bluuebunny/crossref_metadata_2025 huggingface dataset:
Beneficial Effects of Synbiotics on the Gut Microbiome in Individuals with Low Fiber Intake: Secondary Analysis of a Double-Blind, Randomized Controlled Trial
Authors: Mantri Aakash , Klümpen Linda , Seel Waldemar , Krawitz Peter , Stehle Peter , Weber Bernd , Koban Leonie , Plassmann Hilke , Simon Marie-Christine
Published: July 2024

Insufficient dietary fiber intake can negatively affect the intestinal microbiome and, over time, may result in gut dysbiosis, thus potentially harming overall health. This randomized controlled trial aimed to improve the gut microbiome of individuals with low dietary fiber intake (<25 g/day) during a 7-week synbiotic intervention. The metabolically healthy male participants (n = 117, 32 ± 10 y, BMI 25.66 ± 3.1 kg/m2) were divided into two groups: one receiving a synbiotic supplement (Biotic Junior, MensSana AG, Forchtenberg, Germany) and the other a placebo, without altering their dietary habits or physical activity. These groups were further stratified by their dietary fiber intake into a low fiber group (LFG) and a high fiber group (HFG). Stool samples for microbiome analysis were collected before and after intervention. Statistical analysis was performed using linear mixed effects and partial least squares models. At baseline, the microbiomes of the LFG and HFG were partially separated. After seven weeks of intervention, the abundance of SCFA-producing microbes significantly increased in the LFG, which is known to improve gut health; however, this effect was less pronounced in the HFG. Beneficial effects on the gut microbiome in participants with low fiber intake may be achieved using synbiotics, demonstrating the importance of personalized synbiotics.
The Effect of 24 h Shift Work on the Nutritional Status of Healthcare Workers: An Observational Follow-Up Study from Türkiye
Authors: Navruz Varlı Semra , Mortaş Hande
Published: July 2024

This study aimed to determine changes in energy and nutrient intakes over three consecutive days, including the day of the shift, and the days before and after the shift, in healthcare workers working in a 24 h shift system. This study is an observational follow-up study conducted with a total of 500 volunteer healthcare professionals. Food consumption records were taken over 3 consecutive days: pre-shift (off day), shift day (24 h shift), and post-shift (off day). Mean daily intakes of energy, carbohydrate, protein, fat, saturated fat, caffeine, vitamins B1, B2, niacin, B6, folate, and B12, potassium, magnesium, phosphorus, iron, and zinc are listed from highest to lowest as shift day > pre-shift > post-shift (p < 0.05 for all pairwise comparisons). While fiber, vitamin C, and calcium intakes were similar on the shift day and pre-shift day, they were significantly lower on the post-shift day (p < 0.05). The lowest dietary reference intake percentages on the post-shift day were calcium, fiber, and folate, respectively. In the present study, significant differences were detected in the energy, micronutrient-intake, and macronutrient-intake levels between the pre-shift day, shift day, and post-shift day of healthcare workers. Awareness should be increased regarding the decreased nutrient intake seen especially on the first day after a 24 h shift, and appropriate precautions should be taken to increase calcium, fiber, and folate intake levels.
Stress and the Consumption of Ultra-Processed Foods during COVID-19’s Social Distancing: Are Mental Disorders Mediators in This Association? ELSA-Brasil Results
Authors: Pires Raphaela Kistenmacker , Griep Rosane Harter , Scaranni Patricia de Oliveira da Silva , Moreno Arlinda B. , Molina Maria del Carmen B. , Luft Vivian C. , da Fonseca Maria de Jesus Mendes , Cardoso Leticia de Oliveira
Published: July 2024

The COVID-19 pandemic exacerbated various determinants of mental disorders. Several behavioral changes were observed given this increase, including harmful health consequences, such as excessive consumption of ultra-processed foods (UPFs). To assess this relationship, we investigated the meditational role of symptoms of mental disorders (anxiety and depression) in the association between stress resulting from social distancing during the COVID-19 pandemic and the consumption of UPFs in 3884 Brazilian public employees in a supplementary study of the ELSA-Brasil study. Structural equation models (SEMs) were estimated to assess the direct and indirect effects mediated by symptoms of mental disorders. The results suggested a significant and positive mediational effect of the symptoms of mental disorders on the association between the stress resulting from social distancing and the consumption of UPFs. These findings contribute to informing the need for policies and early interventions in potentially stressful situations, with a focus on the promotion of mental health, and may thus help prevent or reduce the consumption of unhealthy foods.
Effects of mHealth Practice Patterns on Improving Metabolic Syndrome Using the Information–Motivation–Behavioral Skills Model
Authors: Park Na-Young , Jang Sarang
Published: July 2024

Chronic diseases contribute to 68% of global mortality, highlighting the importance of early detection and management of conditions such as metabolic syndrome. Effective lifestyle interventions, particularly through mobile health (mHealth), have shown potential in promoting health and reducing cardiometabolic risk. This study utilized mHealth data from public health centers in South Korea, targeting adults with risk factors for metabolic syndrome. The Intervention-Motivation-Behavioral skills (IMB) theoretical model was applied to categorize participants’ practice patterns over time using the Group-Based Trend Model (GBTM). And the Generalized Estimating Equations (GEE) methodology was applied to confirm the effective practice patterns for improving metabolic syndrome. Data were collected over 24 weeks. The dataset encompasses life-log data capable of capturing changes in intervention, self-report surveys, and clinical measurements, all linked to personal identification keys and thereby integrated. Participants demonstrated improved health behaviors, with the healthy eating score increasing from 5.0 to 6.4 and physical activity rates rising from 41.5% to 59%. Health risk factors decreased significantly, with the mean number of risk factors dropping from 2.4 to 1.4. The percentage of subjects with three or more metabolic syndrome components decreased from 42.3% in the initial period to 19.2% in the final period. Practice patterns by IMB components were classified into three categories: continuous type, late decline type, and early decline type. Improvements in health behavior and metabolic syndrome were observed in the continuous type of each IMB component. The mHealth interventions were confirmed to be positively associated with improved health behavior and management of metabolic syndrome in the continuous practice patterns of IMB.
Intermittent Fasting Attenuates Obesity-Induced Triple-Negative Breast Cancer Progression by Disrupting Cell Cycle, Epithelial–Mesenchymal Transition, Immune Contexture, and Proinflammatory Signature
Authors: Son Deok-Soo , Done Kaitlyn A. , Son Jubin , Izban Michael G. , Virgous Carlos , Lee Eun-Sook , Adunyah Samuel E.
Published: July 2024

Obesity is associated with one-fifth of cancer deaths, and breast cancer is one of the obesity-related cancers. Triple-negative breast cancer (TNBC) lacks estrogen and progesterone receptors and human epidermal growth factor receptor 2, leading to the absence of these therapeutic targets, followed by poor overall survival. We investigated if obesity could hasten TNBC progression and intermittent fasting (IF) could attenuate the progression of obesity-related TNBC. Our meta-analysis of the TNBC outcomes literature showed that obesity led to poorer overall survival in TNBC patients. Fasting-mimicking media reduced cell proliferation disrupted the cell cycle, and decreased cell migration and invasion. IF decreased body weight in obese mice but no change in normal mice. Obese mice exhibited elevated plasma glucose and cholesterol levels, increased tumor volume and weight, and enhanced macrophage accumulation in tumors. The obesity-exacerbated TNBC progression was attenuated after IF, which decreased cyclin B1 and vimentin levels and reduced the proinflammatory signature in the obesity-associated tumor microenvironment. IF attenuated obesity-induced TNBC progression through reduced obesity and tumor burdens in cell and animal experiments, supporting the potential of a cost-effective adjuvant IF therapy for TNBC through lifestyle change. Further evidence is needed of these IF benefits in TNBC, including from human clinical trials.
Association between Number of Teeth and Eating out of Home: A 2019 Statistical Survey of the Japanese Representative Population
Authors: Kinugawa Anna , Yamamoto Takafumi , Kusama Taro , Takeuchi Kenji , Osaka Ken
Published: July 2024

Eating out of home (EOH), with its diverse food options, can benefit those with difficulty preparing their meals, especially older adults. Oral health status may be a determinant of EOH, as food accessibility is influenced by oral health, but this association remains unclear. This cross-sectional study used merged data from two national statistical surveys conducted in 2019. Participants were individuals aged ≥ 65 years who responded to both surveys. The frequency of EOH (<once/week or ≥once/week) was the dependent variable. The number of teeth was used as the independent variable (≥20, 10–19, 1–9, and 0). Prevalence ratios (PRs) and 95% confidence intervals (CIs) were calculated using multivariate Poisson regression analysis to identify the association between EOH and the number of teeth, adjusting for possible confounders. We analyzed 2164 participants (mean age = 74.0, women 52.4%). Of these, 456 (21.1%) participants were EOH ≥ once/week; 1142 (52.8%) participants had ≥20 teeth. Compared to those with ≥20 teeth, those with <20 teeth had a lower prevalence of EOH ≥ once/week (10–19: PR = 0.89, 95% CI = 0.72–1.09, 1–9: PR = 0.67, 95% CI = 0.51–0.89, and 0: PR = 0.53, 95% CI = 0.36–0.77, respectively). We observed an association between fewer teeth and a lower frequency of EOH.
Effects of Essential Oils on Biological Characteristics and Potential Molecular Targets in Spodoptera frugiperda
Authors: Oliveira Júlia A. C. , Fernandes Letícia A. , Figueiredo Karolina G. , Corrêa Eduardo J. A. , Lima Leonardo H. F. , Alves Dejane S. , Bertolucci Suzan K. V. , Carvalho Geraldo A.
Published: July 2024

Spodoptera frugiperda control methods have proved to be inefficient, which justifies the search for new control measures. In this search for botanical insecticides for controlling S. frugiperda, the following were evaluated: (i) the toxicity of essential oils (EOs) from Cinnamodendron dinisii, Eugenia uniflora, and Melaleuca armillaris; (ii) the effect of EOs on life table parameters against S. frugiperda; (iii) the chemical characterization of EOs; and (iv) the in silico interaction of the chemical constituents present in the three EOs with the molecular targets of S. frugiperda. The EO from E. uniflora had the lowest LD50 (1.19 µg of EO/caterpillar). The major compounds bicyclogermacrene (18.64%) in C. dinisii and terpinolene (57.75%) in M. armillaris are highly predicted to interact with the octopamine receptor (OctpR). The compound 1,8-cineole (21.81%) in M. armillaris interacts mainly with a tolerant methoprene receptor (MET) and curzerene (41.22%) in E. uniflora, which acts on the OctpR receptor. Minor compounds, such as nerolidol in C. dinisii and β-elemene in E. uniflora, are highly ranked for multiple targets: AChE, MET, OctpR, and 5-HT1. It was concluded that the EO from E. uniflora negatively affects several biological parameters of S. frugiperda development and is promising as an active ingredient in formulations for controlling this insect pest.
Forest Orchids under Future Climate Scenarios: Habitat Suitability Modelling to Inform Conservation Strategies
Authors: Pica Antonio , Vela Daniele , Magrini Sara
Published: July 2024

Orchidaceae is one of the largest and most diverse families of flowering plants in the world but also one of the most threatened. Climate change is a global driver of plant distribution and may be the cause of their disappearance in some regions. Forest orchids are associated with specific biotic and abiotic environmental factors, that influence their local presence/absence. Changes in these conditions can lead to significant differences in species distribution. We studied three forest orchids belonging to different genera (Cephalanthera, Epipactis and Limodorum) for their potential current and future distribution in a protected area (PA) of the Northern Apennines. A Habitat Suitability Model was constructed for each species based on presence-only data and the Maximum Entropy algorithm (MaxEnt) was used for the modelling. Climatic, edaphic, topographic, anthropogenic and land cover variables were used as environmental predictors and processed in the model. The aim is to identify the environmental factors that most influence the current species distribution and the areas that are likely to contain habitats suitable for providing refuge for forest orchids and ensuring their survival under future scenarios. This will allow PA authorities to decide whether to invest more resources in conserving areas that are potential refuges for threatened species.
Genome-Wide Identification of the WRKY Gene Family in Four Cotton Varieties and the Positive Role of GhWRKY31 in Response to Salt and Drought Stress
Authors: Dong Tianyu , Su Jiuchang , Li Haoyuan , Du Yajie , Wang Ying , Chen Peilei , Duan Hongying
Published: July 2024

The WRKY gene family is ubiquitously distributed in plants, serving crucial functions in stress responses. Nevertheless, the structural organization and evolutionary dynamics of WRKY genes in cotton have not been fully elucidated. In this study, a total of 112, 119, 217, and 222 WRKY genes were identified in Gossypium arboreum, Gossypium raimondii, Gossypium hirsutum, and Gossypium barbadense, respectively. These 670 WRKY genes were categorized into seven distinct subgroups and unequally distributed across chromosomes. Examination of conserved motifs, domains, cis-acting elements, and gene architecture collectively highlighted the evolutionary conservation and divergence within the WRKY gene family in cotton. Analysis of synteny and collinearity further confirmed instances of expansion, duplication, and loss events among WRKY genes during cotton evolution. Furthermore, GhWRKY31 transgenic Arabidopsis exhibited heightened germination rates and longer root lengths under drought and salt stress. Silencing GhWRKY31 in cotton led to reduced levels of ABA, proline, POD, and SOD, along with downregulated expression of stress-responsive genes. Yeast one-hybrid and molecular docking assays confirmed the binding capacity of GhWRKY31 to the W box of GhABF1, GhDREB2, and GhRD29. The findings collectively offer a systematic and comprehensive insight into the evolutionary patterns of cotton WRKYs, proposing a suitable regulatory framework for developing cotton cultivars with enhanced resilience to drought and salinity stress.
Lipids and Fatty Acid Composition Reveal Differences between Durum Wheat Landraces and Modern Cultivars
Authors: Mandrioli Mara , Poggi Giovanni Maria , Cai Giampiero , Faleri Claudia , Maccaferri Marco , Tuberosa Roberto , Aloisi Iris , Toschi Tullia Gallina , Corneti Simona
Published: July 2024

Durum wheat (Triticum turgidum L. ssp. durum) landraces, traditional local varieties representing an intermediate stage in domestication, are gaining attention due to their high genetic variability and performance in challenging environments. While major kernel metabolites have been examined, limited research has been conducted on minor bioactive components like lipids, despite their nutritional benefits. To address this, we analyzed twenty-two tetraploid accessions, comprising modern elite cultivars and landraces, to (i) verify if the selection process for yield-related traits carried out during the Green Revolution has influenced lipid amount and composition; (ii) uncover the extent of lipid compositional variability, giving evidence that lipid fingerprinting effectively identifies evolutionary signatures; and (iii) identify genotypes interesting for breeding programs to improve yield and nutrition. Interestingly, total fat did not correlate with kernel weight, indicating lipid composition as a promising trait for selection. Tri- and di-acylglycerol were the major lipid components along with free fatty acids, and their relative content varied significantly among genotypes. In particular, landraces belonging to T. turanicum and carthlicum ecotypes differed significantly in total lipid and fatty acid profiles. Our findings provide evidence that landraces can be a genetically relevant source of lipid variability, with potential to be exploited for improving wheat nutritional quality.
Study on the Performance of Aniline Electrodeposited on MnO2 Nanowire as an Anode for Sodium-Ion Batteries
Authors: Ma Dandan , Yin Xiangyu , Li Xinyi , Qin Xiangge , Qi Meili
Published: July 2024

Manganese dioxide is an ideal anode for sodium-ion batteries due to its rich crystal shapes. However, its low conductivity, low reversible discharge capacity, slow diffusion kinetics, and poor cyclic stability limit its potential for industrial application. The design of manganese dioxide (MnO2) with various morphologies, such as nanowires, nanorods, and nanoflowers, has proven effective in enhancing its electrochemical performance. Stacking nanowire structures is of interest as they increase the open space by forming an interconnected network, thus facilitating favorable diffusion pathways for sodium ions. Concurrently, the substantial increase in the electrolyte contact area efficiently mitigates the strain induced by the volume expansion associated with the repetitive migration and insertion of sodium ions. Based on previous research, this work presents the structural design of flexible MnO2/polyaniline (MnO2/PANI) nanowires assembled on carbon cloth (CC), an innovation in MnO2 modification. Compared to conventional MnO2 nanowires, the MnO2/PANI nanowires exhibit enhanced structural stability and improved dynamic performance, thereby marking a significant advancement in their material properties. This MnO2/PANI composite exhibits a rate capacity of approximately 200 mA h g−1 after 60 cycles at a current density of 0.1 A g−1, and maintains a rate capacity of 182 mA h g−1 even after 200 cycles under the same current density. This study not only provides new insights into the underlying mechanisms governing energy storage in MnO2/PANI nanowires but also paves the way for their further development and optimization as anodes for sodium-ion batteries, thereby opening up fresh avenues for research and application.
Preparation of Green Silver Nanoparticles and Eco-Friendly Polymer–AgNPs Nanocomposites: A Study of Toxic Properties across Multiple Organisms
Authors: Mačák Lívia , Velgosova Oksana , Múdra Erika , Vojtko Marek , Dolinská Silvia , Kromka František
Published: July 2024

This article focuses on the eco-friendly (green) synthesis of silver nanoparticles (AgNPs) and their incorporation into a polymer matrix. For AgNPs synthesis, Lavandula angustifolia (lavender) leaf extract was used as a reducing and stabilizing agent, and as a silver precursor, AgNO3 solution with different concentrations of silver (50, 100, 250, and 500 mg/L) was used. Prepared AgNPs colloids were characterized using UV–vis spectrophotometry, transmission electron microscopy (TEM), and X-ray diffraction (XRD). The spherical morphology of AgNPs with an average size of 20 nm was confirmed across all samples. Further, the antimicrobial properties of the AgNPs were evaluated using the disk diffusion method on algae (Chlorella kessleri) and the well diffusion method on bacteria (Staphylococcus chromogenes, Staphylococcus aureus, and Streptococcus uberis), along with root growth inhibition tests on white mustard (Sinapis alba). Polymer composite (PVA–AgNPs) was prepared by incorporation of AgNPs into the polymer matrix. Subsequently, non-woven textiles and thin foils were prepared. The distribution of AgNPs within the nanocomposites was observed by scanning electron microscopy (SEM). Antibacterial properties of PVA–AgNPs composites were analyzed on bacteria Streptococcus uberis. It was found that not only AgNPs showed good antimicrobial properties, but toxic properties were also transferred to the PVA–AgNPs nanocomposite.
Development of Cellulose Microfibers from Mixed Solutions of PAN-Cellulose in N-Methylmorpholine-N-Oxide
Authors: Makarov Igor , Vinogradov Markel , Golubev Yaroslav , Palchikova Ekaterina , Kulanchikov Yuriy , Grishin Timofey
Published: July 2024

Mixed solutions of PAN with cellulose in N-methylmorpholine-N-oxide (NMMO) were prepared. Systems with a fraction of a dispersed phase of a cellulose solution in NMMO up to 40% are characterized by the formation of fibrillar morphology. The fibrils created as the mixed solution is forced through the capillary take on a more regular order as the cellulose content in the system drops. The systems’ morphology is considered to range from a heterogeneous two-phase solution to regular fibrils. The generated morphology, in which the cellulose fibrils are encircled by the PAN, can be fixed by spinning fibers. Cellulose fibrils have a diameter of no more than a few microns. The length of the fibrils is limited by the size of the fiber being formed. The process of selectively removing PAN was used to isolate the cellulose microfibrils. Several techniques were used to evaluate the mechanical properties of isolated cellulose microfibers. Atomic force microscopy allowed for the evaluation of the fiber stiffness and the creation of topographic maps of the fibers. Cellulose microfibers have a higher Young’s modulus (more than 30 GPa) than cellulose fibers formed in a comparable method, which affects the mechanical properties of composite fibers.
Formation of Olive-like TiO2 Nanospheres in a Polymeric Mesh by Sol-Gel Method
Authors: López Melendez Claudia , Monreal Romero Humberto Alejandro , Carreño-Gallardo Caleb , Martinez Mata Guillermo , Pacheco Santiesteban Rosaura , Pérez Piñon Teresa , Pérez Piñon Dagoberto , López Aguilar Héctor Alfredo , Estrada Macias Marvin Elco , Chacón-Nava José Guadalupe
Published: July 2024

Olive-like TiO2 (titanium dioxide), nanospheres compounds were synthesized. Polysaccharide (1–3 linked β-D galactapyranose and 1.4-linked 3.6 anyhdro-α-L-galactopyranose and titanium isopropoxide (IV) was used as a precursor in its formation. The powder sample was evaluated by scanning tunneling microscope, X-ray diffraction pattern, power spectral density, fast Fourier transform, differential thermal analysis, continuous wavelet transform, and isotropy texture analysis. The results demonstrate that these nanospheres can successfully be synthesized in a solution using a polysaccharide network by means of the sol-gel method. The synthesized olive-like TiO2 nanospheres have diameters ranging from 50 nm to 500 nm. The synthesis parameters, such as temperature, time, and concentration of the polysaccharide, were controlled in solution.
Production of Nanocellulose from Sugarcane Bagasse and Development of Nanocellulose Conjugated with Polylysine for Fumonisin B1 Toxicity Absorption
Authors: Thipchai Parichat , Sringarm Korawan , Punyodom Winita , Jantanasakulwong Kittisak , Thanakkasaranee Sarinthip , Panyathip Rangsan , Arjin Chaiwat , Rachtanapun Pornchai
Published: July 2024

The present study aimed to extract nanocellulose (NC) from sugarcane bagasse agricultural waste through a chemical method (sulfuric acid hydrolysis and ultrasonication). Subsequently, the nanocellulose product was conjugated with polylysine (NC–PL) and assessed for its efficacy in reducing the toxicity of Fumonisin B1 (FB1), a mycotoxin produced by fungi commonly found in corn, wheat, and other grains. Experimental results confirmed the successful conjugation of NC and PL, as evidenced by FTIR peaks at 1635 and 1625 cm−1 indicating amide I and amide II vibrations in polylysine (PL). SEM analysis revealed a larger size due to PL coating, consistent with DLS results showing the increased size and positive charge (38.0 mV) on the NC–PL surface. Moreover, the effect of FB1 adsorption by NC and NC–PL was evaluated at various concentrations (0–200,000 μg/mL). NC–PL demonstrated the ability to adsorb FB1 at concentrations of 2000, 20,000, and 200,000 μg/mL, with adsorption efficiencies of 94.4–100%. Human hepatocellular carcinoma (HepG2) cells were utilized to assess NC and NC–PL cytotoxic effects. This result is a preliminary step towards standardizing results for future studies on their application as novel FB1 binders in food, food packaging, and functional feeds.
Investigating Magnetohydrodynamic Motions of Oldroyd-B Fluids through a Circular Cylinder Filled with Porous Medium
Authors: Fetecau Constantin , Vieru Dumitru
Published: July 2024

We analytically investigated the magnetohydrodynamic motions of electrically conductive, incompressible Oldroyd-B fluids through an infinite circular cylinder filled with a porous medium. A general expression was established for the dimensionless velocity of fluid as a cylinder moves along its symmetry axis with an arbitrary velocity; the expression can generate exact solutions for any motion of this fluid type, solving the discussed problem. Special cases were considered and validated through graphical investigation to illustrate important characteristics of fluid behavior. In application, this is the first presentation of an exact general expression for non-trivial shear stress related to the magnetohydrodynamic motions of Oldroyd-B fluids when a longitudinal time-dependent shear stress is applied to the fluid by a cylinder. Solutions for the motions of rate-type fluids are lacking. The graphical representations show that in the presence of a magnetic field or porous medium, fluids flow more slowly and the steady state is reached earlier.
Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance
Authors: Xie Shengkun , Ho Nathaniel
Published: July 2024

This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods.
Predicting the Impacts of Land Use/Cover and Climate Changes on Water and Sediment Flows in the Megech Watershed, Upper Blue Nile Basin
Authors: Admas Mulugeta , Melesse Assefa M. , Tegegne Getachew
Published: July 2024

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios.
Generation of High Temporal Resolution Fractional Forest Cover Data and Its Application in Accurate Time Detection of Forest Loss
Authors: Shi Wenxi , Zhao Xiang , Yang Hua , Si Longping , Wang Qian , Zhao Siqing , Guo Yinkun
Published: July 2024

Fractional Forest cover holds significance in characterizing the ecological condition of forests and serves as a crucial input parameter for climate and hydrological models. This research introduces a novel approach for generating a 250 m fractional forest cover product with an 8-day temporal resolution based on the updated GLASS FVC product and the annualized MODIS VCF product, thereby facilitating the development of a high-quality, long-time-series forest cover product on a global scale. Validation of the proposed product, employing high spatial resolution GFCC data, demonstrates its high accuracy across various continents and forest cover scenarios globally. It yields an average fit coefficient of determination (R2) of 0.9085 and an average root-mean-square error of 7.22%. Furthermore, to assess the availability and credibility of forest cover data with high temporal resolution, this study integrates the CCDC algorithm to map forest disturbances and quantify the yearly and even monthly disturbed trace area within two sub-study areas of the Amazon region. The achieved sample validation accuracy is over 86%, which substantiates the reliability of the data. This investigation offers a fresh perspective on monitoring forest changes and observing forest disturbances by amalgamating data from diverse sources, enabling the mapping of dynamic forest cover over an extensive time series with high temporal resolution, thereby mitigating data gaps and enhancing the precision of existing products.
Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China
Authors: Zhang Ruiqi , Zhang Lele , Fang Zhice , Oguchi Takashi , Merghadi Abdelaziz , Fu Zijin , Dong Aonan , Dou Jie
Published: July 2024

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.
Comparison of the Water Vapor Budget Evolution of Developing and Non-Developing Disturbances over the Western North Pacific
Authors: Sun Zhihong , Gao Si , Jian Maoqiu
Published: July 2024

Tropical cyclone (TC) genesis prediction remains a major operational challenge. Using multiple satellite datasets and a state-of-the-art reanalysis dataset, this study identifies developing and non-developing tropical disturbances over the western North Pacific from June to November of 2000–2019 and conducts composite analyses of their water vapor budget components and relevant dynamic–thermodynamic parameters in the Lagrangian framework following three-day disturbance tracks. Both groups of disturbances have a similar initial 850 hPa synoptic-scale relative vorticity, while the water vapor budget of developing disturbances exhibits distinct stage-wise evolution characteristics from non-developing cases. Three days prior to TC genesis, developing cases are already associated with significantly higher total precipitable water (TPW), vertically integrated moisture flux convergence (VIMFC), and precipitation, of which TPW is the most important parameter to differentiate two groups of disturbances. One day later, all the water vapor budget components (i.e., TPW, VIMFC, precipitation, and evaporation) strengthened, linked with the enhancement of the mid-to lower-tropospheric vortices. A negative radial gradient of evaporation occurs, suggesting the beginning of the wind−evaporation feedback. On the day prior to TC genesis, the water vapor budget components, as well as the mid-to lower-tropospheric vortices, continue to intensify, eventually leading to TC genesis. By contrast, non-developing disturbances are associated with a drier environment and weaker VIMFC, precipitation, and evaporation during the three-day evolution. All these factors are not favorable for the intensification of the mid-to lower-tropospheric vortices; thus, the disturbances fail to upgrade to TCs. The results may shed light on TC genesis prediction.
Calibration of SAR Polarimetric Images by Covariance Matching Estimation Technique with Initial Search
Authors: Liu Jingke , Liu Lin , Zhou Xiaojie
Published: July 2024

To date, various methods have been proposed for calibrating polarimetric synthetic aperture radar (SAR) using distributed targets. Some studies have utilized the covariance matching estimation technique (Comet) for SAR data calibration. However, practical applications have revealed issues stemming from ill-conditioned problems due to the analytical solution in the iterative process. To tackle this challenge, an improved method called Comet IS is introduced. Firstly, we introduce an outlier detection mechanism which is based on the Quegan algorithm’s results. Next, we incorporate an initial search approach which is based on the interior point method for recalibration. With the outlier detection mechanism in place, the algorithm can recalibrate iteratively until the results are correct. Simulation experiments reveal that the improved algorithm outperforms the original one. Furthermore, we compare the improved method with Quegan and Ainsworth algorithms, demonstrating its superior performance in calibration. Furthermore, we validate our method’s advancement using real data and corner reflectors. Compared with the other two algorithms, the improved performance in crosstalk isolation and channel imbalance is significant. This research provides a more reliable and effective approach for polarimetric SAR calibration, which is significant for enhancing SAR imaging quality.
Joint Wideband Beamforming Algorithm for Main Lobe Jamming Suppression in Distributed Array Radar
Authors: Ma Xiaofeng , Jiang Siqin , Zhang Shurui , Zhang Renli , Sheng Weixing
Published: July 2024

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm.
Low-Power Magnetic Displacement Sensor Based on RISC-V Embedded System
Authors: Sun Tao , Song Yue , Yang Huiyun
Published: July 2024

With the emergence of RISC-V architecture in embedded devices, its inherent low-power features have propelled its extensive adoption across various industrial settings. Displacement sensors leveraging Hall sensors and magnetic flux measurement present notable benefits including cost-effectiveness and compact design. This study undertakes the porting of Hall sensors onto RISC-V architecture embedded devices, validating their functionality within displacement sensors. Empirical investigations substantiate that the ported system consistently delivers comparable outcomes to those obtained from x86 architecture systems employing PM-MFM methods, affirming its reliability and performance in practical applications.
Pulsed Laser-Bleaching Semiconductor and Photodetector
Authors: Huang Chen , Chen Fei , Zhang Ze , Tang Xin , Zhu Meng , Sun Junjie , Chen Yi , Zhang Xin , Yu Jinghua , Zhang Yiwen
Published: July 2024

Pulsed lasers alter the optical properties of semiconductors and affect the photoelectric function of the photodetectors significantly, resulting in transient changes known as bleaching. Bleaching has a profound impact on the control and interference of photodetector applications. Experiments using pump–probe techniques have made significant contributions to understanding ultrafast carrier dynamics. However, there are few theoretical studies to the best of our knowledge. Here, carrier dynamic models for semiconductors and photodetectors are established, respectively, employing the rectified carrier drift-diffusion model. The pulsed laser bleaching effect on seven types of semiconductors and photodetectors from visible to long-wave infrared is demonstrated. Additionally, a continuous bleaching method is provided, and the finite-difference time-domain (FDTD) method is used to solve carrier dynamic theory models. Laser parameters for continuous bleaching of semiconductors and photodetectors are calculated. The proposed bleaching model and achieved laser parameters for continuous bleaching are essential for several applications using semiconductor devices, such as infrared detection, biological imaging, and sensing.
An Improved U-Net Infrared Small Target Detection Algorithm Based on Multi-Scale Feature Decomposition and Fusion and Attention Mechanism
Authors: Fan Xiangsuo , Ding Wentao , Li Xuyang , Li Tingting , Hu Bo , Shi Yuqiu
Published: July 2024

Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively.
Stress Wave Propagation in a Rayleigh–Love Rod with Sudden Cross-Sectional Area Variations Impacted by a Striker Rod
Authors: Wang Chung-Yue , Thang Nguyen Ngoc , Wang Helsin
Published: July 2024

This paper presents an in-depth study of the stress wave behavior propagating in a Rayleigh–Love rod with sudden cross-sectional area variations. The analytical solutions of stress waves are derived for the reflection and transmission propagation behavior at the interface of the cross-sectional area change in the rod, considering inertia and Poisson’s effects on the rod material. Examples solved using the finite element method are provided to verify the correctness of the analytical results. Based on the forward analysis of Rayleigh–Love wave propagation in a rod impacted by a striker rod, an impact-echo-type nondestructive testing (NDT) method is proposed to conduct defect assessment in rod-type structural components with sudden cross-sectional area changes within a cover medium. This proposed NDT method can identify the location, extension, and cross-sectional area drop ratios of an irregular zone in the rod to be inspected.
Wearable Alcohol Monitoring Device for the Data-Driven Transcutaneous Alcohol Diffusion Model
Authors: Jalal Ahmed Hasnain , Arbabi Sepehr , Ahad Mohammad A. , Alam Fahmida , Ahmed Md Ashfaq
Published: July 2024

Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers’ wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration, Cmax, in the stratum corneum from the transient analysis. Lastly, we found that a more significant time difference between BACmax and Cmax was due to greater alcohol consumption for a fixed absorption time.
Piston Error Automatic Correction for Segmented Mirrors via Deep Reinforcement Learning
Authors: Li Dequan , Wang Dong , Yan Dejie
Published: July 2024

The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements compared with other methods. However, it is often difficult to obtain a high accuracy in practical application situations with this method because of the difference between the training model and the actual model. The reinforcement learning algorithm does not need to model the real system when operating the system. However, it still retains the advantages of supervised learning. Thus, in this paper, we placed a mask on the pupil plane of the segmented telescope optical system. Moreover, based on the wide spectrum, point spread function, and modulation transfer function of the optical system and deep reinforcement learning—without modeling the optical system—a large-range and high-precision piston error automatic co-phase method with multiple-submirror parallelization was proposed. Finally, we carried out relevant simulation experiments, and the results indicate that the method is effective.