bluuebunny/crossref_metadata_2025 huggingface dataset:
Melatonin in Human Breast Milk and Its Potential Role in Circadian Entrainment: A Nod towards Chrononutrition?
Authors: Häusler Silke , Lanzinger Emma , Sams Elke , Fazelnia Claudius , Allmer Kevin , Binder Christoph , Reiter Russel J. , Felder Thomas K.
Published: May 2024

Breastfeeding is the most appropriate source of a newborn’s nutrition; among the plethora of its benefits, its modulation of circadian rhythmicity with melatonin as a potential neuroendocrine transducer has gained increasing interest. Transplacental transfer assures melatonin provision for the fetus, who is devoid of melatonin secretion. Even after birth, the neonatal pineal gland is not able to produce melatonin rhythmically for several months (with an even more prolonged deficiency following preterm birth). In this context, human breast milk constitutes the main natural source of melatonin: diurnal dynamic changes, an acrophase early after midnight, and changes in melatonin concentrations according to gestational age and during the different stages of lactation have been reported. Understudied thus far are the factors impacting on (changes in) melatonin content in human breast milk and their clinical significance in chronobiological adherence in the neonate: maternal as well as environmental aspects have to be investigated in more detail to guide nursing mothers in optimal feeding schedules which probably means a synchronized instead of mistimed feeding practice. This review aims to be thought-provoking regarding the critical role of melatonin in chrononutrition during breastfeeding, highlighting its potential in circadian entrainment and therefore optimizing (neuro)developmental outcomes in the neonatal setting.
Predictor Variables in the Spread of Chagas Disease in Rural Areas
Authors: de Sousa Leite Liziana , de Rezende Feres Valéria Christina , Scalize Paulo Sérgio
Published: May 2024

Over a hundred years ago after the discovery of Chagas disease (CD) in Brazil, the World Health Organization estimates a number of 6 to 7 million people infected by Trypanosoma cruzi worldwide. Therefore, the goal of this work was to identify variables related to the spread of infection by T. cruzi in humans living in rural areas, seeking predictor variables. A systematic review of the literature has been conducted, with a search in the Scopus platform, using the search string “Chagas disease” and “rural”, resulting in 85 valid and analyzed scientific studies (1977 and 2022). Twenty-seven predictor variables have been acquired, and 19 of them have been grouped, such as: socioeconomic and educational, housing, environmental, sanitary, and cultural; and 8 variables related to T. cruzi seropositive individuals. The predictor variables yielded significant results (p-value < 0.05) in 59.5% of the cases (195/328), with a median of 66.7%. In other words, studies relating to 50% of the 27 variables showed significance equal to or greater than 66.7% of the time. The independent variables with the highest proportion of significant data (p-value < 0.05) were Education (87.6%), Intradomicile building (70%), Domestic animals (69.6%), and Triatomines (69.2%) in the households. Some variables reached 100%; however, few articles were found, indicating the need for further research, especially for Sanitation and Culture. It has been concluded that, in the several contexts found, the social vulnerability and lack of information led the individual to living in environments where inhabitability is inadequate, to perform limited work activity and develop habits and behaviors which impair them in an environmental insalubrity situation, favorable to the access of vectors and pathogens of anthropozoonoses such as CD.
Association between Paediatric Complementary and Alternative Medicine Use and Parental Health Literacy, Child Health, and Socio-Economic Variables: A Prospective Study
Authors: Denny Abida , Day Andrew S. , Vernon-Roberts Angharad
Published: May 2024

Complementary and Alternative Medicines (CAMs) constitute products and practices not considered allopathic medicine. CAM use is high in children, but little is known about factors that may influence parents using CAM with their child. This study aimed to determine the variables associated with CAM use in children with a prospective study among children and their parents attending a tertiary care hospital in New Zealand (NZ). Outcomes included current CAM use, parental opinions on CAM, parental health literacy and child well-being. This study was completed by 130 parents (85% female), and the mean child age was 6.7 years. CAM use was reported for 59 (45%) children, the most common being oral supplements and body manipulation. Children were more likely to use CAM if their parent had higher health literacy (p = 0.001), and if they had previously attended the emergency department within 12 months (p = 0.03). There was no association between child well-being and CAM use. Parental opinion of using CAM only if a doctor recommended it was associated with CAM use for their child (p = 0.01). Only 40% of parents disclosed their child’s CAM use to the medical team. This study highlights that parental health literacy influences the use of CAM for children in NZ, providing insight for translational research to improve CAM safety and disclosure rates in NZ.
Recent Advances in the Discovery of SIRT1/2 Inhibitors via Computational Methods: A Perspective
Authors: Scarano Naomi , Brullo Chiara , Musumeci Francesca , Millo Enrico , Bruzzone Santina , Schenone Silvia , Cichero Elena
Published: May 2024

Sirtuins (SIRTs) are classified as class III histone deacetylases (HDACs), a family of enzymes that catalyze the removal of acetyl groups from the ε-N-acetyl lysine residues of histone proteins, thus counteracting the activity performed by histone acetyltransferares (HATs). Based on their involvement in different biological pathways, ranging from transcription to metabolism and genome stability, SIRT dysregulation was investigated in many diseases, such as cancer, neurodegenerative disorders, diabetes, and cardiovascular and autoimmune diseases. The elucidation of a consistent number of SIRT–ligand complexes helped to steer the identification of novel and more selective modulators. Due to the high diversity and quantity of the structural data thus far available, we reviewed some of the different ligands and structure-based methods that have recently been used to identify new promising SIRT1/2 modulators. The present review is structured into two sections: the first includes a comprehensive perspective of the successful computational approaches related to the discovery of SIRT1/2 inhibitors (SIRTIs); the second section deals with the most interesting SIRTIs that have recently appeared in the literature (from 2017). The data reported here are collected from different databases (SciFinder, Web of Science, Scopus, Google Scholar, and PubMed) using “SIRT”, “sirtuin”, and “sirtuin inhibitors” as keywords.
Influence of Gut Microbiota-Mediated Immune Regulation on Response to Chemotherapy
Authors: Deng Yufei , Hou Xiaoying , Wang Haiping , Du Hongzhi , Liu Yuchen
Published: May 2024

The involvement of the gut microbiota in anti-cancer treatment has gained increasing attention. Alterations to the structure and function of the gut bacteria are important factors in the development of cancer as well as the efficacy of chemotherapy. Recent studies have confirmed that the gut microbiota and related metabolites influence the pharmacological activity of chemotherapeutic agents through interactions with the immune system. This review aims to summarize the current knowledge of how malignant tumor and chemotherapy affect the gut microbiota, how the gut microbiota regulates host immune response, and how interactions between the gut microbiota and host immune response influence the efficacy of chemotherapy. Recent advances in strategies for increasing the efficiency of chemotherapy based on the gut microbiota are also described. Deciphering the complex homeostasis maintained by the gut microbiota and host immunity provides a solid scientific basis for bacterial intervention in chemotherapy.
Comprehensive Study of Equilibrium Structure of Trans-Azobenzene: Gas Electron Diffraction and Quantum Chemical Calculations
Authors: Pogonin Alexander E. , Kurochkin Ivan Yu. , Eroshin Alexey V. , Zavalishin Maksim N. , Zhabanov Yuriy A.
Published: May 2024

The geometrical re parameters of trans-azobenzene (E-AB) free molecule were refined by gas electron diffraction (GED) method using available experimental data obtained previously by S. Konaka and coworkers. Structural analysis was carried out by various techniques. First of all, these included the widely used molecular orbital constrained gas electron diffraction method and regularization method. The results of the refinements using different models were also compared—a semirigid model, three variants of one-dimensional dynamic models, and a two-dimensional pseudoconformer model. Several descriptions have been used due to the fact that E-AB has a shallow potential energy surface along the rotation coordinates of phenyl groups. Despite this, it turned out that the semirigid model is suitable for use for E-AB and allows good agreement with experimental data to be achieved. According to the results of GED structural analysis, coupled with the results of DLPNO-CCSD(T0) calculations, E-AB has a planar structure. Based only on GED data, it is impossible to unambiguously determine the rotational angle of the phenyl group due to the facts that (i) with rotation over a wide range of angles, the bonded distances in the molecule change insignificantly and (ii) potential function in a structural analysis within a dynamic model is not determined with the necessary accuracy. This work also examines the sensitivity of the GED method to structural changes caused by trans-cis isomerization. The paper also analyzes the applicability of different variants of density functional theory (DFT) calculations in GED structural analysis using E-AB as an example. There are not enough similar methodological works in the literature. This experimental and methodological information is especially important and relevant for planning and implementing GED experiments and corresponding processing of the results for azobenzene derivatives, in which the conformer and isomeric diversity are even more complicated due to the presence of different substituents.
Complex Transitions of the Bounded Confidence Model from an Odd Number of Clusters to the Next
Authors: Deffuant Guillaume
Published: May 2024

The bounded confidence model assumes simple continuous opinion dynamics in which agents ignore opinions which are too far from their own. The two initial variants—Hegselmann–Krause (HK) and Deffuant–Weisbuch (DW)—of the model have attracted significant attention since the early 2000s. This paper revisits the version of the HK model applied to a probability distribution, earlier studied by Jan Lorenz. It shows that the bifurcation diagram depends on the parity of the size of the discretisation and that adding a small noise to the initial conditions leads to complex transitions involving several phases.
Isolation of the Antifungal Compound Alliodorin from the Heartwood of Cordia elaeagnoides A. DC. and the In Silico Analysis of the Laccase
Authors: Guevara-Martínez Santiago José , Villanueva-Mejía Francisco , Zamudio-Ojeda Adalberto , Herrera-Bucio Rafael , Morales-Palacios Fredy Geovannini
Published: May 2024

Cordia elaeagnoides A. DC. is an endemic species of Mexico valued for its timber. Renowned for its durability, resistance, and versatile applications in medicine, this tree holds significant commercial importance. Tetrahydrofuran (THF) extract from the heartwood of C. elaeagnoides was studied. Through chromatographic column purification, the compound 8-(2,5-Dihydroxyphenyl)-2,6-dimethylocta-2,6-dienal, also known as alliodorin, was successfully isolated. Identification of alliodorin was confirmed through comprehensive analysis utilizing NMR, IR, and mass spectrometry techniques. Inhibition tests were conducted using both the THF extract and alliodorin against the rotting fungus Trametes versicolor (L.) Lloyd, employing the agar well diffusion assay. Remarkably, alliodorin exhibited 100% inhibition with a median lethal concentration of 0.079 mg/mL and a total lethal concentration of 0.127 mg/mL, in comparison to the commercial fungicide benomyl, which requires a concentration of 1 mg/mL. In silico analysis through molecular docking on the laccase enzyme was proposed in order to explain the inhibitory activity against the fungus T. versicolor, as this enzyme is one of the main sources of nutrients and development for the fungus. Based on these findings, we deduced that alliodorin holds promise as a potent antifungal agent, potentially applicable in a wide array of technological and environmentally friendly initiatives.
Identification of Damping of Spruce Wood (Picea abies) under Various Levels of Moisture Content Using Time-Scale Decomposition
Authors: Merhar Miran
Published: May 2024

The damping of spruce wood is analysed at different moisture content levels for the first three vibration modes of tangentially and radially vibrating samples. Two methods were used to determine the damping. The first was the vibration envelope fitting as an improved version of the well-known logarithmic decrement, and the second was the newer and recently increasingly used wavelet transform. Both methods showed that the damping of spruce wood first decreases and then increases with moisture content, with the damping in the first vibration mode being about 9% higher in the radial direction than in the tangential direction. In the second and third vibration modes, the damping in the tangential direction was higher than in the radial direction by about 10% and 8.8%, respectively. The measured damping factors from the envelope fitting had, on average, 15.9% higher values than those from the wavelet transform. It can be concluded from the results that the wavelet transform is more accurate for determining the damping factor, as it enables the decoupling of multi-degree of freedom systems if mode coupling is present.
Green Flame-Retardant Blend Used to Improve the Antiflame Properties of Polypropylene
Authors: Cabello-Alvarado Christian J. , Andrade-Guel Marlene , Pérez-Alvarez Marissa , Cadenas-Pliego Gregorio , Bartolo-Pérez Pascual , Martínez-Carrillo Diego , Quiñones-Jurado Zoe V.
Published: May 2024

The flammability properties of polymers and polymeric composites play an important role in ensuring the safety of humans and the environment; moreover, flame-retardant materials ensure a greater number of applications. In the present study, we report the obtaining of polypropylene (PP) composites contain a mixture of two green flame retardants, lignin and clinoptilolite, by melt extrusion. These additives are abundantly found in nature. Fourier transform infrared (FT-IR), thermogravimetric analysis (TGA), mechanical properties, scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM-EDS), cone calorimetry, UL-94, and carbonized residues analysis were carried out. TGA analysis shows that PPGFR-10 and PPGFR-20 compounds presented better thermal stability with respect to PP without flame retardants. The conical calorimetric evaluation of the composites showed that PPGFR-10 and PPGFR-20 presented decreases in peak heat release rates (HRRs) of 9.75% and 11.88%, respectively. The flammability of the composites was evaluated with the UL-94 standard, and only the PPGFR-20 composite presented the V-0 and 5VB classification, which indicates good flame-retardant properties. Additives in the polymer matrix showed good dispersion with few agglomerates. The PPGFR-20 composite showed an FRI value of 1.15, higher percentage of carbonized residues, and UL-94 V-0 and 5VB rating, suggesting some kind of synergy between lignin and clinoptilolite, but only at high flame-retardant concentrations.
Chitosan-2D Nanomaterial-Based Scaffolds for Biomedical Applications
Authors: Naskar Atanu , Kilari Sreenivasulu , Misra Sanjay
Published: May 2024

Chitosan (CS) and two-dimensional nanomaterial (2D nanomaterials)-based scaffolds have received widespread attention in recent times in biomedical applications due to their excellent synergistic potential. CS has garnered much attention as a biomedical scaffold material either alone or in combination with some other material due to its favorable physiochemical properties. The emerging 2D nanomaterials, such as black phosphorus (BP), molybdenum disulfide (MoS2), etc., have taken huge steps towards varying biomedical applications. However, the implementation of a CS-2D nanomaterial-based scaffold for clinical applications remains challenging for different reasons such as toxicity, stability, etc. Here, we reviewed different types of CS scaffold materials and discussed their advantages in biomedical applications. In addition, a different CS nanostructure, instead of a scaffold, has been described. After that, the importance of 2D nanomaterials has been elaborated on in terms of physiochemical properties. In the next section, the biomedical applications of CS with different 2D nanomaterial scaffolds have been highlighted. Finally, we highlighted the existing challenges and future perspectives of using CS-2D nanomaterial scaffolds for biomedical applications. We hope that this review will encourage a more synergistic biomedical application of the CS-2D nanomaterial scaffolds and their utilization clinical applications.
Evaluation of Boron as a Feed Additive to Improve Musculoskeletal Health of Hy-Line W-36 Pullets
Authors: Anderson Mallory G. , Johnson Alexa M. , Clark Alexis , Harrison Cerano , Arguelles-Ramos Mireille , Ali Ahmed
Published: May 2024

Boron supplementation may improve the musculoskeletal health of pullets before entering the lay phase. This study aimed to evaluate the effects of different boron amounts on the performance, muscle deposition, tibia cross-sectional area (CSA) and mineral density (BMD), ash percent, breaking strength, and bone mineralization (bone-specific alkaline phosphatase [BALP] and pro-collagen type 1 n-terminal propeptide [P1NP]) of a white-feathered strain of pullets. A total of 528 Hy-Line W-36 pullets were distributed across 24 pens and fed basal diets containing varying amounts of boron (C: 0 mg/kg; L: 50 mg/kg; M: 100 mg/kg; H: 150 mg/kg) for 17 weeks. Performance parameters (body weight, average daily weight gain/bird, and average daily feed intake/bird) were measured at weeks 4, 7, 10, 13, and 16, while all other measures were taken at 11 and 17 weeks of age. Performance was not impacted by boron supplementation. Pectoralis major weights were higher in H pullets at 11 weeks of age, and we also observed higher pectoralis major, minor, and leg muscle weights in H pullets at 17 weeks of age. Pullets fed the H diet had larger cortical CSA than the other treatment groups at 11 weeks of age. At 17 weeks of age, both the H and M groups had larger cortical CSA than the L and C groups, but the M group had slightly smaller cortical CSA. Pullets fed the H diet had higher BMD values than the other treatment groups at 11 weeks of age. At 17 weeks of age, pullets fed the H diet had the highest total BMD values compared to the other treatment groups, and cortical BMD increased with increasing boron inclusion. Pullets fed the H diet had the highest tibia ash percentages and concentrations of BALP and P1NP. Pullets fed the M and H diets had greater failure load and maximum bending moment than pullets fed the L or C diet at 11 weeks of age, with H pullets having greater stiffness values than other groups. At 17 weeks of age, pullets fed the H diet had greater failure load and maximum bending moment compared to all other treatment groups. Our results suggest that providing boron within the diet at 150 mg/kg can improve the musculoskeletal characteristics of Hy-Line W-36 pullets up to 17 weeks of age, without impacting performance parameters.
Mass Transport and Energy Conversion of Magnetic Nanofluids from Nanoparticles’ Movement and Liquid Manipulation
Authors: Xu Fei , Cao Yaowen , Gong Hanwen , Li Juan , Xu Ying , Shi Lei
Published: May 2024

Magnetic nanofluids, also referred to as ferromagnetic particle levitation systems, are materials with highly responsive magnetic properties. Due to their magnetic responsiveness, excellent controllability, favorable thermal characteristics, and versatility, magnetic nanofluids have sparked considerable interest in both industrial manufacturing and scientific research. Magnetic nanofluids have been used and developed in diverse areas such as materials science, physics, chemistry and engineering due to their remarkable characteristics such as rapid magnetic reaction, elastic flow capacities, and tunable thermal and optical properties. This paper provides a full and in-depth introduction to the diverse uses of ferrofluids including material fabrication, fluid droplet manipulation, and biomedicine for the power and machinery sectors. As a result, magnetic nanofluids have shown promising applications and have provided innovative ideas for multidisciplinary research in biology, chemistry, physics and materials science. This paper also presents an overview of the device construction and the latest developments in magnetic-nanofluid-related equipment, as well as possible challenging issues and promising future scenarios.
Chemical Leasing (Ch.L.) and the Sherwood Plot
Authors: Karakatsanis Georgios , Makropoulos Christos
Published: May 2024

Although the Circular Economy (CE) has made remarkable technological progress by offering a wide range of alternative engineering solutions, an obstacle for its large-scale commercialization is nested in the adoption of those business and financial models that accurately depict the value generated from resource recovery. Recovering a resource from a waste matrix conserves natural reserves in situ by reducing demand for virgin resources, as well as conserving environmental carrying capacities by reducing waste discharges. The standard business model for resource recovery is Industrial Symbiosis (IS), where industries organize in clusters and allocate the process of waste matrices to achieve the recovery of a valuable resource at an optimal cost. Our work develops a coherent microeconomic architecture of Chemical Leasing (Ch.L.) contracts within the analytical framework of the Sherwood Plot (SP) for recovering a Value-Added Compound (VAC) from a wastewater matrix. The SP depicts the relationship between the VAC’s dilution in the wastewater matrix and its cost of recovery. ChL is engineered on the SP as a financial contract, motivating industrial synergies for delivering the VAC at the target dilution level at the market’s minimum cost and with mutual profits. In this context, we develop a ChL market typology where information completeness on which industry is most cost-efficient in recovering a VAC at every dilution level determines market dominance via a Kullback–Leibler Divergence (DKL) metric. In turn, we model how payoffs are allocated between industries via three ChL contract pricing systems, their profitability limits, and their fitting potential by market type. Finally, we discuss the emerging applications of ChL financial engineering in relation to three vital pillars of resource recovery and natural capital conservation.
Necessary Conditions for Running through a Flange by Using Planetary-Geared Magnetic Wheels
Authors: Tanida Masaru , Ono Kosuke , Shiba Takehiro , Takada Yogo
Published: May 2024

To discuss and consider the necessary conditions for magnetic-wheeled robots with planetary-geared magnetic wheels, this paper provides comparing static calculations about three orientations in running a flange with real experiments. SCPREM-I, a magnetic-wheeled robot, was developed for running through a flange from the bottom to the top. This robot has four magnetic wheels with a built-in planetary gearset. In experiments, however, the robot sometimes fails to run through a flange in three orientations. In this study, we statically analyze SCPREM-I to find the conditions necessary for running through the flange. We calculate the forces around the front and rear wheels in the three orientations. As a result, it has been found that the chassis of the SCPREM-I applies a forward force to the wheels when it runs through the flange. In addition, it has been found that the normal force of the A-Legs is balancing with the driving force of the wheels when the SCPREM-I fails to run through the flange.
Hyperspectral Imaging Spectroscopy for Non-Destructive Determination of Grape Berry Total Soluble Solids and Titratable Acidity
Authors: Lyu Hongyi , Grafton Miles , Ramilan Thiagarajah , Irwin Matthew , Sandoval Eduardo
Published: May 2024

Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids (TSS) and titratable acidity (TA) in wine grape berries. A normalized difference spectral index (NDSI) spectral preprocessing method was built and compared with the conventional preprocessing method: multiplicative scatter correction and Savitzky–Golay smoothing (MSC+SG). Different machine learning models were built to examine the performance of the preprocessing methods. The results show that the NDSI preprocessing method demonstrated better performance than the MSC+SG preprocessing method in different classification models, with the best model correctly classifying 93.8% of the TSS and 84.4% of the TA. In addition, the TSS can be predicted with moderate performance using support vector regression (SVR) and MSC+SG preprocessing with a root mean squared error (RMSE) of 0.523 °Brix and a coefficient of determination (R2) of 0.622, and the TA can be predicted with moderate performance using SVR and NDSI preprocessing (RMSE = 0.19%, R2 = 0.525). This study demonstrates that hyperspectral imaging data and NDSI preprocessing have the potential to be a method for grading wine grapes for producing quality wines.
Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study
Authors: Zhang Li , Shi Hao , Gao Shanhong , Li Shun
Published: May 2024

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method.
Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data
Authors: Yang Yining , Shen Xin , Cao Lin
Published: May 2024

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O2 production and CO2 absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m3), followed by the sycamore tree species (557.907 m3), and privet’s was the lowest (16.681 m3).
Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event
Authors: Zhang Jianbin , Gao Zhiqiu , Li Yubin , Jiang Yuncong
Published: May 2024

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.
Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning
Authors: Wang Yuanjian , Cui Ximin , Che Yuhang , Zhao Yuling , Li Peixian , Kang Xinliang , Jiang Yue
Published: May 2024

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.
Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data
Authors: Cheng Yi-Ting , Shin Young-Ha , Shin Sang-Yeop , Koshan Yerassyl , Hodaei Mona , Bullock Darcy , Habib Ayman
Published: May 2024

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.
Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia
Authors: Liu Meng , Wang Juanle , Fetisov Denis , Li Kai , Xu Chen , Jiang Jiawei
Published: May 2024

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.
Stressors Inherent to Beef Cattle Management in the United States of America and the Resulting Impacts on Production Sustainability: A Review
Authors: Winton Toriann Summer , Nicodemus Molly Christine , Harvey Kelsey Margaret
Published: May 2024

Stressors are directly related to major events throughout the beef cattle production cycle. Understanding the impact stressors have on productive outcomes is critical for the efficient implementation of management strategies. Such stressors include environmental extremes, nutritional deprivation, and common management procedures. Environmental extremes such as thermal stress can disturb gestating cows’ normal physiological responses, hindering reproductive efficiency. Thermal stress during the breeding season can affect embryo development causing a decrease in conception rates, although adjusting the scheduling of breeding activities can minimize losses. Additionally, suboptimal nutrition may negatively impact reproductive performance if management strategies including modifying seasonal grazing practices are not implemented. As gestation progresses, nutrient requirements increase; thus, without appropriate dietary management, poor calf performance, the loss of the body condition score, and reduced reproductive performance may result. While weaning is a common management procedure, this event is another major stress within the production system. Applying efficient strategies such as creep feeding or two-step weaning to mitigate weaning stress can maximize production efficiency. This review will explore in-depth the stressors associated with production events in the beef cattle industry and give insight into researched management strategies targeting these stressors that will improve the sustainability of the production system.
Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments
Authors: Khanal Ridip , Choi Yoochan , Lee Joonwhoan
Published: May 2024

Smart farm environments, equipped with cutting-edge technology, require proficient techniques for managing poultry. This research investigates automated chicken counting, an essential part of optimizing livestock conditions. By integrating artificial intelligence and computer vision, it introduces a transformer-based chicken-counting model to overcome challenges to precise counting, such as lighting changes, occlusions, cluttered backgrounds, continual chicken growth, and camera distortions. The model includes a pyramid vision transformer backbone and a multi-scale regression head to predict precise density maps of the crowded chicken enclosure. The customized loss function incorporates curriculum loss, allowing the model to learn progressively, and adapts to diverse challenges posed by varying densities, scales, and appearances. The proposed annotated dataset includes data on various lighting conditions, chicken sizes, densities, and placements. Augmentation strategies enhanced the dataset with brightness, contrast, shadow, blur, occlusion, cropping, and scaling variations. Evaluating the model on the proposed dataset indicated its robustness, with a validation mean absolute error of 27.8, a root mean squared error of 40.9, and a test average accuracy of 96.9%. A comparison with the few-shot object counting model SAFECount demonstrated the model’s superior accuracy and resilience. The transformer-based approach was 7.7% more accurate than SAFECount. It demonstrated robustness in response to different challenges that may affect counting and offered a comprehensive and effective solution for automated chicken counting in smart farm environments.
Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception
Authors: Zhuang Licong , Zhong Xiaorong , Xu Linjie , Tian Chunbao , Yu Wenshuai
Published: May 2024

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual–inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.
Early Eye Disengagement Is Regulated by Task Complexity and Task Repetition in Visual Tracking Task
Authors: Wu Yun , Zhang Zhongshi , Aghazadeh Farzad , Zheng Bin
Published: May 2024

Understanding human actions often requires in-depth detection and interpretation of bio-signals. Early eye disengagement from the target (EEDT) represents a significant eye behavior that involves the proactive disengagement of the gazes from the target to gather information on the anticipated pathway, thereby enabling rapid reactions to the environment. It remains unknown how task difficulty and task repetition affect EEDT. We aim to provide direct evidence of how these factors influence EEDT. We developed a visual tracking task in which participants viewed arrow movement videos while their eye movements were tracked. The task complexity was increased by increasing movement steps. Every movement pattern was performed twice to assess the effect of repetition on eye movement. Participants were required to recall the movement patterns for recall accuracy evaluation and complete cognitive load assessment. EEDT was quantified by the fixation duration and frequency within the areas of eye before arrow. When task difficulty increased, we found the recall accuracy score decreased, the cognitive load increased, and EEDT decreased significantly. The EEDT was higher in the second trial, but significance only existed in tasks with lower complexity. EEDT was positively correlated with recall accuracy and negatively correlated with cognitive load. Performing EEDT was reduced by task complexity and increased by task repetition. EEDT may be a promising sensory measure for assessing task performance and cognitive load and can be used for the future development of eye-tracking-based sensors.
A Low-Frequency Fiber Bragg Grating Acceleration Sensor Based on Spring Support and Symmetric Compensation Structure with Flexible Hinges
Authors: Meng Lijun , Zhu Panpan , Tan Xin , Huang Xiao
Published: May 2024

To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor’s structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies.