bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complementary Filter to Enhance Indoor Mini-Drone Localization under Varying Conditions
Authors: Basiri Amin , Mariani Valerio , Glielmo Luigi
Published: June 2023

Mini-drones can be used for a variety of tasks, ranging from weather monitoring to package delivery, search and rescue, and also recreation. In outdoor scenarios, they leverage Global Positioning Systems (GPS) and/or similar systems for localization in order to preserve safety and performance. In indoor scenarios, technologies such as Visual Simultaneous Localization and Mapping (V-SLAM) are used instead. However, more advancements are still required for mini-drone navigation applications, especially in the case of stricter safety requirements. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-Direct Monocular Visual Odometry (SVO) via an Adaptive Complementary Filter (ACF), the proposed strategy achieves better position estimates under various conditions (low light in low-surface-texture environments and high flying speed), showing an average percentage error of 18.1% and 25.9% smaller than that of ORB-SLAM and SVO against the ground-truth.
Finite-Size Relaxational Dynamics of a Spike Random Matrix Spherical Model
Authors: de Freitas Pimenta Pedro H. , Stariolo Daniel A.
Published: June 2023

We present a thorough numerical analysis of the relaxational dynamics of the Sherrington–Kirkpatrick spherical model with an additive non-disordered perturbation for large but finite sizes N. In the thermodynamic limit and at low temperatures, the perturbation is responsible for a phase transition from a spin glass to a ferromagnetic phase. We show that finite-size effects induce the appearance of a distinctive slow regime in the relaxation dynamics, the extension of which depends on the size of the system and also on the strength of the non-disordered perturbation. The long time dynamics are characterized by the two largest eigenvalues of a spike random matrix which defines the model, and particularly by the statistics concerning the gap between them. We characterize the finite-size statistics of the two largest eigenvalues of the spike random matrices in the different regimes, sub-critical, critical, and super-critical, confirming some known results and anticipating others, even in the less studied critical regime. We also numerically characterize the finite-size statistics of the gap, which we hope may encourage analytical work which is lacking. Finally, we compute the finite-size scaling of the long time relaxation of the energy, showing the existence of power laws with exponents that depend on the strength of the non-disordered perturbation in a way that is governed by the finite-size statistics of the gap.
Measuring Oral Reading Fluency (ORF) Computer-Based and Paper-Based: Examining the Mode Effect in Reading Accuracy and Reading Fluency
Authors: Jungjohann Jana , DeVries Jeffrey M. , Gebhardt Markus
Published: June 2023

Internationally, teachers use oral reading fluency (ORF) measurements to monitor learning progress in reading and adapt instruction to the individual needs of students. In ORF measures, the child reads aloud single syllables, words, or short passages, and the teacher rates in parallel at which items the child makes a mistake. Since administering paper-based ORF requires increased effort on the part of teachers, computer-based test administration is available. However, there are still concerns about the comparability of paper-based and computer-based test modes. In our study, we examine mode effects between paper-based and computer-based test scores for both reading speed and reading accuracy using a German-language ORF assessment for progress monitoring. 2nd- and 3rd-year-students (N = 359) with and without special education needs participated in the study. Results show comparable and high reliability (r > 0.76) and no differential item functioning for both test modes. Furthermore, students showed significantly higher reading speed on the paper-based test, while no differences were found in reading accuracy. In the absence of differential item functioning, we discuss how mean differences can be accounted for, how teachers can be trained to use the different test modes, and how computer-based tests can be safeguarded in practice.
Environmentally Specific Servant Leadership and Brand Citizenship Behavior: The Role of Green-Crafting Behavior and Employee-Perceived Meaningful Work
Authors: Elshaer Ibrahim A. , Azazz Alaa M. S. , Kooli Chokri , Alshebami Ali Saleh , Zeina Mohammad M. A. , Fayyad Sameh
Published: June 2023

Sustainability and environmental concerns have become increasingly important in the business world, with organizations seeking to integrate sustainable practices and enhance their brand citizenship behavior. Servant leadership that is focused on the environment is a type of leadership approach that gives prominence to preserving and promoting environmental sustainability. This study aims to examine the impact of environmentally specific servant leadership on brand citizenship behavior, with a focus on the mediating roles of green-crafting behavior and employee-identified meaningful work. Drawing on data from a survey of 319 employees working in hotels, this study conducted partial least square–structural equation modeling (PLS-SEM) to test a dual-moderated mediation model to explore the direct and indirect effects of environmentally specific servant leadership on brand citizenship behavior. The results of this study reveal that environmentally specific servant leadership has a significant and positive impact on green-crafting behavior and employee meaningful work. Moreover, green-crafting behavior and employee-perceived meaningful work both mediate the link between environmentally specific servant leadership and brand citizenship behavior. Specifically, green-crafting behavior acts as a mediator between environmentally specific servant leadership and employee-perceived meaningful work, while employee-perceived meaningful work mediates the link between green-crafting behavior and brand citizenship behavior. These findings have important implications for managers and organizations that seek to enhance their sustainability and brand citizenship behavior. Specifically, this study highlights the critical role of environmentally specific servant leadership (ESSL) in promoting green-crafting behavior and employee-perceived meaningful work, which in turn influence brand citizenship behavior. Therefore, organizations can improve their brand citizenship performance by developing ESSL behaviors and practices that foster green-crafting behavior and employee-perceived meaningful work.
Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review
Authors: Emek Soylu Busra , Guzel Mehmet Serdar , Bostanci Gazi Erkan , Ekinci Fatih , Asuroglu Tunc , Acici Koray
Published: June 2023

The task of semantic segmentation holds a fundamental position in the field of computer vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent times, significant advancements have been achieved in the field of semantic segmentation through the application of Convolutional Neural Networks (CNN) techniques based on deep learning. This paper presents a comprehensive and structured analysis of approximately 150 methods of semantic segmentation based on CNN within the last decade. Moreover, it examines 15 well-known datasets in the semantic segmentation field. These datasets consist of 2D and 3D image and video frames, including general, indoor, outdoor, and street scenes. Furthermore, this paper mentions several recent techniques, such as SAM, UDA, and common post-processing algorithms, such as CRF and MRF. Additionally, this paper analyzes the performance evaluation of reviewed state-of-the-art methods, pioneering methods, common backbone networks, and popular datasets. These have been compared according to the results of Mean Intersection over Union (MIoU), the most popular evaluation metric of semantic segmentation. Finally, it discusses the main challenges and possible solutions and underlines some future research directions in the semantic segmentation task. We hope that our survey article will be useful to provide a foreknowledge to the readers who will work in this field.
A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
Authors: Zhang Jiachi , Wang Xueyun , Liu Liu
Published: June 2023

The proliferation of flying ad hoc networks (FANETs) enables multiple applications in various scenarios. In order to construct and maintain an effective hierarchical structure in FANETs where mobile nodes proceed at high mobility, we propose a novel FANET clustering algorithm by using the Kalman-filter-predicted location and velocity information. First, we use the Silhouette coefficient to determine the number of clusters and the k-means++ method is utilized to group nodes into clusters. Regarding the external disturbances in highly mobile scenarios, a Kalman filter is used to predict locations and velocities for all nodes. When clustering, the relative speeds together with relative distances are considered, and the previous selected cluster heads (CHs) are utilized to initialize current centroids. Furthermore, we propose two metrics, including the cluster stability and the ratio of changed edges, to evaluate the network performance. Relevant simulation results reveal that our proposal can yield a cumulative distribution function (CDF) of cluster stability values close to the sensor-measurement-based data. Moreover, it can reduce communication overheads significantly.
CEEMDAN-ICA-Based Radar Monitoring of Adjacent Multi-Target Vital Signs
Authors: Dong Xichao , Feng Yun , Cui Chang , Lu Jun
Published: June 2023

In recent years, radar, especially frequency-modulated continuous wave (FMCW) radar, has been extensively used in non-contact vital signs (NCVS) research. However, current research does not work when multiple human targets are close to each other, especially when adjacent human targets lie in the same resolution cell. In this paper, a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–independent component analysis (ICA) was proposed to obtain the vital-sign information (including respiratory rate and heart rate) of adjacent human targets by using a single FMCW radar. Firstly, the data observed at a single angle were decomposed by the CEEMDAN separation algorithm to construct virtual multi-angle observations. It can effectively transform the undetermined blind source separation (UBSS) problem into an overdetermined blind source separation (BSS) problem. Thus, a BSS algorithm based on FastICA can be used to reconstruct each person’s vital-sign signal and then calculate their respiratory rate/heart rate. To validate the effectiveness of the proposed method, experiments based on the measured data were conducted and the results show that the proposed method can obtain multi-target vital-sign information even when they are in the same resolution cell.
Lightweight Small Target Detection Algorithm with Multi-Feature Fusion
Authors: Yang Rujin , Zhang Jingwei , Shang Xinna , Li Wenfa
Published: June 2023

Unmanned aerial vehicles (UAVs) are a highly sought-after technology with numerous applications in both military and non-military uses. The identification of targets is a crucial aspect of UAV applications, but there are challenges associated with complex detection models and difficulty in detecting small targets. To address these issues, this study proposes the lightweight L-YOLO algorithm for target detection tasks from a UAV perspective. The L-YOLO algorithm improves on YOLOv5 by improving the model’s detection performance for small targets while reducing the number of parameters and computational effort. The GhostNet module replaces the relevant convolution in the YOLOv5 model to create a lightweight model. The EIoU loss is used as the loss function of the algorithm to accelerate convergence and improve regression accuracy. Furthermore, feature-level extensions based on YOLOv5 are implemented, and a new detection head is proposed to improve the model’s detection accuracy for small targets. The size of the anchor boxes is redesigned to suit the small targets using the K-means++ clustering algorithm. The experiments were conducted on the VisDrone-2022 dataset, and the L-YOLO algorithm demonstrated a reduction in computational effort by 42.42% and number of parameters by 48.6% compared to the original algorithm. Furthermore, recall and mAP@0.5 improved by 2.1% and 1.4%, respectively. These results demonstrate that the L-YOLO algorithm not only has better detection performance for small targets but is also a lighter model, indicating promising prospects for target detection from a UAV perspective.
MGFCTFuse: A Novel Fusion Approach for Infrared and Visible Images
Authors: Hao Shuai , Li Jiahao , Ma Xu , Sun Siya , Tian Zhuo , Cao Le
Published: June 2023

Traditional deep-learning-based fusion algorithms usually take the original image as input to extract features, which easily leads to a lack of rich details and background information in the fusion results. To address this issue, we propose a fusion algorithm, based on mutually guided image filtering and cross-transmission, termed MGFCTFuse. First, an image decomposition method based on mutually guided image filtering is designed, one which decomposes the original image into a base layer and a detail layer. Second, in order to preserve as much background and detail as possible during feature extraction, the base layer is concatenated with the corresponding original image to extract deeper features. Moreover, in order to enhance the texture details in the fusion results, the information in the visible and infrared detail layers is fused, and an enhancement module is constructed to enhance the texture detail contrast. Finally, in order to enhance the communication between different features, a decoding network based on cross-transmission is designed within feature reconstruction, which further improves the quality of image fusion. In order to verify the advantages of the proposed algorithm, experiments are conducted on the TNO, MSRS, and RoadScene image fusion datasets, and the results demonstrate that the algorithm outperforms nine comparative algorithms in both subjective and objective aspects.
Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles
Authors: Yang Wenqiang , Zhu Xinxin , Nie Fuquan , Jiao Hongwei , Xiao Qinge , Yang Zhile
Published: June 2023

Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of balancing peaks and filling valleys can be realized. However, the complexity of DED has increased with the emergence of the penetration of plug-in electric vehicles. This paper proposes a model that takes into account the day-ahead, hourly-based scheduling of power systems and the impact of PEVs. To solve the model, an improved chaos moth flame optimization algorithm (CMFO) is introduced. This algorithm has a faster convergence rate and better global optimization capabilities due to the incorporation of chaotic mapping. The feasibility of the proposed CMFO is validated through numerical experiments on benchmark functions and various generation units of different sizes. The results demonstrate the superiority of CMFO compared with other commonly used swarm intelligence algorithms.
Identification of Indoor Radio Environment Properties from Channel Impulse Response with Machine Learning Models
Authors: Kocevska Teodora , Javornik Tomaž , Švigelj Aleš , Rashkovska Aleksandra , Hrovat Andrej
Published: June 2023

The design and optimization of next-generation indoor wireless communication networks require detailed and precise descriptions of the indoor environments. Environmental awareness can serve as a fundamental basis for the dynamic adaptation of the wireless system to channel conditions and can improve the system’s performance. Methods that combine wireless technology with machine learning are promising for identifying the properties of the indoor radio environment (RE) without requiring specialized equipment or manual intervention. In the paper, we propose an approach for identifying the materials of the surfaces using channel impulse response (CIR) and RE identification models built with machine learning. To train the models and assess their performance, we acquired radio propagation data from rooms with different sizes and materials using ray tracing. We explored tree-based methods, ensemble-based methods, kernel-based methods, and neural networks for training the models. The performance of the models is evaluated in three realistic scenarios defined by the location of the radio nodes and the room sizes. The multilayer perceptron models performed best in most of the evaluation settings. The results show that the models are capable of accurately predicting the materials in rooms with sizes that were not included in the training procedure. Including CIRs from a large number of rooms with different sizes and surface materials estimated with different radio node positions in the training process results in models with wider practical applicability.
Hybrid Source Multi-Port Quasi-Z-Source Converter with Fuzzy-Logic-Based Energy Management
Authors: Say Gorkem , Hosseini Seyed Hossein , Esmaili Parvaneh
Published: June 2023

In this paper, a fuzzy-logic-based energy management system and a multi-port quasi-z-source converter that utilizes hybrid renewable energy sources are proposed. The system ensures that each energy source module can be used individually by employing fuzzy logic to define the power modes. This approach also helps to prevent switching losses resulting from the extra switching of the source modules. In addition, the proposed energy management does not have a mathematical model, so its applicability is simple, and it is suitable for different multiple-input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system owing to linguistic rules. Moreover, the switching losses of the multiport modules were significantly reduced by adopting the quasi-z-source network to the end of the multiport converter. Furthermore, different errors, such as the root mean square error (RMSE), average squared error (ASE), average absolute error (AAE), average time-weighted absolute error (ATWAE), tracking error (TE), and unscaled mean bounded relative absolute error (UMBRAE), were applied to evaluate the fuzzy logic performance from different perspectives. The simulation results were obtained using MATLAB Simulink, and the experimental results were obtained by connecting the circuit to MATLAB Simulink using an Arduino Due.
Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity
Authors: Barros Amanda M. P. , Angelim Jorge H. , Affonso Carolina M.
Published: June 2023

This paper presents a comparative analysis of the effects of short-range and long-range electric vehicles charging on transformer life. Long-range vehicles are expected to become more common in the future. They have higher battery capacity and charge at higher power levels, modifying demand profile. A probabilistic analysis is performed using the Monte Carlo Simulation, evaluating the transformer hottest-spot temperature and the aging acceleration factor. Residential demand is modeled based on real electricity measurements, and EVs’ demand is modeled based on real data collected from a trial project developed in the United Kingdom. Simulations are conducted considering the influence of ambient temperature analyzing summer and winter seasons and several EV penetration levels. Results show the impacts caused by long-range vehicles are more severe because they charge at higher power levels, especially during winter, when residential demand is higher. For penetration level of 50% during summer, the use of long-range EVs brings a minimum equivalent aging factor of 5.2, which means the transformer aged 124.8 h in a cycle of only 24 h, decreasing its lifetime.
A Review of Groundwater Heat Pump Systems in the Italian Framework: Technological Potential and Environmental Limits
Authors: Gizzi Martina , Vagnon Federico , Taddia Glenda , Lo Russo Stefano
Published: June 2023

For new buildings in densely urbanised cities, groundwater heat pump systems (GWHPs) represent a concrete, effective solution for decarbonising existing energy systems. Environmental factors must be considered to limit the GWHP system’s impact on the subsurface. Particular attention must be given to the long-term sustainability of groundwater abstraction modalities and the development of a thermally affected zone around re-injection wells. Simplified solutions and numerical models have been applied to predict subsurface heat transport mechanisms; these simulations allow researchers to consider site-specific geological conditions, transient heat and groundwater flow regimes, and anisotropies in the subsurface media. This paper presents a comprehensive overview of the current research on GWHPs and discusses the benefits and limitations of their diffusion in Italy. The sources used provide information on and examples of the correct methodological approaches for depicting the induced variations while avoiding the overestimation or underestimation of the impact that GWHPs have on exploited aquifers.
A Study on the Soundscape Preferences of the Elderly in the Urban Forest Parks of Underdeveloped Cities in China
Authors: Luo Lei , Zhang Qi , Mao Yingming , Peng Yanyan , Wang Tao , Xu Jian
Published: June 2023

Against the backdrop of the global aging trend, the proportion of the elderly population is severely increasing in the urban areas of underdeveloped regions. Despite evidence that urban forest parks are effective at enhancing the physical and mental well-being of the elderly, little has been done to investigate the connection between urban forest parks and the elderly in underdeveloped regions, and landscape studies in particular are lacking. This study attempted to address this gap, using a subjective evaluation method in which 725 elderly respondents were engaged in a questionnaire survey on their soundscape preferences in the urban forest parks of an underdeveloped city in China. The results revealed the elderly people’s preferences for soundscapes, and a further analysis demonstrated the relationships between these preferences and landscape features. The effects of personal traits and living situations on soundscape preferences were determined by analyzing the impacts of living conditions, occupation, and education on soundscape preferences. By building a model with regression coefficients, the most powerful factors influencing soundscape choice were investigated. It was found that (1) the types of sound sources preferred by the elderly, in descending order, were natural sound, livestock sound, bird song, musical sound, other sounds. (2) The differences among education, occupation, and age all affected the participants’ soundscape preferences, i.e., the mean values of the soundscape preferences among older adults varied with education, occupation, and age. The mean value of soundscape preference was higher among older adults who had received higher education, were government officials and business managers, and belonged to higher age groups. (3) Among the various factors influencing the soundscape preference of the elderly, the most influential factors were the length of time spent in the waterfront environment, the time spent in the forest park, and the importance of road signs. (4) The preference for soundscapes was strongly connected with happiness in life. (5) Wearing a mask significantly reduced soundscape perception scores under epidemic conditions, while vaccinated individuals were more tolerant of various noises. Recommendations for landscape design to improve the soundscape perception of elderly people are accordingly provided.
Construction of Core Collection and Phenotypic Evaluation of Toona sinensis
Authors: Dai Jianhua , Fan Yanru , Diao Shu , Yin Hengfu , Han Xiaojiao , Liu Jun
Published: June 2023

Toona sinensis is an important “vegetable and timber” tree species that is widely distributed in eastern and southeastern Asia. An efficient mechanism for preserving and utilizing germplasm resources is crucial for speeding up the process of genetic improvement of T. sinensis. We collected a total of 1040 germplasm resources of T. sinensis from China, Britain, and New Zealand. Using a total of 27,040 markers, we analyzed the genetic diversity, genetic structure, and core collection of these resources. Additionally, a comprehensive evaluation method for phenotypes of T. sinensis was established by principal component analysis (PCA) and the technique for order preference by similarity to ideal solution (TOPSIS). Our analysis included three main results: Firstly, the breeding population, consisting of a total of 1040 individuals, contains a diverse genetic pool and can be divided into two genetic clusters. Secondly, the best core collection was obtained from the alternative population, consisting of a total of 208 individuals and comprising 20% of the breeding population. It was screened out based on Core Hunter 3 software with the average entry-to-nearest-entry (E-NE) algorithm. Finally, a comprehensive evaluation method was established based on eight selected agronomic traits of the core collection. This method allowed us to screen the best individual plants of the core collection, including N0237, N0972, and N0409. Overall, our research findings will enable better preservation and utilization of T. sinensis germplasm resources and result in faster improvement of T. sinensis varieties.
A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019
Authors: Gao Shijuan , Zhu Jianjun , Fu Haiqiang
Published: June 2023

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas.
Optimization of Cold Brew Coffee Using Central Composite Design and Its Properties Compared with Hot Brew Coffee
Authors: Zakaria Nur Hadiyani , Whanmek Kanyawee , Thangsiri Sirinapa , Chathiran Wimonphan , Srichamnong Warangkana , Suttisansanee Uthaiwan , Santivarangkna Chalat
Published: June 2023

The cold brew coffee (CBC) trend is increasing globally; nevertheless, there is limited literature on this popular beverage. Many studies have focused on the health benefits of green coffee beans and coffee brewed by conventional hot water methods. Thus, whether cold brew conveys similar benefits is still unclear. This study aimed to investigate the influences of brewing conditions on physicochemical properties using response surface methodology in order to optimize the brewing parameters and compare the resulting CBC with the coffee from the French Press method. Central Composite Design was used to evaluate the effects and optimize the brewing parameters (i.e., water temperature, coffee-to-water ratio (C2WR), coffee mesh size, and extraction time) on total dissolved solids (TDS). Physicochemical properties, antioxidant activity, volatile compounds, and organic acids were compared between CBC and its French Press counterpart. Our results showed that water temperature, C2WR, and coffee mesh size significantly influenced the TDS of CBC. The optimized brewing conditions were water temperature (4 °C), C2WR (1:14), coffee mesh size (0.71 mm), and 24-h extraction time. At similar TDS, caffeine content, volatile compounds, and organic acids were higher in CBC, while other properties showed no significant difference. In conclusion, this study showed that at similar TDS, CBC has characteristics generally similar to hot brew coffee, except for the caffeine and sensory-related compound content. The model for the prediction of TDS from this study may benefit food services or industries for the optimization of brewing conditions to obtain different characteristics of CBC.
A Review on Isolation, Characterization, Modification, and Applications of Proso Millet Starch
Authors: Kumar Simmi Ranjan , Tangsrianugul Nuttinee , Suphantharika Manop
Published: June 2023

Proso millet starch (PMS) as an unconventional and underutilized millet starch is becoming increasingly popular worldwide due to its health-promoting properties. This review summarizes research progress in the isolation, characterization, modification, and applications of PMS. PMS can be isolated from proso millet grains by acidic, alkaline, or enzymatic extraction. PMS exhibits typical A-type polymorphic diffraction patterns and shows polygonal and spherical granular structures with a granule size of 0.3–17 µm. PMS is modified by chemical, physical, and biological methods. The native and modified PMS are analyzed for swelling power, solubility, pasting properties, thermal properties, retrogradation, freeze–thaw stability, and in vitro digestibility. The improved physicochemical, structural, and functional properties and digestibility of modified PMS are discussed in terms of their suitability for specific applications. The potential applications of native and modified PMS in food and nonfood products are presented. Future prospects for research and commercial use of PMS in the food industry are also highlighted.
Effect of Ultrasonic Pretreatment on the Far-Infrared Drying Process and Quality Characteristics of Licorice
Authors: Shang Jianwei , Zhang Qian , Wang Tongxun , Xu Yanrui , Zang Zepeng , Wan Fangxin , Yue Yuanman , Huang Xiaopeng
Published: June 2023

In this paper, the effects of different ultrasonic pretreatment processes on the far-infrared drying characteristics, quality indexes, and microstructure of licorice are evaluated. The results showed that ultrasonic pretreatment, combined with far-infrared drying, significantly reduced the drying time and moisture content of licorice compared with those of the control group. The highest total flavonoid content was obtained at an ultrasound power of 80 W. The total phenolic content (0.686 mg gallic acid equivalent/g) was higher than that in the control group, the increase was 19.4%, and its content was the highest at the sonication frequency of 20 kHz. The antioxidant capacity tended to increase and then decrease with the increase in sonication time, sonication power, and sonication frequency, and was the highest at 30 min of sonication. The soluble sugar content (31.490 mg glucose equivalent/g) was the highest at 30 kHz and 30 min. Observation of the microstructure revealed that the surface structure of the ultrasonic pretreated licorice slices changed significantly, forming more micropore channels, which facilitated the mass heat transfer during the drying process. In conclusion, ultrasonic pretreatment can significantly improve the quality of licorice tablets and significantly reduce the time required for subsequent drying. The combination of pretreatment parameters of 60 W ultrasonic power and 40 kHz ultrasonic frequency for 30 min was found to be an optimal combination of pretreatment parameters; therefore, this study may provide a technical reference for the industrialization of licorice drying.
Potential of Near-Infrared Spectroscopy (NIRS) for Efficient Classification Based on Postharvest Storage Time, Cultivar and Maturity in Coconut Water
Authors: Shen Xiaojun , Wang Tao , Wei Jingyi , Li Xin , Deng Fuming , Niu Xiaoqing , Wang Yuanyuan , Kan Jintao , Zhang Weimin , Yun Yong-Huan , Chen Fusheng
Published: June 2023

Coconut water (CW) is a popular and healthful beverage, and ensuring its quality is crucial for consumer satisfaction. This study aimed to explore the potential of near-infrared spectroscopy (NIRS) and chemometric methods for analyzing CW quality and distinguishing samples based on postharvest storage time, cultivar, and maturity. CW from nuts of Wenye No. 2 and Wenye No. 4 cultivars in China, with varying postharvest storage time and maturities, were subjected to NIRS analysis. Partial least squares regression (PLSR) models were developed to predict reducing sugar and soluble sugar contents, revealing moderate applicability but lacking accuracy, with the residual prediction deviation (RPD) values ranging from 1.54 to 1.83. Models for TSS, pH, and TSS/pH exhibited poor performance with RPD values below 1.4, indicating limited predictability. However, the study achieved a total correct classification rate exceeding 95% through orthogonal partial least squares discriminant analysis (OPLS-DA) models, effectively discriminating CW samples based on postharvest storage time, cultivar, and maturity. These findings highlight the potential of NIRS combined with appropriate chemometric methods as a valuable tool for analyzing CW quality and efficiently distinguishing samples. NIRS and chemometric techniques enhance quality control in coconut water, ensuring consumer satisfaction and product integrity.
Back to the Future: “De-Transition” to Low-Car Cities
Authors: Taillandier Chloé , Dijk Marc , Vialleix Martial
Published: June 2023

Current urban mobility systems in Europe, characterized by high car mobility shares, have negative environmental and health impacts but struggle to mitigate these for fear of sacrificing accessibility. Ironically, before the car mobility transition (in the 1950s and 1960s in Western countries and the 1990s in Eastern Europe), most cities were accessible by walking, cycling, public transport, and by the few cars there were. Through a longitudinal case study of a medium-sized urban area in Clermont-Ferrand, France (1950–2022), this paper explores the potential to ‘de-transition’, i.e., to reverse the urban transition process towards ‘accessible, low-car cities’ by reshaping infrastructures to constrain car use whilst accommodating walking, cycling, and public transport. We answer the following questions: To what extent can cities reverse the urban car mobility transition? How could such a process be further encouraged? Our analysis adopts a social practices perspective and uses a mixed-methods approach by combining semi-structured interviews, a survey, and a document analysis. On the one hand, our findings highlight the difficulty of an urban modality shift to car alternatives: (1) the limited reach of public transformation networks (in Clermont-Ferrand, the tramline); (2) the fact that many feel unsafe or assume they need excellent health conditions to cycle, which is associated with leisure and sports; and (3) strong convictions concerning the usefulness of vehicle ownership, which is believed to maximise comfort. On the other hand, based on a historic analysis, we offer practical recommendations to de-transition to low-car urban areas: (1) the creation of an extensive regional tramway network; (2) the development of a full cycling network; and (3) the promotion of an extensive car-free city centre.
On the Evolution of, and Hot Gas in, Wind-Blown Bubbles around Massive Stars - Wind Bubbles Are Not Energy-Conserving
Authors: Dwarkadas Vikram V.
Published: June 2023

The structure and evolution of wind-blown bubbles (WBBs) around massive stars has primarily been investigated using an energy-conserving model of wind-blown bubbles. While this model is useful in explaining the general properties of the evolution, several problems remain, including inconsistencies between observed wind luminosities and those derived using this formulation. Major difficulties include the low X-ray temperature and X-ray luminosity, compared to the model. In this paper, we re-examine the evolution, dynamics, and kinematics of WBBs around massive stars, using published ionization gasdynamic simulations of wind-blown bubbles. We show that WBBs can cool efficiently due to the presence of various instabilities and turbulence within the bubble. The expansion of WBBs is more consistent with a momentum-conserving solution, rather than an energy-conserving solution. This compares well with the dynamics and kinematics of observed wind bubbles. Despite the cooling of the bubble, the shocked wind temperature is not reduced to the observed values. We argue that the X-ray emission arise mainly from clumps and filaments within the hot shocked wind region, with temperatures just above 106 K. The remainder of the plasma can contribute to a lesser extent.
Integrated Transcriptomics and Metabolomics Analysis of the Fructan Metabolism Response to Low-Temperature Stress in Garlic
Authors: Bian Haiyan , Zhou Qianyi , Du Zhongping , Zhang Guangnan , Han Rui , Chen Laisheng , Tian Jie , Li Yi
Published: June 2023

As the main reserve carbohydrate in garlic, fructan contributes to garlic’s yield and quality formation. Numerous studies have shown that plant fructan metabolism induces a stress response to adverse environments. However, the transcriptional regulation mechanism of garlic fructan in low-temperature environments is still unknown. In this study, the fructan metabolism of garlic seedlings under low-temperature stress was revealed by transcriptome and metabolome approaches. With the extension of stress time, the number of differentially expressed genes and metabolites increased. Using weighted gene co-expression network analysis (WGCNA), three key enzyme genes related to fructan metabolism were screened (a total of 12 transcripts): sucrose: sucrose 1-fructosyltransferase (1-SST) gene; fructan: fructan 6G fructosyltransferase (6G-FFT) gene; and fructan 1-exohydrolase (1-FEH) gene. Finally, two hub genes were obtained, namely Cluster-4573.161559 (6G-FFT) and Cluster-4573.153574 (1-FEH). The correlation network and metabolic heat map analysis between fructan genes and carbohydrate metabolites indicate that the expression of key enzyme genes in fructan metabolism plays a positive promoting role in the fructan response to low temperatures in garlic. The number of genes associated with the key enzyme of fructan metabolism in trehalose 6-phosphate was the highest, and the accumulation of trehalose 6-phosphate content may mainly depend on the key enzyme genes of fructan metabolism rather than the enzyme genes in its own synthesis pathway. This study not only obtained the key genes of fructan metabolism in garlic seedlings responding to low temperatures but also preliminarily analyzed its regulatory mechanism, providing an important theoretical basis for further elucidating the cold resistance mechanism of garlic fructan metabolism.
Translation Arrest: A Key Player in Plant Antiviral Response
Authors: Vermeulen Annemarie , Takken Frank L. W. , Sánchez-Camargo Victor A.
Published: June 2023

Plants evolved several mechanisms to protect themselves against viruses. Besides recessive resistance, where compatible host factors required for viral proliferation are absent or incompatible, there are (at least) two types of inducible antiviral immunity: RNA silencing (RNAi) and immune responses mounted upon activation of nucleotide-binding domain leucine-rich repeat (NLR) receptors. RNAi is associated with viral symptom recovery through translational repression and transcript degradation following recognition of viral double-stranded RNA produced during infection. NLR-mediated immunity is induced upon (in)direct recognition of a viral protein by an NLR receptor, triggering either a hypersensitive response (HR) or an extreme resistance response (ER). During ER, host cell death is not apparent, and it has been proposed that this resistance is mediated by a translational arrest (TA) of viral transcripts. Recent research indicates that translational repression plays a crucial role in plant antiviral resistance. This paper reviews current knowledge on viral translational repression during viral recovery and NLR-mediated immunity. Our findings are summarized in a model detailing the pathways and processes leading to translational arrest of plant viruses. This model can serve as a framework to formulate hypotheses on how TA halts viral replication, inspiring new leads for the development of antiviral resistance in crops.
Integration of GWAS and RNA-Seq Analysis to Identify SNPs and Candidate Genes Associated with Alkali Stress Tolerance at the Germination Stage in Mung Bean
Authors: Xu Ning , Chen Bingru , Cheng Yuxin , Su Yufei , Song Mengyuan , Guo Rongqiu , Wang Minghai , Deng Kunpeng , Lan Tianjiao , Bao Shuying , Wang Guifang , Guo Zhongxiao , Yu Lihe
Published: June 2023

Soil salt-alkalization seriously impacts crop growth and productivity worldwide. Breeding and applying tolerant varieties is the most economical and effective way to address soil alkalization. However, genetic resources for breeders to improve alkali tolerance are limited in mung bean. Here, a genome-wide association study (GWAS) was performed to detect alkali-tolerant genetic loci and candidate genes in 277 mung bean accessions during germination. Using the relative values of two germination traits, 19 QTLs containing 32 SNPs significantly associated with alkali tolerance on nine chromosomes were identified, and they explained 3.6 to 14.6% of the phenotypic variance. Moreover, 691 candidate genes were mined within the LD intervals containing significant trait-associated SNPs. Transcriptome sequencing of alkali-tolerant accession 132–346 under alkali and control conditions after 24 h of treatment was conducted, and 2565 DEGs were identified. An integrated analysis of the GWAS and DEGs revealed six hub genes involved in alkali tolerance responses. Moreover, the expression of hub genes was further validated by qRT-PCR. These findings improve our understanding of the molecular mechanism of alkali stress tolerance and provide potential resources (SNPs and genes) for the genetic improvement of alkali tolerance in mung bean.
Geomatics in the Era of Citizen Science
Authors: Claramunt Christophe , Lotfian Maryam
Published: June 2023

Geomatics has long been recognized as an information-technology-oriented discipline whose objective is to integrate and deliver multiple sources of geolocated data to a wide range of environmental and urban sciences [...]
Low Frequency Cyclic Mechanical Loading of Till Deposits from Northern Germany under Oedometric Conditions
Authors: Hailemariam Henok , Wuttke Frank
Published: June 2023

Glacial deposits are of significant importance to geotechnical engineers and geologists in northern Europe, North America, and Northern Asia, as vast areas of these land surfaces were historically covered with ice leading to the formation of a wide variety of till deposits. The use of these areas for various engineering purposes warrants their subjection to mechanical loads (of static and cyclic forms) from manmade structures, as well as natural hazards such as earthquakes. This paper focuses on the experimental investigation of the cyclic mechanical loading behavior of two glacial tills from northern Germany under one-dimensional loading or oedometric conditions, and in different soil wetting conditions. The experimental results show a significant dependence of the cyclic mechanical response of the glacial tills on wetting condition and number of loading cycles. The recorded values of accumulated plastic strains of the glacial tills generally increase with an increase in wetting or moisture content, with the highest measured value for the two tills being around 3.9% after 19 cycles of loading. The findings of the experimental cyclic mechanical tests of the glacial tills are discussed in view of the intrinsic soil behavior and fabric.
Neighborhood Deprivation and Racial Disparities in Early Pregnancy Impaired Glucose Tolerance
Authors: Dolin Cara D. , Mullin Anne M. , Ledyard Rachel F. , Bender Whitney R. , South Eugenia C. , Durnwald Celeste P. , Burris Heather H.
Published: June 2023

Objective: There is mounting evidence that neighborhoods contribute to perinatal health inequity. We aimed (1) to determine whether neighborhood deprivation (a composite marker of area-level poverty, education, and housing) is associated with early pregnancy impaired glucose intolerance (IGT) and pre-pregnancy obesity and (2) to quantify the extent to which neighborhood deprivation may explain racial disparities in IGT and obesity. Study Design: This was a retrospective cohort study of non-diabetic patients with singleton births ≥ 20 weeks’ gestation from 1 January 2017–31 December 2019 in two Philadelphia hospitals. The primary outcome was IGT (HbA1c 5.7–6.4%) at <20 weeks’ gestation. Addresses were geocoded and census tract neighborhood deprivation index (range 0–1, higher indicating more deprivation) was calculated. Mixed-effects logistic regression and causal mediation models adjusted for covariates were used. Results: Of the 10,642 patients who met the inclusion criteria, 49% self-identified as Black, 49% were Medicaid insured, 32% were obese, and 11% had IGT. There were large racial disparities in IGT (16% vs. 3%) and obesity (45% vs. 16%) among Black vs. White patients, respectively (p < 0.0001). Mean (SD) neighborhood deprivation was higher among Black (0.55 (0.10)) compared with White patients (0.36 (0.11)) (p < 0.0001). Neighborhood deprivation was associated with IGT and obesity in models adjusted for age, insurance, parity, and race (aOR 1.15, 95%CI: 1.07, 1.24 and aOR 1.39, 95%CI: 1.28, 1.52, respectively). Mediation analysis revealed that 6.7% (95%CI: 1.6%, 11.7%) of the Black-White disparity in IGT might be explained by neighborhood deprivation and 13.3% (95%CI: 10.7%, 16.7%) by obesity. Mediation analysis also suggested that 17.4% (95%CI: 12.0%, 22.4%) of the Black–White disparity in obesity may be explained by neighborhood deprivation. Conclusion: Neighborhood deprivation may contribute to early pregnancy IGT and obesity–surrogate markers of periconceptional metabolic health in which there are large racial disparities. Investing in neighborhoods where Black patients live may improve perinatal health equity.