bluuebunny/crossref_metadata_2025 huggingface dataset:
Barley in the Production of Cereal-Based Products
Authors: Lukinac Jasmina , Jukić Marko
Published: December 2022

Barley (Hordeum vulgare L.) is unjustly neglected today as a food grain. Interest in the use of barley in the food industry has increased recently. The reason for this is its content of dietary fibre, especially β-glucan, which has been shown to reduce blood cholesterol and lower blood sugar levels. The main nutritional components of barley and barley products, besides the mentioned β-glucan, are starch, sugar, proteins, fat and ash. Although not common in the production of bakery products, barley can be very easily involved in the production of the same products, and such products have improved nutritional characteristics and acceptable sensory characteristics, which make them desirable. Barley has great potential for use in a wide range of cereal-based foods as a partial or full replacement for currently used grains (such as wheat, oats, rice and corn). This article provides basic and general information about the use of barley in food and the processing of barley grains for use in the manufacturing of cereal-based products, with particular attention to the use of barley in the manufacturing of bread (flatbread and leavened bread), noodles and pasta, muffins and cakes and cookies and biscuits.
Irrigation Effect on Yield, Skin Blemishes, Phellem Formation, and Total Phenolics of Red Potatoes
Authors: Jiang Manlin , Shinners-Carnelley Tracy , Gibson Darin , Jones Debbie , Joshi Jyoti , Wang-Pruski Gefu
Published: December 2022

Dark Red Norland is an important potato cultivar in the fresh market due to its attractive bright, red colour, and good yield. However, skin blemishes such as silver patch, surface cracking, and russeting can negatively influence the tuber skin quality and marketability. It is well known that potato is a drought-sensitive plant. This study was conducted to determine whether irrigation would affect Dark Red Norland’s yield and skin quality. A three-year field trial was conducted by Peak of the Market in Manitoba, Canada. Plants were treated under both irrigation and rainfed conditions. The results show that irrigation increased the total yield by 20.6% and reduced the severity of surface cracking by 48.5%. Microscopy imaging analysis demonstrated that tubers from the rainfed trials formed higher numbers of suberized cell layers than those of the irrigated potatoes, with a difference of 0.360 to 0.652 layers in normal skins. Surface cracking and silver patch skins had more suberized cell layers than the normal skins, with ranges of 7.805 to 8.333 and 7.740 to 8.496, respectively. A significantly higher amount of total polyphenols was found in the irrigated samples with a mean of 77.30 mg gallic acid equivalents (GAE)/100 g fresh weight (fw) than that of the rainfed samples (69.80 mg GAE/100 g fw). The outcome of this study provides a better understanding of the water regime effect causing these skin blemishes, which could potentially be used to establish strategies to improve tuber skin quality and minimize market losses.
Genetic Diversity and Population Structure of Chinese Chestnut (Castanea mollissima Blume) Cultivars Revealed by GBS Resequencing
Authors: Jiang Xibing , Fang Zhou , Lai Junsheng , Wu Qiang , Wu Jian , Gong Bangchu , Wang Yanpeng
Published: December 2022

Chinese chestnut (Castanea mollissima Bl.) is one of the earliest domesticated and cultivated fruit trees, and it is widely distributed in China. Because of the high quality of its nuts and its high resistance to abiotic and biotic stresses, Chinese chestnut could be used to improve edible chestnut varieties worldwide. However, the unclear domestication history and highly complex genetic background of Chinese chestnut have prevented the efficiency of breeding efforts. To explore the genetic diversity and structure of Chinese chestnut populations and generate new insights that could aid chestnut breeding, heterozygosity statistics, molecular variance analysis, ADMIXTURE analysis, principal component analysis, and phylogenetic analysis were conducted to analyze single nucleotide polymorphism data from 185 Chinese chestnut landraces from five geographical regions in China via genotyping by sequencing. Results showed that the genetic diversity level of the five populations from different regions was relatively high, with an observed heterozygosity of 0.2796–0.3427. The genetic diversity level of the population in the mid-western regions was the highest, while the population north of the Yellow River was the lowest. Molecular variance analysis showed that the variation among different populations was only 2.07%, while the intra-group variation reached 97.93%. The Chinese chestnut samples could be divided into two groups: a northern and southern population, separated by the Yellow River; however, some samples from the southern population were genetically closer to samples from the northern population. We speculate that this might be related to the migration of humans during the Han dynasty due to the frequent wars that took place during this period, which might have led to the introduction of chestnut to southern regions. Some samples from Shandong Province and Beijing City were outliers that did not cluster with their respective groups, and this might be caused by the special geographical, political, and economic significance of these two regions. The findings of our study showed the complex genetic relationships among Chinese chestnut landraces and the high genetic diversity of these resources.
15N Natural Abundance of C3 and C4 Herbaceous Plants and Its Response to Climatic Factors along an Agro-Pastoral Zone of Northern China
Authors: Liu Xianzhao , Li Yang , Zhang Yong , Su Qing , Feng Teng , Song Yan
Published: December 2022

The nitrogen isotope composition of plants (δ15N) can comprehensively reflect information on climate change and ecosystems’ nitrogen cycle. By collecting common herbs and soil samples along the 400 mm isoline of mean annual precipitation (MAP) in the agro-pastoral zone of North China (APZNC) and measuring their δ15N values, the statistical characteristics of foliar δ15N of herbs and the responses of foliar δ15N to the MAP and mean annual temperature (MAT) were analyzed. The results showed that: (1) the δ15N values of all herbs investigated varied from −5.5% to 15.25%. Among them, the δ15N value range of C3 herbs (−5.5~15.00%) was wider than that of C4 herbs (−2.17~15.25%), but the average value (3.27%) of C3 herbs was significantly lower than that of C4 herbaceous plants (5.55%). This difference provides an important method for identifying plants of different photosynthetic types by nitrogen isotope technology. (2) Along the transect from northeast to southwest, the δ15N of both C3 and C4 herbs decreased with the increase in the MAP, but not significantly for C3 herbs. The inverse relationship between the nitrogen isotopic signatures of herbs and MAP is consistent with previous studies. However, the MAP in the APZNC is found to only explain a small amount of the observed variance in the δ15N herbs (C3 herbs: 10.40%; C4 herbs: 25.03%). (3) A strong negative relationship was found between δ15N of herbs and MAT across the transect (C3 herbs: −0.368%/°C; C4 herbs: −0.381%/°C), which was contrary to the global pattern and some regional patterns. There was no significant difference in the δ15N responses of two different photosynthetic herbs to temperature, but the effect of temperature on the variances of δ15N of C3 and C4 herbs was significantly greater than that of precipitation. This suggests that temperature is a key factor affecting foliar δ15N of herbs in this transect. The above findings may be of value to global change researchers studying the processes of the nitrogen cycle and gaining an insight into climate dynamics of the past.
TPC1-Type Channels in Physcomitrium patens: Interaction between EF-Hands and Ca2+
Authors: Mérida-Quesada Franko , Vergara-Valladares Fernando , Rubio-Meléndez María Eugenia , Hernández-Rojas Naomí , González-González Angélica , Michard Erwan , Navarro-Retamal Carlos , Dreyer Ingo
Published: December 2022

Two-pore channels (TPCs) are members of the superfamily of ligand-gated and voltage-sensitive ion channels in the membranes of intracellular organelles of eukaryotic cells. The evolution of ordinary plant TPC1 essentially followed a very conservative pattern, with no changes in the characteristic structural footprints of these channels, such as the cytosolic and luminal regions involved in Ca2+ sensing. In contrast, the genomes of mosses and liverworts encode also TPC1-like channels with larger variations at these sites (TPC1b channels). In the genome of the model plant Physcomitrium patens we identified nine non-redundant sequences belonging to the TPC1 channel family, two ordinary TPC1-type, and seven TPC1b-type channels. The latter show variations in critical amino acids in their EF-hands essential for Ca2+ sensing. To investigate the impact of these differences between TPC1 and TPC1b channels, we generated structural models of the EF-hands of PpTPC1 and PpTPC1b channels. These models were used in molecular dynamics simulations to determine the frequency with which calcium ions were present in a coordination site and also to estimate the average distance of the ions from the center of this site. Our analyses indicate that the EF-hand domains of PpTPC1b-type channels have a lower capacity to coordinate calcium ions compared with those of common TPC1-like channels.
Chemical Composition of Thymus leucotrichus var. creticus Essential Oil and Its Protective Effects on Both Damage and Oxidative Stress in Leptodictyum riparium Hedw. Induced by Cadmium
Authors: Maresca Viviana , Badalamenti Natale , Ilardi Vincenzo , Bruno Maurizio , Bontempo Paola , Basile Adriana
Published: December 2022

The chemical profile of the essential oil (EO) of the aerial parts of Thymus leucotrichus var. creticus (Lamiaceae), a taxon not previously studied, was investigated by GC–MS analysis, using a DB–Wax polar column. Oxygenated monoterpenes and monoterpene hydrocarbons dominate the EO, with thymol (46.97%) and p-cymene (28.64%) as the main constituent of these two classes, respectively. The ability of the EO of T. leucotrichus to reduce Cd toxicity was studied in aquatic moss Leptodictyum riparium. To study EO-induced tolerance to Cd toxicity, apex growth, number of dead cells, DNA damage and antioxidant response in gametophytes were examined. The exogenous application of the EO yields a resumption of growth rate and a reduction in the number of dead cells; it also reduces the oxidative stress induced by Cd, as demonstrated by the reduction of the ROS content (with a decrease of 1.52% and 5%) and by the increased activity of antioxidant enzymes such as superoxide dismutase (SOD) (with an increase of 1.44% and 2.29%), CAT catalase (1.46% and 2.91%) and glutathione-S-transferase GST (1.57% and 1.90%). Furthermore, the application of the EO yields a reduction of DNA damage. These results clearly indicate the protective capacity of the EO of T. leucotrichus in modulating the redox state through the antioxidant pathway by reducing the oxidative stress induced by Cd.
Elevated CO2 Alters the Physiological and Transcriptome Responses of Pinus densiflora to Long-Term CO2 Exposure
Authors: Kim Tae-Lim , Lim Hyemin , Chung Hoyong , Veerappan Karpagam , Oh Changyoung
Published: December 2022

Physiological response and transcriptome changes were observed to investigate the effects on the growth, metabolism and genetic changes of Pinus densiflora grown for a long time in an environment with an elevated atmospheric CO2 concentration. Pine trees were grown at ambient (400 ppm) and elevated (560 ppm and 720 ppm) CO2 concentrations for 10 years in open-top chambers. The content of nonstructural carbohydrates was significantly increased in elevated CO2. It was notable that the contents of chlorophylls significantly decreased at an elevated CO2. The activities of antioxidants were significantly increased at an elevated CO2 concentration of 720 ppm. We analyzed the differences in the transcriptomes of Pinus densiflora at ambient and elevated CO2 concentrations and elucidated the functions of the differentially expressed genes (DEGs). RNA-Seq analysis identified 2415 and 4462 DEGs between an ambient and elevated CO2 concentrations of 560 ppm and 720 ppm, respectively. Genes related to glycolysis/gluconeogenesis and starch/sucrose metabolism were unchanged or decreased at an elevated CO2 concentration of 560 ppm and tended to increase at an elevated CO2 concentration of 720 ppm. It was confirmed that the expression levels of genes related to photosynthesis and antioxidants were increased at an elevated CO2 concentration of 720 ppm.
Study of High-Temperature-Induced Morphological and Physiological Changes in Potato Using Nondestructive Plant Phenotyping
Authors: Lazarević Boris , Carović-Stanko Klaudija , Safner Toni , Poljak Milan
Published: December 2022

Potato (Solanum tuberosum L.) is vulnerable to high temperatures, which are expected to increase in frequency and duration due to climate change. Nondestructive phenotyping techniques represent a promising technology for helping the adaptation of agriculture to climate change. In this study, three potato cultivars (Agria, Bellarosa and Desiree) were grown under four temperature treatments: 20/15 °C (T1), 25/20 °C (T2), 30/25 °C (T3), and 35/30 °C (T4). Multispectral and chlorophyll fluorescence imaging, 3D multispectral scanning, and gas exchange analysis were used to study the effect of moderate heat stress on potato morphology and physiology and select phenotypic traits most responsive to increased temperatures. The most responsive morphological traits to increased temperatures are related to decreased leaf area, which were detected already at T2. Increased temperatures (already T2) also changed leaf spectral characteristics, indicated by increased red, green, and blue reflectance and decreased far-red reflectance and anthocyanin index (ARI). Regarding chlorophyll fluorescence, increasing temperatures (T2) caused an increase in minimal fluorescence of both dark-adapted (F0) and light-adapted (F0’) plants. Stomatal conductance, transpiration rate, photosynthetic rate, instantaneous water use efficiency (WUE), and intrinsic water use efficiency increased from T1 to T3 and decreased again in T4. Using recursive partitioning analysis, the most responsive potato phenotypic traits to increased temperature were leaf area projected (LAP), ARI, F0, and WUE. These traits could be considered marker traits for further studying potato responses to increased temperatures.
Volatile Chemical Variation of Essential Oils and Their Correlation with Insects, Phenology, Ontogeny and Microclimate: Piper mollicomum Kunth, a Case of Study
Authors: de Brito-Machado Daniel , Ramos Ygor Jessé , Defaveri Anna Carina Antunes e , de Queiroz George Azevedo , Guimarães Elsie Franklin , de Lima Moreira Davyson
Published: December 2022

The aim of this study was to monitor the volatile chemical composition from leaves and reproductive organs of Piper mollicomum Kunth (PM), in its reproduction period, as well as register inflorescence visitors, microclimate and phenological information. The essential oils (EOs) obtained from the different fresh organs by hydrodistillation were identified and quantified by Gas Chromatography/Mass Spectrometry (GC/MS) and by GC coupled to a Flame Ionization Detector (GC/FID), respectively. The cercentage content of some volatiles present in reproductive organs, such as limonene, 1,8-cineole, linalool and eupatoriochromene, increased during the maturation period of the inflorescences, and decreased during the fruiting period, suggesting a defense/attraction activities. Furtermore, a biosynthetic dichotomy between 1,8-cineole (leaves) and linalool (reproductive organs) was recorded. A high frequency of bee visits was registered weekly, and some correlations showed a positive relationship between this variable and terpenes. Microclimate has an impact on this species’ phenological cycles and insect visiting behavior. All correlations between volatiles, insects, phenology and microclimate allowed us to present important data about the complex information network in PM. These results are extremely relevant for the understanding of the mechanisms of chemical–ecological plant–insect interactions in Piperaceae, a basal angiosperm.
Towards a Circular Economy: Study of the Mechanical, Thermal, and Electrical Properties of Recycled Polypropylene and Their Composite Materials
Authors: Jamnongkan Tongsai , Intraramongkol Nitchanan , Samoechip Wesarach , Potiyaraj Pranut , Mongkholrattanasit Rattanaphol , Jamnongkan Porntip , Wongwachirakorn Piyada , Sugimoto Masataka , Ito Hiroshi , Huang Chih-Feng
Published: December 2022

This research focuses on the mechanical properties of polypropylene (PP) blended with recycled PP (rPP) at various concentrations. The rPP can be added at up to 40 wt% into the PP matrix without significantly affecting the mechanical properties. MFI of blended PP increased with increasing rPP content. Modulus and tensile strength of PP slightly decreased with increased rPP content, while the elongation at break increased to up to 30.68% with a 40 wt% increase in rPP content. This is probably caused by the interfacial adhesion of PP and rPP during the blending process. The electrical conductivity of materials was improved by adding carbon black into the rPP matrices. It has a significant effect on the mechanical and electrical properties of the composites. Stress-strain curves of composites changed from ductile to brittle behaviors. This could be caused by the poor interfacial interaction between rPP and carbon black. FTIR spectra indicate that carbon black did not have any chemical reactions with the PP chains. The obtained composites exhibited good performance in the electrical properties tested. Finally, DSC results showed that rPP and carbon black could act as nucleating agents and thus increase the degree of crystallinity of PP.
Molecular Dynamics Simulations of Polydopamine Nanosphere’s Structure Based on Experimental Evidence
Authors: Manzanares-Gómez Jesús , León Salvador , Climent-Pascual Esteban , García-Armada María Pilar
Published: December 2022

In this work, we show how to obtain internal monodispersed gold nanoparticles inside polydopamine (PDA) nanospheres that are also externally decorated with gold. The number of internal nanoparticles is affected by the size of the PDA nanosphere used, and the lower limit in the number of gold nanoparticles in the center of decorated nanospheres, one single gold nanoparticle, has been reached. In addition, extensive molecular dynamics simulations of PDA nanospheres based on four different chemical motifs, in the presence of water and with different sizes, have been performed to gain insight into the arrangements capable of accommodating cavities. In particular, PDA nanospheres based on pyranoacridinotrione (PYR) units provide good agreement with the experimental attainment of internal metal nanoparticles. In these, the stacking of PYR units leads to a particular morphology, with large portions of space occupied by the solvent, that would explain the observed formation of gold nanoparticles inside the PDA nanosphere.
Masterbatch of Chitosan Nanowhiskers for Preparation of Nylon 6,10 Nanocomposite by Melt Blending
Authors: Jin Se Bin , Hao Lam Tan , Hwang Sung Yeon , Oh Dongyeop X. , Koo Jun Mo , Jeon Hyeonyeol , Park Sung Bae , Park Jeyoung
Published: December 2022

Composite materials have been extensively studied to optimize properties such as lightness and strength, which are the advantages of plastics. We prepared a highly concentrated (30 wt %) nylon/chitosan nanowhisker (CSW) masterbatch by blending nylon 6,10 and CSW by solvent casting to achieve high dispersion efficiency while considering an industrial setting. Subsequently, 0.3 wt % nylon/CSW nanocomposites were prepared with a large quantity of nylon 6,10 via melt blending. During preparation, the materials were stirred in the presence of formic acid at different times to investigate the effect of stirring time on the structure of the CSW and the physical properties of the composite. The formation of nanocomposites by the interactions between nylon and CSW was confirmed by observing the change in hydrogen bonding using FT-IR spectroscopy and the rise in melting temperature and melting enthalpy through differential scanning calorimetry. The results demonstrated increases in complex viscosity and shear thinning. The rheological properties of the composites changed due to interactions between CSW and nylon, as indicated by the loss factor. The mechanical properties produced by the nanocomposite stirred for 1.5 h were superior, suggesting that formic acid caused minimal structural damage, thus verifying the suitability of the stirring condition.
Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique
Authors: Abed Ahmed M. , Seddek Laila F. , Elattar Samia
Published: December 2022

Scholars seek to recycle wasted energy to produce electricity by integrating thermoelectric generators (TEGs) with internal combustion engines (ICE), which rely on the electrical conductivity, β, of the thermal conductor strips. The TEG legs are alloyed from iron, aluminum and copper in a strip shape with specific characteristics that guarantee maximum thermo-electric transformation, which has fluctuated between a uniform, Gaussian, and exponential distribution according to the structure of the alloy. The ICE exhaust and intake gates were chosen as the TEG sides. The digital simulator twin model checks the integration efficiency through two sequential stages, beginning with recording the causes of thermal conductivity failure via filming and extracting their data by neural network procedures in the feed of the second stage, which reveal that the cracks are a major obstacle in reducing the TEG-generated power. Therefore, the interest of the second stage is predicting the cracks’ positions, Pi,j, and their intensity, QP, based on the ant colony algorithm which recruits imaging data (STTF-NN-ACO) to install the thermal conductors far away from the cracks’ positions. The proposed metaheuristic (STTF-NN-ACO) verification shows superiority in the prediction over [Mat-ACO] by 8.2% and boosts the TEGs’ efficiency by 32.21%. Moreover, increasing the total generated power by 12.15% and working hours of TEG by 20.39%, reflects reduced fuel consumption by up to 19.63%.
Development of a Continuous Hydrothermal Treatment Process for Efficient Dewatering of Industrial Wastewater Sludge
Authors: Zijlstra Douwe S. , Cobussen-Pool Esther , Slort Dennis J. , Visser Mark , Nanou Pavlina , Pels Jan R. , Wray Heather E.
Published: December 2022

Sludges from the papermaking industry represent a challenging residue stream that is difficult to dewater using conventional processes. The successful development and scale-up of innovative processes from lab- to pilot- to industrial-scale are required to tackle challenges for waste treatment, including paper sludges. Biological paper sludge was treated via a mild hydrothermal carbonization process (TORWASH®) to improve dewaterability of the sludge, including long-duration, continuous testing. Initial lab-scale experiments indicated the optimal treatment temperature for sludge dewatering was 190 °C. Dewaterability improved with increasing temperature, but the obtained solid yield decreased. Scaling-up to a continuous flow pilot plant required a temperature of 200 °C to achieve optimum dewatering. Pilot-scale hydrothermal treatment and dewatering resulted in solid cakes with an average dry matter content of 38% and a solid yield of 39%. This study demonstrates the benefits of hydrothermal carbonization for the dewatering of biological paper sludge without the use of dewatering aids such as fiber sludge or polyelectrolytes. The results also demonstrate the successful adaptation of a lab-scale batch process to a pilot-scale continuous flow process for hydrothermal carbonization of industrial wastewater sludge.
Specification and Simplification of Analytical Methods to Determine Wine Color
Authors: Hensel Marcel , Di Nonno Sarah , Mayer Yannick , Scheiermann Marina , Fahrer Jörg , Durner Dominik , Ulber Roland
Published: December 2022

The color of wine is an important quality parameter essential for the first impression of consumers. The International Organization of Vine and Wine (OIV) recommends two methods to describe wine color: color calculation according to Glories and the determination of coordinates in the CIE L*a*b* color space. The measurement of wine color is often not feasible for winemakers because the required instrumentation is expensive and bulky. In this study, the influence of photometer settings on the calculated color was investigated based on 14 wines. Furthermore, the CIE L*a*b* and Glories system were compared using 56 red and 56 white wines. Photometer settings were found to influence the reproducibility of color determination. In addition, CIE L*a*b* system do not correlate in all wines with the Glories system and Glories probably provides less information about wine color. Using interpolation, CIE L*a*b* coordinates were calculated from single wavelength measurements taken by a small-sized and inexpensive portable analysis system, which could be used by winemakers in the future.
“Mystical Spirituality” in Second Temple Period Judaism? Light from the Decorated Stone in the Magdala Synagogue
Authors: Cirafesi Wally V.
Published: December 2022

While “Merkavah mysticism” as a religious movement is a phenomenon of Late Ancient and Medieval Judaism, scholars have debated whether the origins of this movement are traceable to traditions of the divine Merkavah (chariot-throne) preserved in some early Jewish apocalyptic literature from the Second Temple period. Furthermore, scholars have emphasized that these early Merkavah traditions reflect individualistic religious experiences that emerged historically in contexts of small esoteric groups of initiates who claimed a privileged experience and knowledge of the divine. In this article, I wish to do two things: (1) to establish methodologically the point that, from an analytical perspective, we can, indeed, speak of a kind of Jewish “mystical spirituality” present in the Second Temple period akin to later Jewish mystical traditions; and (2) to argue that, in light of some iconographic features on the decorated stone from the first-century synagogue at Magdala, early Jewish “mystical spirituality” was not a phenomenon restricted to the individual but could also involve an assembled community’s experience of divine presence.
VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
Authors: Falcioni Renan , Gonçalves João Vitor Ferreira , Oliveira Karym Mayara de , Antunes Werner Camargos , Nanni Marcos Rafael
Published: December 2022

VIS-NIR-SWIR hyperspectroscopy is a significant technique used in remote sensing for classification of prediction-based chemometrics and machine learning. Chemometrics, together with biophysical and biochemical parameters, is a laborious technique; however, researchers are very interested in this field because of the benefits in terms of optimizing crop yields. In this study, we investigated the hypothesis that VIS-NIR-SWIR could be efficiently applied for classification and prediction of leaf thickness and pigment profiling of green lettuce in terms of reflectance, transmittance, and absorbance data according to the variety. For this purpose, we used a spectroradiometer in the visible, near-infrared, and shortwave ranges (VIS-NIR-SWIR). The results showed many chemometric parameters and fingerprints in the 400–2500 nm spectral curve range. Therefore, this technique, combined with rapid data mining, machine learning algorithms, and other multivariate statistical analyses such as PCA, MCR, LDA, SVM, KNN, and PLSR, can be used as a tool to classify plants with the highest accuracy and precision. The fingerprints of the hyperspectral data indicated the presence of functional groups associated with biophysical and biochemical components in green lettuce, allowing the plants to be correctly classified with higher accuracy (99 to 100%). Biophysical parameters such as thickness could be predicted using PLSR models, which showed R2P and RMSEP values greater than >0.991 and 6.21, respectively, according to the relationship between absorbance and reflectance or transmittance spectroscopy curves. Thus, we report the methodology and confirm the ability of VIS-NIR-SWIR hyperspectroscopy to simultaneously classify and predict data with high accuracy and precision, at low cost and with rapid acquisition, based on a remote sensing tool, which can enable the successful management of crops such as green lettuce and other plants using precision agriculture systems.
Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests
Authors: Simović Isidora , Šikoparija Branko , Panić Marko , Radulović Mirjana , Lugonja Predrag
Published: December 2022

Remote sensing of phenology is adopted as the practice in greenery monitoring. Now research is turned towards the fusion of data from various sensors to fill in the gap in time series and allow monitoring of pests and disturbances. Poplar species were monitored for the determination of the best approach for detecting phenology and disturbances. With the adjustments that include a choice of indices, wavelengths, and a setup, a multispectral camera may be used to calibrate satellite images. The image processing pipeline included different denoising and interpolation methods. The correlation of the changes in a signal of top and lateral imaging proved that the contribution of the whole canopy is reflected in satellite images. Normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) successfully distinguished among phenophases and detected leaf miner presence, unlike enhanced vegetation index (EVI). Changes in the indices were registered before, during, and after the development of the disease. NDRE is the most sensitive as it distinguished among the different intensities of damage caused by pests but it was not able to forecast its occurrence. An efficient and accurate system for detection and monitoring of phenology enables the improvement of the phenological models’ quality and creates the basis for a forecast that allows planning in various disciplines.
Application of Resistivity and Seismic Refraction Tomography for Landslide Stability Assessment in Vallcebre, Spanish Pyrenees
Authors: Himi Mahjoub , Anton Mickel , Sendrós Alex , Abancó Clàudia , Ercoli Maurizio , Lovera Raúl , Deidda Gian Piero , Urruela Aritz , Rivero Lluís , Casas Albert
Published: December 2022

Geophysical surveys are a noninvasive reliable tool to improve geological models without requiring extensive in situ borehole campaigns. The usage of seismic refraction tomography (SRT), electrical resistivity tomography (ERT) and borehole data for calibrating is very appropriate to define landslide body geometries; however, it is still only used occasionally. We present here the case of a Spanish Pyrenees slow-moving landslide, where ERT, SRT and lithological log data were integrated to obtain a geological three-dimensional model. The high contrasts of P-wave velocity and electrical resistivity values of the upper materials (colluvial debris and clayey siltstone) provided accurate information on the geometry of the materials involved in the landslide body, as well as the sliding surface. Geophysical prospecting allowed us to identify the critical sliding surface over a large area and at a reduced cost and, therefore, gives the geophysical method an advantage over borehole data. The three-dimensional model was used to carry out stability analyses of a landslide in 2D and 3D, which, coherently with previous studies, reveal that the lower part is more unstable than the upper units.
Seasonal Variations in the Vertical Wavenumber Spectra of Stratospheric Gravity Waves in the Asian Monsoon Region Derived from COSMIC-2 Data
Authors: Qu Tao , Zhang Lifeng , Wang Yuan , Wang Xu , Guan Jiping
Published: December 2022

We used the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) dry temperature profile data from December 2019 to November 2021 to study the vertical wavenumber spectra of the potential energy of stratospheric gravity waves (GWs Ep) in the Asian monsoon region (15–45°N, 70–150°E). The GW Ep decreases with increasing vertical wavenumber, and the spectral slope varies with wavenumber. The spectral slope becomes smaller over a wavenumber range of 0.1–0.45 km−1, and larger from 0.45–1 km−1, with increasing wavenumber. The energy density distribution at middle and low latitudes shows seasonal variations. Over a wavenumber range of 0.05–0.5 km−1, the energy density in winter is higher at middle latitudes than at low latitudes, and the opposite is observed in summer over a wavenumber range from 0.1 to 1 km−1. Both the spectral amplitude and characteristic wavelength exhibit band-like patterns, and the large-value bands and their centers vary significantly with the season. In winter, the middle latitude spectral amplitude is larger than that at low latitudes, and the significant large-value band-like distribution is at ~40°N. In summer, the distribution is opposite, with large-value band regions over the Bay of Bengal and Indo-China Peninsula. The large-value region of the middle latitude spectral amplitude corresponds to a longer characteristic wavelength, while the large-value region of the low latitude spectral amplitude corresponds to a shorter characteristic wavelength. There is also significant seasonal variation in the distribution of spectral slopes. Over a wavenumber range of 0.1 to 0.5 km−1, the slope is smaller at middle latitudes and larger at low latitudes in winter; the opposite is observed in summer. There is a significant annual cycle of spectral amplitude at middle and low latitudes, and a 4.8 month cycle at middle latitudes.
Changes in Onset of Vegetation Growth on Svalbard, 2000–2020
Authors: Karlsen Stein Rune , Elvebakk Arve , Tømmervik Hans , Belda Santiago , Stendardi Laura
Published: December 2022

The global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. The most prominent changes occur at northern latitudes such as our study area, which is Svalbard, located between 76°30′N and 80°50′N. A cloud-free time series of MODIS-NDVI data was processed. The dataset was interpolated to daily data during the 2000–2020 period with a 231.65 m pixel resolution. The onset of vegetation growth was mapped with a NDVI threshold method which corresponds well with a recent Sentinel-2 NDVI-based mapping of the onset of vegetation growth, which was in turn validated by a network of in-situ phenological data from time lapse cameras. The results show that the years 2000 and 2008 were extreme in terms of the late onset of vegetation growth. The year 2020 had the earliest onset of vegetation growth on Svalbard during the 21-year study. Each year since 2013 had an earlier or equally early timing in terms of the onset of the growth season compared with the 2000–2020 average. A linear trend of 0.57 days per year resulted in an earlier onset of growth of 12 days on average for the entire archipelago of Svalbard in 2020 compared to 2000.
A Study on the Effect of Multispectral LiDAR Data on Automated Semantic Segmentation of 3D-Point Clouds
Authors: Vierhub-Lorenz Valentin , Kellner Maximilian , Zipfel Oliver , Reiterer Alexander
Published: December 2022

Mobile mapping is an application field of ever-increasing relevance. Data of the surrounding environment is typically captured using combinations of LiDAR systems and cameras. The large amounts of measurement data are then processed and interpreted, which is often done automated using neural networks. For the evaluation the data of the LiDAR and the cameras needs to be fused, which requires a reliable calibration of the sensors. Segmentation solemnly on the LiDAR data drastically decreases the amount of data and makes the complex data fusion process obsolete but on the other hand often performs poorly due to the lack of information about the surface remission properties. The work at hand evaluates the effect of a novel multispectral LiDAR system on automated semantic segmentation of 3D-point clouds to overcome this downside. Besides the presentation of the multispectral LiDAR system and its implementation on a mobile mapping vehicle, the point cloud processing and the training of the CNN are described in detail. The results show a significant increase in the mIoU when using the additional information from the multispectral channel compared to just 3D and intensity information. The impact on the IoU was found to be strongly dependent on the class.
Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks
Authors: Siddiqui Asra Abid , Zabit Usman , Bernal Olivier D.
Published: December 2022

Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved. We have also proposed a method for automatic labelling of SMI fringes under variable optical feedback to facilitate the generation of a large training dataset. Specifically, we have trained two deep neural network models, namely Yolov5 and EfficientDet, and analysed the performance of these networks on various experimental SMI signals acquired by using different laser-diode-based sensors operating under different noise and speckle conditions. The performance has been quantified in terms of fringe detection accuracy, signal to noise ratio, depth of modulation, and execution time parameters. The impact of network architecture on real-time sensing is also discussed.
Does Vehicle-2-X Radio Transmission Technology Need to Be Considered within Accident Analysis in the Future?
Authors: Bauder Maximilian , Kubjatko Tibor , Helmer Thomas , Schweiger Hans-Georg
Published: December 2022

In this analysis, Cooperative Intelligent Transportation System relevant scenarios are created to investigate the need to differentiate Vehicle-to-X transmission technologies on behalf of accident analysis. For each scenario, the distances between the vehicles are calculated 5 s before the crash. Studies on the difference between Dedicated Short-Range Communication (IEEE 802.11p) and Cellular Vehicle-to-X communication (LTE-V2C PC5 Mode 4) are then used to assess whether both technologies have a reliable connection over the relevant distance. If this is the case, the transmission technology is of secondary importance for future investigations on Vehicle-to-X communication in combination with accident analysis. The results show that studies on freeways and rural roads can be carried out independently of the transmission technology and other boundary conditions (speed, traffic density, non-line of sight/line of sight). The situation is different for studies in urban areas, where both technologies may not have a sufficiently reliable connection range depending on the traffic density.
A Resilient Method for Visual–Inertial Fusion Based on Covariance Tuning
Authors: Li Kailin , Li Jiansheng , Wang Ancheng , Luo Haolong , Li Xueqiang , Yang Zidi
Published: December 2022

To improve localization and pose precision of visual–inertial simultaneous localization and mapping (viSLAM) in complex scenarios, it is necessary to tune the weights of the visual and inertial inputs during sensor fusion. To this end, we propose a resilient viSLAM algorithm based on covariance tuning. During back-end optimization of the viSLAM process, the unit-weight root-mean-square error (RMSE) of the visual reprojection and IMU preintegration in each optimization is computed to construct a covariance tuning function, producing a new covariance matrix. This is used to perform another round of nonlinear optimization, effectively improving pose and localization precision without closed-loop detection. In the validation experiment, our algorithm outperformed the OKVIS, R-VIO, and VINS-Mono open-source viSLAM frameworks in pose and localization precision on the EuRoc dataset, at all difficulty levels.
Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning
Authors: Negera Worku Gachena , Schwenker Friedhelm , Debelee Taye Girma , Melaku Henock Mulugeta , Ayano Yehualashet Megeresa
Published: December 2022

The orchestration of software-defined networks (SDN) and the internet of things (IoT) has revolutionized the computing fields. These include the broad spectrum of connectivity to sensors and electronic appliances beyond standard computing devices. However, these networks are still vulnerable to botnet attacks such as distributed denial of service, network probing, backdoors, information stealing, and phishing attacks. These attacks can disrupt and sometimes cause irreversible damage to several sectors of the economy. As a result, several machine learning-based solutions have been proposed to improve the real-time detection of botnet attacks in SDN-enabled IoT networks. The aim of this review is to investigate research studies that applied machine learning techniques for deterring botnet attacks in SDN-enabled IoT networks. Initially the first major botnet attacks in SDN-IoT networks have been thoroughly discussed. Secondly a commonly used machine learning techniques for detecting and mitigating botnet attacks in SDN-IoT networks are discussed. Finally, the performance of these machine learning techniques in detecting and mitigating botnet attacks is presented in terms of commonly used machine learning models’ performance metrics. Both classical machine learning (ML) and deep learning (DL) techniques have comparable performance in botnet attack detection. However, the classical ML techniques require extensive feature engineering to achieve optimal features for efficient botnet attack detection. Besides, they fall short of detecting unforeseen botnet attacks. Furthermore, timely detection, real-time monitoring, and adaptability to new types of attacks are still challenging tasks in classical ML techniques. These are mainly because classical machine learning techniques use signatures of the already known malware both in training and after deployment.
Breast Tumor Tissue Image Classification Using DIU-Net
Authors: Lee Jiann-Shu , Wu Wen-Kai
Published: December 2022

Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network, that is, to avoid the segmentation network from learning low-level features, we proposed the Complementary Color Conversion Scheme in the training phase. In addition, due to the disparity between the area of the nucleus and the background in the pathology image, there is an inherent data imbalance phenomenon, dice loss and focal loss were used to overcome this problem. In order to further strengthen the classification performance of the model, this study adopted a joint training scheme, so that the output of the classification network can not only be used to optimize the classification network itself, but also optimize the segmentation network. In addition, this model can also provide the pathologist model’s attention area, increasing the model’s interpretability. The classification performance verification of the proposed method was carried out with the BreaKHis dataset. Our method obtains binary/multi-class classification accuracy 97.24/93.75 and 98.19/94.43 for 200× and 400× images, outperforming existing methods.
Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering
Authors: Song Jiayin , Shi Jie , Zhou Hongwei , Song Wenlong , Zhou Hongju , Zhao Yue
Published: December 2022

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.
Helmet Wearing State Detection Based on Improved Yolov5s
Authors: Zhang Yi-Jia , Xiao Fu-Su , Lu Zhe-Ming
Published: December 2022

At many construction sites, whether to wear a helmet is directly related to the safety of the workers. Therefore, the detection of helmet use has become a crucial monitoring tool for construction safety. However, most of the current helmet wearing detection algorithms are only dedicated to distinguishing pedestrians who wear helmets from those who do not. In order to further enrich the detection in construction scenes, this paper builds a dataset with six cases: not wearing a helmet, wearing a helmet, just wearing a hat, having a helmet, but not wearing it, wearing a helmet correctly, and wearing a helmet without wearing the chin strap. On this basis, this paper proposes a practical algorithm for detecting helmet wearing states based on the improved YOLOv5s algorithm. Firstly, according to the characteristics of the label of the dataset constructed by us, the K-means method is used to redesign the size of the prior box and match it to the corresponding feature layer to increase the accuracy of the feature extraction of the model; secondly, an additional layer is added to the algorithm to improve the ability of the model to recognize small targets; finally, the attention mechanism is introduced in the algorithm, and the CIOU_Loss function in the YOLOv5 method is replaced by the EIOU_Loss function. The experimental results indicate that the improved algorithm is more accurate than the original YOLOv5s algorithm. In addition, the finer classification also significantly enhances the detection performance of the model.