bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization Design for Sparse Planar Array in Satellite Communications
Authors: He Yuanzhi , Wang Changxu
Published: April 2023

The antenna is one of the key components of satellite communication load. To address the evolving requirements of future satellite communication systems, the sparse planar array has become an important device for transmitting and receiving electromagnetic waves in emerging antenna systems. The advantages of this technology include low cost, low system complexity, and robust anti-interference ability, which have attracted widespread attention within the industry. In this paper, we investigate an optimization design of sparse planar arrays in satellite communication scenarios. Firstly, we introduce the mathematical foundation of the array antennas and establish the optimization design model of the sparse planar array. Secondly, we analyze and compare the impact of different array layout methods on the sparse planar array antenna pattern, and then introduce the latest design trend of array material design. Thirdly, we review some classical optimization methods for optimizing sparse planar arrays and the recent research advancements in promising and novel methods. Lastly, on the basis of the present research status, we propose three future research directions and two critical challenges for optimal design of sparse planar arrays in satellite communication scenarios, which can facilitate the development and realization of array technology under future B5G and 6G wireless networks.
Speech Enhancement Performance Based on the MANNER Network Using Feature Fusion
Authors: Wang Shijie , Li Ji , Shao Lei , Liu Hongli , Zhu Lihua , Zhu Xiaochen
Published: April 2023

The problems that the multi-view attention network for noise erasure (MANNER) cannot take into account are the local and global features in the speech enhancement of long sequences. An attention and feature fusion MANNER (AF-MANNER) network is proposed, which improves the multi-view attention (MA) module in MANNER and replaces the global and local attention in the module. AF-MANNER also designs the feature-weighted fusion module to fuse the features of flash attention and neighborhood attention to enhance the feature expression of the network. The final ablation studies show that this network exhibits a good performance in speech enhancement and that its structure is valuable for improving the intelligibility and perceptual quality of speech.
Automatic Fluency Assessment Method for Spontaneous Speech without Reference Text
Authors: Liu Jiajun , Wumaier Aishan , Fan Cong , Guo Shen
Published: April 2023

The automatic fluency assessment of spontaneous speech without reference text is a challenging task that heavily depends on the accuracy of automatic speech recognition (ASR). Considering this scenario, it is necessary to explore an assessment method that combines ASR. This is mainly due to the fact that in addition to acoustic features being essential for assessment, the text features output by ASR may also contain potentially fluency information. However, most existing studies on automatic fluency assessment of spontaneous speech are based solely on audio features, without utilizing textual information, which may lead to a limited understanding of fluency features. To address this, we propose a multimodal automatic speech fluency assessment method that combines ASR output. Specifically, we first explore the relevance of the fluency assessment task to the ASR task and fine-tune the Wav2Vec2.0 model using multi-task learning to jointly optimize the ASR task and fluency assessment task, resulting in both the fluency assessment results and the ASR output. Then, the text features and audio features obtained from the fine-tuned model are fed into the multimodal fluency assessment model, using attention mechanisms to obtain more reliable assessment results. Finally, experiments on the PSCPSF and Speechocean762 dataset suggest that our proposed method performs well in different assessment scenarios.
Artificial Neural Network for Photonic Crystal Band Structure Prediction in Different Geometric Parameters and Refractive Indexes
Authors: Hsiao Fu-Li , Lee Hsin-Feng , Wang Su-Chao , Weng Yu-Ming , Tsai Ying-Pin
Published: April 2023

In this study, an artificial neural network that can predict the band structure of 2-D photonic crystals is developed. Three kinds of photonic crystals in a square lattice, triangular lattice, and honeycomb lattice and two kinds of materials with different refractive indices are investigated. Using the length of the wave vectors in the reduced Brillouin zone, band number, r/a ratio, and the refractive indices as the dataset, the desired ANN is trained to predict the eigenfrequencies of the photonic modes and depict the photonic band structures with a correlation coefficient greater than 0.99. By increasing the number of neurons in the hidden layer, the correlation coefficient can be further increased over 0.999.
LUVS-Net: A Lightweight U-Net Vessel Segmentor for Retinal Vasculature Detection in Fundus Images
Authors: Islam Muhammad Talha , Khan Haroon Ahmed , Naveed Khuram , Nauman Ali , Gulfam Sardar Muhammad , Kim Sung Won
Published: April 2023

This paper presents LUVS-Net, which is a lightweight convolutional network for retinal vessel segmentation in fundus images that is designed for resource-constrained devices that are typically unable to meet the computational requirements of large neural networks. The computational challenges arise due to low-quality retinal images, wide variance in image acquisition conditions and disparities in intensity. Consequently, the training of existing segmentation methods requires a multitude of trainable parameters for the training of networks, resulting in computational complexity. The proposed Lightweight U-Net for Vessel Segmentation Network (LUVS-Net) can achieve high segmentation performance with only a few trainable parameters. This network uses an encoder–decoder framework in which edge data are transposed from the first layers of the encoder to the last layer of the decoder, massively improving the convergence latency. Additionally, LUVS-Net’s design allows for a dual-stream information flow both inside as well as outside of the encoder–decoder pair. The network width is enhanced using group convolutions, which allow the network to learn a larger number of low- and intermediate-level features. Spatial information loss is minimized using skip connections, and class imbalances are mitigated using dice loss for pixel-wise classification. The performance of the proposed network is evaluated on the publicly available retinal blood vessel datasets DRIVE, CHASE_DB1 and STARE. LUVS-Net proves to be quite competitive, outperforming alternative state-of-the-art segmentation methods and achieving comparable accuracy using trainable parameters that are reduced by two to three orders of magnitude compared with those of comparative state-of-the-art methods.
Review of Wafer Surface Defect Detection Methods
Authors: Ma Jianhong , Zhang Tao , Yang Cong , Cao Yangjie , Xie Lipeng , Tian Hui , Li Xuexiang
Published: April 2023

Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas.
Three-Way Selection Random Forest Optimization Model for Anomaly Traffic Detection
Authors: Zhang Chunying , Zhang Meng , Yang Guanghui , Xue Tao , Zhang Zichi , Liu Lu , Wang Liya , Hou Wei , Chen Zhihai
Published: April 2023

With the application and development of Internet technology, network traffic is growing rapidly, and the situation of network security is becoming more and more serious. As an important way to protect network security, abnormal traffic detection has been paid more and more attention. In this paper, the uncertainty of the samples in the abnormal traffic detection dataset is studied. Combining the three-way decision idea with the random forest algorithm, a three-way selection random forest optimization model for abnormal traffic detection is proposed. Firstly, the three-way decision idea is integrated into the random selection process of feature attributes, and the attribute importance based on decision boundary entropy is calculated. The feature attributes are divided into the normal domain, abnormal domain, and uncertain domain, and the three-way attribute random selection rules are designed to randomly select the feature attributes that conform to the rules from different domains. Secondly, the classifier evaluation function is constructed by combining pure accuracy and diversity, and the anomaly traffic detection base classifier with a high evaluation value is selected for integration to eliminate the unstable factors caused by randomness in the process of base classifier generation. Thirdly, the optimal node weight combination of the base classifier is obtained by iterative calculation of the gray wolf optimization algorithm to further improve the prediction effect and robustness of the model. Finally, the model is applied to the abnormal traffic detection dataset. The experimental results show that the prediction accuracy of the three-way selection random forest optimization model on CIC-IDS2017, KDDCUP99, and NSLKDD datasets is 96.1%, 95.2%, and 95.3%, respectively, which has a better detection effect than other machine learning algorithms.
A Multi-Objective Crow Search Algorithm for Influence Maximization in Social Networks
Authors: Wang Ping , Zhang Ruisheng
Published: April 2023

Influence maximization is a key topic of study in social network analysis. It refers to selecting a set of seed users from a social network and maximizing the number of users expected to be affected. Many related research works on the classical influence maximization problem have concentrated on increasing the influence spread, omitting the cost of seed nodes in the diffusion process. In this work, a multi-objective crow search algorithm (MOCSA) is proposed to optimize the problem with maximum influence spread and minimum cost based on a redefined discrete evolutionary scheme. Specifically, the parameter setting based on the dynamic control strategy and the random walk strategy based on black holes are adopted to improve the convergence efficiency of MOCSA. Six real social networks were selected for experiments and analyzed in comparison with other advanced algorithms. The results of experiments indicate that our proposed MOCSA algorithm performs better than the benchmark algorithm in most cases and improves the total objective function value by more than 20%. In addition, the running time of the MOCSA has also been effectively shortened.
AI and Expert Insights for Sustainable Energy Future
Authors: Danish Mir Sayed Shah
Published: April 2023

This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.
Assessment of the Prospects of Polish Non-Food Energy Agriculture in the Context of a Renewable Energy Source
Authors: Dybek Barbara , Anders Dorota , Hołaj-Krzak Jakub T. , Hałasa Łukasz , Maj Grzegorz , Kapłan Magdalena , Klimek Kamila , Filipczak Gabriel , Wałowski Grzegorz
Published: April 2023

This paper describes examples of energy crops that are profitable to burn, and whose cultivation is not complicated or expensive. Rapid growth of biomass, especially the green mass of energy crops, is indicated, which means that, in relation to fossil fuels, energy crops are considered renewable raw materials. An assessment of Polish non-food energy agriculture was conducted in the context of the prospects of a renewable energy source, namely, biomass. Recommendations for crop cultivation, the size of possible yields and the most important parameters of the obtained biomass, which have the greatest impact on the suitability of energy use, are presented. Materials of biological origin for combustion are divided into three groups: wood waste, by-products and plant products for the energy industry. It is indicated that 2 tons (Mg) of dry wood or straw is energetically equivalent to 1 Mg of coal, and 1 m3 of biogas is energetically equivalent to 1 kg of Polish coal. A novelty of this article is the interpretation of obtaining primary energy, including energy from renewable sources, in the European Union and Poland, taking into account the production of wood waste, straw, cereals and energy crops. The mechanism of the impact of the production parameters of energy crops was revealed during the prepared analysis of the prospects of Polish energy agriculture. Additionally, we conducted an analysis of the potential of biomass as a source of energy in the context of: obtaining primary energy, including energy from renewable sources, in the European Union and Poland; the number of biogas plants in Poland; and the area of agricultural land that is potentially useful for the cultivation of energy crops.
AI-Based Scheduling Models, Optimization, and Prediction for Hydropower Generation: Opportunities, Issues, and Future Directions
Authors: Villeneuve Yoan , Séguin Sara , Chehri Abdellah
Published: April 2023

Hydropower is the most prevalent source of renewable energy production worldwide. As the global demand for robust and ecologically sustainable energy production increases, developing and enhancing the current energy production processes is essential. In the past decade, machine learning has contributed significantly to various fields, and hydropower is no exception. All three horizons of hydropower models could benefit from machine learning: short-term, medium-term, and long-term. Currently, dynamic programming is used in the majority of hydropower scheduling models. In this paper, we review the present state of the hydropower scheduling problem as well as the development of machine learning as a type of optimization problem and prediction tool. To the best of our knowledge, this is the first survey article that provides a comprehensive overview of machine learning and artificial intelligence applications in the hydroelectric power industry for scheduling, optimization, and prediction.
Development and Application of a Simulator for Simulating the Behaviors of a Geological System When Replacing CH4 from Hydrate-Bearing Reservoirs by CO2
Authors: Li Yan , Tian Hailong
Published: April 2023

Conventional techniques for hydrate production may cause the deconstruction of hydrate, changing the geomechanical stresses of the reservoir, which could trigger the subsidence of the seafloor. A new method for replacing CH4 from the hydrate lattice by CO2, without damaging the mechanical structure of sediment, has been proposed. This approach can achieve both the objectives of long-term CO2 sequestration and the safe production of CH4 from hydrates. By coupling the Chen-Guo model into Tough+Hydrate V1.5, an updated simulator CO2-EGHRSim V.10 (CO2 Enhanced Gas Hydrate Recovery simulator) was developed in this work to describe the replacing processes of CH4 from the hydrate lattice by CO2 and to evaluate the storage potential of CO2 and the recovery efficiency of CH4 from the hydrate-bearing reservoirs. The developed simulator was verified using measured data obtained from laboratory experiments. The verification suggested that CO2-EGHRSim performed well in predicting the replacing processes of CH4 with CO2. The simulator was applied to calculate the CO2 storage potential combined with the CH4 recovery from hydrates at the site of Iġnik Sikumi on the North Slope of Alaska. The simulated results indicated that the CO2–CH4 exchange mostly occurred inside the gas plume, and the CO2 hydrate was only present around the production well. The simulated CO2 storage ratio was 0.58, and the CH4 recovery efficiency was 25.95%.
Frequency Analysis of Equivalent Property-Damage-Only (EPDO) Crashes at Intersections
Authors: Sharafeldin Mostafa , Ksaibati Khaled , Gerow Ken
Published: April 2023

Traffic crashes are burdening societies with severe costs in terms of public health and economic loss. Intersection-related crashes are responsible for a large proportion of these losses due to their higher frequency and severity. Understanding the factors contributing to intersection crash frequency and severity is essential to mitigate their grave consequences. This study covered the analysis of roadway risk factors, influencing the frequency of equivalent property-damage-only (EPDO) crashes at intersections. The study included developing a negative binomial modeling framework to examine nine years of intersection crash records in the state of Wyoming. The modeling results revealed the key role of pavement friction in intersection safety and EPDO frequency. The findings also demonstrated that intersection location, grade, road functional classification, road surface type, the presence of guardrails, right shoulder type, and horizontal curvature all influence the EPDO crash frequency at intersections.
Applied Chemical Ecology of the Western Pine Beetle, an Important Pest of Ponderosa Pine in Western North America
Authors: Fettig Christopher J. , Audley Jackson P. , Homicz Crystal S. , Progar Robert A.
Published: April 2023

Western pine beetle (Dendroctonus brevicomis LeConte) is a major cause of ponderosa pine (Pinus ponderosa Dougl. ex. Laws.) mortality in western North America. Twenty-first century epidemics are among the largest in history and have affected hundreds of thousands of hectares. We synthesize literature on the chemical ecology of western pine beetle and on efforts to exploit our understanding of the western pine beetle-ponderosa pine system to reduce host tree losses. This literature dates back to the early 20th century and focuses on populations in California and Oregon, U.S., where western pine beetle exerts its largest impacts. Research in the 1960s–1970s yielded an effective semiochemical attractant (exo-brevicomin, frontalin, and myrcene) that helped inform understanding of the biology, ecology, and management of this species. Later, research focused on isolation and identification of semiochemical repellents. To date, Verbenone Plus (acetophenone, (E)-2-hexen-1-ol + (Z)-2-hexen-1-ol, and verbenone) is the only semiochemical repellent demonstrated effective for protecting ponderosa pines from mortality attributed to western pine beetle in multiple studies in Canada and the U.S.
Preparation of Hydrophobic Optically Transparent Wood via An Efficient UV-Assisted Route
Authors: Wang Hang , Yang Hong , Mu Hongbo
Published: April 2023

In the context of the double carbon goal, the green, low-carbon and environmentally friendly modern wood construction market is promising and is expected to be further promoted and applied in the construction market. Optically transparent wood is a new building light-transmitting material with excellent performance, designed to reduce the energy consumption of buildings. An efficient and green method for the preparation of hydrophobic optically transparent wood is proposed in this study, in which its microstructure, chemical composition, surface wettability and optical properties are investigated. Hydrophobic optically transparent wood (90% light transmission, 80% haze, 130° water contact angle) with identical optical properties on the positive and negative sides was obtained by UV-assisted hydrogen peroxide treatment of natural wood to remove lignin chromogenic groups in situ, followed by dipping the epoxy resin into the wood substrate template and finally combining it with PDMS low surface energy modifications on the surface. The scanning electron microscopy and chemical composition analysis showed that the epoxy resin was successfully immersed in the internal pores of the wood and exhibited a homogeneous interface with the wood cell walls. All results confirm that this optically transparent wood preparation method is effective, and the resulting hydrophobic optically transparent wood is a new wood composite alternative material with excellent optical and hydrophobic properties, which broadens the application area of traditional wood and offers potential applications in energy-efficient buildings, smart windows and solar cells.
Branchwood Properties of Two Tilia Species Collected from Natural Secondary Forests in Northeastern China
Authors: Guo Pingping , Zhao Xiping , Feng Qi , Yang Yongqiang
Published: April 2023

Tilia amurensis Rupr. and Tilia mandshurica Rupr. and Maxim. are two essential commercial species, though there is surprisingly little concern about whether their branches can be used in the current situation of a wood shortage in China. In this study, tissue proportions and fiber morphology, physical and mechanical properties, and chemical composition of the branchwood were studied and compared with stemwood to evaluate the potential for papermaking. The branchwood and stemwood showed similar cell arrangement but different tissue proportions and fiber morphology. The branchwood had more than 40% fiber proportion, 90%–97% below 0.9 mm in length, 75%–90% less than 33 in slenderness ratio, and 80% less than 1 in Runkel ratio. The branchwood was as light and soft as stemwood with a density of 0.32–0.36 g/cm3 and a compressive strength of about 30 MPa. The branchwood had 6% water extractives, 66% holocellulose, and 22% lignin for T. amurensis, 58% holocellulose and 30% lignin for T. mandshurica. The results suggest the branchwood is favorable for mechanical chipping, has the potential to obtain high pulp yield and its fibers can be mixed with wide, long and thick fibers from other tree species to produce specific paper products. In contrast, T. mandshurica branchwood may not be suitable for chemical pulping.
Interspecies Association and Community Stability of Plants in the Core Distribution Area of Thuja sutchuenensis
Authors: Wang Xiangfu , Liu Yong , Li Yuanhui , Jin Jiangqun , Guo Quanshui , Pei Shunxiang
Published: April 2023

The protection of endangered species is a hot topic for scholars worldwide, and interspecific association and community stability analysis are important methods with which to explore the structure and function of the endangered species community. Thuja sutchuenensis Franch. is an endangered species; however, studies on the T. sutchuenensis community remain insufficient. Here, based on the data of the plot survey of the T. sutchuenensis community in the Ta-pa Mountains and Xuebao Mountains, we analyzed the interspecific associations, niche width, and niche overlap of major species in the arborous and shrub layers, as well as community stability. The results showed that the overall interspecies association between the species of the arbor layer was non-significantly negatively associated, while the shrub layer was non-significantly positively associated. The Chi-square test results showed that the species pairs without interspecific association in the arbor layer and the shrub layer were much higher than those with significant interspecific association; in other words, the interspecific association of species in the T. sutchuenensis community was loose. The results of interspecific association coefficient analysis showed that the number of negatively associated species pairs was significantly higher than that of positive association species pairs, but Pearson’s correlation coefficient and Morisita’s niche overlap index analysis showed that the degree of competition between species in the community was small. In the arbor layer, the niche width of T. sutchuenensis was the widest, while in the shrub layer, the niche width of T. sutchuenensis was relatively high, indicating that T. sutchuenensis had a strong ability to adapt to the environment and use available resources. In addition, the intersection point of community stability (32.11, 67.89) in the vegetative community of T. sutchuenensis suggested that the community displayed a better stability. The survival strategy of T. sutchuenensis is based on its strong adaptability to the harsh environment to escape the competition among species, and this knowledge can provide a reference for the protection and restoration of endangered species.
A Review of Wood Compression along the Grain—After the 100th Anniversary of Pleating
Authors: Báder Mátyás , Németh Róbert
Published: April 2023

This study focuses on the compression of wood along the grain (also known as pleating), a modification that improves the pliability of higher-density hardwoods with a moisture content above 20%. Pleated wood can be bent into small curves in any direction. The success of the industrial pleating process in some parts of the world is influenced by many factors, such as wood species, wood quality, moisture content, compression ratio, fixation time, etc. Pleating by 20% causes the modulus of elasticity to decrease to one-third for oak and beech, and the bending ratio can be increased above ½. Bending stress decreases and the absorbed energy increases multiple times. The impact bending strength also increases significantly. The walls of cells crinkle by pleating and the microfibrils of the fibres become distorted. Many patents, articles, and books on this subject have been published since 1917, and this review attempts to introduce and, where necessary, critically analyse them.
Harvest Intensity Impacts Nutrient Status and Young Stand Development in Latvian Hemiboreal Forest
Authors: Klavins Ivars , Bardule Arta , Klavina Zane , Libiete Zane
Published: April 2023

This study aimed to compare the effects of whole-tree and stem-only harvesting in hemiboreal forests in Latvia. Chemistry of soil solution, precipitation, litter and needles, as well as tree parameters in regenerated stands were measured from 2012 to 2021 in oligotrophic and mesotrophic Scots pine sites with mineral soils and a eutrophic Norway spruce site with drained peat soil. Compared to the undisturbed control, the soil solution pH was significantly lower and N-NO3− and K+ concentrations were significantly higher after stem-only harvesting at the oligotrophic site, stem-only and whole-tree harvesting at the mesotrophic site and whole-tree harvesting at the eutrophic site. The height growth of the regenerated stands in all sites was similar for both harvesting methods six years after planting. More slender trees were observed after whole-tree harvesting than after stem-only harvesting at the eutrophic site. Whole-tree harvesting produced significant negative short-term effects on height growth in the oligotrophic site four to six years after harvest. The nutrient levels of needles differed with harvest intensity but did not indicate insufficient nutrient availability with any type of harvesting.
Color and Chemical Composition of Timber Woods (Daniellia oliveri, Isoberlinia doka, Khaya senegalensis, and Pterocarpus erinaceus) from Different Locations in Southern Mali
Authors: Traoré Mohamed , Martínez Cortizas Antonio
Published: April 2023

Wood characteristics and properties are related to various factors connected to the biochemical processes that occur in the tree during wood formation, but also, to the interactions with the environmental conditions at the tree growing location. In addition to climatic factors, several investigations drew attention to the significance of the influence of other environmental parameters at the tree growing location. In this perspective, this work aimed to characterize the variation in color and chemical composition of timber wood from different locations in southern Mali, of trees growing under the same climatic conditions. To do so, a total of 68 grounded wood samples, from 4 timber wood species (Daniellia oliveri, Isoberlinia doka, Khaya senegalensis, and Pterocarpus erinaceus), were analyzed using CIELab color space and FTIR-ATR. Overall, the results indicated that the variation in wood color and chemical properties can be related to the local environmental conditions. Pterocarpus erinaceus presented significant differences between samples from the three areas according to the highest number of variables (color parameters, molecular composition determined by FTIR-ATR spectroscopy, and FTIR-ATR ratios). Daniellia oliveri and Khaya senegalensis, however, showed significant differences between areas of provenance for a lower number of variables. Isoberlinia doka, for its part, showed no significant differences and seems to be less sensitive to environmental factors. Furthermore, the results revealed that important differences exist between wood samples from Kati and Kéniéba.
Vegetation Greening Enhanced the Regional Terrestrial Carbon Uptake in the Dongting Lake Basin of China
Authors: Wang Shihan , Feng Huihui , Zou Bin , Yang Zhuolin , Wang Shu
Published: April 2023

Terrestrial ecosystem carbon uptake is essential to achieving a regional carbon neutrality strategy, particularly in subtropical humid areas with dense vegetation. Due to the complex spatial and temporal heterogeneity of the carbon uptake of ecosystems, it is difficult to quantify the influence and contribution of different factors. With the aid of multisource remote sensing data, the spatiotemporal characteristics of carbon uptake and the impact of vegetation change were explored in the Dongting Lake Basin from 2001 to 2020. Based on the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model, we simulated the land use of the basin under different development scenarios in 2030. Our results showed that the basin has demonstrated a significant greening trend in the last 20 years, with a multiyear average NDVI (normalized difference vegetation index) of 0.60 and an increasing trend (y = 0.0048x − 9.069, R2 = 0.89, p < 0.01). In this context, the multiyear mean of net ecosystem productivity (NEP) was 314.95 g C·m−2·a−1 and also showed a significant increasing trend (y = 1.8915x + 295.09, R2 = 0.23, p < 0.05). Moreover, though the future carbon uptake might decrease because of the enhanced anthropogenic activities, the ecological conservation scenario might mitigate the reduction by 0.05 × 107 t. In conclusion, the greening trend enhanced the ecosystem carbon uptake in the Dongting lake basin. Considering the representativeness of the Dongting Lake Basin, the results of our study would provide useful clues for understanding the trend and pattern of terrestrial carbon uptake and for guiding the carbon neutrality strategy in the subtropical humid area.
An Integrative Analysis of Metabolome and Transcriptome Reveals the Molecular Regulatory Mechanism of the Accumulation of Flavonoid Glycosides in Different Cyclocarya paliurus Ploidies
Authors: Yu Yanhao , Qu Yinquan , Wang Shuyang , Wang Qian , Shang Xulan , Fu Xiangxiang
Published: April 2023

Cyclocarya paliurus (Batal) Iljinskaja is mainly used for harvesting leaves as materials for tea production and ingredients for the food industry. As its most important component, the contents of its total or specific flavonoids are supposed to vary at different ploidy levels. In the present study, two ploidy levels of C. paliurus are used to study their metabolome and transcriptome profiles. Though the total content of the flavonoids in leaves that were collected in September (the main harvesting season) presented insignificant differences between the two ploidies, flavonoid glucuronides were significantly accumulated in the tetraploid C. paliurus. Several structural genes related to the biosynthesis of these flavonoid glucuronides were expressed differentially, including PAL, 4CL, CHS, and CpUGTs. A weighted gene co-expression network analysis (WGCNA) revealed that nine genes were highly correlated with the flavonoid glucuronide contents. Furthermore, 3 CpMYB39 and 3 CpUGT71 were highly associated with this accumulation of flavonoid glucuronides in tetraploid C. paliurus. These results can provide a new perspective on how different polyploid levels alter the quantitative and qualitative patterns of the secondary metabolite production in C. paliurus.
Response of Soil Microbial Communities to Elevation Gradient in Central Subtropical Pinus taiwanensis and Pinus massoniana Forests
Authors: Huang Kexin , Xiang Jian , Ma Yuying , Cheng Jinping , Gu Jie , Hu Meng , Yang Yuan , Fang Yanming , Wang Genmei , Zhang Huanchao
Published: April 2023

In forest ecosystems, elevation gradient is one of the most influential factors on soil characteristics, vegetation types, and soil microorganisms. However, it remains unclear how the elevation gradient and the soil environment under its influence affect soil microbial communities under two distinct vegetation types. In this study, high-throughput sequencing technology from Illumina was utilized to examine the response of soil microbial communities to elevation and their driving factors in forests of Pinus taiwanensis and Pinus massoniana in various Jiangxi Province locales. The results demonstrated that the elevation gradients of the two pines had significant effects on soil organic carbon (SOC) and total nitrogen (TN), both in unimodal mode as well as on the alpha diversity of soil microbes. The community structure of soil bacteria is more sensitive to elevation than that of soil fungus. At different elevations in the two pine forests, Acidobacteria, Proteobacteria, Chloroflexi, Verrucomicrobia, Bacteroidetes, Patescibacteria, and Thaumarchaeota are the dominant bacterial phyla, and Ascomycota, Basidiomycota, and Mucoromycota are the dominant fungal phyla. This investigation revealed that SOC and TN were the two most influential factors on the alteration of the soil microbial community in two pine forests. In summary, there were substantial changes in soil microbial diversity and community composition across the two different pine forests, with elevation and soil characteristics (SOC and TN) serving as the primary drivers.
Genome-Wide Identification and Expression Analysis of the TCP Genes in Jatropha curcas L. Reveals Its Roles in Involvement of Leaf Shape
Authors: Zou Rong , Peng Yang , Zhao Yang , Wang Xiurong
Published: April 2023

Jatropha curcas var. nigroviensrugosus CV Yang (Jn) exhibits wrinkled leaves and higher flowering and fruiting rates compared to Jatropha curcas L. (Jc). Teosinte branched1/Cincinnata/Proliferating cell factors (TCPs) are known to play crucial roles in plant development and physiological processes. However, it remains unknown whether or not the TCP gene family regulates in leaf development of Jc and Jn. Here, we systematically performed a genome-wide analysis of the Jc TCP family and investigated the differences in the expression of TCP in different leaf morphologies. In total, our results showed that 18 TCP members were identified in the whole genome sequence of Jatropha curcas L.; Jc TCP genes were classified into two categories by phylogenetic tree construction, among which there were 11 members in the Class I subfamily, seven members in the Class Ⅱ subfamily. It was shown that 12 members of Jc TCP genes were located at the seven chromosomes, and proteins belonging to the same TCP group exhibit higher similarity than those from different groups. Furthermore, the expression profiles of 15 TCP genes were discovered at different leaf developmental stages of Jc and Jn. Jc TCP 4, Jc TCP 5, Jc TCP 8, Jc TCP 13, Jc TCP 14, and Jc TCP 16 showed significantly different expressions, and can be used as candidate genes for regulating leaf development. Therefore, the TCP genes play important roles in regulating the leaf development in Jc, and the manipulation of Jc TCP genes can potentially be an important tool used for the genetic improvement of the leaf.
Single-Cell Optical Nanomotion of Candida albicans in Microwells for Rapid Antifungal Susceptibility Testing
Authors: Radonicic Vjera , Yvanoff Charlotte , Villalba Maria Ines , Devreese Bart , Kasas Sandor , Willaert Ronnie G.
Published: April 2023

Candida albicans is an emerging multidrug-resistant opportunistic pathogen representing an important source of invasive disease in humans and generating high healthcare costs worldwide. The development of a rapid and simple antifungal susceptibility test (AFST) could limit the spread of this disease, increase the efficiency of treatment, and lower the risk of developing resistant strains. We developed a microfluidic chip containing an array of microwells that were designed to trap the cells and perform rapid antifungal susceptibility tests using optical nanomotion detection (ONMD). Yeast cell entrapment in a microwell allows for a very rapid exchange of growth medium with the antifungal, which enables performing single-cell ONMD measurements on the same cell before and after antifungal treatment. The exposure to a low concentration of the antifungal caspofungin or flucanozole induced a significant decrease in the nanomotion signal, demonstrating the high sensitivity of this technique. We used this chip to quantify the real-time response of individual C. albicans cells to the antifungal treatment in as fast as 10 min. This simple and label-free technique could be further developed into a simple-to-use device that allows the performance of fast AFST as part of a routine hospital procedure in developed and also eventually developing world countries.
Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning
Authors: Jamshidi Mohammad (Behdad) , Moztarzadeh Omid , Jamshidi Alireza , Abdelgawad Ahmed , El-Baz Ayman S. , Hauer Lukas
Published: April 2023

The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine’s performance.
A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
Authors: Otta Soumya Prakash , Panda Subhrakanta , Gupta Maanak , Hota Chittaranjan
Published: April 2023

The unauthorized usage of various services and resources in cloud computing is something that must be protected against. Authentication and access control are the most significant concerns in cloud computing. Several researchers in this field suggest numerous approaches to enhance cloud authentication towards robustness. User names and associated passwords have been a common practice for long as Single Factor Authentication. However, advancements in the speed of computing and the usage of simple methods, starting from the Brute Force technique to the implementation of advanced and efficient crytographic algorithms, have posed several threats and vulnerabilities for authentication systems, leading to the degradation of their effectiveness. Multi-factor authentication has emerged as a robust means of securing the cloud using simultaneous and multiple means of authentication factors. This employs multiple levels of cascaded authentication checks. This paper covers an extensive and systematic survey of various factors towards their adoption and suitability for authentication for multi-factor authentication mechanisms. The inference drawn from the survey is in terms of arriving at a unique authentication factor that does not require any additional, specialized hardware or software for multi-factor authentication. Such authentication also uses the distinct biometric characteristics of the concerned user in the process. This arrangement augments the secured and robust user authentication process. The mechanism is also assessed as an effective means against impersonation attacks.
Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem
Authors: Loudermilk Eva Louise , Pokswinski Scott , Hawley Christie M. , Maxwell Aaron , Gallagher Michael R. , Skowronski Nicholas S. , Hudak Andrew T. , Hoffman Chad , Hiers John Kevin
Published: April 2023

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models.
Winery By-Products as Sources of Bioactive Tryptophan, Serotonin, and Melatonin: Contributions to the Antioxidant Power
Authors: Baenas Nieves , García-Viguera Cristina , Domínguez-Perles Raúl , Medina Sonia
Published: April 2023

The amino acid tryptophan and its derived molecules serotonin and melatonin are involved in a wide range of physiological functions that contribute significantly to human health, namely antioxidant, immune-active, and neurological properties. Grapes and wine are a source of these compounds, but their presence in wine by-products remains underexplored. Therefore, the aim of this work was the identification and quantification of tryptophan, serotonin, and melatonin in winery by-products (grape stems, grape pomace, and wine lees) by ultra-high performance liquid chromatography coupled to electrospray ionization and mass spectrometer with triple-quadrupole technology (UHPLC-ESI-QqQ-MS/MS), as well as the evaluation of the extracts obtained (by applying specific extraction conditions for each of them) for their antioxidant and reducing capacity (by three different and complementary methods: FRAP, ABTS•+, and ORAC). Furthermore, correlation analyses were developed to establish the contribution of the different analytes to the total antioxidant activity. The main results obtained pointed out grape stems as the by-product with the highest tryptophan content (96.28 mg/kg dw) and antioxidant capacity (142.86, 166.72, and 363.24 mmol TE/kg dw, FRAP, ABTS•+, and ORAC, respectively), while serotonin and melatonin were the predominant derivatives in grape pomace (0.086 and 0.902 µg/kg dw, respectively). The antioxidant capacity of the standards was also analysed at the concentrations found in the matrices studied. A significant correlation was found between the concentration of the pure tryptophan standard and the antioxidant capacity (ABTS•+, r2 = 0.891 at p < 0.001 (***); FRAP, r2 = 0.885 at p < 0.01 (**); and ORAC, r2 = 0.854 at p < 0.01 (**)). According to these results, winery by-products can be highlighted as valuable materials to be used as novel ingredients containing tryptophan, serotonin, and melatonin, while tryptophan was identified as the most relevant contributor (out of phenolic compounds) to the antioxidant capacity exhibited by wine by-products.
δ34S and Geochemical Analyses for the Determination of, and Discrimination between, Salt Samples of Different Geographic Origin: A Feasibility Study
Authors: Horacek Micha
Published: April 2023

The geographic origin of salt is usually regarded as unimportant, as it is a one-quality product produced in vast quantities. However, certain salt brands, especially sea salt (fleur de sel), are sold at significantly higher prices. Thus, it is necessary to control the declared geographic origin of salt. Such controls are already frequently carried out for foodstuffs, but salt is an inorganic commodity. Thus, δ34S analysis combined with element concentration analysis was carried out. The results show very similar δ34S values for all sea salt samples, which is to be expected due to the homogenous marine δ34S value. Still, slightly higher values have been found in Mediterranean salt samples. Rock salt samples show differing δ34S values depending on the time they were formed, and if the salt samples are of marine or terrestrial origin. Terrestrial/continental salt samples are characterized by elemental patterns significantly differing from marine ones. However, within marine samples (sea salt and rock salt) there also exist differences enabling the differentiation of samples.