bluuebunny/crossref_metadata_2025 huggingface dataset:
Stability and Bioaccessibility of Carotenoids from Sea Buckthorn Pomace Encapsulated in Alginate Hydrogel Beads
Authors: Gherasim Cristina Elena , Focşan Monica , Ciont Călina , Bunea Andrea , Rugină Dumitriţa , Pintea Adela
Published: August 2024

Carotenoids, the natural pigments that confer the bright orange color of sea buckthorn berries, are also associated with several health benefits, such as antioxidant activity and skin and eye protection. Due to their lipophilic nature and localization, carotenoids are largely retained in the sea buckthorn pomace (SBP) resulting from juice production. Carotenoids from SBP (70.03 mg/100 g DW), extracted and characterized by HPLC-PDA, contained zeaxanthin (free and esterified) and beta-carotene as major compounds. The SBP carotenoids-enriched sunflower oil was further encapsulated in Ca-alginate hydrogel beads (98.4% encapsulation efficiency) using ionotropic gelation. The hydrogel beads were characterized by confocal laser scanning microscopy and scanning electron microscopy. Fairly good stability (>64%) of the encapsulated carotenoids in the alginate hydrogel beads during storage (30 days, 4 °C and 25 °C) was found, with zeaxanthin esters being the most stable compounds, for all the experimental conditions. The bioaccessibility of the total carotenoids (INFOGEST protocol) was 42.1 ± 4.6% from hydrated, and, respectively, 40.8 ± 4% from dehydrated SBP alginate hydrogel beads. The addition of yogurt to the dehydrated hydrogel beads had a positive effect on the bioaccessibility of free and esterified zeaxanthin, but not on that of the carotenes. In conclusion, SBP is a valuable source of carotenoids which can be protected by encapsulation in alginate hydrogel beads, thus still retaining a good bioaccessibility.
The Impact of Following Instagram Influencers on Women’s Body Dissatisfaction and Eating Disorder Symptoms
Authors: Bocci Benucci Sara , Fioravanti Giulia , Silvestro Valeria , Spinelli Maria Chiara , Brogioni Giulietta , Casalini Alessia , Allegrini Lara , Altomare Arianna Ida , Castellini Giovanni , Ricca Valdo , Rotella Francesco
Published: August 2024

According to the Tripartite Influence Model, social media is one of the primary sources influencing women’s body dissatisfaction. However, the role of social media influencers as a potential driver for impacting users’ body image evaluation when disseminating content on social networks has been little investigated. The present research aims to explore the relationship between following three Instagram influencers (i.e., nutrition, fitness, and entertainment) and eating disorder (ED) symptoms and body dissatisfaction among a group of female users. A sample of 5060 women (mean age = 35.33 ± 9.33) were recruited with the collaboration of three Italian influencers, and self-reported questionnaires were administered. Women who followed the nutritional influencer account reported significantly higher scores on ED symptoms and body dissatisfaction than women who followed the fitness and entertainment influencer accounts (η2 = 0.05 and η2 = 0.02, respectively). Overall, following nutrition and fitness accounts (compared to entertainment accounts) and spending more time daily on social networks positively predicts ED symptoms (β = 0.28, p < 0.001, β = 0.10, p < 0.001, β = 0.11, p < 0.001, respectively) and body dissatisfaction (β = 0.07, p < 0.001, β = 0.04, p < 0.001, β = 0.07, p < 0.001, respectively). Moreover, following nutritional influencers compared to fitness influencers positively predicts ED symptoms (β = 0.17, p < 0.001) but not body dissatisfaction. The current results suggest that being exposed to dieting and weight loss topics on social media might be particularly harmful for individuals with specific vulnerabilities. Practical implications will be discussed.
Sex- and Age-Specific Differences in Mice Fed a Ketogenic Diet
Authors: Sprankle Kenyon W. , Knappenberger Mya A. , Locke Erica J. , Thompson Jack H. , Vinovrski Madison F. , Knapsack Kaylin , Kolwicz Stephen C.
Published: August 2024

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that results in the elevation of serum ketone bodies, known as ketosis. This metabolic consequence has been suggested as a method for treating neurological conditions, improving exercise performance, and facilitating weight loss for overweight individuals. However, since most research primarily uses male populations, little is known about the potential sex differences during the consumption of the KD. In addition, the effects of the KD on aging are relatively unexplored. Therefore, the purpose of this study was to explore sex- and age-specific differences in mice fed the KD. Male and female C57BL/6N mice at either 12 wks or 24 wks of age were randomly assigned to a KD (90% fat, 1% carbohydrate) or chow (13% fat, 60% carbohydrate) group for 6 wks. KD induced weight gain, increased adiposity, induced hyperlipidemia, caused lipid accumulation in the heart and liver, and led to glycogen depletion in the heart, liver, and muscle with varying degrees of changes depending on age and sex. While younger and older male mice on the KD were prone to glucose intolerance, the KD acutely improved rotarod performance in younger females. Overall, this study highlights potential sex and aging differences in the adaptation to the KD.
Identification of Enteric Pathogen Reservoirs and Transmission Pathways Associated with Short Childhood Stature in the Kolkata Indian Site of the Global Enteric Multicenter Study
Authors: Long Kurt Z. , Gunanti Inong R. , Stride Chris , Sanchez Johanna , Sur Dipika , Manna Byomkesh , Ramamurthy Thandavarayan , Kanungo Suman , Nataro James P. , Powell Helen , Roose Anna , Nasrin Dilruba , Sommerfelt Halvor , Levine Myron , Kotloff Karen
Published: August 2024

Age-stratified path analyses modeled associations between enteric pathogen reservoirs, transmission pathways and height-for-age z-scores (HAZ) to identify determinants of childhood growth in the Kolkata, India site of the Global Enteric Multicenter Study (GEMS). Models tested direct associations of potential pathogen reservoirs with HAZ at 60-day follow-up in separate moderate and severe diarrhea (MSD) case and control cohorts or indirectly when mediated by enteric infections. In the MSD cohort, rotavirus and typical EPEC (tEPEC) infections among children 0–11 months of age and ST-ETEC infections among children 12–23 months of age were associated with lower HAZ. Handwashing after defecating and before cooking reduced impaired growth through reductions in rotavirus and tEPEC infections. Water storage increased rotavirus and ST-ETEC infection risks, resulting in increased impaired growth, but was reduced with reported child feces disposal. The GII norovirus variant was inversely associated with HAZ among children 12–59 months of age in the control cohort. Reported handwashing before the handling of children reduced GII infections and impaired growth. Boiling water and the disposal of children’s feces mediated by stored water were positively associated with HAZ. The targeting of pathogen-specific reservoirs and transmission pathways may more effectively improve childhood linear growth in South Asian urban communities.
Knowledge, Attitudes, and Perceptions of Maternity Care Providers on the Implementation of Calcium Supplementation during Pregnancy in Three Public Hospitals in Argentina: A Qualitative Study
Authors: Di Marco Martín Hernán , Cabrera Wanda , Rivas Tomas I. , Maury-Sintjago Eduard , López María N. , Cormick Gabriela
Published: August 2024

The aim of this study was to explore maternity care providers’ knowledge, attitudes, and perceptions about the use of calcium supplements during pregnancy for the prevention of preeclampsia in three hospitals from Metropolitan Buenos Aires, Argentina. We conducted semi-structured interviews and followed a thematic analysis framework. Maternity care providers’ knowledge, attitudes, and practices regarding calcium supplementation during pregnancy are linked to barriers to the potential implementation of calcium supplementation. Free provision of calcium supplements by the government, coupled with training that reinforces the scientific evidence supporting their use to prevent preeclampsia, along with documented recommendations from credible sources, would be crucial to ensure that health providers adopt the use of calcium supplements in antenatal care. Future studies should assess pregnant women and policymakers’ perceptions about calcium supplementation during pregnancy, as well as local infrastructure to provide access to free-of-charge calcium supplements in antenatal care settings. Economic evaluation with local information could inform policymakers and advocate for the implementation of strategies to reduce preeclampsia.
Polyphenols in the Prevention and Treatment of Colorectal Cancer: A Systematic Review of Clinical Evidence
Authors: López-Gómez Laura , Uranga Jose Antonio
Published: August 2024

Polyphenols are plant metabolites with potential anti-inflammatory and anti-proliferative effects, which may be advantageous for disorders like colorectal cancer (CRC). Despite promising in vitro and in vivo evidence, human clinical trials have yielded mixed results. The present study aimed to evaluate the clinical evidence of polyphenols for CRC prevention or treatment. A systematic review was performed according to PRISMA. Based on a PROSPERO registered protocol (CRD42024560044), online databases (PubMed and COCHRANE) were utilized for the literature search. A total of 100 studies articles were initially identified. After reviewing, 12 studies with a low risk of bias were selected, examining the effect of a variety of compounds. Curcumin demonstrated promise in various trials, mainly decreasing inflammatory cytokines, though results varied, and it did not lower intestinal adenomas or improve outcomes after chemotherapy. Neither epigallocatechin gallate nor artepillin C reduced the incidence of adenomas. Finally, fisetin seemed to improve the inflammatory status of patients under chemotherapy (5-fluorouracil). In summary, although certain polyphenols appear to exert some effect, their role in the prevention or treatment of CRC is inconclusive, and more clinical studies under more controlled conditions are needed.
Heat-Processed Soybean Germ Extract and Lactobacillus gasseri NK109 Supplementation Reduce LPS-Induced Cognitive Impairment and Colitis in Mice
Authors: Yun Soo-Won , Lee Dong-Yun , Park Hee-Seo , Kim Dong-Hyun
Published: August 2024

Soybean alleviates cognitive impairment. In our preparatory experiment, we found that dry-heat (90 °C for 30 min)-processed soybean embryo ethanol extract (hSE) most potently suppressed lipopolysaccharide (LPS)-induced tumor necrosis factor (TNF)-α expression in BV2 cells among dry-heat-, steaming-, and oil exclusion-processed soybean embryo ethanol extracts (SEs). Heat processing increased the absorbable soyasaponin Bb content of SE. Therefore, we investigated whether hSE and its supplement could mitigate LPS-impaired cognitive function in mice. Among dry-heat-, steaming-, and oil exclusion-processed SEs, hSE mitigated LPS-impaired cognitive function more than parental SE. hSE potently upregulated LPS-suppressed brain-derived neurotropic factor (BDNF) expression in the hippocampus, while LPS-induced TNF-α and IL-1β expression in the hippocampus and colon were downregulated. Lactobacillus gasseri NK109 additively increased the cognitive function-enhancing activity of hSE in mice with LPS-induced cognitive impairment as follows: the hSE and NK109 mix potently increased cognitive function and hippocampal BDNF expression and BDNF-positive neuron cell numbers and decreased TNF-α expression and NF-κB-positive cell numbers in the hippocampus and colon. These findings suggest that hSE and its supplement may decrease colitis and neuroinflammation by suppressing NF-κB activation and inducing BDNF expression, resulting in the attenuation of cognitive impairment.
Vernonia amygdalina Leaf Extract Induces Apoptosis in HeLa Cells: A Metabolomics and Proteomics Study
Authors: Samutrtai Pawitrabhorn , Yingchutrakul Yodying , Faikhruea Kriangsak , Vilaivan Tirayut , Chaikeeratisak Vorrapon , Chatwichien Jaruwan , Krobthong Sucheewin , Aonbangkhen Chanat
Published: August 2024

Medicinal plants produce various bioactive molecules with potential anti-cancer properties with favorable safety profiles. We aimed to investigate the comprehensive composition of Vernonia amygdalina leaf extract and its cytotoxic effects via apoptosis in HeLa cells. The metabolomics approach using LC-MS/MS was conducted to gather the metabolite profile of the extract. Proteomics was performed to understand the comprehensive mechanistic pathways of action. The apoptosis was visualized by cellular staining and the apoptotic proteins were evaluated. V. amygdalina leaf extract exhibited dose-dependent cytotoxic effects on both HeLa and Vero cells after 24 h of exposure in the MTT assay with the IC50 values of 0.767 ± 0.0334 and 4.043 ± 0.469 µg mL−1, respectively, which demonstrated a higher concentration required for Vero cell cytotoxicity. The metabolomic profile of 112 known metabolites specified that the majority of them were alkaloids, phenolic compounds, and steroids. Among these metabolites, deacetylvindoline and licochalcone B were suggested to implicate cytotoxicity. The cytotoxic pathways involved the response to stress and cell death which was similar to doxorubicin. The upstream regulatory proteins, phosphatase and tensin homolog deleted on chromosome ten (PTEN) and X-box binding protein 1 (XBP1), were significantly altered, supporting the regulation of apoptosis and cell death. The levels of apoptotic proteins, c-Jun N-terminal kinases (JNK), p53, and caspase-9 were significantly increased. The novel insights gained from the metabolomic profiling and proteomic pathway analysis of V. amygdalina leaf extract have identified crucial components related to apoptosis induction, highlighting its potential to develop future chemotherapy.
Impact of Hot-Melt Extrusion on Glibenclamide’s Physical and Chemical States and Dissolution Behavior: Case Studies with Three Polymer Blend Matrices
Authors: Zupan Nina , Yous Ines , Danede Florence , Verin Jeremy , Kouach Mostafa , Foulon Catherine , Dudognon Emeline , Florin Muschert Susanne
Published: August 2024

This research work dives into the complexity of hot-melt extrusion (HME) and its influence on drug stability, focusing on solid dispersions containing 30% of glibenclamide and three 50:50 polymer blends. The polymers used in the study are Ethocel Standard 10 Premium, Kollidon SR and Affinisol HPMC HME 4M. Glibenclamide solid dispersions are characterized using thermal analyses (thermogravimetric analysis (TGA) and differential scanning calorimetry), X-ray diffraction and scanning electron microscopy. This study reveals the transformation of glibenclamide into impurity A during the HME process using mass spectrometry and TGA. Thus, it enables the quantification of the extent of degradation. Furthermore, this work shows how polymer–polymer blend matrices exert an impact on process parameters, the active pharmaceutical ingredient’s physical state, and drug release behavior. In vitro dissolution studies show that the polymeric matrices investigated provide extended drug release (over 24 h), mainly dictated by the polymer’s chemical nature. This paper highlights how glibenclamide is degraded during HME and how polymer selection crucially affects the sustained release dynamics.
Antibiofilm and Antihemolytic Activities of Actinostemma lobatum Extract Rich in Quercetin against Staphylococcus aureus
Authors: Lee Jin-Hyung , Kim Yong-Guy , Choi Ji-Su , Jeong Yong Tae , Hwang Buyng Su , Lee Jintae
Published: August 2024

Staphylococcus aureus biofilm formation is a pivotal mechanism in the development of drug resistance, conferring resilience against conventional antibiotics. This study investigates the inhibitory effects of Actinostemma lobatum (A. lobatum) Maxim extracts on S. aureus biofilm formation and their antihemolytic activities, with a particular focus on identifying the active antibiofilm and antihemolysis compound, quercetin. Seven solvent extracts and twelve sub-fractions were evaluated against four S. aureus strains. The ethyl acetate fraction (10 to 100 μg/mL) significantly hindered biofilm formation by both methicillin-sensitive and -resistant strains. Bioassay-guided isolation of the ethyl acetate extract identified quercetin as the major antibiofilm compound. The ethyl acetate extract was found to contain 391 μg/mg of quercetin and 30 μg/mg of kaempferol. Additionally, the A. lobatum extract exhibited antihemolytic activity attributable to the presence of quercetin. The findings suggest that quercetin-rich extracts from A. lobatum and other quercetin-rich foods and plants hold promise for inhibiting resilient S. aureus biofilm formation and attenuating its virulence.
Fine-Scale Lithogeochemical Features Influence Plant Distribution Patterns in Alpine Grasslands in the Western Alps of Italy
Authors: Cazzavillan Anna , Gerdol Renato , Marrocchino Elena , Vaccaro Carmela , Brancaleoni Lisa
Published: August 2024

Bedrock geology is crucial in structuring alpine plant communities. Old studies mainly focused on the compositional differences between alpine plant communities on carbonate rocks and crystalline rocks, i.e., calcareous vs. siliceous vegetation. Increasing attention is being paid to bedrock types other than calcareous or siliceous ones, viz. those which have intermediate geochemical characteristics between pure calcareous and pure siliceous ones. Among these types of ‘intermediate’ bedrocks, calc-schists and serpentines are generally characterized by vegetation comprised of a mixture of basiphilous and acidophilous species. We selected several sites in alpine grasslands in the Western Italian Alps, on calc-schist and serpentine bedrocks, located at 2500 ± 100 m above sea level. X-ray fluorescence quantification of major and trace elements, combined with stereomicroscopic examination of bedrock samples with a petrographic approach, revealed a much broader range of bedrock types than recognized by inspection of geological maps. The vegetation investigated in our study was mostly composed of a set of species found more or less frequently in alpine silicicolous or calcicolous plant communities of the Alps and other European mountains. The carbonate content in the bedrock was one of the main drivers of variation in grassland vegetation, not necessarily related to soil pH. There were no distinctive species uniquely characterizing grassland vegetation on serpentines or calc-schists.
Mapping Asia Plants: Plant Diversity and a Checklist of Vascular Plants in Indonesia
Authors: Sun Jing , Liu Bo , Rustiami Himmah , Xiao Huiyun , Shen Xiaoli , Ma Keping
Published: August 2024

Indonesia, located in Southeast Asia, is the world’s largest tropical island country. It is globally recognized as a unique center of biodiversity in the Asian–Australian transitional zone. To date, however, no national plant checklist of Indonesia has been published. Here, we collected botanical information on the flora of Indonesia and presented for the first time a checklist of known native species of vascular plants in the country. Our checklist included 30,466 species belonging to 2968 genera and 317 families, representing 8.7% of the 351,180 vascular plant species worldwide. Among the seven regions, Sumatra had the highest number of species at 10,902, followed by Indonesian New Guinea (9935), Java (9289), Kalimantan (9191), Sulawesi (7048), Maluku (5294), and Lesser Sunda Islands (4514). In contrast, Indonesian New Guinea had a much higher proportion of locally endemic species than other regions (59%). The seven regions were divided into three phytogeographical areas: the Sunda Shelf, the Sahul Shelf, and the Wallacea, based on similarities in floristic composition. Our checklist for Indonesia provides basic information for biodiversity conservation and associated research.
Influence of Water and Fertilizer Reduction on Respiratory Metabolism in Sugar Beet Taproot (Beta vulgaris L.)
Authors: Chang Yuxin , Li Guolong , Jian Caiyuan , Zhang Bowen , Sun Yaqing , Li Ningning , Zhang Shaoying
Published: August 2024

Inner Mongolia, a major region in China for growing sugar beet, faces challenges caused by unscientific water and fertilizer management. This mismanagement restricts the improvement of sugar beet yield and quality and exacerbates water waste and environmental pollution. This study aims to evaluate the effects of reduced water and fertilizer on the growth and physiological metabolism of sugar beet taproot. Field experiments were conducted in Ulanqab, Inner Mongolia, in 2022 and 2023, using a split-plot design with three levels each of fertilization and irrigation. The study analyzed the effects of reduced water and fertilizer treatments on fresh taproot weight, respiration rate, energy metabolism, respiratory enzyme activity, and gene expression in sugar beet taproot. It was found that a 10% reduction in fertilizer significantly increased the beet taproot fresh weight. Further research revealed that during the rapid leaf growth phase and the taproot and sugar growth period, a 10% reduction in fertilizer upregulated HK and IDH gene expression and downregulated G6PDH gene expression in the beet taproot. This increased HK and IDH activities, decreased G6PDH activity, enhanced the activity of the EMP-TCA pathway, and inhibited the PPP. Taproot weight was positively correlated with the respiration rate, ATP content, EC, and ATPase, HK, and IDH activities, thereby increasing the taproot growth rate and taproot fresh weight, with an average increase of 4.0% over two years. These findings introduce a novel method for optimizing fertilizer use, particularly beneficial in water-scarce regions. Implementing this strategy could help farmers in western Inner Mongolia and similar areas improve crop yield and sustainability. This study offers new insights into resource-efficient agricultural practices, highlighting the importance of customized fertilization strategies tailored to local environmental conditions.
From Molecular Design to Practical Applications: Strategies for Enhancing the Optical and Thermal Performance of Polyimide Films
Authors: Li Liangrong , Jiang Wendan , Yang Xiaozhe , Meng Yundong , Hu Peng , Huang Cheng , Liu Feng
Published: August 2024

Polyimide (PI) films are well recognized for their outstanding chemical resistance, radiation resistance, thermal properties, and mechanical strength, rendering them highly valuable in advanced fields such as aerospace, sophisticated electronic components, and semiconductors. However, improving their optical transparency while maintaining excellent thermal properties remains a significant challenge. This review systematically checks over recent advancements in enhancing the optical and thermal performance of PI films, focusing on various strategies through molecular design. These strategies include optimizing the main chain, side chain, non-coplanar structures, and endcap groups. Rigid and flexible structural characteristics in the proper combination can contribute to the balance thermal stability and optical transparency. Introducing fluorinated substituents and bulky side groups significantly reduces the formation of charge transfer complexes, enhancing both transparency and thermal properties. Non-coplanar structures, such as spiro and cardo configurations, further improve the optical properties while maintaining thermal stability. Future research trends include nanoparticle doping, intrinsic microporous PI polymers, photosensitive polyimides, machine learning-assisted molecular design, and metal coating techniques, which are expected to further enhance the comprehensive optical and thermal performance of PI films and expand their applications in flexible displays, solar cells, and high-performance electronic devices. Overall, systematic molecular design and optimization have significantly improved the optical and thermal performance of PI films, showing broad application prospects. This review aims to provide researchers with valuable references, stimulate more innovative research and applications, and promote the deep integration of PI films into modern technology and industry.
Computational Methodologies in Synthesis, Preparation and Application of Antimicrobial Polymers, Biomolecules, and Nanocomposites
Authors: Rezić Iva , Somogyi Škoc Maja
Published: August 2024

The design and optimization of antimicrobial materials (polymers, biomolecules, or nanocomposites) can be significantly advanced by computational methodologies like molecular dynamics (MD), which provide insights into the interactions and stability of the antimicrobial agents within the polymer matrix, and machine learning (ML) or design of experiment (DOE), which predicts and optimizes antimicrobial efficacy and material properties. These innovations not only enhance the efficiency of developing antimicrobial polymers but also enable the creation of materials with tailored properties to meet specific application needs, ensuring safety and longevity in their usage. Therefore, this paper will present the computational methodologies employed in the synthesis and application of antimicrobial polymers, biomolecules, and nanocomposites. By leveraging advanced computational techniques such as MD, ML, or DOE, significant advancements in the design and optimization of antimicrobial materials are achieved. A comprehensive review on recent progress, together with highlights of the most relevant methodologies’ contributions to state-of-the-art materials science will be discussed, as well as future directions in the field will be foreseen. Finally, future possibilities and opportunities will be derived from the current state-of-the-art methodologies, providing perspectives on the potential evolution of polymer science and engineering of novel materials.
Optimization of Glass-Powder-Reinforced Recycled High-Density Polyethylene (rHDPE) Filament for Additive Manufacturing: Transforming Bottle Caps into Sound-Absorbing Material
Authors: Atsani Sarah Iftin , Sing Swee Leong
Published: August 2024

Additive manufacturing presents promising potential as a sustainable processing technology, notably through integrating post-consumer recycled polymers into production. This study investigated the recycling of high-density polyethylene (rHDPE) into 3D printing filament, achieved by the following optimal extrusion parameters: 180 °C temperature, 7 rpm speed, and 10% glass powder addition. The properties of the developed rHDPE filament were compared with those of commonly used FDM filaments such as acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) to benchmark the performance of rHDPE against well-established materials in the 3D printing industry, providing a practical perspective for potential users. The resulting filament boasted an average tensile strength of 25.52 MPa, slightly exceeding ABS (25.41 MPa) and comparable to PLA (28.55 MPa). Despite diameter fluctuations, the filament proved usable in 3D printing. Mechanical tests compared the rHPDE filament 3D printed objects with ABS and PLA, showing lower strength but exceptional ductility and flexibility, along with superior sound absorption. A life cycle analysis underscored the sustainability advantages of rHDPE, reducing environmental impact compared to conventional disposal methods. While rHDPE falls behind in mechanical strength against virgin filaments, its unique attributes and sustainability position it as a valuable option for 3D printing, showcasing recycled materials’ potential in sustainable innovation.
Torque Mapping as a Novel Method for In-Line Characterisation of Spherical Agglomeration Process Kinetics
Authors: Schreier Julia , Bröckel Ulrich
Published: August 2024

This study presents a novel method to determine the kinetics of spherical agglomeration in-line via monitoring the stirrer torque. In order to confirm that the torque can be used to characterise the process phases, samples were taken at different times during the agglomeration process and the morphology of agglomerates is examined using X-ray microtomography. It can be shown that if the stirrer torque is decreasing, the fast growth regime starts. Additionally, the influence of binding-liquid concentration as well as the primary particle shape was studied. Both have an influence on the process kinetics as well as on the different growth mechanisms of agglomerates during spherical agglomeration. These two growth mechanisms, distribution and immersion, can be identified by the morphological parameters sphericity, packing density and fractal dimension. In particular, the concept of fractal dimension was not used so far on agglomerates obtained from spherical agglomeration.
DualNet-PoiD: A Hybrid Neural Network for Highly Accurate Recognition of POIs on Road Networks in Complex Areas with Urban Terrain
Authors: Zhang Yongchuan , Long Caixia , Liu Jiping , Wang Yong , Yang Wei
Published: August 2024

For high-precision navigation, obtaining and maintaining high-precision point-of-interest (POI) data on the road network is crucial. In urban areas with complex terrains, the accuracy of traditional road network POI acquisition methods often falls short. To address this issue, we introduce DualNet-PoiD, a hybrid neural network designed for the efficient recognition of road network POIs in intricate urban environments. This method leverages multimodal sensory data, incorporating both vehicle trajectories and remote sensing imagery. Through an enhanced dual-attention dilated link network (DAD-LinkNet) based on ResNet18, the system extracts static geometric features of roads from remote sensing images. Concurrently, an improved gated recirculation unit (GRU) captures dynamic traffic characteristics implied by vehicle trajectories. The integration of a fully connected layer (FC) enables the high-precision identification of various POIs, including traffic light intersections, gas stations, parking lots, and tunnels. To validate the efficacy of DualNet-PoiD, we collected 500 remote sensing images and 50,000 taxi trajectory data samples covering road POIs in the central urban area of the mountainous city of Chongqing. Through comprehensive area comparison experiments, DualNet-PoiD demonstrated a high recognition accuracy of 91.30%, performing robustly even under conditions of complex occlusion. This confirms the network’s capability to significantly improve POI detection in challenging urban settings.
Study on Daytime Atmospheric Mixing Layer Height Based on 2-Year Coherent Doppler Wind Lidar Observations at the Southern Edge of the Taklimakan Desert
Authors: Su Lian , Xia Haiyun , Yuan Jinlong , Wang Yue , Maituerdi Amina , He Qing
Published: August 2024

The long-term atmospheric mixing layer height (MLH) information plays an important role in air quality and weather forecasting. However, it is not sufficient to study the characteristics of MLH using long-term high spatial and temporal resolution data in the desert. In this paper, over the southern edge of the Taklimakan Desert, the diurnal, monthly, and seasonal variations in the daytime MLH (retrieved by coherent Doppler wind lidar) and surface meteorological elements (provided by the local meteorological station) in a two-year period (from July 2021 to July 2023) were statistically analyzed, and the relationship between the two kinds of data was summarized. It was found that the diurnal average MLH exhibits a unimodal distribution, and the decrease rate in the MLH in the afternoon is much higher than the increase rate before noon. From the seasonal and monthly perspective, the most frequent deep mixing layer (>4 km) was formed in June, and the MLH is the highest in spring and summer. Finally, in terms of their mutual relationship, it was observed that the east-pathway wind has a greater impact on the formation of the deep mixing layer than the west-pathway wind; the dust weather with visibility of 1–10 km contributes significantly to the formation of the mixing layer; the temperature and relative humidity also exhibit a clear trend of a concentrated distribution at about the height of 3 km. The statistical analysis of the MLH deepens the understanding of the characteristics of dust pollution in this area, which is of great significance for the treatment of local dust pollution.
Estimation of Soil Organic Carbon Density on the Qinghai–Tibet Plateau Using a Machine Learning Model Driven by Multisource Remote Sensing
Authors: Chen Qi , Zhou Wei , Shi Wenjiao
Published: August 2024

Soil organic carbon (SOC) plays a vital role in the global carbon cycle and soil quality assessment. The Qinghai–Tibet Plateau is one of the largest plateaus in the world. Therefore, in this region, SOC density and the spatial distribution of SOC are highly sensitive to climate change and human intervention. Given the insufficient understanding of the spatial distribution of SOC density in the Qinghai–Tibet Plateau, this study utilized machine learning (ML) algorithms to estimate the density and distribution pattern of SOC density in the region. In this study, we first collected multisource data, such as optical remote sensing data, synthetic aperture radar) (SAR) data, and other environmental variables, including socioeconomic factors, topographic factors, climate factors, and soil properties. Then, we used ML algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to estimate the topsoil SOC density and spatial distribution patterns of SOC density. We also aimed to investigate any driving factors. The results are as follows: (1) The average SOC density is 5.30 kg/m2. (2) Among the three ML algorithms used, LightGBM showed the highest validation accuracy (R2 = 0.7537, RMSE = 2.4928 kgC/m2, MAE = 1.7195). (3) The normalized difference vegetation index (NDVI), valley depth (VD), and temperature are crucial in predicting the spatial distribution of topsoil SOC density. Feature importance analyses conducted using the three ML models all showed these factors to be among the top three in importance, with contribution rates of 14.08%, 12.29%, and 14.06%; 17.32%, 20.73%, and 24.62%; and 16.72%, 11.96%, and 20.03%. (4) Spatially, the southeastern part of the Qinghai–Tibet Plateau has the highest topsoil SOC density, with recorded values ranging from 8.41 kg/m2 to 13.2 kg/m2, while the northwestern part has the lowest density, with recorded values ranging from 0.85 kg/m2 to 2.88 kg/m2. Different land cover types showed varying SOC density values, with forests and grasslands having higher SOC densities compared to urban and bare land areas. The findings of this study provide a scientific basis for future soil resource management and improved carbon sequestration accounting in the Qinghai–Tibet Plateau.
A Novel Framework for Integrally Evaluating the Impacts of Climate Change and Human Activities on Water Yield Services from Both Local and Global Perspectives
Authors: Ouyang Kehao , Huang Min , Gong Daohong , Zhu Daoye , Lin Hui , Xiao Changjiang , Fan Yewen , Altan Orhan
Published: August 2024

With global climate change and irrational human activities, regional water resource conflicts are becoming more and more pronounced. The availability of water resource in watersheds can be indicated by the water yield. Exploring the factors that influence the water yield is crucial in responding to climate change and protecting water resource. Previous research on the factors influencing the water yield has frequently adopted a macro-level perspective, which has failed to reflect the influencing mechanisms of changes at the local scale adequately. Therefore, this study proposes a novel framework for integrally evaluating the impacts of climate change and human activities on water yield services from both local and global perspectives. Taking Ganzhou City, the source of the Ganjiang River, as an example, the results show the following: (1) Ganzhou City had the largest water yield of 1307.29 mm in 2016, and the lowest was only 375.32 mm in 2011. The spatial distribution pattern was mainly affected by the surface environment, and the high-value water yield regions in the study area were predominantly located in urban areas with flat terrain. (2) At the local scale, regions where human activities contribute more than 80% accounted for 25% of the area. In comparison, the impact of climate change accounted for 0.95%. The contribution rate of human activities to the water yield in Ganzhou City was significantly greater than that of climate change. (3) At the global scale, the simulation results of four scenarios show that climate change contributed (>98%) to the water yield, which is significantly higher than human activities (<2%). This study puts forward pioneering views on the research of water yield driving forces and provides a valuable theoretical basis for water resource protection and ecological environment construction.
The Use of Vis-NIR-SWIR Spectroscopy and X-ray Fluorescence in the Development of Predictive Models: A Step forward in the Quantification of Nitrogen, Total Organic Carbon and Humic Fractions in Ferralsols
Authors: Lima Bruna Coelho de , Demattê José A. M. , Santos Carlos H. dos , Tiritan Carlos S. , Poppiel Raul R. , Nanni Marcos R. , Falcioni Renan , Oliveira Caio A. de , Vedana Nicole G. , Zimmermann Guilherme , Reis Amanda S.
Published: August 2024

The objective was to verify the performance of spectral techniques as well as validation models in the prediction of nitrogen, total organic carbon, and humic fractions under different cultivation conditions. Chemical analyses for the determination of nitrate, total nitrogen, total organic carbon, and the chemical fractionation of soil organic matter were performed, as well as spectral analyses by Vis-NIR-SWIR and X-ray fluorescence. The results of the spectroscopy were processed using RStudio v. 4.1.3, and PLSR and support vector machine learning algorithms were applied to validate the models. The Vis-NIR-SWIR and XRF spectroscopic techniques showed high performance and are indicated for the prediction of nitrogen, total organic carbon, and humic fractions in Ferralsols of medium sandy texture. However, it is important to highlight that each technique has its own characteristic mechanism of action: Vis-NIR-SWIR detects the element based on harmonic tones, while XRF is based on the atomic number of the element or elemental association. The PLSR and SVM models showed excellent validation results, allowing them to fit the experimental data, emphasizing that they are different statistical methods.
Refined Modeling of Heterogeneous Medium for Ground-Penetrating Radar Simulation
Authors: Liu Hai , Dai Dingwu , Zou Lilong , He Qin , Meng Xu , Chen Junhong
Published: August 2024

Ground-penetrating radar (GPR) has been widely used for subsurface detection and testing. Numerical simulations of GPR signal are commonly performed to aid the interpretation of subsurface structures and targets in complex environments. To enhance the accuracy of GPR simulations on heterogeneous medium, this paper proposes a hybrid modeling method that combines the discrete element method with a component fusion strategy (DEM–CFS). Taking the asphalt pavement as an example, three 3D stochastic models with distinctly different porosities are constructed by the DEM–CFS method. Firstly, the DEM is utilized to establish the spatial distribution of random coarse aggregates. Then, the component fusion strategy is employed to integrate other components into the coarse aggregate skeleton. Finally, the GPR response of the constructed asphalt models is simulated using the finite-difference time-domain method. The proposed modeling method is validated through both numerical and laboratory experiments and demonstrates high precision. The results indicate that the proposed modeling method has high accuracy in predicting the dielectric constant of heterogeneous media, as generated models are closely aligned with real-world conditions.
Research on eLoran Weak Signal Extraction Based on Wavelet Hard Thresholding Processing
Authors: Cheng Langlang , Zhang Shougang , Qi Zhen , Wang Xin , Chen Yingming , Feng Ping
Published: August 2024

As the eLoran signal propagates, its strength gradually diminishes with increasing distance, making subsequent signal capture and terminal development challenging. To address this phenomenon, this paper proposes an improved method based on wavelet hard thresholding. This method applies hierarchical processing to the coefficients obtained after wavelet decomposition, based on the signal’s center frequency. It effectively addresses issues like the disappearance of trailing edges and the presence of the noise with large coefficients. Simulation results show that the improved method has the largest output signal-to-noise ratio and effectively improves the problem of tailing vanishing and eliminates the noise with large coefficients. In analog source signal testing, the results show that the method can extract signals of 30 dBμv/m and above well. In actual signal testing, the improved method can extract eLoran signals transmitted over a distance of approximately 1000 km. Based on the results, it can be deduced that the input signal-to-noise ratio is −28.8 dB. Therefore, this method is a suitable and effective solution for extracting weak eLoran signals, providing strong support for signal monitoring in areas at the coverage boundaries of eLoran signals.
Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring
Authors: Wang Chengguan , Wang Guangping , Wang Tao , Xiong Xiyao , Ouyang Zhongchuan , Gong Tao
Published: August 2024

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model’s performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model’s mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.
Air Traffic Controller Workload Detection Based on EEG Signals
Authors: Shao Quan , Li Hui , Sun Zhe
Published: August 2024

The assessment of the cognitive workload experienced by air traffic controllers is a complex and prominent issue in the research community. This study introduces new indicators related to gamma waves to detect controllers’ workload and develops experimental protocols to capture their EEG data and NASA-TXL data. Then, statistical tests, including the Shapiro–Wilk test and ANOVA, were used to verify whether there was a significant difference between the workload data of the controllers in different scenarios. Furthermore, the Support Vector Machine (SVM) classifier was employed to assess the detection accuracy of these indicators across four categorizations. According to the outcomes, hypotheses suggesting a strong correlation between gamma waves and an air traffic controller’s workload were put forward and subsequently verified; meanwhile, compared with traditional indicators, the indicators associated with gamma waves proposed in this paper have higher accuracy. In addition, to explore the applicability of the indicator, sensitive channels were selected based on the mRMR algorithm for the indicator with the highest accuracy, β + θ + α + γ, showcasing a recognition rate of a single channel exceeding 95% of the full channel, which meets the requirements of convenience and accuracy in practical applications. In conclusion, this study demonstrates that utilizing EEG gamma wave-associated indicators can offer valuable insights into analyzing workload levels among air traffic controllers.
Design of Three-Dimensional Magnetic Probe System for Space Plasma Environment Research Facility (SPERF)
Authors: Yang Jihua , Xie Jiayin , Ling Wenbin , Guan Jian , Huang Kai , Chen Fupeng , Peng Gaoyuan , Tang Huibo , Zhou Hua , E Peng
Published: August 2024

A three-dimensional magnetic probe system has been designed and implemented at the Space Plasma Environment Research Facility (SPERF). This system has been developed to measure the magnetic field with high spatial and temporal resolution, enabling studies of fundamental processes in space physics, such as magnetic reconnection at the Earth’s magnetopause, on the basis of SPERF. The system utilizes inductive components as sensors, arranged in an array and soldered onto a printed circuit board (PCB), achieving a spatial resolution of 2.5 mm. The system’s electrical parameters have been measured, and its amplitude–frequency response characteristics have been simulated. The system has demonstrated good performance with response capabilities below 50 kHz. The experimental setup and results are discussed, highlighting the system’s effectiveness in accurately measuring weak magnetic signals and its suitability for magnetic reconnection experiments.
Enhancing Tennis Practice: Sensor Fusion and Pose Estimation with a Smart Tennis Ball
Authors: Foo Yu , Li Xi , Ghannam Rami
Published: August 2024

This article demonstrates the integration of sensor fusion for pose estimation and data collection in tennis balls, aiming to create a smaller, less intrusive form factor for use in progressive learning during tennis practice. The study outlines the design and implementation of the Bosch BNO055 smart sensor, which features built-in managed sensor fusion capabilities. The article also discusses deriving additional data using various mathematical and simulation methods to present relevant orientation information from the sensor in Unity. Embedded within a Vermont practice foam tennis ball, the final prototype product communicates with Unity on a laptop via Bluetooth. The Unity interface effectively visualizes the ball’s rotation, the resultant acceleration direction, rotations per minute (RPM), and the orientation relative to gravity. The system successfully demonstrates accurate RPM measurement, provides real-time visualization of ball spin and offers a pathway for innovative applications in tennis training technology.