning Based GNSS Scene Recognition Method for Detailed Urban Static Positioning Task via Low-Cost Receivers
Authors: Li Yubo , Jiang Zhuojun , Qian Chuang , Huang Wenjing , Yang Zeen
Published: August 2024

Global Navigation Satellite Systems (GNSS)-based position service is widely applied in cities, but the precision varies significantly in different obstruction scenes. Scene recognition is critical for developing scene-adaptive GNSS algorithms. However, the complexity of urban environments and the unevenness of received signal especially in low-cost receivers limit the performance of GNSS-based scene recognition models. Therefore, our study aims to construct a scene recognition model suitable for urban static positioning and low-cost GNSS receivers. Firstly, we divide the scenes into five categories according to application requirements, including open area, high urban canyon, unilateral urban canyon, shade of tree and low urban canyon. We then construct feature vectors from original observation data and consider the geometric relationships between satellites and receivers. The different sensitivity to different scenes is discovered through an analysis of the performance of each feature vector in recognition. Therefore, a GNSS positioning scene recognition model based on multi-channel LSTM (MC-LSTM) is proposed. The results of experiments show that an accuracy of 99.14% can be achieved by our model. Meanwhile, only 0.75 s and 1.95 ms are required in model training per epoch and model prediction per data on a CPU, which presents a significant improvement of over 90% compared with existing works. Furthermore, our model can be transferred into different time periods quickly and can maintain robustness in situations where one or two types of observation data are missed. A maximum accuracy of 81.13% can be achieved when two channels are missed, while 96.06% is attainable when one channel is missed. Therefore, our model has the potential for real applications in complex urban environments.
Multimodal Semantic Collaborative Classification for Hyperspectral Images and LiDAR Data
Authors: Wang Aili , Dai Shiyu , Wu Haibin , Iwahori Yuji
Published: August 2024

Although the collaborative use of hyperspectral images (HSIs) and LiDAR data in land cover classification tasks has demonstrated significant importance and potential, several challenges remain. Notably, the heterogeneity in cross-modal information integration presents a major obstacle. Furthermore, most existing research relies heavily on category names, neglecting the rich contextual information from language descriptions. Visual-language pretraining (VLP) has achieved notable success in image recognition within natural domains by using multimodal information to enhance training efficiency and effectiveness. VLP has also shown great potential for land cover classification in remote sensing. This paper introduces a dual-sensor multimodal semantic collaborative classification network (DSMSC2N). It uses large language models (LLMs) in an instruction-driven manner to generate land cover category descriptions enriched with domain-specific knowledge in remote sensing. This approach aims to guide the model to accurately focus on and extract key features. Simultaneously, we integrate and optimize the complementary relationship between HSI and LiDAR data, enhancing the separability of land cover categories and improving classification accuracy. We conduct comprehensive experiments on benchmark datasets like Houston 2013, Trento, and MUUFL Gulfport, validating DSMSC2N’s effectiveness compared to various baseline methods.
Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach
Authors: Ouyang Yating , Zhang Yuhong , Feng Ming , Boschetti Fabio , Du Yan
Published: August 2024

Sea surface salinity (SSS) observed by satellite has been widely used since the successful launch of the first salinity satellite in 2009. However, compared with other oceanographic satellite products (e.g., sea surface temperature, SST) that became operational in the 1980s, the SSS product is less mature and lacks effective validation from the user end. We employed an unsupervised machine learning approach to classify the Level 3 SSS bias from the Soil Moisture Active Passive (SMAP) satellite and its observing environment. The classification model divides the samples into fifteen classes based on four variables: satellite SSS bias, SST, rain rate, and wind speed. SST is one of the most significant factors influencing the classification. In regions with cold SST, satellite SSS has an accuracy of less than 0.2 PSU (Practical Salinity Unit), mainly due to the higher uncertainty in the cold environment. A small number of observations near the seawater freezing point show a significant fresh bias caused by sea ice. A systematic bias of the SMAP SSS product is found in the mid-latitudes: positive bias tends to occur north (south) of 45°N(S) and negative bias is more common in 25°N(S)–45°N(S) bands, likely associated with the SMAP calibration scheme. A significant bias also occurs in regions with strong ocean currents and eddy activities, likely due to spatial mismatch in the highly dynamic background. Notably, satellite SSS and in situ data correlations remain good in similar environments with weaker ocean dynamic activities, implying that satellite salinity data are reliable in dynamically active regions for capturing high-resolution details. The features of the SMAP SSS shown in this work call for careful consideration by the data user community when interpreting biased values.
Effect of Albedo Footprint Size on Relationships between Measured Albedo and Forest Attributes for Small Forest Plots
Authors: Ramtvedt Eirik Næsset , Ørka Hans Ole , Bollandsås Ole Martin , Næsset Erik , Gobakken Terje
Published: August 2024

The albedo of boreal forests depends on the properties of the forest and is a key parameter for understanding the climate impact of forest management practices at high northern latitudes. While high-resolution albedo retrievals from satellites remain challenging, unmanned aerial vehicles (UAVs) offer the ability to obtain albedo corresponding to the typical size of forest stands or even smaller areas, such as forest plots. Plots and pixels of sizes in the typical range of 200–400 m2 are used as the basic units in forest management in the Nordic countries. In this study, the aim was to evaluate the effect of the differences in the footprint size of the measured albedo and fixed-area forest plots on the relationship between albedo and forest attributes. This was performed by examining the correlation between albedo and field-measured forest attributes and metrics derived from airborne laser scanner data using linear regression models. The albedo was measured by a UAV above 400 m2, circular forest plots (n = 128) for seven different flight heights above the top of the canopy. The flight heights were chosen so the plots were always smaller than the footprint of the measured albedo, and the area of a forest plot constituted 30–90% of the measured albedo. The applied pyranometer aboard the UAV measured the albedo according to a cosine response across the footprint. We found the strongest correlation when there was the greatest correspondence between the spatial size of the albedo footprint and the size of the forest plots, i.e., when the target area constituted 80–90% of the measured albedo. The measured albedo of the plots in both regeneration forests and mature forests were highly sensitive (p-values ≤ 0.001) to the footprint size, with a mean albedo difference of 11% between the smallest and largest footprints. The mean albedo of regeneration forests was 33% larger than that of mature forests for footprint sizes corresponding to 90%. The study demonstrates the importance of corresponding spatial sizes of albedo measurements and the target areas subject to measurements.
Wavelength Cut-Off Error of Spectral Density from MTF3 of SWIM Instrument Onboard CFOSAT: An Investigation from Buoy Data
Authors: Luo Yuexin , Xu Ying , Qin Hao , Jiang Haoyu
Published: August 2024

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively.