bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Invasive, Targeted Nanoparticle-Mediated Drug Delivery across a Novel Human BBB Model
Authors: Kaya Shona , Callan Bridgeen , Hawthorne Susan
Published: May 2023

The blood–brain barrier (BBB) is a highly sophisticated system with the ability to regulate compounds transporting through the barrier and reaching the central nervous system (CNS). The BBB protects the CNS from toxins and pathogens but can cause major issues when developing novel therapeutics to treat neurological disorders. PLGA nanoparticles have been developed to successfully encapsulate large hydrophilic compounds for drug delivery. Within this paper, we discuss the encapsulation of a model compound Fitc-dextran, a large molecular weight (70 kDa), hydrophilic compound, with over 60% encapsulation efficiency (EE) within a PLGA nanoparticle (NP). The NP surface was chemically modified with DAS peptide, a ligand that we designed which has an affinity for nicotinic receptors, specifically alpha 7 nicotinic receptors, found on the surface of brain endothelial cells. The attachment of DAS transports the NP across the BBB by receptor-mediated transcytosis (RMT). Assessment of the delivery efficacy of the DAS-conjugated Fitc-dextran-loaded PLGA NP was studied in vitro using our optimal triculture in vitro BBB model, which successfully replicates the in vivo BBB environment, producing high TEER (≥230 ) and high expression of ZO1 protein. Utilising our optimal BBB model, we successfully transported fourteen times the concentration of DAS-Fitc-dextran-PLGA NP compared to non-conjugated Fitc-dextran-PLGA NP. Our novel in vitro model is a viable method of high-throughput screening of potential therapeutic delivery systems to the CNS, such as our receptor-targeted DAS ligand-conjugated NP, whereby only lead therapeutic compounds will progress to in vivo studies.
Therapeutic Applications of Mesenchymal Stem Cell Loaded with Gold Nanoparticles for Regenerative Medicine
Authors: Cheng Wen-Yu , Yang Meng-Yin , Yeh Chun-An , Yang Yi-Chin , Chang Kai-Bo , Chen Kai-Yuan , Liu Szu-Yuan , Tang Chien-Lun , Shen Chiung-Chyi , Hung Huey-Shan
Published: May 2023

In the present study, the various concentrations of AuNP (1.25, 2.5, 5, 10 ppm) were prepared to investigate the biocompatibility, biological performances and cell uptake efficiency via Wharton’s jelly mesenchymal stem cells and rat model. The pure AuNP, AuNP combined with Col (AuNP-Col) and FITC conjugated AuNP-Col (AuNP-Col-FITC) were characterized by Ultraviolet–visible spectroscopy (UV-Vis), Fourier-transform infrared spectroscopy (FTIR) and Dynamic Light Scattering (DLS) assays. For in vitro examinations, we explored whether the Wharton’s jelly MSCs had better viability, higher CXCR4 expression, greater migration distance and lower apoptotic-related proteins expression with AuNP 1.25 and 2.5 ppm treatments. Furthermore, we considered whether the treatments of 1.25 and 2.5 ppm AuNP could induce the CXCR4 knocked down Wharton’s jelly MSCs to express CXCR4 and reduce the expression level of apoptotic proteins. We also treated the Wharton’s jelly MSCs with AuNP-Col to investigate the intracellular uptake mechanisms. The evidence demonstrated the cells uptake AuNP-Col through clathrin-mediated endocytosis and the vacuolar-type H+-ATPase pathway with good stability inside the cells to avoid lysosomal degradation as well as better uptake efficiency. Additionally, the results from in vivo examinations elucidated the 2.5 ppm of AuNP attenuated foreign body responses and had better retention efficacy with tissue integrity in animal model. In conclusion, the evidence demonstrates that AuNP shows promise as a biosafe nanodrug delivery system for development of regenerative medicine coupled with Wharton’s jelly MSCs.
Impacts of PEGylation and Glycosylation on the Biological Properties of Host Defense Peptide IDR1018
Authors: Etayash Hashem , Yip Fione , Hancock Robert E. W.
Published: May 2023

The multifunctional properties of host defense peptides (HDPs) make them promising drug candidates to tackle bacterial infections and tissue inflammation. However, these peptides tend to aggregate and can harm host cells at high doses, potentially limiting their clinical use and applications. In this study, we explored the influences of both pegylation and glycosylation on the biocompatibility and biological properties of HDPs, particularly the innate defense regulator IDR1018. Two peptide conjugates were designed by attaching either polyethylene glycol (PEG6) or a glucose moiety to the peptide towards the N-terminus. Significantly, both derivatives reduced the aggregation, hemolysis, and cytotoxicity of the parent peptide by orders of magnitude. In addition, while the pegylated conjugate, PEG6-IDR1018, retained an excellent immunomodulatory profile, similar to that observed for IDR1018 itself, the glycosylated conjugate, Glc-IDR1018, significantly outperformed the parent peptide in inducing anti-inflammatory mediators, MCP1 and IL-1RA and in suppressing the level of lipopolysaccharide-induced proinflammatory cytokine IL-1β. Conversely, the conjugates led to a partial reduction in antimicrobial and antibiofilm activity. These findings underline the impacts of both pegylation and glycosylation on the biological properties of the HDP IDR1018 and indicate the potential of glycosylation to enhance the design of highly effective immunomodulatory peptides.
Soil Amendments and Foliar Melatonin Reduced Pb Uptake, and Oxidative Stress, and Improved Spinach Quality in Pb-Contaminated Soil
Authors: Iqbal Naeem , Tanzeem-ul-Haq Hafiz Syed , Gull-e-Faran  , Turan Veysel , Iqbal Muhammad
Published: May 2023

Amending Pb-affected soil with biochar (BH) and magnesium potassium phosphate cement (MKC) reduces Pb uptake in plants. Moreover, foliar applications of melatonin and proline are also known to reduce plant oxidative stress and Pb uptake. However, little is known about combining both techniques, i.e., adding a combo immobilizing dose (CIA = mixture of BH and MKC at 50:50 ratio) in Pb-polluted soil and foliar application of proline and melatonin for reducing Pb uptake and oxidative stress in spinach. Control, proline, melatonin, CIA, CIA+proline, and CIA+melatonin were the treatments utilized in this pot study to see their effects on reducing plant oxidative stress, Pb uptake, and improving spinach quality in Pb-polluted soil. Moreover, Pb bioavailability, enzymatic activities, and numbers of bacteria, fungi, and actinomycetes in the soil were also evaluated. The effect of CIA on reducing Pb in the soil-plant system and improving soil enzymes and microbial numbers was more pronounced than melatonin alone. The most effective treatment was CIA+melatonin reducing Pb availability in soil (77%), shoots (95%), and roots (84%), alleviating oxidative stress, and improving plant biomass (98%) and nutrients. Soil enzymatic activities and the number of microorganisms in the rhizosphere were also highest with CIA+melatonin. Results highlight the significance of CIA+melatonin, as an inexpensive approach, in remediating Pb-polluted soil and improving spinach quality. However, further research is needed to understand the significance of CIA+melatonin on different crops and various soil Pb concentrations before employing this technique commercially in agriculture and environment sectors.
Potential of Novel Magnesium Nanomaterials to Manage Bacterial Spot Disease of Tomato in Greenhouse and Field Conditions
Authors: Liao Ying-Yu , Pereira Jorge , Huang Ziyang , Fan Qiurong , Santra Swadeshmukul , White Jason C. , De La Torre-Roche Roberto , Da Silva Susannah , Vallad Gary E. , Freeman Joshua H. , Jones Jeffrey B. , Paret Mathews L.
Published: May 2023

Bacterial spot of tomato is among the most economically relevant diseases affecting tomato plants globally. In previous studies, non-formulated magnesium oxide nanoparticles (nano-MgOs) significantly reduced the disease severity in greenhouse and field conditions. However, the aggregation of nano-MgO in liquid suspension makes it challenging to use in field applications. Therefore, we formulated two novel MgO nanomaterials (SgMg #3 and SgMg #2.5) and one MgOH2 nanomaterial (SgMc) and evaluated their physical characteristics, antibacterial properties, and disease reduction abilities. Among the three Mg nanomaterials, SgMc showed the highest efficacy against copper-tolerant strains of Xanthomonas perforans in vitro, and provided disease reduction in the greenhouse experiments compared with commercial Cu bactericide and an untreated control. However, SgMc was not consistently effective in field conditions. To determine the cause of its inconsistent efficacy in different environments, we monitored particle size, zeta potential, morphology, and crystallinity for all three formulated materials and nano-MgOs. The MgO particle size was determined by the scanning electron microscopy (SEM) and dynamic light scattering (DLS) techniques. An X-ray diffraction (XRD) study confirmed a change in the crystallinity of MgO from a periclase to an Mg(OH)2 brucite crystal structure. As a result, the bactericidal activity correlated with the high crystallinity present in nano-MgOs and SgMc, while the inconsistent antimicrobial potency of SgMg #3 and SgMg #2.5 might have been related to loss of crystallinity. Future studies are needed to determine which specific variables impair the performance of these nanomaterials in the field compared to under greenhouse conditions. Although SgMc did not lead to significant disease severity reduction in the field, it still has the potential to act as an alternative to Cu against bacterial spot disease in tomato transplant production.
Regulation of NADP-Malic Enzyme Activity in Maize (Zea mays L.) under Salinity with Reference to Light and Darkness
Authors: Sarkar Bipul , Das Abir , Pal Sayan , Kundu Ankita , Hasanuzzaman Mirza , Fujita Masayuki , Adak Malay Kumar
Published: May 2023

We revealed the functional characterization of C4-NADP-malic enzyme (NADP-ME), extracted and partially purified from maize (Zea mays L. cv. Kaveri 50). The leaf discs were previously activated under 1000–1200 µE m−2 s−1, incubated in bicarbonate (2.0 mM) solution, and subjected to salt stress (100 mM NaCl). Initially, salt stress was evident from the accumulations of proline, chlorophyll content, carbohydrate profile, and Hill activity influencing the C4 enzyme. Primarily, in illuminated tissues, the activity of the enzyme recorded a reduced trend through salinity irrespective of light and darkness compared to the control. On illumination, the kinetic parameters such as Vmax of the enzyme increased by 1.36-fold compared to in the dark under salinity whereas Km was decreased by 20% under the same condition. The extent of light induction was proportionate to limiting (0.01 mM) and saturated (4.0 mM) malate concentrations for enzyme activity. Moreover, the catalytic properties of the enzyme were also tested on concomitant responses to activator (citrate and succinate) and inhibitor (oxalate and pyruvate) residues. The sensitivity to light and dark effects was also tested for reducing agents such as dithiothreitol, suggesting the effect of the changes in redox on the regulatory properties of the enzyme. The ratio of enzyme activity under light and darkness in the presence or absence of a reducing agent was concomitantly increased with varying malate concentrations. At the molecular level, protein polymorphism of the enzyme represented minor variations in band intensities, however, not in numbers through salinity subjected to light and darkness. Therefore, salinity-induced changes in the decarboxylation reaction, evident by NADP-ME activity, may be based on the redox property of regulatory sites and sensitivity to light and darkness.
Genetic Diversity and Population Structure of European Soybean Germplasm Revealed by Single Nucleotide Polymorphism
Authors: Andrijanić Zoe , Nazzicari Nelson , Šarčević Hrvoje , Sudarić Aleksandra , Annicchiarico Paolo , Pejić Ivan
Published: May 2023

Soybean is the most grown high-protein crop in the world. Despite the rapid increase of acreage and production volume, European soybean production accounts for only 34% of its consumption in Europe. This study aims to support the optimal exploitation of genetic resources by European breeding programs by investigating the genetic diversity and the genetic structure of 207 European cultivars or American introductions registered in Europe, which were genotyped by the SoySNP50K array. The expected heterozygosity (He) was 0.34 for the entire collection and ranged among countries from 0.24 for Swiss cultivars to 0.32 for American cultivars (partly reflecting differences in sample size between countries). Cluster analysis grouped all genotypes into two main clusters with eight subgroups that corresponded to the country of origin of cultivars and their maturity group. Pairwise Fst values between countries of origin showed the highest differentiation of Swiss cultivars from the rest of the European gene pool, while the lowest mean differentiation was found between American introductions and all other European countries. On the other hand, Fst values between maturity groups were much lower compared to those observed between countries. In analysis of molecular variance, the total genetic variation was partitioned either by country of origin or by maturity group, explaining 9.1% and 3.5% of the total genetic variance, respectively. On the whole, our results suggest that the European soybean gene pool still has sufficient diversity due to the different historical breeding practices in western and eastern countries and the relatively short period of breeding in Europe.
iTRAQ-Based Proteomic and Physiological Analyses Reveal the Mechanisms of Dehydration and Cryopreservation Tolerance of Sophora tonkinensis Gagnep. Seeds
Authors: Luo Yongjian , Zhang Yixin , Jiang Yu , Dai Zhangyan , Li Qing , Mou Jiaolin , Xu Li , Deng Shiming , Li Jitao , Wang Ru , Liu Jun , Deng Zhijun
Published: May 2023

Sophora tonkinensi is a shrub of the genus Sophora in the family Fabaceae with anti-inflammatory and pain-relieving effects. While the cultivation, chemical makeup, and medicinal properties of S. tonkinensis have been reported, the physiological mechanisms governing its dehydration and cryopreservation tolerance of seeds remain unclear. In this study, we investigated the morphological, physiological, biochemical, and protein expression characteristics of S. tonkinensis seeds subjected to dehydration and cryopreservation techniques via the observation of cell microstructure, determination of antioxidant enzyme activity, and iTRAQ-based proteomic analysis, respectively. The results of the study demonstrated that the seeds possessed a certain level of tolerance to dehydration. The highest germination percentage of 83.0% was observed after 2 h of dehydration (10.1% water content), which was identified as the optimal time point for cryopreservation. However, the germination percentage was reduced to only 30.5% when the water content reached 5.4%, indicating that S. tonkinensis seeds exhibit intermediate storage behavior. Further investigation revealed that during seed dehydration and cryopreservation treatment, liposomes were gradually and highly fused, whereas the activities of ROS scavenging and stress defense were significantly enhanced. During dehydration, the seed tissues formed a protective mechanism of stress resistance based on protein processing in the endoplasmic reticulum and antioxidant system, which was related to the dehydration tolerance. Moreover, only three differentially expressed LEA proteins were identified, and it is speculated that the strengthening of intracellular metabolism and the absence of specific LEA and dehydrins could be crucial factors for the reduced germination percentage after excessive dehydration and cryopreservation.
Identification of Superior Barley Genotypes Using Selection Index of Ideal Genotype (SIIG)
Authors: Zali Hassan , Barati Ali , Pour-Aboughadareh Alireza , Gholipour Ahmad , Koohkan Shirali , Marzoghiyan Akbar , Bocianowski Jan , Bujak Henryk , Nowosad Kamila
Published: May 2023

The main objective of the study was to evaluate and select the superior barley genotypes based on grain yield and some pheno-morphological traits using a new proposed selection index (SIIG). For this purpose, one-hundred-eight pure and four local cultivars (Norouz, Auxin, Nobahar, and WB-97-11) were evaluated as reference genotypes in four warm regions of Iran, including Ahvaz, Darab, Zabol, and Gonbad, during the 2020–2021 cropping seasons. The results of REML analysis showed that the heritability of all traits (except plant height) was higher in Gonbad than in other environments, while the lowest values were estimated in Ahvaz and Zabol environments. In addition, among the measured traits, the thousand kernel weight and grain filling period showed the highest and lowest values of heritability (0.83 and 0.01, respectively). The results showed that the seed yield of genotypes 1, 108, 3, 86, 5, 87, 19, 16, 15, 56, and 18 was higher than the four reference genotypes, and, on the other hand, the SIIG index of these genotypes was greater than or equal to 0.60. Based on the SIIG discriminator index, 4, 8, 31, and 28 genotypes with values greater than or equal to 0.60 were identified as superior for Darab, Ahvaz, Zabol, and Gonbad environments, respectively. As a conclusion, our results revealed that the SIIG index has ideal potential to identify genotypes with high yield and desirable traits. Therefore, the use of this index can be beneficial in screening better genotypes in the early stages of any breeding program for any crop.
The Effect of Cadmium on Plants in Terms of the Response of Gene Expression Level and Activity
Authors: Moravčíková Dagmar , Žiarovská Jana
Published: May 2023

Cadmium (Cd) is a heavy metal that can cause damage to living organisms at different levels. Even at low concentrations, Cd can be toxic to plants, causing harm at multiple levels. As they are unable to move away from areas contaminated by Cd, plants have developed various defence mechanisms to protect themselves. Hyperaccumulators, which can accumulate and detoxify heavy metals more efficiently, are highly valued by scientists studying plant accumulation and detoxification mechanisms, as they provide a promising source of genes for developing plants suitable for phytoremediation techniques. So far, several genes have been identified as being upregulated when plants are exposed to Cd. These genes include genes encoding transcription factors such as iron-regulated transporter-like protein (ZIP), natural resistance associated macrophage protein (NRAMP) gene family, genes encoding phytochelatin synthases (PCs), superoxide dismutase (SOD) genes, heavy metal ATPase (HMA), cation diffusion facilitator gene family (CDF), Cd resistance gene family (PCR), ATP-binding cassette transporter gene family (ABC), the precursor 1-aminocyclopropane-1-carboxylic acid synthase (ACS) and precursor 1-aminocyclopropane-1-carboxylic acid oxidase (ACO) multigene family are also influenced. Thanks to advances in omics sciences and transcriptome analysis, we are gaining more insights into the genes involved in Cd stress response. Recent studies have also shown that Cd can affect the expression of genes related to antioxidant enzymes, hormonal pathways, and energy metabolism.
Arbuscular Mycorrhizal Fungi and Compost-Based Biostimulants Enhance Fitness, Physiological Responses, Yield, and Quality Traits of Drought-Stressed Tomato Plants
Authors: Soussani Fatima Ezzahra , Boutasknit Abderrahim , Ben-Laouane Raja , Benkirane Rachid , Baslam Marouane , Meddich Abdelilah
Published: May 2023

Climate change-driven water resource constraints cause tomatoes to suffer from drought. The use of biostimulants has emerged as an important approach to enhancing resilience to drought. However, the roles of biostimulants in the physicochemical characteristics of tomatoes in response to drought are poorly understood. In this study, we evaluated the ability of arbuscular mycorrhizal fungi (AMF) and compost (versus NPK application) to improve the agro-physiology, yield, and fruit quality of tomato plants and their tolerance to drought by comparing them with conventional chemical fertilizers (NPK). Under drought conditions, plant growth traits associated with yield and fruit bioactive compounds (carotenoids: 73%; lycopene: 53%; polyphenols: 310%; and flavonoids: 158%) were increased in the AMF-tomato treatment. Compost significantly enhanced sugars (ca. 60%) and protein contents (ca. 20%). Moreover, AMF protected the photosynthetic apparatus from drought-induced oxidative stress, improved photosynthetic efficiency, leaf water potential, and osmolytes, and reduced malondialdehyde (MDA) and hydrogen peroxide (H2O2) accumulation by increasing peroxidase (POX) (140%) and polyphenol oxidase (PPO) (340%) activities compared to their controls. Our findings revealed that NPK is an important nutrient-based fertilizer for plant growth and development. However, its efficiency as a fertilizer is quite low. In addition, we highlighted different mechanisms mediated by AMF and compost, inducing drought tolerance in tomato plants.
Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case
Authors: Fan Junfu , Zhang Mengzhen , Chen Jiahao , Zuo Jiwei , Shi Zongwen , Ji Min
Published: May 2023

Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images.
Evaluating Atmospheric Correction Methods for Sentinel−2 in Low−to−High−Turbidity Chinese Coastal Waters
Authors: Zhang Shuyi , Wang Difeng , Gong Fang , Xu Yuzhuang , He Xianqiang , Zhang Xuan , Fu Dongyang
Published: May 2023

Inaccuracies in the atmospheric correction (AC) of data on coastal waters significantly limit the ability to quantify the parameters of water quality. Many studies have compared the effects of the atmospheric correction of data provided by the Sentinel−2 satellites, but few have investigated this issue for coastal waters in China owing to a limited amount of in situ spectral data. The authors of this study compared four processors for the atmospheric correction of data provided by Sentinel−2—the Atmospheric Correction for OLI ‘lite’(ACOLITE), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System (SeaDAS), Polynomial-based algorithm applied to MERIS (POLYMER), and Case 2 Regional Coast Colour (C2RCC)—to identify the most suitable one for water bodies with different turbidities along the coast of China. We tested the algorithms used in these processors for turbid waters and compared the resulting inversion of the remote sensing reflectance (Rrs) using in situ reflectance data from three stations with varying levels of coastal turbidity (HTYZ, DONG’OU, and MUPING). All processors significantly underestimated the results on data from the HTYZ station, which is located along waters with high turbidity, with the SeaDAS delivering the best performance, with an average band RMSE of 0.0146 and an average MAPE of 29.80%. It was followed by ACOLITE, with an average band RMSE of 0.0213 and an average MAPE of 43.43%. The performance of two AC algorithms used in ACOLITE, dark spectrum fitting (DSF) and exponential extrapolation (EXP), was also evaluated by comparing their results with in situ measurements at the HTYZ site. The ACOLITE-EXP algorithm delivered a slight improvement in results for the blue band compared with the DSF algorithm in highly turbid water, but led to no significant improvement in the green and red bands. C2RCC delivered the best performance on data from the DONG’OU station, which is located along water with medium turbidity, and from the MUPING station (water with low turbidity), with values of the MAPE of 18.58% and 28.41%, respectively.
Spatiotemporal Variations in the Sensitivity of Vegetation Growth to Typical Climate Factors on the Qinghai–Tibet Plateau
Authors: Wu Kai , Chen Jiahao , Yang Han , Yang Yue , Hu Zhongmin
Published: May 2023

Gaining knowledge about vegetation sensitivity in response to climate change is a current research priority in the context of accelerated shifts generated by global warming, especially for the Qinghai–Tibet Plateau (QTP), where vegetation is known to be highly sensitive to ongoing climate change. However, the temporal variability of vegetation sensitivity in response to climate change is still poorly understood on the QTP. Here, we articulate the interannual variability of the vegetation sensitivity in response to typical climate factors, including temperature, solar radiation, and water availability, on the QTP during 2000–2021, using a variety of indicators characterizing vegetation dynamics, including the Leaf Area Index (LAI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and solar-induced chlorophyll fluorescence (SIF) data. The results indicate that temperature exerted positive impacts on forests, grasslands, and barren or sparsely vegetated areas (BSVs). However, all the land-cover types showed decreasing sensitivity to temperature variability. Solar radiation had a positive impact on forests, while it had a negative impact on grasslands and BSVs. An increasing trend was observed for forests, while a decreasing trend was found for grasslands and BSVs regarding their sensitivity to solar radiation. Water availability exerted a positive impact on grasslands and BSVs, and no obvious impact direction could be determined for forests. Over the last two decades, forests and BSVs exhibited increasing sensitivity to water availability, and no obvious trend was observed for grasslands. Overall, temperature was the most important climate factor, followed by solar radiation and water availability, regarding the regulation of vegetation sensitivity on the QTP. Spatially, temperature and solar radiation jointly dominated the vegetation sensitivity in the central to eastern QTP. Conversely, water availability dominated the sensitivity of forests in the southeastern QTP and grasslands in the northeastern and southwestern QTP. This study provides theoretical support for the ecological conservation and management of the QTP in the context of ongoing climate change.
Applying Time-Expended Sampling to Ensemble Assimilation of Remote-Sensing Data for Short-Term Predictions of Thunderstorms
Authors: Zhang Huanhuan , Gao Jidong , Xu Qin , Ran Lingkun
Published: May 2023

By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or phase errors) but also triple the analysis ensemble size for covariance construction without increasing the forecast ensemble size. In this study, TES was applied to the convection-allowing ensemble-based warn-on-forecast system (WoFS), for four severe storm events, to reduce the computational costs that constrain real-time applications in the assimilation of remote-sensing data from radars and the geostationary satellite GOES-16. For each event, TES was implemented against a 36-member control experiment (E36) by reducing the forecast ensemble size to 12 but tripling the analysis ensemble size to 12 × 3 = 36 with τ = 2.5 min, 5 min and 7.5 min in three TES experiments, named E12×3τ2.5, E12×3τ5 and E12×3τ7.5, respectively. A 0–6-h forecast was created hourly after the second hour during the assimilation in each experiment. The assimilation statistics were evaluated for each experiment applied to each event and were found to be little affected by the TES, while reducing the computational cost. The forecasts produced in each experiment were verified against multi-sensor observed/estimated rainfall, reported tornadoes and damaging winds for each event. The verifications indicated that the forecasts produced in the three TES experiments had about the same capability and quality as that in the E36 for predicting hourly rainfall and the probabilities of tornadoes and damaging winds; in addition, the predictive capability and quality were not sensitive to τ, although they were slightly enhanced by selecting τ = 7.5 min. These results suggest that TES is attractive and useful for cost-saving real-time applications of WoFS in the assimilation of remote-sensing data and the generation of short-term severe-weather forecasts.
Application of UAV Push-Broom Hyperspectral Images in Water Quality Assessments for Inland Water Protection: A Case Study of Zhang Wei Xin River in Dezhou Distinct, China
Authors: Yi Lina , Zhang Guifeng , Zhang Bowen
Published: May 2023

A water quality parameter retrieval scheme based on the UAV push-broom hyperspectral images was designed and validated for assessing the ecological health of Zhang Wei Xin River in Dezhou distinct, China. First, a UAV carrying a push-broom hyperspectral imager that is lightweight and has a small size was used to acquire high spatial and hyperspectral resolution images. Then, the mosaicked reflectance data of the whole river were produced by a seamless image mosaicking method with high geometrical accuracy and spectral fidelity. Next, the in-field measurements of different parameters and the corresponding spectral reflectance from the mosaicked images at the sampling points were used to build the water quality parameter retrieval models for total phosphorus (TP), chlorophyll a (Chla), and total suspended solids (TSS). To validate the model, the retrieval results of the testing sampling points were compared with the measured parameters. The coefficients of determination R2 of TP, Chla, and TSS were 0.886, 0.918, and 0.968, respectively. The retrieved TP, Chla, and TSS maps showed that the water pollution of Zhang Wei Xin River is serious, the total phosphorus exceeds the standard, and the water body is in a state of eutrophication. The UAV-based hyperspectral remote sensing technique provides a cost-effective method for inland water monitoring at a local scale with high accuracy.
Combining LSTM and PLUS Models to Predict Future Urban Land Use and Land Cover Change: A Case in Dongying City, China
Authors: Zhao Xin , Wang Ping , Gao Songhe , Yasir Muhammad , Islam Qamar Ul
Published: May 2023

Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km2 of construction land is converted to cropland, and 424.11 km2 of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km2 and 148.42 km2, respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively.
Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning
Authors: Islam Md Didarul , Di Liping , Qamer Faisal Mueen , Shrestha Sravan , Guo Liying , Lin Li , Mayer Timothy J. , Phalke Aparna R.
Published: May 2023

This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data processing issues, selecting a suitable machine learning model that can learn from few available time-series data, and understanding the non-linear relationship between historical crop yield and remote sensing and meteorological factors. This study applied a series of data processing techniques and a customized ML model to improve the accuracy of crop yield estimation at the district level in Nepal. It was found that remote sensing-derived NDVI product alone was not sufficient for accurate estimation of crop yield. After incorporating other meteorological variables into the ML models, estimation accuracy improved dramatically. Along with NDVI, the meteorological variables of rainfall, soil moisture, and evapotranspiration also exhibited a strong association with rice yield. This study also found that stacking multiple tree-based regression models together could achieve better accuracy than benchmark linear regression or standalone ML models. Due to the unique and distinct physio-geographical setting of each district, a variation in estimation accuracy from district to district could be observed. Our data processing and ML model workflow achieved an average of 92% accuracy of yield estimation with RMSE 328.06 kg/ha and MAE 317.21 kg/ha. This methodological workflow can be replicated in other study areas and the results can help the local authorities and stakeholders understand the factors affecting crop yields as well as estimating crop yield before harvesting season to ensure food security and sustainability.
Similar or Different Training Cultures? German and Chinese Companies in Their Home and Host Countries
Authors: Pilz Matthias , Fuchs Martina , Li Junmin , Finken Lena , Westermeyer Johannes
Published: May 2023

For internationally producing companies, training and recruiting skilled workers is particularly challenging. At various international locations, companies often encounter a “skills ecosystem” that differs to a greater or lesser extent from that of the home country. This article considers this problem and examines German companies in Germany and China and, conversely, Chinese companies in China and Germany. Specifically, it examines (1) how the training cultures of companies differ between the home country and abroad, (2) which influencing factors determine the training cultures realized in each case, and (3) what influence the respective nationally prevailing skills ecosystems specifically exert. The findings from these four perspectives document that German companies in Germany use the formal vocational training system of dual training, whereas German companies in China are strongly influenced by the local training culture of in-company learning. Moreover, Chinese companies in China also use this training, and only cooperate to a limited extent with the formal school-based vocational education and training system. These findings show that Chinese companies in Germany do not adopt the dual training system used in Germany. This article analyses these results and discusses the reasons behind them, drawing of a distinction between institutional logic and internal logic.
Parent–Child Relationships from Adolescence to Adulthood: An Examination of Children’s and Parent’s Reports of Intergenerational Solidarity by Race, Ethnicity, Gender, and Socioeconomic Status from 1994–2018 in the United States
Authors: Zhang Xing , Grant Annaliese
Published: May 2023

Background: Parent–child relationships are increasingly central in family life due to the delayed transition to adulthood. Methods: Using data from Add Health and the Add Health Parent Study from 1994–2018, we examine changes in parent–child relationships reported by the child and the parent across gender, race, ethnicity, and socioeconomic status (SES) from ages 12–43 years old. We examine three broad categories of intergenerational solidarity: Affectual (closeness, relationship quality, and emotional support), associational (satisfaction with communication), and functional (financial and instrumental support). Results: For the overall sample, parent–child closeness remains high but decreases from ages 12–43. By gender, we find that daughters report lower relationship quality with their mothers and fathers in adolescence. By race and ethnicity, we find that parent–child closeness is highest among Black respondents. By SES, we find that respondents whose mothers attained less than a high school degree report the greatest mother–child closeness. Conclusions: Overall, parents remain an important source of emotional, instrumental, and financial support in the transition to adulthood, especially for Black and low-SES adolescents. It is important to pay attention to the intersectional nature of the parent–child relationship with regard to the timing of the life course and race, ethnicity, gender, and SES.
ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability
Authors: Kayakus Mehmet , Tutcu Burçin , Terzioglu Mustafa , Talaş Hasan , Ünal Uyar Güler Ferhan
Published: May 2023

Return on equity (ROE) and return on assets (ROA) are important indicators that reveal the sustainability of a company’s profitability performance for both managers and investors. The correct prediction of these indicators will provide a basis for the strategic decisions made by the company managers. The estimation of these signs is a significant factor in supporting the decisions and up-to-date knowledge of potential investors. In this study, return on equity and return on assets were estimated using artificial neural networks (ANNs), multiple linear regression (MLR), and support vector regression (SVR) on the financial data of thirteen companies operating in the iron and steel sector. The success of predicting ROA in the designed model was 86.4% for ANN, 79.9% for SVR, and 74% for MLR. The success of estimating the ROE of the same model was 85.8% for ANN, 80.9% for SVR, and 63.8% for MLR. It is concluded that ANN and SVR can produce successful prediction results for ROA and ROE both accurately and reasonably.
Research on the Effect of Information Infrastructure Construction on Low-Carbon Technology Knowledge Flow
Authors: Wang Xiaonan , Wang Weidong , Wu Yufeng , Jin Shunlin
Published: May 2023

The cross-regional diffusion of low-carbon technology knowledge is conducive to the development of low-carbon technology and increases the emission reduction contribution of the wider land. However, the flow of low-carbon technology knowledge faces many obstacles such as information, geography, and local protectionism. This paper focuses on whether and how information infrastructure construction can promote the flow of low-carbon technology knowledge under the digital wave. Through the low-carbon technology patents of category Y02 under the classification standard of Cooperative Patent Classification (CPC) and by matching them to the city level to measure the low-carbon technology knowledge flow, empirical research finds that: information infrastructure cannot only directly promote the low-carbon technology knowledge flow, but also indirectly promote the low-carbon technology diffusion by increasing foreign direct investment and human capital. The role of information infrastructure in promoting low-carbon technology knowledge is more obvious in the central and western regions and non-resource-based cities. Finally, in view of the current development situation of low-carbon technology knowledge flows in China, the paper puts forward policy recommendations to vigorously promote the construction of information infrastructure, to gradually increase the network density and the number of network nodes, release the digital economic dividend, and form a spatial pattern of coordinated development of regional knowledge flows.
Toward New Value-Added Products Made from Anaerobic Digestate: Part 2—Effect of Loading Level on the Densification of Solid Digestate
Authors: Łysiak Grzegorz , Kulig Ryszard , Kowalczyk-Juśko Alina
Published: May 2023

A comprehensive understanding of the mechanisms associated with the pelletization of an anaerobic digestate is necessary to optimize the pellet production process and achieve better and more sustainable management of the digestate. This work evaluated the digestate behavior during cyclic loading and unloading in a closed matrix. The results presented here are a continuation of those observed in previous work that evaluated the effect of moisture content on the behavior of the digestate under cyclic loading/unloading conditions in a closed matrix. The effect of moisture content on the distribution of permanent and elastic strain energy demonstrated in the previous study was verified in the present work under different loading conditions. A Zwick universal machine was used for the experiments. The samples were loaded with amplitudes of 8, 11, 14, 17, and 20 kN for 10 cycles. Two distinct moisture levels of the digestate—10% and 22%—were analyzed. The results of the present study confirmed that the elastic energy dissipated was independent of the moisture content of the digestate and remained relatively constant for a wide range of the applied loads. Higher values of elastic strain energy were observed for the digestate with higher moisture content only when higher loads were applied. In the range of the studied loads, characteristic differences were noted in loading/unloading curves regardless of the load magnitude. The increase in the applied load led to an increase in pellet strength, but only when the moisture content of the digestate was 10%. The results of the pellet strength reflect well the results of irreversible energy and the conclusions about the area enclosed between loading and unloading curves.
Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth
Authors: Borucka Anna
Published: May 2023

Demand forecasting plays a key role in supply chain planning, management and its sustainable development, but it is a challenging process as demand depends on numerous, often unidentified or unknown factors that are seasonal in nature. Another problem is limited availability of information. Specifically, companies lacking modern IT systems are constrained to rely on historical sales observation as their sole source of information. This paper employs and contrasts a selection of mathematical models for short-term demand forecasting for products whose sales are characterized by high seasonal variations and a development trend. The aim of this publication is to demonstrate that even when only limited empirical data is available, while other factors influencing demand are unknown, it is possible to identify a time series that describes the sales of a product characterized by strong seasonal fluctuations and a trend, using selected mathematical methods. This study uses the seasonal ARIMA (autoregressive integrated moving average) model, ARIMA with Fourier terms model, ETS (exponential smoothing) model, and TBATS (Trigonometric Exponential Smoothing State Space Model with Box–Cox transformation, ARMA errors, Trend and Seasonal component). The models are presented as an alternative to popular machine learning models, which are more complicated to interpret, while their effectiveness is often similar. The selected methods were presented using a case study. The results obtained were compared and the best solution was identified, while emphasizing that each of the methods used could improve demand forecasting in the supply chain.
Economic Potential Gain, Income Uncertainty, and Rural Migrants’ Urban Homeownership: Evidence from China
Authors: Chen Jie , Wang Wei , Song Yan
Published: May 2023

Citizenization of rural migrants is considered as a labeling urbanization goal in developing countries. Homeownership has always been regarded as the most important cornerstone of citizenization for individuals and families in China. Despite the existence of plenty of literature on migrants’ homeownership, some critical influencing factors are still under-investigated. On the basis of the large nation-wide micro data of the China Migrant Dynamic Survey (CMDS), this study investigates the correlations among economic potential gain, income uncertainty, and rural migrants’ homeownership propensity in their host cities. The empirical results suggest that economic potential gain is positively correlated with the likelihood of rural migrants’ homeownership in their host cities, whereas there is negative association between income uncertainty and urban homeownership propensity among rural migrant households. In addition, we found that larger income uncertainty lowers the positive association between economic potential gain and rural migrants’ homeownership propensity in their host cities. Furthermore, the heterogeneity of these correlations across demographic characteristics and regions were investigated. We conclude this paper by making several suggestions, including offering a level playing field for rewarding migrants’ human capital endowment reasonably in terms of income, accelerating the hukou system reform, and eliminating institutional discrimination imposed on rural migrants to increase income stability.
Effect of Multimedia E-Book Use on the Information Literacy of Nursing Students and Health Communication in Student-Led Large- and Small-Group Community Health Education Sessions
Authors: Wu Ting-Ting , Lu Yi-Chen , Huang Yueh-Min
Published: May 2023

The integration of digital technology into healthcare is critical for health communication. This study analyzed a group of nursing students who applied multimedia health education e-books to different groups of varying sizes to explore the efficiency of implementing health communication and nursing information literacy in the nursing industry. If medical personnel can make appropriate use of technology, combine medical operations with information systems, and disseminate the purpose of health to groups of different sizes properly, the quality of patient care will improve. Thirty-two junior nursing students at the college level were divided into three groups, each subject to a 3-week internship. After the internship, a questionnaire survey was conducted. Additionally, nine nursing students were interviewed in a 45-min semistructured format. Regarding the effectiveness of nursing students using multimedia e-books to implement health communication and the development of nursing information literacy, the statistical analysis results demonstrated no significant differences between large groups and small groups. However, their 5-point Likert scale average values were all greater than 4, indicating that regardless of group size, their feedback on using e-books was positive. This means multimedia e-books can effectively help nursing students practice health communication application effectiveness and develop nursing information literacy.