bluuebunny/crossref_metadata_2025 huggingface dataset:
Follow-Up Period Affects the Association between Serum 25-Hydroxyvitamin D Concentration and Incidence of Dementia, Alzheimer’s Disease, and Cognitive Impairment
Authors: Grant William B.
Published: September 2024

Background/Objectives: Vitamin D’s effect on risk health outcomes is often evaluated using prospective cohort studies. For vitamin D, risk ratios (RRs) are based on health outcomes with respect to serum 25-hydroxyvitamin D [25(OH)D] concentrations measured at time of enrollment. Serum 25(OH)D concentrations vary over time, thereby diluting the effect of 25(OH)D for long follow-up periods. Inverse relationships between RR and follow-up period have been reported for all-cause mortality rate and cancer incidence rates. Here, the effect for neurological outcomes is evaluated. Methods: The analysis examines how follow-up period affected results from nine cohort studies of all-cause dementia, six studies of Alzheimer’s disease, and nine for cognitive impairment with respect to vitamin D deficiency. Results: For all-cause dementia, Alzheimer’s disease, and cognitive impairment, respectively, the linear regression fits are RR = 2.9 − 0.14 × years, r = 0.73, p = 0.02; RR = 2.9 − 0.14 × years, r = 0.69, p = 0.13; and RR = 1.8 − 0.066 × years, r = 0.72, p = 0.03. The regression fit to RR for the shortest follow-up period for each outcome is considered the best estimate of vitamin D deficiency’s effect on risk. Those values are approximately twice that found by averaging all RRs without considering the effect of follow-up period. Conclusions: Vitamin D’s effect on risk of neurological conditions is inversely correlated with mean follow-up period in prospective cohort studies. This effect should be considered in the design and analysis of such studies. Additional studies should also be conducted regarding raising serum 25(OH)D concentrations to reduce risk of brain function decline.
Caffeine Placebo Effect in Sport and Exercise: A Systematic Review
Authors: Vega-Muñoz Alejandro , Contreras-Barraza Nicolás , Salazar-Sepúlveda Guido , Lay Nelson , Gil-Marín Miseldra , Muñoz-Urtubia Nicolás
Published: September 2024

The objective of this review article is to systematically identify the caffeine placebo effect in sport and exercise activities. We selected randomized controlled trial studies to better understand the impact of caffeine and its placebo effect on sports performance. We extracted a set of articles that refer strictly to the topics of caffeine and its placebo effect in sport and exercise, considering the databases integrated to the Core Collection Web of Science and Scopus, as well as the registration of the documents in PubMed®, a system with a selection process aligned with the guidelines for the PRISMA methodology, establishing the eligibility criteria of the articles with the PICOS tool, to which a systematic review is applied. Finally, the results show that caffeine improves anaerobic capacity and endurance, while placebo perceived as caffeine can also increase performance by reducing pain and improving concentration. This finding underscores the influence of expectations and placebo on physical performance, suggesting that managing these expectations may be an effective strategy for improving athletic performance.
Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
Authors: Almeida Cristina Ferreira , Palmeira Andreia , Valente Maria João , Correia-da-Silva Georgina , Vinggaard Anne Marie , Sousa Maria Emília , Teixeira Natércia , Amaral Cristina
Published: September 2024

Background: Breast cancer therapy has been facing remarkable changes. Classic treatments are now combined with other therapies to improve efficacy and surpass resistance. Indeed, the emergence of resistance demands the development of novel therapeutic approaches. Due to key estrogen signaling, estrogen receptor-positive (ER+) breast cancer treatment has always been focused on aromatase inhibition and ER modulation. Lately, the effects of phytocannabinoids, mainly Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), have been evaluated in different cancers, including breast. However, Cannabis sativa contains more than 120 phytocannabinoids less researched and understood. Methods: Here, we evaluated, both in silico and in vitro, the ability of 129 phytocannabinoids to modulate important molecular targets in ER+ breast cancer: aromatase, ER, and androgen receptor (AR). Results: In silico results suggested that some cannabinoids may inhibit aromatase and act as ERα antagonists. Nine selected cannabinoids showed, in vitro, potential to act either as ER antagonists with inverse agonist properties, or as ER agonists. Moreover, these cannabinoids were considered as weak aromatase inhibitors and AR antagonists with inverse agonist action. Conclusions: Overall, we present, for the first time, a comprehensive analysis of the actions of the phytocannabinoids in targets of ER+ breast tumors, pointing out their therapeutic potential in cancer and in other diseases.
The Impact of Silver Nanoparticles Functionalized with Spirulina Protein Extract on Rats
Authors: Rudi Ludmila , Zinicovscaia Inga , Cepoi Liliana , Chiriac Tatiana , Grozdov Dmitrii , Kravtsova Alexandra
Published: September 2024

Background/Objectives: This study investigates the biocompatibility and physiological impacts of silver nanoparticles (AgNPs) functionalized with Spirulina protein extract (SPE) on laboratory rats. The objective was to assess and compare the systemic distribution, organ accumulation, and changes in hematological and biochemical parameters between biofunctionalized and non-functionalized silver nanoparticles. Methods: AgNPs were functionalized with SPE. Adult Wistar rats were administered these nanoparticles to assess their distribution across various organs using ICP-MS analysis. Hematological and biochemical markers were measured to evaluate systemic effects. Results: Functionalized silver nanoparticles demonstrated preferential accumulation in the brain, liver, and testicles, with significant clearance observed post-administration. The persistence of AgNPs SPE in reproductive organs was established. Hematological analysis revealed moderate changes, suggesting mild immune activation. Biochemical tests indicated transient increases in liver enzymes, signaling reversible hepatic stress. Conclusions: The biofunctionalization of AgNPs with Spirulina protein extract modifies the nanoparticles’ systemic behavior and organ distribution, enhancing their biocompatibility while inducing minimal physiological stress. These findings support the potential of Spirulina-based coatings to mitigate the toxicity and enhance the therapeutic efficacy of nanomedical agents.
Enhancing Radiotherapy Sensitivity in Prostate Cancer with Lentinan-Functionalized Selenium Nanoparticles: Mechanistic Insights and Therapeutic Potential
Authors: Zou Yani , Xu Helin , Wu Xiu , Liu Xuesong , Zhao Jianfu
Published: September 2024

Radiation therapy is a cornerstone of prostate cancer (PCa) treatment. However, its limited tumor sensitivity and severe side effects restrict its clinical utility. Lentinan-functionalized selenium nanoparticles (LET-SeNPs) have shown promise in enhancing radiotherapy sensitivity and exhibiting antitumor activity. In this study, we investigated the radiotherapy sensitization mechanism of LET-SeNPs in PCa. Our results demonstrate that the combination of LET-SeNPs and X-ray therapy (4 Gy) significantly inhibited the growth and colony formation of PCa cells by inducing apoptosis, surpassing the effects of individual treatments. This combined approach modulated DNA damage through the p53, MAPK (mitogen-activated protein kinase), and AKT pathways. Furthermore, LET-SeNPs increased PC3 cell sensitivity to X-ray-induced apoptosis by downregulating TrxR (Thioredoxin reductase) expression and inducing reactive oxygen species (ROS) overproduction, thereby activating mitochondria-mediated apoptosis signaling pathways. Additionally, LET-SeNPs regulated PARP (poly (ADP-ribose) polymerase) to prevent DNA damage repair. In vivo studies confirmed that the combination treatment inhibited PCa growth by synergistically activating the p53 pathway to induce cell apoptosis. These findings highlight LET-SeNPs’ potential as a radiotherapy sensitizer and suggest that combining LET-SeNPs with X-ray therapy could be a promising strategy for clinical application, leveraging selenium-modified nanoparticles’ antitumor effects.
Comparative Analysis of Chemical Profiles and Biological Activities of Essential Oils Derived from Torreya grandis Arils and Leaves: In Vitro and In Silico Studies
Authors: Deng Pengfei , Wang Huiling , Xu Xiaoniu
Published: September 2024

Torreya grandis (T. grandis, Taxaceae) is a well-known nut tree species. Its fruit aril and leaves possess a unique aroma, making it an ideal natural raw material for extracting essential oils (EOs). This study aims to comprehensively compare the composition, biological activities, and pharmacological mechanism of EOs extracted from the arils (AEO) and leaves (LEO) of T. grandis. The results revealed that the chemical composition of the two EOs was highly consistent, with α-pinene and D-limonene as the main components. Both EOs significantly reduced cellular melanin production and inhibited tyrosinase activity in α-MSH-stimulated B16 cells (p < 0.05). AEO and LEO suppressed inflammatory responses in LPS-stimulated RAW 264.7 macrophages, significantly inhibiting cellular NO production and proinflammatory cytokines such as TNF-α and IL-6 (p < 0.05). A network pharmacology analysis reveals that AEO and LEO share similar molecular mechanisms and pharmacological pathways for treating skin pigmentation and inflammation. Regulating inflammatory cytokines may be a critical pathway for AEO and LEO in treating skin pigmentation. These findings suggest that AEO and LEO have potential for cosmetic applications. The leaves of T. grandis could be a valuable source of supplementary materials for producing T. grandis aril EO.
Virus-Induced galactinol-sucrose galactosyltransferase 2 Silencing Delays Tomato Fruit Ripening
Authors: Zhang Pengcheng , Wang Jingjing , Yang Yajie , Pan Jingjing , Bai Xuelian , Zhou Ting , Lai Tongfei
Published: September 2024

Tomato fruit ripening is an elaborate genetic trait correlating with significant changes at physiological and biochemical levels. Sugar metabolism plays an important role in this highly orchestrated process and ultimately determines the quality and nutritional value of fruit. However, the mode of molecular regulation is not well understood. Galactinoal-sucrose galactosyltransferase (GSGT), a key enzyme in the biosynthesis of raffinose family oligosaccharides (RFOs), can transfer the galactose unit from 1-α-D-galactosyl-myo-inositol to sucrose and yield raffinose, or catalyze the reverse reaction. In the present study, the expression of SlGSGT2 was decreased by Potato Virus X (PVX)-mediated gene silencing, which led to an unripe phenotype in tomato fruit. The physiological and biochemical changes induced by SlGSGT2 silencing suggested that the process of fruit ripening was delayed as well. SlGSGT2 silencing also led to significant changes in gene expression levels associated with ethylene production, pigment accumulation, and ripening-associated transcription factors (TFs). In addition, the interaction between SlGSGT2 and SlSPL-CNR indicated a possible regulatory mechanism via ripening-related TFs. These findings would contribute to illustrating the biological functions of GSGT2 in tomato fruit ripening and quality forming.
Dual-Stream Architecture Enhanced by Soft-Attention Mechanism for Plant Species Classification
Authors: Khan Imran Ullah , Khan Haseeb Ali , Lee Jong Weon
Published: September 2024

Plants play a vital role in numerous domains, including medicine, agriculture, and environmental balance. Furthermore, they contribute to the production of oxygen and the retention of carbon dioxide, both of which are necessary for living beings. Numerous researchers have conducted thorough research in the classification of plant species where certain studies have focused on limited numbers of classes, while others have employed conventional machine-learning and deep-learning models to classify them. To address these limitations, this paper introduces a novel dual-stream neural architecture embedded with a soft-attention mechanism specifically developed for accurately classifying plant species. The proposed model utilizes residual and inception blocks enhanced with dilated convolutional layers for acquiring both local and global information. Following the extraction of features, both streams are combined, and a soft-attention technique is used to improve the distinct characteristics. The efficacy of the model is shown via extensive experimentation on varied datasets, including several plant species. Moreover, we have contributed a novel dataset that comprises 48 classes of different plant species. The results demonstrate a higher level of performance when compared to current models, emphasizing the capability of the dual-stream design in improving accuracy and model generalization. The integration of a dual-stream architecture, dilated convolutions, and soft attention provides a strong and reliable foundation for the botanical community, supporting advancement in the field of plant species classification.
Characterization of Plant-Growth-Promoting Rhizobacteria for Tea Plant (Camellia sinensis) Development and Soil Nutrient Enrichment
Authors: Wang Mengjiao , Sun Haiyan , Dai Huiping , Xu Zhimin
Published: September 2024

Plant-growth-promoting rhizobacteria (PGPR) play an important role in plant growth and rhizosphere soil. In order to evaluate the effects of PGPR strains on tea plant growth and the rhizosphere soil microenvironment, 38 PGPR strains belonging to the phyla Proteobacteria with different growth-promoting properties were isolated from the rhizosphere soil of tea plants. Among them, two PGPR strains with the best growth-promoting properties were then selected for the root irrigation. The PGPR treatment groups had a higher Chlorophyll (Chl) concentration in the eighth leaf of tea plants and significantly promoted the plant height and major soil elements. There were significant differences in microbial diversity and metabolite profiles in the rhizosphere between different experimental groups. PGPR improved the diversity of beneficial rhizosphere microorganisms and enhanced the root metabolites through the interaction between PGPR and tea plants. The results of this research are helpful for understanding the relationship between PGPR strains, tea plant growing, and rhizosphere soil microenvironment improvement. Moreover, they could be used as guidance to develop environmentally friendly biofertilizers with the selected PGPR instead of chemical fertilizers for tea plants.
Abiotic Stress Effect on Agastache mexicana subsp. mexicana Yield: Cultivated in Two Contrasting Environments with Organic Nutrition and Artificial Shading
Authors: Morales-Barrera Judith , Reséndiz-Muñoz Juan , Cruz-Lagunas Blas , Fernández-Muñoz José Luis , Godínez-Jaimes Flaviano , de Jesús Adame-Zambrano Tania , Vázquez-Villamar Mirna , Romero-Rosales Teollincacihuatl , Zagaceta-Álvarez María Teresa , Aguilar-Cruz Karen Alicia , Estrada-Martínez Jorge , Gruintal-Santos Miguel Angel
Published: September 2024

Research on medicinal plants is essential for their conservation, propagation, resistance to environmental stress, and domestication. The use of organic nutrition has been demonstrated to improve soil fertility and plant quality. It is also important to study the effects of the Basic Cation Saturation Ratio (BCSR) approach, which is a topic where there is currently controversy and limited scientific information. Evaluating the growth and yields of Agastache mexicana subsp. mexicana (Amm) in different environments is crucial for developing effective propagation and domestication strategies. This includes examining warm and subhumid environments with rain in summer in comparison to mild environments with summer rain. Significant differences were observed in the effects of cold, waterlogging, and heat stresses on the plant’s biomass yield and the morphometric-quantitative modeling by means of isolines. The biomass yield was 56% higher in environment one compared to environment two, 19% higher in environment one with organic nutrition, and 48% higher in environment two with organic nutrition compared to using only BCSR nutrition. In the second harvesting cycle, the plants in environment one did not survive, while the plants in environment two managed to survive without needing additional nutrition. Statistical and mathematical analyses provided information about the population or sample. Additionally, further analysis using isolines as a new approach revealed new insights into understanding phenology and growth issues.
Genetic Diversity of Tulipa alberti and T. greigii Populations from Kazakhstan Based on Application of Expressed Sequence Tag Simple Sequence Repeat Markers
Authors: Yermagambetova Moldir , Almerekova Shyryn , Ivashchenko Anna , Turuspekov Yerlan , Abugalieva Saule
Published: September 2024

The genus Tulipa L., renowned for its ornamental and ecological significance, encompasses a diversity of species primarily concentrated in the Tian Shan and Pamir-Alay Mountain ranges. With its varied landscapes, Kazakhstan harbors 42 Tulipa species, including the endangered Tulipa alberti Regel and Tulipa greigii Regel, which are critical for biodiversity yet face significant threats from human activities. This study aimed to assess these two species’ genetic diversity and population structure using 15 expressed sequence tag simple sequence repeat (EST-SSR) markers. Leaf samples from 423 individuals across 23 natural populations, including 11 populations of T. alberti and 12 populations of T. greigii, were collected and genetically characterized using EST-SSR markers. The results revealed relatively high levels of genetic variation in T. greigii compared to T. alberti. The average number of alleles per locus was 1.9 for T. alberti and 2.8 for T. greigii. AMOVA indicated substantial genetic variation within populations (75% for T. alberti and 77% for T. greigii). The Bayesian analysis of the population structure of the two species indicated an optimal value of K = 3 for both species, splitting all sampled populations into three distinct genetic clusters. Populations with the highest level of genetic diversity were identified in both species. The results underscore the importance of conserving the genetic diversity of Tulipa populations, which can help develop strategies for their preservation in stressed ecological conditions.
Advanced Polymeric Scaffolds for Stem Cell Engineering and Regenerative Medicine
Authors: Silva João Carlos , Ferreira Frederico Castelo
Published: September 2024

Polymeric scaffolds play a pivotal role in tissue engineering (TE) and regenerative medicine strategies, as they offer the possibility to closely mimic the architectural features of the native tissues’ extracellular matrix (ECM) and support cell performance both in vitro and in vivo, creating a favourable regenerative microenvironment [...]
High-Quality Foaming and Weight Reduction in Microcellular-Injection-Molded Polycarbonate Using Supercritical Fluid Carbon Dioxide under Gas Counter Pressure
Authors: Agustion Yogi Hendra , Chen Shia-Chung , Feng Ching-Te , Iskandar Bermawi Priyatna
Published: September 2024

Microcellular injection molding (MuCell®) using supercritical fluid (SCF) as a foaming agent to achieve weight reduction has become popular in carbon emission reduction. In the typical MuCell® process, SCF N2 is commonly used. Although SCF CO2 exhibits high solubility and can achieve a high weight reduction, controlling the foaming is not easy, and its foaming cells are usually larger and less uniform, which limits its industrial application. Our previous studies have shown that gas counter pressure (GCP) can improve the foaming quality effectively. Here, we investigated whether or not the CO2 SCF foaming quality could be improved, and weight reduction was achieved for polycarbonate (PC) material. This is quite important for the electronics industry, in which most of the housing for devices is made of PC materials. MuCell® was subjected to molding experiments using the parameters of the SCF dosage, melt temperature, mold temperature, and injection speed. The results revealed that using CO2 gas for the PC material can reduce the size of microcellular cells to 40 µm and increase the cell densities to 3.97 × 106 cells/cm3. Using GCP significantly improved the microcellular injection-molded parts by reducing the cell size to 20.9 µm (a 45.41% improvement) and increasing the cell density to 8.04 × 106 cells/cm3 (a 102.48% improvement). However, implementing GCP may slightly decrease the target weight reduction. This study reveals that microcellular injection molding of PC parts using SCF CO2 can achieve high-quality foaming and reduce the weight by about 30%.
Three-Dimensionally Printed K-Band Radar Stealth Lightweight Material with Lotus Leaf Structure
Authors: Liu Chuangji , Xu Yingjie , Huang Beiqing , Zhang Wan , Wang Yuxin
Published: September 2024

K-band radar waves have high penetration and low attenuation coefficients. However, the wavelength of this radar wave is relatively short; thus, designing and preparing both broadband and wide-angle radar wave absorbers in this band presents considerable challenges. In this study, a resin-based K-band radar wave absorber with a biomimetic lotus leaf structure was designed and formed by UV curing. Here, microscale lotus leaf papillae and antireflection structures were prepared using a DLP 3D printer, and the contact angle between the material and water droplets was increased from 56° to 130°. In addition, the influence of the geometric parameters of the lotus leaf antireflection structure on the electromagnetic absorption performance and mechanical strength was investigated. After simulation optimization, the maximum electromagnetic loss of the lotus leaf structure 3D-printed sample was −32.3 dB, and the electromagnetic loss was below −10 dB in the 20.8–26.5 GHz frequency range. When the radar incidence angle was 60°, the maximum electromagnetic loss was still less than −10 dB. The designed lotus leaf structure has a higher mechanical energy absorption per unit volume (337.22 KJ/m3) and per unit mass (0.55 KJ/Kg) than commonly used honeycomb lightweight structures during the elastic deformation stage, and we expect that the designed structure can be used as an effective lightweight material for K-band radar stealth.
Analysis and Evaluation of Load-Carrying Capacity of CFRP-Reinforced Steel Structures
Authors: Zhao Jian , Huang Yongxing , Gong Kun , Wen Zhiguo , Liu Sinan , Hou Yanyan , Hong Xuewu , Tong Xuecheng , Shi Kai , Qu Ziyi
Published: September 2024

Carbon Fiber Reinforced Polymer (CFRP) can be used to reinforce steel structures depending on its high strength and lightweight resistance. To analyze and evaluate the load-carrying capacity of CFRP-reinforced steel structures. This study uses the Finite Element Analysis (FEA) and the experimental tests combined to investigate the influence that the reinforcement patterns and the relevant parameters have on the load-carrying capacity. We made specimens with different reinforcement patterns. Take the steel beam specimen with full reinforcement as an example. Compared with the load-carrying capacity of the steel beam reinforced by two-layer CFRP cloth, that respectively increases by 5.16% and 11.1% when the number of the CFRP cloth increases to four and six, respectively. Based on a specimen set consisting of CFRP-reinforced steel structures under different reinforcement patterns, the random forest algorithm is used to develop an evaluation model for the load carrying. The performance test results show that the MAE (Mean Absolute Error) of the evaluation model can reach 0.12 and the RMSE (Root Mean Square Error) is 0.25, presenting a good prediction accuracy, which lays a solid foundation for the research on the CFRP-based reinforcement technology and process.
Interpretable Machine Learning-Based Influence Factor Identification for 3D Printing Process–Structure Linkages
Authors: Liu Fuguo , Chen Ziru , Xu Jun , Zheng Yanyan , Su Wenyi , Tian Maozai , Li Guodong
Published: September 2024

Three-dimensional printing technology is a rapid prototyping technology that has been widely used in manufacturing. However, the printing parameters in the 3D printing process have an important impact on the printing effect, so these parameters need to be optimized to obtain the best printing effect. In order to further understand the impact of 3D printing parameters on the printing effect, make theoretical explanations from the dimensions of mathematical models, and clarify the rationality of certain important parameters in previous experience, the purpose of this study is to predict the impact of 3D printing parameters on the printing effect by using machine learning methods. Specifically, we used four machine learning algorithms: SVR (support vector regression): A regression method that uses the principle of structural risk minimization to find a hyperplane in a high-dimensional space that best fits the data, with the goal of minimizing the generalization error bound. Random forest: An ensemble learning method that constructs a multitude of decision trees and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. GBDT (gradient boosting decision tree): An iterative ensemble technique that combines multiple weak prediction models (decision trees) into a strong one by sequentially minimizing the loss function. Each subsequent tree is built to correct the errors of the previous tree. XGB (extreme gradient boosting): An optimized and efficient implementation of gradient boosting that incorporates various techniques to improve the performance of gradient boosting frameworks, such as regularization and sparsity-aware splitting algorithms. The influence of the print parameters on the results under the feature importance and SHAP (Shapley additive explanation) values is compared to determine which parameters have the greatest impact on the print effect. We also used feature importance and SHAP values to compare the importance impact of print parameters on results. In the experiment, we used a dataset with multiple parameters and divided it into a training set and a test set. Through Bayesian optimization and grid search, we determined the best hyperparameters for each algorithm and used the best model to make predictions for the test set. We compare the predictive performance of each model and confirm that the extrusion expansion ratio, elastic modulus, and elongation at break have the greatest influence on the printing effect, which is consistent with the experience. In future, we will continue to delve into methods for optimizing 3D printing parameters and explore how interpretive machine learning can be applied to the 3D printing process to achieve more efficient and reliable printing results.
A General On-Orbit Absolute Radiometric Calibration Method Compatible with Multiple Imaging Conditions
Authors: Fan Liming , Jiang Zhongjin , Yu Shuhai , Liu Yunhe , Wang Dong , Chen Maosheng
Published: September 2024

On-orbit absolute radiometric calibration is not only a prerequisite for the quantitative application of optical remote sensing satellite data but also a key step in ensuring the accuracy and reliability of satellite observation data. Due to the diversity of imaging conditions for optical remote sensing satellite sensors, on-orbit absolute radiometric calibration usually requires a large number of imaging tasks and manual labor to calibrate each imaging condition. This seriously limits the timeliness of on-orbit absolute radiometric calibration and is also an urgent problem to be solved in the context of the explosive growth of satellite numbers. Based on this, we propose a general on-orbit absolute radiometric calibration method compatible with multiple imaging conditions. Firstly, we use a large amount of laboratory radiometric calibration data to explore the mathematical relationship between imaging conditions (row transfer time, integration level and gain), radiance, and DN, and successfully build an imaging condition compatibility model. Secondly, we combine the imaging condition compatibility model with cross calibration to achieve a general on-orbit absolute radiometric calibration method. We use cross calibration to obtain the reference radiance and corresponding DN of the target satellites, which calculates the general coefficient by using row transfer time, integration level, and gain, and use the general coefficient to calibrate all imaging conditions. Finally, we use multiple imaging tasks of the JL1GF03D11 satellites to verify the effectiveness of the proposed method. The experiments show that the average relative difference was reduced to 2.79% and the RMSE was reduced to 1.51, compared with the laboratory radiometric calibration method. In addition, we also verify the generality of the proposed method by using 10 satellites of the Jilin-1 GF03D series. The experiment shows that the goodness of fit of the general coefficient is all greater than 95%, and the average relative difference between the reference radiance and the calibrated radiance of the proposed method is 2.46%, with an RMSE of 1.67. To sum up, by using the proposed method, all imaging conditions of optical remote sensing satellite sensor can be calibrated in one imaging task, which greatly improves the timeliness and accuracy of on-orbit absolute radiometric calibration.
Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Temperate Lakes across New York State Using Sentinel-2 Images: Application of Google Earth Engine for Efficient Satellite Image Processing
Authors: Akbarnejad Nesheli Sara , Quackenbush Lindi J. , McCaffrey Lewis
Published: September 2024

Harmful algae blooms (HABs) have been reported with greater frequency in lakes across New York State (NYS) in recent years. In situ sampling is used to assess water quality, but such observations are time intensive and therefore practically limited in their spatial extent. Previous research has used remote sensing imagery to estimate phytoplankton pigments (typically chlorophyll-a or phycocyanin) as HAB indicators. The primary goal of this study was to validate a remote sensing-based method to estimate cyanobacteria concentrations at high temporal (5 days) and spatial (10–20 m) resolution, to allow identification of lakes across NYS at a significant risk of algal blooms, thereby facilitating targeted field investigations. We used Google Earth Engine (GEE) as a cloud computing platform to develop an efficient methodology to process Sentinel-2 image collections at a large spatial and temporal scale. Our research used linear regression to model the correlation between in situ observations of chlorophyll-a (Chl-a) and phycocyanin and indices derived from Sentinel-2 data to evaluate the potential of remote sensing-derived inputs for estimating cyanobacteria concentrations. We tested the performance of empirical models based on seven remote-sensing-derived indices, two in situ measurements, two cloud mitigation approaches, and three temporal sampling windows across NYS lakes for 2019 and 2020. Our best base model (R2 of 0.63), using concurrent sampling data and the ESA cloud masking—i.e., the QA60 bitmask—approach, related the maximum peak height (MPH) index to phycocyanin concentrations. Expanding the temporal match using a one-day time window increased the available training dataset size and improved the fit of the linear regression model (R2 of 0.71), highlighting the positive impact of increasing the training dataset on model fit. Applying the Cloud Score+ method for filtering cloud and cloud shadows further improved the fit of the phycocyanin estimation model, with an R2 of 0.84, but did not result in substantial improvements in the model’s application. The fit of the Chl-a models was generally poorer, but these models still had good accuracy in detecting moderate and high Chl-a values. Future work will focus on exploring alternative algorithms that can incorporate diverse data sources and lake characteristics, contributing to a deeper understanding of the relationship between remote sensing data and water quality parameters. This research provides a valuable tool for cyanobacteria parameter estimation with confidence quantification to identify lakes at risk of algal blooms.
Feature-Selection-Based Unsupervised Transfer Learning for Change Detection from VHR Optical Images
Authors: Chen Qiang , Yue Peng , Xu Yingjun , Cao Shisong , Zhou Lei , Liu Yang , Luo Jianhui
Published: September 2024

Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes.
Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling
Authors: Zhao Aoyun , Cheng Xinjie , Cao Rong , Huang Liuyuan , Hou Zhengyang
Published: September 2024

With the drastic reduction in wetland areas, it is essential to conduct an annual monitoring of the biomass or carbon content of wetland ecosystems to support international initiatives and agreements focused on sustainable development, climate change, and carbon equity. Forests in wetland ecosystems play a crucial role in carbon sequestration; however, the monitoring of small, fragmented forest components in wetlands remains insufficient, leading to an underestimation of their ecological and carbon sequestration functions. This study utilizes a model-assisted (MA) estimator, a monitoring procedure that is asymptotically design-unbiased and incorporates remote sensing, to assess the status and trends in the above-ground biomass (AGB) of forest components in wetlands, while also proposing a method of optimizing the sample size to enable continuous monitoring. Based on the population of the forest component of Baiyangdian wetland, major findings indicate that: (1) neglecting the forest component of Baiyangdian wetland will lead to an underestimation of the total aboveground biomass by 224.34 t/ha and 243.64 t/ha in the years 2022 and 2023, respectively; (2) in either year-specific monitoring or interannual change monitoring, the MA estimator is more cost-effective than the expansion estimator, a comparable procedure that relies solely on field observations; (3) the method used to optimize sample size can effectively tackle the cost-related concerns of subsequent continuous monitoring. Overall, the neglect of forest components is inevitably bound to give rise to an underestimation of wetlands, and use of an MA estimator and optimizing the sample size could effectively address the cost issue in continuous monitoring. This holds significant importance when developing management strategies to prevent the further degradation of wetland ecological functions and carbon sink capabilities.
Spatial and Temporal Variations of Total Suspended Matter Concentration during the Dry Season in Dongting Lake in the Past 35 Years
Authors: Shao Yifan , Shen Qian , Yao Yue , Zhou Yuting , Xu Wenting , Li Wenxin , Gao Hangyu , Shi Jiarui , Zhang Yuting
Published: September 2024

Dongting Lake is the second largest freshwater lake in China, located in the middle reaches of the Yangtze River. Since the 21st century, it has faced intensified human activities, particularly the Three Gorges Dam impoundment and sand mining. The water quality of Dongting Lake has significantly changed due to human activities and climate change. Currently, quantitative studies on the spatial–temporal variations of total suspended matter (TSM) during Dongting Lake’s dry season and the human impacts on its concentration are lacking. This study utilizes Landsat-5 TM and Landsat-8 OLI data to estimate the changes in TSM concentration during the dry season from 1986 to 2021, analyzing their spatial–temporal variations and driving mechanisms. By evaluating the atmospheric calibration accuracy and model precision metrics, we select a model based on the ratio of red to green band, achieving an R2 of 0.84, RMSE of 18.94 mg/L, and MRE of 27.32%. Applying this model to the images, we map the distribution of the TSM concentration during the dry season from 1986 to 2021, analyzing its spatial pattern and inter-annual variation, and further investigate the impacts of natural factors and human activities on the TSM concentration. Our results show the following: (1) From 1986 to 2021, the TSM concentration during the dry season ranges from 0 to 200 mg/L of Dongting Lake, with an area-wide average value between 41.61 and 75.44 mg/L. (2) The TSM concentration from 1986 to 2021 is significantly correlated with the water level. Before 2006, it correlates positively, but no significant correlation exists from 2006 onward. (3) From 2006 onward, the mean TSM concentration is notably decreased compared to that before 2006, likely due to the Three Gorges Dam, while our analysis indicates a significant positive correlation between the TSM concentration and sand mining intensity during this period. This study highlights the influence of the Three Gorges Dam and sand mining on the TSM concentration in Dongting Lake during the dry season, providing valuable insights for related research on similar lakes.
Dynamic Variable Precision Attribute Reduction Algorithm
Authors: Li Xu , Dong Ruibo , Chen Zhanwei , Ren Jiankang
Published: September 2024

Dynamic reduction algorithms have become an important part of attribute reduction research because of their ability to perform dynamic updates without the need to retrain the original model. To enhance the efficiency of variable precision reduction algorithms in processing dynamic data, research has been conducted from the perspective of the construction process of the discernibility matrix. By modifying the decision values of some samples through an absolute majority voting strategy, a connection between variable precision reduction and positive region reduction has been established. Considering the increase and decrease of samples, dynamic variable precision reduction algorithms have been proposed. For four cases of sample increase, four corresponding scenarios have been discussed, and judgment conditions for the construction of the discernibility matrix have been proposed, which has led to the development of a dynamic variable precision reduction algorithm for sample increasing (DVPRA-SI). Simultaneously, for the scenario of sample deletion, three corresponding scenarios have been proposed, and the judgment conditions for the construction of the discernibility matrix have been discussed, which has resulted in the development of a dynamic variable precision reduction algorithm for sample deletion (DVPRA-SD). Finally, the proposed two algorithms and existing dynamic variable precision reduction algorithms were compared in terms of the running time and classification precision, and the experiments demonstrated that both algorithms are feasible and effective.
On a Randomly Censoring Scheme for Generalized Logistic Distribution with Applications
Authors: Hasaballah Mustafa M. , Balogun Oluwafemi Samson , Bakr Mahmoud E.
Published: September 2024

In this paper, we investigate the inferential procedures within both classical and Bayesian frameworks for the generalized logistic distribution under a random censoring model. For randomly censored data, our main goals were to develop maximum likelihood estimators and construct confidence intervals using the Fisher information matrix for the unknown parameters. Additionally, we developed Bayes estimators with gamma priors, addressing both squared error and general entropy loss functions. We also calculated Bayesian credible intervals for the parameters. These methods were applied to two real datasets with random censoring to provide valuable insights. Finally, we conducted a simulation analysis to assess the effectiveness of the estimated values.
Research on Pricing and Dynamic Replenishment Planning Strategies for Perishable Vegetables Based on the RF-GWO Model
Authors: Pu Yongjun , Huang Zhonglin , Wang Junjie , Zhang Qianrong
Published: September 2024

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model’s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization.
Trajectory Privacy-Protection Mechanism Based on Multidimensional Spatial–Temporal Prediction
Authors: Xi Ji , Shi Meiyu , Zhang Weiqi , Xu Zhe , Liu Yanting
Published: September 2024

The popularity of global GPS location services and location-enabled personal terminal applications has contributed to the rapid growth of location-based social networks. Users can access social networks at anytime and anywhere to obtain services in the relevant location. While accessing services is convenient, there is a potential risk of leaking users’ private information. In data processing, the discovery of issues and the generation of optimal solutions constitute a symmetrical process. Therefore, this paper proposes a symmetry–trajectory differential privacy-protection mechanism based on multi-dimensional prediction (TPPM-MP). Firstly, the temporal attention mechanism is designed to extract spatiotemporal features of trajectories from different spatiotemporal dimensions and perform trajectory-sensitive prediction. Secondly, class-prevalence-based weights are assigned to sensitive regions. Finally, the privacy budget is assigned based on the sensitive weights, and noise conforming to localized differential privacy is added. Validated on real datasets, the proposed method in this paper enhanced usability by 22% and 37% on the same dataset compared with other methods mentioned, while providing equivalent privacy protection.
(Re-)Emergence of Oropouche Virus (OROV) Infections: Systematic Review and Meta-Analysis of Observational Studies
Authors: Riccò Matteo , Corrado Silvia , Bottazzoli Marco , Marchesi Federico , Gili Renata , Bianchi Francesco Paolo , Frisicale Emanuela Maria , Guicciardi Stefano , Fiacchini Daniel , Tafuri Silvio , Cascio Antonio , Giuri Pasquale Gianluca , Siliquini Roberta
Published: September 2024

Oropouche Virus (OROV; genus of Orthobunyavirus) is the causal agent of Oropouche Fever (OF). Due to the lack of specific signs and symptoms and the limited availability of diagnostic tests, the actual epidemiology of OROV infections and OF has been extensively disputed. In this systematic review with meta-analysis, a literature search was carried out in PubMed, Scopus, EMBASE, and MedRxiv in order to retrieve relevant articles on the documented occurrence of OROV infections. Pooled detection rates were then calculated for anti-OROV antibodies and virus detection (i.e., viral RNA detected by viral cultures and/or real-time polymerase chain reaction [RT-qPCR]). Where available, detection rates for other arboviruses (i.e., Dengue [DENV], Chikungunya [CHKV], and Zika Virus [ZIKV]) were calculated and compared to those for OROV. A total of 47 studies from South America and the Caribbean were retrieved. In individuals affected by febrile illness during OROV outbreaks, a documented prevalence of 0.45% (95% confidence interval [95%CI] 0.16 to 1.12) for virus isolation, 12.21% (95%CI 4.96 to 27.09) for seroprevalence (including both IgM and IgG class antibodies), and 12.45% (95%CI 3.28 to 37.39) for the detection of OROV-targeting IgM class antibodies were eventually documented. In the general population, seroprevalence was estimated to be 24.45% (95%CI 7.83 to 55.21) for IgG class antibodies. The OROV detection rate from the cerebrospinal fluids of suspected cases of viral encephalitis was estimated to be 2.40% (95%CI 1.17 to 5.03). The occurrence of OROV infections was consistently lower than that of DENV, CHKV, and ZIKV during outbreaks (Risk Ratio [RR] 24.82, 95%CI 21.12 to 29.16; RR 2.207, 95%CI 1.427 to 3.412; and RR 7.900, 95%CI 5.386 to 11.578, respectively) and in the general population (RR 23.614, 95%CI 20.584 to 27.129; RR 3.103, 95%CI 2.056 to 4.685; and RR 49.500, 95%CI 12.256 to 199.921, respectively). In conclusion, our study stresses the possibly high underestimation of OROV prevalence in the general population of South America, the potential global threat represented by this arbovirus infection, and the potential preventive role of a comprehensive “One Health approach”.