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Lung and respiratory ailments are among the leading causes of illness and fatalities. Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, has convinced the world that early and affordable detection improves treatment. X-ray imaging systems are inexpensive and widely available. Chest X-ray (CXR) images are inadequate due to the acquiring environment and technician skill. Hence, CXR image contrast enhancement is necessary for a correct diagnosis. Various lung diseases create variable spatial variation in CXR image contrast and brightness; hence, a single contrast enhancement procedure cannot improve it. In the proposed method CXR images are first classified into four categories depending upon their quality defined by their statistical parameters, before applying adaptive gamma correction for contrast enhancement. The performance of the proposed method is compared with existing methods on four datasets for five different types of lung diseases. The performance of the proposed algorithm is evaluated using parameters, such as Root Mean Square Contrast (RMSC) to determine the relation of contrast enhancement between the original and enhanced image, Contrast Improvement Index (CII) to measure the achieved contrast enhancement and Tenengrad which calculates the variation of intensity in the direction of maximum gradient descent. The qualitative and quantitative performance of the proposed method is found better than the existing methods for CXR images for all five lung diseases, which shows the stable performance of the proposed method and improvement in the processed images. [ABSTRACT FROM AUTHOR]

Postprandial hyperglycemia (PPG) exacerbates endothelial dysfunction and impairs vascular function in diabetes as well in healthy people. Though synthetic drugs are available to regulate PPG, the severe gastrointestinal side effects of those medications have prompted the search for alternative treatments. Recently, some phytochemicals captured the attention because of their inhibitory effects on α-amylase to control diabetes. The aim of this study was to investigate and identify potential alpha-amylase inhibitors in C. indica and W. coagulans. This study also aims to understand one of the possible mechanisms of action of plants for their anti-diabetic activity. A total of 36 phytochemical ligands were subjected for protein–ligand docking analysis. Among the phytochemicals, Taraxerol and Epoxywithanolide-I demonstrated significant binding free energy of − 10.2 kcal/mol and − 11.9 kcal/mol respectively, which was higher than the reference acarbose with − 8.6 kcal/mol. These molecules were subjected for molecular dynamics simulation (MDS) analysis with alpha-amylase protein for a duration of 150 ns. Among the three complexes, Taraxerol and Epoxywithanolide-I complexes demonstrates strong potential as inhibitors of the target protein. MDS results were analyzed via root mean square deviation (RMSD), fluctuation of residues, potential energy, radii of gyration and solvent access surface area analysis. Taraxerol demonstrated a significantly low potential energy of − 1,924,605.25 kJ/mol, and Epoxywithanolide-I demonstrated − 1,964,113.3 kJ/mol of potential energy. RMSD plot shows that Epoxywithanolide-I has much higher stability than the other MDS complexes. Drugability and toxicity studies show that the test ligands are demonstrating strong potential as drug like molecules. The results of the study conclude that, Taraxerol of C. indica and Epoxywithanolide-I of W. coagulans are strong inhibitors of alpha-amylase enzyme and that, this is one of the possible mechanisms of action of the plants for their reported anti-diabetic activities. Further in-vitro analysis is in demand to prove the observed results. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics is used to analyze the influence of the horseshoe vortex on the wake features of a simplified geometry representing an underwater vehicle sail (i.e. Rood wing). The sail wake features are of interest as they influence the performance of the downstream components of an underwater vehicle such as the aft appendages and propeller. This paper uses the Rood wing, a generic wing body, mounted on a flat plate as its low aspect ratio is comparable to the underwater vehicle sail and there are substantial published experimental data for validation. Two main simulation schemes were adopted in this paper, i.e. the Reynolds-averaged Navier–Stokes (RANS) and hybrid RANS–large Eddy simulation (LES) incorporating several turbulence models. Both schemes were also examined in their ability to predict the downstream wake features as the literature available to date have primarily focused only on the near-field flow features around the wing root. Three main parameters were investigated including the pressure distribution along the wing's body, the mean streamwise velocity, and its root mean square fluctuation at three different downstream planes, two in the near field and one in the far field. Results show that the RANS and the hybrid RANS–LES models are capable of predicting the wing-body pressure distribution and the paths of the horseshoe vortex (HSV) as it moves downstream with acceptable numerical dissipation. It was found that different models provided higher accuracy when compared to the experiment depending on the downstream location of the plane. The re-normalization group k-epsilon model with enhanced wall treatment (RNG KE-EN) model captured the wake properties with the highest accuracy within the near field, while further downstream (in the far field), the scale adaptive simulation (SAS) model predicted the flow field with the highest accuracy followed by the RNGKE-EN model. [ABSTRACT FROM AUTHOR]

Since the dawn of civilization, sandstone has been a fantastic building material. Numerous causes have been observed in the past for sandstone damage or deterioration, one of which is sulphur-oxidizing bacteria (SOB) and cyanobacteria. In general, SOB is present in the soil, air, water, humidity, and human activity. The oxidation of sulphur compounds, such as hydrogen sulphide, thiosulphate, or elements of sulphur, provides energy for SOB. These microorganisms contribute to the decay of buildings materials, especially those made of stone, metal, or concrete. The sulphur oxidizing process affects the mechanical properties of sandstone. Mechanical properties are related to strength. Losses of mechanical properties may be the reason for deflection, cracking, collapse, and catastrophic failure of sandstone. Monitoring and evaluation gives an idea about the behavior of structure and the prevention of catastrophic failure. This research paper contains the application of electromechanical impedance with surface bonded Piezoelectric Lead Zirconate Titanate. The sandstone samples and soil samples have been collected from the historical site. Two sets of cylindrical types of sandstone specimens have been used in experimental work. The conductance signature and susceptance signature have been measured every six months. The variation and shifting of the signature curve have been used to identify the structural behavioral change. Statistical methods like the root mean square deviation have been used for the quantification of damage. An equitation has been generated on the basis of the percentage root mean square deviation to quantify the prediction of damages. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) is a vital component of the human–computer interaction system. The traditional Deep learning-based speech SER schemes show poor time-domain representation, class imbalance issues due to uneven samples in the training datasets, less feature distinctiveness, and inferior long-term dependency on global and local attributes of the speech. This article introduces lightweight, long short memory (LSTM) along with multiple acoustic features such as Mel frequency spectrum coefficients (MFCC), chroma, root mean square (RMS), and Tonnetz features for boosting the time-domain representation and long-term dependency of emotional speech. Further, it used data augmentation techniques such as noise addition, shifting, stretching, and feature extraction to lessen the class imbalance problem. The proposed methods have an average accuracy of 95% for SAVEE, 94.57% for EMOVO, 97.16% for EMODB, and 96.66% for BAVED, which has shown noteworthy improvement over the traditional state of arts. [ABSTRACT FROM AUTHOR]

The vibrations generated in the two-wheeled vehicles like motorcycles due to road irregularities such as cracks, potholes, and bumps on the road cause discomfort for the rider as well as the pillion. These vibrations are reported to cause lower back pains, musculoskeletal effects, fatigue, and long-term health issues. Particularly, electric two-wheelers are more susceptible to these vibrations caused by the road and need attention. This paper presents an innovative technique for the reduction of vibrations at prominent locations in the electric two-wheeler to improve the rider's comfort. All measured accelerations are about vertical direction (along z-axis as per ISO 2631-1 standard). Passive and Semi-active damping treatments namely, Room temperature vulcanizing Silicone rubber and Magnetorheological elastomer (MRE) were applied on the test vehicle at strategic locations of vibration. Both were compared for their effectiveness in reducing the vibrations. Results showed that MRE based damping technique proved better vibration isolation at the strategic locations. The weighted root mean square acceleration as well as vibration dose values were found to decrease with the help of damping treatments thus improving the rider's overall comfort level. [ABSTRACT FROM AUTHOR]

Electromagnetic tracking of endovascular instruments has the potential to substantially decrease radiation exposure of patients and personnel. In this study, we evaluated the in vivo accuracy of a vessel-based method to register preoperative computed tomography angiography (CTA) images to physical coordinates using an electromagnetically tracked guidewire. Centerlines of the aortoiliac arteries were extracted from preoperative CTA acquired from five swine. Intravascular positions were obtained from an electromagnetically tracked guidewire. An iterative-closest-point algorithm registered the position data to the preoperative image centerlines. To evaluate the registration accuracy, a guidewire was placed inside the superior mesenteric, left and right renal arteries under fluoroscopic guidance. Position data was acquired with electromagnetic tracking as the guidewire was pulled into the aorta. The resulting measured positions were compared to the corresponding ostia manually identified in the CTA images after applying the registration. The three-dimensional (3D) Euclidean distances were calculated between each corresponding ostial point, and the root mean square (RMS) was calculated for each registration. The median 3D RMS for all registrations was 4.82 mm, with an interquartile range of 3.53–6.14 mm. A vessel-based registration of CTA images to vascular anatomy is possible with acceptable accuracy and encourages further clinical testing. Relevance statement: This study shows that the centerline algorithm can be used to register preoperative CTA images to vascular anatomy, with the potential to further reduce ionizing radiation exposure during vascular procedures. Key Points: Preoperative images can be used to guide the procedure without ionizing intraoperative imaging. Preoperative imaging can be the only imaging modality used for guidance of vascular procedures. No need to use external fiducial markers to register/match images and spatial anatomy. Acceptable accuracy can be achieved for navigation in a preclinical setting. [ABSTRACT FROM AUTHOR]

Fabric Defect (FD) detection has played an important role in the process of fabric production. FDs lead to a reduction in cloth prices, causing a significant loss of 45% to 65% for the cloth manufacturer. The detection of FDs becomes a challenging task in the fabric industry due to its complicated shapes and a considerable amount of FDs. Thus, a new detection technique, which has great detection speed and accuracy, is desired to replace manual work. With the emergence of Convolution Neural Networks (CNN) and the development of machine vision and Deep Learning (DL), various detection techniques, combining the benefits of DL and machine vision, have emerged, which replace manual and image processing approaches. The study presents an Automated Fabric Defect Detection using a Hybrid Particle Cat Swarm Optimizer with a Deep Learning (AFDD-HPCSODL) algorithm. AFDD-HPCSODL method aims to detect and categorize the existence of defects in fabric production. The objective is to automate and improve the performance of fabric defect detection, addressing the complexity of different defect patterns and characteristics in textile images. In the presented AFDD-HPCSODL technique, two stages of data pre-processing take place namely contrast enhancement and bilateral filtering (BF)-based noise elimination. Besides, a fusion of feature extraction processes is carried out using Inception V3 and EfficientNetB3. Moreover, the Attention Convolutional Long Short-Term Memory (ACLSTM) network is exploited for the detection and classification of FDs. Furthermore, the HPCSO technique carries out the hyperparameter selection of the ACLSTM model. Finally, the Root Mean Square Propagation (RMSProp) optimizer with the NestNet model is utilized for the defect segmentation process. The stimulation validation of the AFDD-HPCSODL method is studied on the two FD datasets. Widespread comparative results emphasized the improved performance of the AFDD-HPCSODL technique on FD classification with maximum accuracy of 97.92% and 98.89% on the Kaggle FD dataset and the ZJU-Leaper dataset, correspondingly. [ABSTRACT FROM AUTHOR]

High-precision Global Navigation Satellite System (GNSS) orbit and clock products are crucial for precise applications. An evenly distributed global network enables continuous tracking for GNSS satellites, while a regional network may result in tracking gaps in the areas where monitoring stations are not deployed. This also means that the orbit determination accuracy based on a regional network is not comparable to that with a global network. Integrating the measurements from regional ground stations and Low Earth Orbit (LEO) satellites onboard receivers is a potential approach for generating GNSS orbit and clock products with centimeter-level accuracy, which is particularly important for BDS and the local commercial providers relying on a regional network. In the integrated Precise Orbit Determination (POD), LEO satellites are used to compensate for the drawback of regional ground stations in the precise orbit and clock determination of GNSS satellites. To validate the role of LEO satellites in the orbit determination with a regional network, 6 International GNSS Service stations around China and 13 LEO satellites from January 20 to 26, 2019, including GRACE-C/D, SWARM-A/B/C, Jason-3, Sentinel-3A/B, and SAT-A/B/C/D/E are selected in this study to perform the integrated POD. The orbit and clock accuracies of GPS and LEO satellites are evaluated by comparison with precise products. The average Root Mean Square (RMS)of GPS orbit errors in the radial (R), along-track (T) and cross-track (N) directions are 2.27 cm, 3.45 cm, and 3.08 cm, respectively, and the clock accuracy is better than 0.15 ns based on a comparison with the final products provided by Center for Orbit Determination in Europe (CODE). The LEO orbit accuracy is better than 2 cm in the R direction, and the position errors are mostly within 4 cm. The results indicate that the integrated POD can generate high-precision orbit and clock products for GPS and LEO satellites based on regional network stations. Finally, the integrated POD products are assessed for Precise Point Positioning (PPP). Simulated kinematic PPP has a comparable performance in terms of the convergence time and positioning accuracy. With more LEO satellites available, the orbit and clock determination accuracy and PPP positioning accuracy can be improved. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the performance and factors affecting the species classification of convolutional neural network (CNN) architecture using whole-part and earlywood-part cross-sectional datasets of six Korean Quercus species. The accuracy of species classification for each condition was analyzed using the datasets, data augmentation, and optimizers—stochastic gradient descent (SGD), adaptive moment estimation (Adam), and root mean square propagation (RMSProp)—based on a CNN architecture with three to four convolutional layers. The model trained with the augmented dataset yielded significantly superior results in terms of classification accuracy compared to the model trained with the non-augmented dataset. The augmented dataset was the only factor affecting classification accuracy in the final five epochs. In contrast, four factors in the entire epoch, such as the Adam and SGD optimizers and the earlywood-part and whole-part datasets, affected species classification. The arrangement of earlywood vessels, broad rays, and axial parenchyma was identified as a major influential factor in the CNN species classification using gradient-weighted class activation mapping (Grad-CAM) analysis. The augmented whole-part dataset with the Adam optimizer achieved the highest classification accuracy of 85.7% during the final five epochs of the test phase. [ABSTRACT FROM AUTHOR]

The exclusion mechanism of food contaminants such as bisphenol A (BPA), Flavonoids (FLA), and Goitrin (GOI) onto the novel gallium–metal organic framework (MOF) and functionalized MOF with oxalamide group (MOF-OX) is evaluated by utilizing molecular dynamics (MD) and Metadynamics simulations. The atoms in molecules (AIM) analysis detected different types of atomic interactions between contaminant molecules and substrates. To assess this procedure, a range of descriptors including interaction energies, root mean square displacement, radial distribution function (RDF), density, hydrogen bond count (HB), and contact numbers are examined across the simulation trajectories. The most important elements in the stability of the systems under examination are found to be stacking π–π and HB interactions. It was confirmed by a significant value of total interaction energy for BPA/MOF-OX (− 338.21 kJ mol−1) and BPA/MOF (− 389.95 kJ mol−1) complexes. Evaluation of interaction energies reveals that L–J interaction plays an essential role in the adsorption of food contaminants on the substrates. The free energy values for the stability systems of BPA/MOF and BPA/MOF-OX complexes at their global minima reached about BPA/MOF = − 254.29 kJ mol−1 and BPA/MOF-OX = − 187.62 kJ mol−1, respectively. Nevertheless, this work provides a new strategy for the preparation of a new hierarchical tree-dimensional of the Ga-MOF hybrid material for the adsorption and exclusion of food contaminates and their effect on human health. [ABSTRACT FROM AUTHOR]

Background: Although potato virus Y (PVY) is the most serious virus-infecting potato plants worldwide, the losses concurred by it remain unmanageable due to the lack of efficient anti-PVY agents. Hence, the objective of this study was to assess the antiviral properties of secondary metabolite compounds obtained from culture filtrates of four Trichoderma spp. isolates. The assessment was conducted using computational methods, including molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, as well as molecular dynamics simulations. The aim was to develop novel and effective agents for combating PVY. Results: The GC–MS analysis of the studied Trichoderma spp. secondary metabolites revealed 24 compounds with relative amounts exceeding 10%. Molecular docking was then performed using MOE software to evaluate the activity of these compounds against the PVY protein coat (PDB-ID: 6HXX). Ningnanmycin and ribavirin, known plant virus inhibitors, were employed as reference ligands for comparison. Among the compounds tested, C9, C10, C13, and C19 exhibited superior docking scores, root mean square deviation (RMSD) values, and binding modes compared to the reference ligands. In addition, these compounds successfully passed the ADMET analysis. Further investigation focused on compounds C13 and C19, which underwent in-depth analysis through MDs for 100 ns. The MDs trajectories demonstrated that both complexes exhibited favorable stability, compactness, and binding modes throughout the simulation period. However, the C19/PVY-CP complex outperformed the C13 complex in all calculated parameters such as RMSD, root mean square fluctuation (RMSF), radius of gyration (RoG), solvent-accessible surface area (SASA), and intermolecular hydrogen bonds. Interestingly, these findings aligned with the results obtained from the docking analysis, indicating that C9 and C10 possess high potential against PVY, as they exhibited binding modes like that of C19. Conclusion: These promising outcomes provide a solid foundation for considering the potential use of compounds C9, C10, C13, and C19 as antiviral agents. Further experimental validation and in-depth studies are warranted to assess the efficacy and safety of these compounds and their potential as antiviral therapeutics. To our knowledge, this is the first report to study the biological activities of the Trichoderma-based bioactive compounds against PVY using computational techniques. [ABSTRACT FROM AUTHOR]

The investigation involves a comparative analysis of linear bearings fabricated by two different manufacturers. One manufacturer is known for producing original, high-quality bearings that come with a higher price tag, while the other provides more affordable alternatives through reverse engineering of the original bearings. The primary aim is to understand the practical behavior of these replicated bearings compared to the original ones and determine whether the cost-effective replicated bearings can be chosen over the more expensive originals for specific applications. This requires assessing their performance under various operational conditions (speeds of 6000, 8000, and 10,000 mm/min, and loads of 500 and 1050 g) and evaluating the impact on functionality in real-world scenarios. Time-domain statistical techniques are incorporated to quantify the differences between these bearings. Statistical scalar indicators, such as root-mean-square value, crest factor, peak, skewness, and kurtosis, are used to characterize the health status of the bearings, with the monitored parameter being the vibration signal. It has been observed that these indicators are consistently higher for the replicated bearings compared to the original ones, the defects are introduced may be during the manufacturing process or utilization low-quality materials for production of replicated bearings. The analysis, therefore, highlights notable deviations and potential issues with the replicated bearings, reinforcing the superior performance and stability of the original bearings despite their higher cost. Thus, original bearings are better suited for applications. [ABSTRACT FROM AUTHOR]

The degradation state of a bearing can be monitored effectively by using a wear indicator (WI). A WI curve having smoothness and monotonicity can lay a good foundation for predicting the remaining useful life of the bearing. Most traditional models for bearing WI construction, such as time–frequency indicators and signal decomposition, are complicated; for example, some WI construction models need several models to select from, and their fusion depends on the manual experience of engineers. For example, single and mixed traditional time–frequency indicators, such as the root mean square (RMS), Kurtosis, multiple time–frequency domain fusion. However, the mentioned-above time–frequency domain indicators are difficult to adaptively reflect the operating status of the equipment when the operating conditions of the mechanical system change. Some signal decomposition models are combined with other models and rely on manual experience to extract WI, such as the selection of effective intrinsic mode function components function, parameter setting of empirical mode decomposition and ensemble empirical mode decomposition model, etc. Deep learning models, such as stacked auto encoder (SAE) and convolution neural network, have been widely used in bearing health monitoring and WI construction, because its powerful learning and feature extraction capabilities of multiple hidden layer structures. But these deep learning models are designed to use output labels. Particularly when the data volume is large, it requires manpower, material resources, and experienced engineers to label the data, or it is difficult to label and distinguish the categories of data samples. Therefore, to solve these problems and eliminate the need for manual labor, such as labeling data and selecting models for fusion, we propose using SAE without an output label layer to extract WI from original signals directly. However, an extracted WI curve without good monotonicity (Mon) will result in a poor remaining useful life prediction accuracy. To improve the monotonicity of the extracted WI and reduce the complexity of the WI construction model, we propose an unsupervised enhanced SAE without an output layer, named SINSAE, by adding a sine function of an average value which is calculated form start time to current at each hidden layer to eliminate concussion. Moreover, to demonstrate that our proposed model is better than other models, such as the RMS, Kurtosis, multiple time–frequency domain fusion, SAE, SAE without an output layer, and signal decomposition models, the Mon indicator in this study is used to compare the monotonicity of the extracted WI. Lastly, the results of our experiments using different bearing datasets and various working conditions show that the smoothness and monotonicity of the WI curve extracted by the SINSAE is better than that of other models. Moreover, compared to the traditional commonly used single and multiple time–frequency domain indicators, supervised deep learning and basic unsupervised deep models, the unsupervised SINSAE model can increase the Mon indicators from [0.1, 0.8], [0.02, 0.1], [0.1, 0.8] to above 0.9, respectively. [ABSTRACT FROM AUTHOR]

The degradation temperature in high-energy materials is vital, and by predicting, it is possible to prevent the dangers of the degradation of high-energy materials and increase their safety. In this study, the degradation temperature of 63 energetic ionic liquids was investigated based on imidazole cation, and a mathematical model was presented to predict the degradation temperature of this class of materials. In this model, 20% of the data were randomly used as test data, and the rest were used as training sets. Correlation prediction (R2) in training and test data was 0.946 and 0.909, respectively. Also, in the internal leave-many-out cross-validation method, the average correlation is 0.953. The average absolute deviation and the root mean square deviation were calculated for both training and test groups. The results showed that the presented model for predicting the degradation temperature in energetic ionic liquids is highly accurate. [ABSTRACT FROM AUTHOR]

Phenological shifts are one of the most visible signs of climatic variability and change in the biosphere. However, modeling plant phenological responses has always been a key challenge due to climatic variability and plant adaptation. Grapevine is a phenologically sensitive crop and, thus, its developmental stages are affected by the increase in temperature. The goal of this study was to develop a temperature-based grapevine phenology model (GPM) for predicting key developmental stages for different table grape cultivars for a non-traditional viticulture zone in south Asia. Experiments were conducted in two vineyards at two locations (Chakwal and Islamabad) in the Pothawar region of Pakistan during the 2019 and 2020 growing seasons for four cultivars including Perlette, King's Ruby, Sugraone and NARC Black. Detailed phenological observations were obtained starting in January until harvest of the grapes. The Mitscherlich monomolecular equation was used to develop the phenology model for table grapes. There was a strong non-linear correlation between the Eichhorn and Lorenz phenological (ELP) scale and growing degree days (GDD) for all cultivars with coefficient of determinations (R2) ranging from 0.90 to 0.94. The results for model development indicated that GPM was able to predict phenological stages with high skill scores, i.e., a root mean square (RMSE) of 2.14 to 2.78 and mean absolute error (MAE) of 1.86 to 2.26 days. The prediction variability of the model for the onset timings of phenological stages was up to 3 days. The results also reveal that the phenology model based on GDD approach provides an efficient planning tool for viticulture industry in different grape growing regions. The proposed methodology, being a simpler one, can be easily applied to other regions and cultivars as a predictor for grapevine phenology. [ABSTRACT FROM AUTHOR]

Introduction: Huntington's disease (HD) is known as a neurodegenerative disease with movement disorder and cognitive impairment; autonomic involvement is also becoming common in some recent studies. The aim of this study is to demonstrate the presence of cardiac autonomic involvement in HD patients. Method: Time and frequency domain parameters obtained from the 24-h Holter ECG(hECG) were compared between 20 HD patients and 20 healthy control subjects. Results: Fourteen HD patients had tachycardia, bradycardia, and extra beats. Interval between two heartbeats, normal-to-normal (NN), standard deviation of all normal-to-normal (SDNN), square root of the mean of the sum of the squares of the differences between consecutive N-N intervals in ms (rMSSD), and the ratio of the number of consecutive pairs of N-N intervals that differ by more than 50 ms to the total number of N-N intervals (pNN50) were all significantly higher in the patient group than in the control group during 24-h hECG monitoring. However, hECG monitoring showed that the patient group had significantly higher values of the frequency-domain metrics high frequency (HF) than the control group did (P = 0.003). Very low frequency (VLF) was lower in the patient group (P = 0.009). There was no difference in low frequency (LF) in both groups. In comparison to the control group, LF/HF was much reduced in the patient group (P = 0.001). Conclusion: Cardiac disfunction increases, and autonomic functions change in HD, but more comprehensive studies are needed to distinguish sympathetic and parasympathetic involvement. [ABSTRACT FROM AUTHOR]

In the frame of the European funded H2020 project RETALT (retro propulsion-assisted landing technologies), the unsteady aerodynamics of vertically descending and landing launchers have been investigated. In this paper, experimental data of the landing burn tested in the Vertical Free-Jet Facility Cologne at DLR in Cologne are presented. The landing burn was simulated with a cold gas jet of pressurized air opposing the wind tunnel free stream. Tests with several jet conditions were compared to results without active jet. Proper orthogonal decomposition of schlieren recordings and spectral analyses of their time histories are performed and are compared to frequencies in pressure measurements. Dominant frequencies were found, which are strongest at Mach 0.8. Especially, a Strouhal number of 0.2 was found to be most dominant. The intensity of the dominant frequencies can be lowered if the engine is active. The normalized root mean square pressure fluctuations are between 0.1 and 0.3 during the landing maneuver. Additionally, the steady flow features scale well with the ambient pressure ratio and the momentum flux ratio. The unsteady flow field dynamics of the subsonic retro propulsion flow field can likely be linked to large-scale turbulent structures in the supersonic jet, triggering large-scale pressure fluctuations and altering the overall flow field. [ABSTRACT FROM AUTHOR]

With the advent of numerous global precipitation estimates (GPEs) in the recent decades, dependability of hydrologists has lessened on the station data as the GPEs can be readily availed and utilized. Since the skills of GPEs may differ from region-to-region, it is vital to analyse their ability in resolving the regional precipitation climatology using appropriate statistical methods. In this study, a total of five GPEs, viz., APHRODITE, PERSIANN-CDR, CHIRPS, CMORPH, and IMERG were evaluated for their abilities in resolving regional precipitation climatology of WHR with respect to gridded precipitation product of India Meteorological Department (IMD). Different performance indicators i.e., Probability of Detection (POD), False Alarm Ratio (FAR), Normalised Root Mean Square Deviation (NRMSD), Pearson Correlation Coefficient (CC) and Skill Score (SS) were used for evaluating the GPEs. Multicriterion Decision Making (MCDM)approaches i.e., Compromise Programming (CP), Cooperative Game Theory (CGT), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Average Technique (WAT), and Fuzzy TOPSIS were used for ranking the GPEs across different grids in WHR. Entropy based weight assignment to NRMSD, CC, and SS were performed while applying them in MCDM methods. Group Decision Making (GDM) approach utilizing spearman correlation coefficient and additive ranking rule was employed to obtain the final ranking of GPEs from multiple rankings assigned through different MCDM methods. Across 115 grids, APHRODITE exhibits superior performance compared to other GPEs in 89 grids. Conversely, CHIRPS and CMORPH emerge as the least favorable products among the five GPEs across more than 70 grids, being consistently ranked either 4th or 5th. Notably, IMERG was identified as the best-performing product in 14 grids and as the second-best product in 63 grids, positioning it as the second most suitable option after APHRODITE for monthly rainfall time series analysis. Similar results, as detailed in the paper, were also obtained for month-wise rainfall time series analysis. [ABSTRACT FROM AUTHOR]

Terahertz imaging has significant development prospects in terahertz technology due to its strong penetration and high spatial resolution. Currently, optical system designs are generally limited to the visible light range. To design an optical system suitable for terahertz imaging, an analysis of optical materials and system structures has been conducted. TPX (4-methylpentene-1) material and inverse telephoto structure were selected as the design basis. A terahertz optical system with a full field angle of 50°, a relative aperture of 1/4 and a focal length of 100 mm is designed using ZEMAX. The optimization results show that the root mean square radius of the spot in each field of view is smaller than the airy disk radius, indicating good imaging quality of the system. [ABSTRACT FROM AUTHOR]

This study utilizes molecular dynamics simulations to explore the tribological behavior of rough surfaces across various lubrication regimes—boundary, mixed, and elastohydrodynamic (EHL)—at the nanoscale. The analysis not only highlights the distinct interactions between asperity contacts and lubricant films but also introduces novel insights into how lubricant film thickness and surface roughness interplay to dictate frictional dynamics. The analysis reveals that in dry contact, surface roughness quickly stabilizes despite an initial increase in contact area, indicating a dominant role of asperity interlocking that influences frictional resistance. Transitioning to boundary lubrication, the presence of a lubricant layer modifies the contact mechanics, leading to a slower root mean square (RMS) reduction of surface roughness and a more gradual increase in friction force, emphasizing the lubricant's protective effect against asperity flattening. Nevertheless, under lower loads, this protective effect can lead to increased frictional forces compared to dry contact due to higher final roughness. Mixed lubrication maintains a near-constant contact area after initial contact, with any variations in friction force primarily occurring in the early cycles due to rapid asperity flattening. In the EHL regime, the formation of a complete lubricant film between surfaces results in the lowest friction forces, dominated by viscous shear rather than direct asperity contact. Unique to this study is the quantification of these effects under realistic roughness conditions (RMS of 1.2 nm). These regime-specific behaviors underline the nuanced interplay between RMS, contact area, and friction forces, and enhance our understanding of the fundamental principles guiding the design of lubrication strategies for advanced mechanical systems. [ABSTRACT FROM AUTHOR]

Despite therapeutic advancements, cervical cancer caused by high-risk subtypes of the human papillomavirus (HPV) remains a leading cause of cancer-related deaths among women worldwide. This study aimed to discover potential drug candidates from the Asian medicinal plant Andrographis paniculata, demonstrating efficacy against the E6 protein of high-risk HPV-16 subtype through an in-silico computational approach. The 3D structures of 32 compounds (selected from 42) derived from A. paniculata, exhibiting higher binding affinity, were obtained from the PubChem database. These structures underwent subsequent analysis and screening based on criteria including binding energy, molecular docking, drug likeness and toxicity prediction using computational techniques. Considering the spectrometry, pharmacokinetic properties, docking results, drug likeliness, and toxicological effects, five compounds—stigmasterol, 1H-Indole-3-carboxylic acid, 5-methoxy-, methyl ester (AP7), andrographolide, apigenin and wogonin—were selected as the potential inhibitors against the E6 protein of HPV-16. We also performed 200 ns molecular dynamics simulations of the compounds to analyze their stability and interactions as protein–ligand complexes using imiquimod (CID-57469) as a control. Screened compounds showed favorable characteristics, including stable root mean square deviation values, minimal root mean square fluctuations and consistent radius of gyration values. Intermolecular interactions, such as hydrogen bonds and hydrophobic contacts, were sustained throughout the simulations. The compounds displayed potential affinity, as indicated by negative binding free energy values. Overall, findings of this study suggest that the selected compounds have the potential to act as inhibitors against the E6 protein of HPV-16, offering promising prospects for the treatment and management of CC. [ABSTRACT FROM AUTHOR]

Radiologists employ mammograms for the detection of breast cancer in patients, particularly as breast cancer exhibits higher incidence rates in women. Early identification of breast cancer significantly reduces the risk of mortality. Mammograms serve as a valuable imaging technique for the early detection of breast cancer. However, accurately characterizing breast cancer images poses a considerable challenge. A recent research study introduced an innovative algorithm for Mammogram Pectoral Muscle Removal, leveraging Entropy-Based Fuzzy C-means clustering and Classification using RMCNN (Root Mean Squares Convolutional Neural Network). The process involves extracting and pre-processing the input breast image from the dataset through Gaussian filtering. Subsequently, pectoral muscle removal is achieved via entropy-based fuzzy C-Means clustering, followed by DCT (Discrete Cosine Transform) and DWT (Discrete wavelet transforms) feature extraction. The Root Mean Square Value-based Convolutional Neural Network classifier effectively clusters mammography images into normal, malignant, and benign classes, achieving an impressive 99.45% accuracy far better than existing methods with accuracies of 93%, 89%, and 60%. [ABSTRACT FROM AUTHOR]

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure–activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of − 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment. [ABSTRACT FROM AUTHOR]

Previous studies have explored the effect of differing heat and relative humidity (RH) environments on the performance of multiple anaerobic high-intensity interval training (HIIT). Still, its impact on physiological responses and performance following aerobic HIIT has not been well studied. This study examined the effects of differing RH environments on physiological responses and performance in college football players following HIIT. Twelve college football completed HIIT under four different environmental conditions: (1) 25 °C/20% RH (Control group); (2) 35 °C/20% RH (H20 group); (3) 35 °C/40% RH (H40 group); (4) 35 °C/80% RH (H80 group). The heart rate (HR), mean arterial pressure (MAP), lactate, tympanic temperature (TT), skin temperature (TS), thermal sensation (TS), and rating of perceived exertion (RPE) were recorded continuously throughout the exercise. The heart rate variability (HRV): including root mean squared differences of the standard deviation (RMSSD)、standard deviation differences of the standard deviation (SDNN)、high frequency (HF), low frequency (LF), squat jump height (SJH), cycling time to exhaustion (TTE), and sweat rate (SR) were monitored pre-exercise and post-exercise. The HR, MAP, lactate, TT, Ts, TS, and RPE in the 4 groups showed a trend of rapid increase, then decreased gradually. There was no significant difference in HR, MAP, TT, or RPE between the 4 groups at the same time point (p > 0.05), in addition to this, when compared to the C group, the lactate, Ts, TS in the other 3 groups significant differences were observed at the corresponding time points (p < 0.05). The RMSSD, SDNN, HF, and LF levels in the 4 groups before exercise were not significantly different. The RMSSD and HF in the H40 and H80 groups were significantly decreased and other HRV indicators showed no significant difference after exercise. In sports performance measurement, the SJH and TTE were significantly decreased, but there was no significant difference in the 4 groups. The SR was no significant difference in the 4 groups after exercise. In conclusion, heat and humidity environments elicited generally greater physiological effects compared with the normal environment but did not affect sports performance in college football players. [ABSTRACT FROM AUTHOR]

Diffuse solar radiation (DSR) plays a critical role in renewable energy utilization and efficient agricultural production. However, there is a scarcity of high-precision, long-term, and spatially continuous datasets for DSR in the world, and particularly in China. To address this gap, a 41-year (1982–2022) daily diffuse solar radiation dataset (CHDSR) is constructed with a spatial resolution of 10 km, based on a new ensemble model that combines the clear-sky irradiance estimated by the REST2 model and a machine-learning technique using precise cloud information derived from reanalysis data. Validation against ground-based measurements indicates strong performance of the new hybrid model, with a correlation coefficient, root mean square error and mean bias error (MBE) of 0.94, 13.9 W m−2 and −0.49 W m−2, respectively. The CHDSR dataset shows good spatial and temporal continuity over the time horizon from 1982 to 2022, with a multi-year mean value of 74.51 W m−2. This dataset is now freely available on figshare to the potential benefit of any analytical work in solar energy, agriculture, climate change, etc (https://doi.org/10.6084/m9.figshare.21763223.v3). [ABSTRACT FROM AUTHOR]

The study investigated the effects of temperature and centrifugation time on the efficacy of removing uncured resin from 3D-printed clear aligners. Using a photo-polymerizable polyurethane resin (Tera Harz TC-85, Graphy Inc., Seoul, Korea), aligners were printed and subjected to cleaning processes using isopropyl alcohol (IPA) or centrifugation (g-force 27.95g) at room temperature (RT, 23 °C) and high temperature (HT, 55 °C) for 2, 4, and 6 min. The control group received no treatment (NT). Cleaning efficiency was assessed through rheological analysis, weight measurement, transparency evaluation, SEM imaging, 3D geometry evaluation, stress relaxation, and cell viability tests. Results showed increased temperature and longer centrifugation times significantly reduced aligner viscosity, weight (P < 0.05), and transmittance. IPA-cleaned aligners exhibited significantly lower transparency and rougher surfaces in SEM images. All groups met ISO biocompatibility standards in cytotoxicity tests. The NT group had higher root mean square (RMS) values, indicating greater deviation from the original design. Stress relaxation tests revealed over 95% recovery in all groups after 60 min. The findings suggest that a 2-min HT centrifugation process effectively removes uncured resin without significantly impacting the aligners' physical and optical properties, making it a clinically viable option. [ABSTRACT FROM AUTHOR]

Multivariate regression models, including classical least square, principal component regression, and partial least square, combined with UV-Vis absorbance spectra, have been developed and validated to simultaneously determine furosemide and spironolactone in tablets. The central composite design method is used to design calibration and validation concentration sets for the models. The method was applied in the concentration ranges of 4.4–15.6 mg/L for spironolactone and 2.4–13.6 mg/L for furosemide. The method exhibited good correlation coefficients (R2) with low root mean square error of calibration values of 0.22, 0.16, and 0.17 mg/L for spironolactone and 0.20, 0.17, and 0.18 mg/L for furosemide, respectively. The root mean square error of prediction values for spironolactone and furosemide are less than 10% of the average concentration values, corresponding to 1 mg/L for spironolactone and 0.8 mg/L for furosemide. Precision assessment encompassed repeatability and intermediate precision, while comparison with the HPLC method revealed no statistically significant disparity (α = 0.05). Notably, classical least square, principal component regression, and partial least square methods exhibited successful simultaneous quantification of spironolactone and furosemide within the Spiromide-40 combination tablet. Additionally, the functions and code for the analytical methods implemented in R are provided, facilitating easy usage for the readers. [ABSTRACT FROM AUTHOR]

This paper presents a novel measurement method that aims to qualitatively and quantitatively assess uniform deformation during displacement- and force-controlled tensile tests of ductile metals. The method utilizes digital image correlation technology to record the strain distribution during tensile testing, followed by the calculation of the floating root mean square (RMS) value of the strain amplitude along the specimen axis. By implementing this approach, the RMS-based profiles of strain amplitude are identified in different metals and alloys, including austenitic stainless steels, structural steel, copper, and aluminium alloys. Moreover, the proposed method holds potential for predicting important deformation characteristics such as distribution of intensive plastic zones, necking effect, and delocalization effect. Thus, it establishes a link between macroscale and microscale during the analysis of plastic deformation behaviour. The effectiveness of the new method is compared with existing strain and strain-rate methods. The novel approach demonstrates promising advantages in the context of the identification of metal-forming parameters. [ABSTRACT FROM AUTHOR]

In recent years, micro-vehicles have been increasingly involved in urban mobility following the actual trend towards light, more affordable, and eco-friendly means of transportation. Among this vehicle category, the electric kick scooters (e-scooters) represent the most popular example driven by app-based sharing mobility services. Despite the positive implications, poor safety requirements and issues of discomfort are also related to this new segment. The recent spread of e-scooters is motivating the scientific community in investigating performance and ride comfort, in the attempt of improving vehicle design and safety regulations. The aim of this study is to evaluate e-scooter vibrations in driving in a realistic environment, constituted by bike path with seven speed bumps. Fourteen healthy young participants (seven males and seven females) are asked to conduct the test at two different constant velocities ( 5 km/h and 25 km/h). Accelerations are acquired at the main human body segments as well as on the e-scooter. The assessment is based on identifying maxima and root mean squares from signal time histories. A non-parametrical statistical analysis is performed focusing on vibrations transmitted from vehicle to human body, e-scooter velocity, and some rider's characteristics such as gender, mass, dominant arm, and dominant foot. Root mean squares and tests at low velocity generally underline a larger number of significant differences. Moreover, the parameter which mostly influences the system is the rider's mass. Overall, the proposed methodology proves to be an efficient tool to investigate the vehicle-rider vibrational influence. [ABSTRACT FROM AUTHOR]

Heart disease is the world's leading cause of death. Diagnostic models based on electrocardiograms (ECGs) are often limited by the scarcity of high-quality data and issues of data imbalance. To address these challenges, we propose a conditional generative adversarial network (CECG-GAN). This strategy enables the generation of samples that closely approximate the distribution of ECG data. Additionally, CECG-GAN addresses waveform jitter, slow processing speeds, and dataset imbalance issues through the integration of a transformer architecture. We evaluated this approach using two datasets: MIT-BIH and CSPC2020. The experimental results demonstrate that CECG-GAN achieves outstanding performance metrics. Notably, the percentage root mean square difference (PRD) reached 55.048, indicating a high degree of similarity between generated and actual ECG waveforms. Additionally, the Fréchet distance (FD) was approximately 1.139, the root mean square error (RMSE) registered at 0.232, and the mean absolute error (MAE) was recorded at 0.166. [ABSTRACT FROM AUTHOR]

Background: Computer-aided modeling and design (CAM/CAD) of patient anatomy from computed tomography (CT) imaging and 3D printing technology enable the creation of tangible, patient-specific anatomic models that can be used for surgical guidance. These models have been associated with better patient outcomes; however, a lack of CT imaging guidelines risks the capture of unsuitable imaging for patient-specific modeling. This study aims to investigate how CT image pixel size (X-Y) and slice thickness (Z) impact the accuracy of mandibular models. Methods: Six cadaver heads were CT scanned at varying slice thicknesses and pixel sizes and turned into CAD models of the mandible for each scan. The cadaveric mandibles were then dissected and surface scanned, producing a CAD model of the true anatomy to be used as the gold standard for digital comparison. The root mean square (RMS) value of these comparisons, and the percentage of points that deviated from the true cadaveric anatomy by over 2.00 mm were used to evaluate accuracy. Two-way ANOVA and Tukey-Kramer post-hoc tests were used to determine significant differences in accuracy. Results: Two-way ANOVA demonstrated significant difference in RMS for slice thickness but not pixel size while post-hoc testing showed a significant difference in pixel size only between pixels of 0.32 mm and 1.32 mm. For slice thickness, post-hoc testing revealed significantly smaller RMS values for scans with slice thicknesses of 0.67 mm, 1.25 mm, and 3.00 mm compared to those with a slice thickness of 5.00 mm. No significant differences were found between 0.67 mm, 1.25 mm, and 3.00 mm slice thicknesses. Results for the percentage of points deviating from cadaveric anatomy greater than 2.00 mm agreed with those for RMS except when comparing pixel sizes of 0.75 mm and 0.818 mm against 1.32 mm in post-hoc testing, which showed a significant difference as well. Conclusion: This study suggests that slice thickness has a more significant impact on 3D model accuracy than pixel size, providing objective validation for guidelines favoring rigorous standards for slice thickness while recommending isotropic voxels. Additionally, our results indicate that CT scans up to 3.00 mm in slice thickness may provide an adequate 3D model for facial bony anatomy, such as the mandible, depending on the clinical indication. [ABSTRACT FROM AUTHOR]

BeiDou global navigation satellite system (BDS-3), a developed GNSS by China, has the ability to support five different signals, including B1I, B3I, B1C, B2a, and B2b. Meanwhile, BDS-3 has officially provided the satellite-based precise point positioning (PPP) service through the B2b signal (PPP-B2b) since 2021. It's necessary to conduct a comprehensive analysis on multi-frequency PPP with PPP-B2b corrections. In this study, a multi-frequency undifferenced and uncombined PPP model (UDUC) using PPP-B2b corrections was employed to investigate dual-frequency to five-frequency real-time PPP performance. The results show that compared with the conventional dual-frequency solutions, multi-frequency solutions can improve both the convergence performances and positioning accuracy of PPP-B2b service, especially during the convergence stage. The quad-frequency and five-frequency solutions can achieve the best positioning performance. The static solutions of multi-frequency PPP models reach the centimeter-level accuracy after convergence. In kinematic mode, the convergence time of the five-frequency PPP results is reduced by 23.5% compared with the dual-frequency results. The root mean square (RMS) errors of the five-frequency PPP in the E, N, and U components are 7.1 cm, 4.8 cm, and 12.4 cm, which are improved by 6.8%, 11.5%, and 5.5%, respectively. [ABSTRACT FROM AUTHOR]

In forensic commingled contexts, when the disarticulation occurs uniquely at the atlantoaxial joint, the correct match of atlas and axis may lead to the desirable assembly of the entire body. Notwithstanding the importance of this joint in such scenarios, no study has so far explored three-dimensional (3D) methodologies to match these two adjoining bones. In the present study, we investigated the potential of re-associating atlas and axis through 3D–3D superimposition by testing their articular surfaces congruency in terms of point-to-point distance (Root Mean Square, RMS). We analysed vertebrae either from the same individual (match) and from different individuals (mismatch). The RMS distance values were assessed for both groups (matches and mismatches) and a threshold value was determined to discriminate matches with a sensitivity of 100%. The atlas and the corresponding axis from 41 documented skeletons (18 males and 23 females), in addition to unpaired elements (the atlas or the axis) from 5 individuals, were superimposed, resulting in 41 matches and 1851 mismatches (joining and non-joining elements). No sex-related significant differences were found in matches and mismatches (p = 0.270 and p = 0.210, respectively), allowing to pool together the two sexes in each group. RMS values ranged between 0.41 to 0.77 mm for matches and between 0.37 and 2.18 mm for mismatches. Significant differences were found comparing the two groups (p < 0.001) and the highest RMS of matches (0.77 mm) was used as the discriminative value that provided a sensitivity of 100% and a specificity of 41%. In conclusion, the 3D–3D superimposition of the atlanto-axial articular facets cannot be considered as a re-association method per se, but rather as a screening one. However, further research on the validation of the 3D approach and on its application to other joints might provide clues to the complex topic of the reassociation of crucial adjoining bones. [ABSTRACT FROM AUTHOR]

High-dimensional quantum key distribution (HD-QKD) encoded by orbital angular momentum (OAM) presents significant advantages in terms of information capacity. However, perturbations caused by free-space atmospheric turbulence decrease the performance of the system by introducing random fluctuations in the transmittance of OAM photons. Currently, the theoretical performance analysis of OAM-encoded QKD systems exists a gap when concerning the statistical distribution under the free-space link. In this article, we analyzed the security of QKD systems by combining probability distribution of transmission coefficient (PDTC) of OAM with decoy-state BB84 method. To address the problem that the invalid key rate is calculated in the part transmittance interval of the post-processing process, an intelligent threshold method based on neural network is proposed to improve OAM-encoded QKD, which aims to conserve computing resources and enhance system efficiency. Our findings reveal that the ratio of root mean square (RMS) OAM-beam radius to Fried constant plays a crucial role in ensuring secure key generation. Meanwhile, the training error of neural network is at the magnitude around 10−3, indicating the ability to predict optimization parameters quickly and accurately. Our work contributes to the advancement of parameter optimization and prediction for free-space OAM-encoded HD-QKD systems. Furthermore, it provides valuable theoretical insights to support the development of free-space experimental setups. [ABSTRACT FROM AUTHOR]

The count, location, and size of shrimp larvae are important observations for their intensive breeding, which could evaluate the culture density, growth, and yield. Recent works on the counting of dense aquatic objects in an image are mainly accomplished by the density map regression based on convolutional neural network (CNN). These regression methods only give the estimated density map and the corresponding count, but cannot locate and size the objects accurately. To realize the noninvasive counting, locating, and sizing during aquatic breeding, we propose a novel detection architecture in computer vision for the larvae of Cherax quadricarinatus. In particular, a new shrimp larvae counting network (SLCNet) using the density map regression is designed to output the estimated density map of the shrimp larvae image and predict the corresponding number of the shrimp larvae. The local peak filtering (LPF) is applied to the estimated density map to locate the coordinates of every shrimp larva. We then use the K-nearest neighbor (KNN) and the bounding box search to find the fitting bounding boxes of the shrimp larvae in sparse regions and calculate the body length of the shrimp larvae in the fitting bounding boxes with the aid of the least squares regression method. Compared with typical CNN-based counting models, the mean absolute error and root mean square error of the proposed SLCNet are 4.13 and 5.75, respectively, and the counting accuracy is up to 98.57%. Then, we perform the detection of the location and size of the shrimp larvae using the density map estimated by the SLCNet. The locating and sizing accuracy can reach 90.6% (i.e., F1 value) and 90.28%, respectively. It proves that this detection architecture could be used as technical support for subsequent studies on larva growth and behavior recognition. [ABSTRACT FROM AUTHOR]

Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated. [ABSTRACT FROM AUTHOR]

CHB converters have been intensively considered for various applications. However, a reliability issue has emerged due to the higher number of power devices, particularly power semiconductor devices and capacitors, which have been identified as major sources of failure. Although continuous efforts have been made to enhance the reliability of power semiconductors through methods such as active thermal control, there is a noticeable absence of discussion regarding the reliability of capacitors. This is significant since capacitors are the components most prone to failure in power converters. In this study, the root mean square (RMS) current of the DC-link capacitor in a CHB converter is derived when applying thermally compensated discontinuous modulation for power semiconductors. To validate the obtained findings, the derived equations are experimentally tested using a proof-of-concept setup. [ABSTRACT FROM AUTHOR]

Along with regular position-related applications, observations from navigation satellite constellations can also be utilised for atmospheric studies. The present study deals with the 3D water vapour tomography carried out for the first time using Indian GPS stations set up in the Himalayan region. A voxel-based approach is applied to retrieve the vertical profile of precipitable water vapour (PWV). Horizontal 4×4 grids have been formed between 29°–31° latitude and 77.5°–79.5° longitude with 0.5° spatial resolution. To construct a tomography model, voxels are formed over these grids up to 10 km with 0.5 km vertical resolution. Water vapour density (WVD) is estimated for different days to observe its variations under different atmospheric conditions. The results show that the estimated vertical variation of water vapour closely matches the AIRS WVD profile. To compare the results, AIRS WVD observations are interpolated at the same altitude where the tomographic estimations are available. The average root mean square (RMS) error, standard deviation (SD), and mean absolute error (MAE) between estimated water vapour density and AIRS WVD are observed to be 1.52, 1.32 and 2.02 g/m3, respectively. The coefficient of determination (R2) value is observed to be close to 0.9, reflecting an excellent linearity between estimated and observed values. The results also show that the WVD is high up to the 5 km voxel and it is between 0 and 5 g/m3 above 5 km altitude. A good estimation of vertical water vapour profile has been obtained using CORS observations and can be used for further atmospheric analyses in this region. [ABSTRACT FROM AUTHOR]

Insufficient hole cleaning in deviated eccentric annulus yields many drilling problems due to excessive accumulation of cuttings. The majority of the implemented studies in such issues are computational or experimental making them complicated, expensive, and time-consuming. This paper presents a statistical data-driven model to predict cuttings concentration (CA), as a hole cleaning efficiency indicator, under different presumed eccentricity (ε) values of 0.0, 0.4, and 0.8. The model was constructed on the basis of Buckingham Pi theorem utilizing oil field data collected from six offshore deviated wells for developing reliable CA predictive tool. Least linear regression approach was employed for each ε value considering seven dimensionless groups of drilling parameters, rheological parameters, cuttings density, and hole cleaning efficiency indicators, such as transport velocity ratio (VTR), carrying capacity index (CCI), and equivalent circulating density (ECD). The field data were filtered and divided into 75% for training and 25% for validation. Furthermore, a sensitivity study was conducted by parametric differentiation of independent variables regarding the developed CA correlations. Results showed that, VTR, CCI followed by ECD had the highest significance, especially in eccentric annuli. In contrast, cuttings density had the lowest significance in all cases. The proposed method outperformed Duan and Khaled models with root mean square error of 1.45, 1.77, 2.89, and mean absolute percentage error of 34.6, 41.3, 54.5% for ε = 0.0, 0.4, and 0.8, respectively. This study could provide promising and practical methodology in estimating CA and improving deviated drilling efficiency up to 61.8° from vertical. [ABSTRACT FROM AUTHOR]

In this study, we evaluate the soil moisture and precipitation products obtained from two reanalysis models: the National Centers for Environment Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF). The study centers on Kuwait's semi-arid region, during the wet season (November to May) from 2008 to 2018. For the precipitation-related evaluation dataset, rain gauge records from the Kuwait Automatic Weather Observation System (KAWOS) were used, while the ground-truth soil moisture values were taken from the Climate Change Initiative (CCI-SM). Initially, to ensure CCI-SM reliability, we compare it with in-situ soil sensor measurements deployed at a desert site. The analysis revealed a maximum CCI-SM overestimation in winter, decreasing progressively throughout the year with 20% mean bias. The bias-corrected CCI-SM dataset is used for the comprehensive evaluation of the soil moisture reanalysis data. Accuracy metrics, such as mean bias (MB), correlation coefficient (R), and unbiased Root Mean Square Difference (ubRMSD), were used for this purpose. The results indicate that ERA5 consistently underestimates (~ 50% MB) soil moisture, but responds well under high soil moisture conditions. NCEP mostly overestimates soil moisture by a similar magnitude, providing even twice as high values during spring months. Mean monthly precipitation (MP) is also overestimated by NCEP, particularly during extreme episodes, yet found to be reliable enough regarding annual accumulated precipitation. ERA5 has shown strong (R ~ 0.6–0.9) predictive capabilities under both frontal and convective precipitation conditions, with ~ 3% median bias for MP, making it a promising alternative data source, particularly in regions with limited weather station coverage. [ABSTRACT FROM AUTHOR]

The roughness of the contact surface exerts a vital role in rubbing. It is still a significant challenge to understand the microscopic contact of the rough surface at the atomic level. Herein, the rough surface with a special root mean square (RMS) value is constructed by multivariate Weierstrass–Mandelbrot (W–M) function and the rubbing process during that the chemical mechanical polishing (CMP) process of diamond is mimicked utilizing the reactive force field molecular dynamics (ReaxFF MD) simulation. It is found that the contact area A/A0 is positively related with the load, and the friction force F depends on the number of interfacial bridge bonds. Increasing the surface roughness will increase the friction force and friction coefficient. The model with low roughness and high lubrication has less friction force, and the presence of polishing liquid molecules can decrease the friction force and friction coefficient. The RMS value and the degree of damage show a functional relationship with the applied load and lubrication, i.e., the RMS value decreases more under larger load and higher lubrication, and the diamond substrate occurs severer damage under larger load and lower lubrication. This work will generate fresh insight into the understanding of the microscopic contact of the rough surface at the atomic level. [ABSTRACT FROM AUTHOR]

Cyclodextrins (CDs) can enhance the stability and bioavailability of pharmaceutical compounds by encapsulating them within their cavities. This study utilized molecular dynamics simulations to investigate the interaction mechanisms between hydrocortisone (HC) and various methylated CD derivatives. The results reveal that the loading of HC into CD cavities follows different mechanisms depending on the degree and position of methylation. Loading into βCD and 6-MeβCD was more complete, with the hydroxyl groups of HC facing the primary hydroxyl rim (PHR) and the ketone side facing the secondary hydroxyl rim (SHR). In contrast, 2,3-D-MeβCD and 2,6-D-MeβCD showed a different loading mechanism, with the ketone side facing the PHR and the hydroxyl groups facing the SHR. The root mean square fluctuation (RMSF) analysis demonstrated that methylation increases the flexibility of CD heavy atoms, with 3-MeβCD and 2,3-D-MeβCD exhibiting the highest flexibility. However, upon inclusion of HC, 3-MeβCD, 2,3-D-MeβCD, 2-MeβCD, and 6-MeβCD showed a significant reduction in flexibility, suggesting a more rigid structure that effectively retains HC within their cavities. The radial distribution function revealed a significant reduction in the number of water molecules within the innermost layer of the methylated CD cavities, particularly in TMeβCD, indicating a decrease in polarity. The presence of HC led to the release of high-energy water molecules, creating more favorable conditions for HC loading. Conformational analysis showed that methylation caused a partial decrease in the area of the PHR, a significant decrease in the area of the middle rim, and a notable decrease in the area of the SHR. The loading of HC increased the area of the PHR in most derivatives, with the most pronounced increase observed in 2,6-D-MeβCD and 6-MeβCD. The analysis of interaction energies and binding free energies demonstrated that the binding of HC to methylated CD derivatives is thermodynamically more favorable than to βCD, with the strongest association observed for 6-MeβCD, 2-MeβCD, and 2,3-D-MeβCD. [ABSTRACT FROM AUTHOR]

We aimed to determine if continuous perioperative heart rate variability (HRV) monitoring could improve risk stratification compared to a short preoperative measurement in radical cystectomy patients. Electrocardiography (ECG) recordings were collected continuously preoperatively to discharge in 83 patients. Two, 5-min ECG signal segments (preoperative and at 24-h post ECG placement) were analyzed offline to extract HRV metrics. HRV metric discriminatory ability to identify patients with 30-day postoperative complications were analyzed using receiver operating characteristics curves. Sixty participants were included for analysis of which 27 (45%) developed a complication within 30 days postoperative. HRV was reduced in patients with complications. Postoperative standard deviation NN intervals and root mean square of successive differences had area under the curves (AUC) of 0.67 (95% CI 0.54 to 0.81) and 0.68 (95% CI 0.54 to 0.82), respectively. Significant discriminatory abilities were also reported for postoperative frequency metrics of absolute low frequency (LF) [AUC = 0.65 (95% CI 0.51 to 0.79)] and high frequency (HF) powers [AUC = 0.69 (95% CI 0.55 to 0.83)] and total power [AUC = 0.66 (95% CI 0.53 to 0.80)]. Postoperative acquired HRV metrics demonstrated improved discriminatory ability. Our findings suggest that longer-term perioperative HRV monitoring presents with superior ability to stratify complication risk. [ABSTRACT FROM AUTHOR]

Passive translational acceleration (PTA) has been demonstrated to induce the stress response and regulation of autonomic balance in healthy individuals. Electrodermal activity (EDA) and heart rate variability (HRV) measurements are reliable indicators of the autonomic nervous system (ANS) and can be used to assess stress levels. The objective of this study was to investigate the potential of combining EDA and HRV measurements in assessing the physiological stress response induced by PTA. Fourteen healthy subjects were randomly assigned to two groups of equal size. The experimental group underwent five trials of elevator rides, while the control group received a sham treatment. EDA and HRV indices were obtained via ultra-short-term analysis and compared between the two groups to track changes in the ANS. In addition, the complexity of the EDA time series was compared between the 4 s before and the 2–6 s after the onset of PTA to assess changes in the subjects' stress levels in the experimental group. The results revealed a significant increase in the skin conductance response (SCR) frequency and a decrease in the root mean square of successive differences (RMSSD) and high frequency (HF) components of HRV. In terms of stress assessment, the results showed an increase in the complexity of the EDA time series 2–6 s after the onset of PTA. These results indicate an elevation in sympathetic tone when healthy subjects were exposed to a translational transport scenario. Furthermore, evidence was provided for the ability of EDA complexity to differentiate stress states in individual trials of translational acceleration. [ABSTRACT FROM AUTHOR]

Variations in the autonomic nervous system activity during exercise therapy in patients with cardiovascular diseases may lead to adverse events. Aromatherapy may reduce these adverse events by enhancing parasympathetic nervous activity (PNA). However, the effects of aromatherapy during exercise remain relatively unknown. This study aimed to evaluate the effect of aromatherapy on autonomic nervous activity during exercise and recovery. This randomized crossover study included 20 healthy men subjected to both aroma and placebo conditions which involved rest and moderate-intensity aerobic exercise on a cycle ergometer, followed by recovery. Blood pressure, heart rate variability indices, and SpO2 were measured during the rest, exercise, and recovery phases. Moreover, aroma preferences and emotional changes in response to the aroma were assessed. Under the placebo condition, high frequency (HF), root mean square of successive differences indices, and heart rate showed delayed recovery (P < 0.05). Furthermore, a moderate positive correlation was identified between aroma preference, pleasant emotions induced by aromatherapy, and the HF index (P < 0.05). These results indicate that aromatherapy facilitates the recovery of PNA after exercise. Furthermore, these effects were more pronounced among individuals who exhibited a stronger preference for and more positive emotions toward aromas. [ABSTRACT FROM AUTHOR]

The ever-growing demands from emerging industrial applications and services require revolutionary technology beyond fifth-generation (5G). The sixth-generation (6G) networks are the promising technology to satisfy the high data rate demands for future industrial applications. This paper examined wideband radio propagation characteristics at 28 GHz obtained through measurements in an industrial environment and compared them with those obtained at 108 GHz. The characteristics were expressed in terms of path loss, K-factor, power delay profile, root mean square delay spread, and channel capacity. The measured path-loss was used to obtain empirical propagation models in the line of sight using the floating intercept and close-in free space reference distance and they fitted very well. The obtained propagation models were then used to obtain an empirical channel capacity prediction model in terms of antenna gain, transmission frequency, distance, and propagation exponent. For the non-LoS scenario, significant degradation of channel capacity was observed, and hence the usage of intelligent reflection surface technique to compensate for the lost capacity was investigated. This work is part of the foundation needed to establish the feasibility of using IRS technique in 6G networks operating at mmWave and sub-THz bands for industrial applications. [ABSTRACT FROM AUTHOR]

Inter-satellite link (ISL) plays an essential role in current and future Global Navigation Satellite System (GNSS). In this study, we investigate the impact of ISL observations on precise orbit determination for BeiDou-3 Navigation Satellite System (BDS-3) Medium Earth Orbit (MEO) satellites based on different Extended CODE Orbit Models (ECOM). Thanks to the better observation geometry of the Ka-band ISL data compared to the L-band data for BDS-3 MEO satellites, the ISL solution substantially reduces Orbit Boundary Discontinuity (OBD) errors, except for C30, which suffers from unstable Ka-band hardware delay. From the external quality analysis, ISL significantly enhances the reliability of the orbit of MEO satellites manufactured by the China Academy of Space Technology (CAST). The standard deviation (STD) of the satellite laser ranging (SLR) residuals is approximately 2.5 cm, and the root mean square (RMS) is reduced by 10–23% compared to L-band solutions. Besides, the Sun-elongation angle dependent systematic error in SLR residuals nearly vanishes based on the reduced 5-parameter ECOM (ECOM1) or extended 7-parameter ECOM (ECOM2) with ISL data. This is because the ISL reduces the correlation between state parameters and solar radiation pressure (SRP) parameters as well as those among SRP parameters, leading to a more accurate estimation of both orbit and SRP perturbations, particularly those along B direction. This confirms that the deficiency of the SRP models for BDS-3 CAST satellites can be compensated by using better observation geometry from ISL data. On the other hand, for the satellite manufactured by Shanghai Engineering Center for Microsatellites (SECM), the ISL allows for a more accurate estimation of the Bc1 parameter in the ECOM1 model. This only reduces linear systematic error, possibly because the impact generated by the satellite bus cannot be entirely absorbed by the B-direction parameters. [ABSTRACT FROM AUTHOR]

Genes related to MAPK-ERK signaling pathways, and epithelial-mesenchymal transition induction is evolutionarily conserved and has crucial roles in the regulation of important cellular processes, including cell proliferation. In this study, six cannabinoids from Cannabis sativa were docked with MAPK-ERK signaling pathways to identify their possible binding interactions. The results showed that all the cannabinoids have good binding affinities with the target proteins. The best binding affinities were MEK- tetrahydrocannabinol (– 8.8 kcal/mol) and P13k-cannabinol (– 8.5 kcal/mol). The root mean square deviation was calculated and used two alternative variants (rmsd/ub and rmsd/lb) and the values of rmsd/lb fluctuated 8.6–2.0 Å and for rmsd/ub from 1.0 to 2.0 Å that suggests the cannabinoids and protein complex are accurate and cannot destroy on binding. The study analyzed the pharmacokinetic and drug-likeness properties of six cannabinoids from C. sativa leaves using the SwissADME web tool. Lipinski's rule of five was used to predict drug-likeness and showed that all compounds have not violated it and the total polar surface area of cannabinoids was also according to Lipinski's rule that is benchmarked of anticancer drugs. Cannabinoids are meet the requirements of leadlikeness and synthetic accessibility values showed they can be synthesized. The molecular weight, XLOGP3, solubility (log S), and flexibility (FLEX) are according to the bioavailability radar. The bioavailability score and consensus Log Po/w fall within the acceptable range for the suitable drug. Pharmacokinetics parameters showed that cannabinoids cannot cross the blood–brain barrier, have high GI absorption as well as cannabinoids are substrates of (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) but no substrate of P-glycoprotein. Based on these findings, the study suggests that cannabinoids are suitable drugs that could be used as effective inhibitors for target proteins involved in cancer pathways. Among the six cannabinoids, cannabinol and tetrahydrocannabinol exerted maximum binding affinities with proteins of MAPK-ERK signaling pathways, and their pharmacokinetics and drug-likeness-related profiles suggest that these cannabinoids could be superlative inhibitors in cancer treatment. Further in vitro, in vivo, and clinical studies are needed to explore their potential in cancer treatment. [ABSTRACT FROM AUTHOR]

Purpose: We examined heart rate variability (HRV) and baroreflex sensitivity (BRS) disease- and age-related response at 10-and 60-min after an acute high-intensity interval (HIIE) and moderate continuous exercise (MICE) in older adults with and without type 2 diabetes mellitus (T2DM) and healthy young adults. Methods: Twelve older male adults with (57–84 years) and without T2DM (57–76 years) and 12 healthy young male adults (20–40 years) completed an isocaloric acute bout of HIIE, MICE, and a non-exercise condition in a randomized order. Time and Wavelets-derived frequency domain indices of HRV and BRS were obtained in a supine position and offline over 2-min time-bins using Matlab. Results: HIIE but not MICE reduced natural logarithm root mean square of successive differences (Ln-RMSSD) (d = − 0.85; 95% CI − 1.15 to − 0.55 ms, p < 0.001), Ln-high-frequency power (d = − 1.60; 95% CI − 2.24 to − 0.97 ms2; p < 0.001), and BRS (d = − 6.32; 95% CI − 9.35 to − 3.29 ms/mmHg, p < 0.001) in adults without T2DM (averaged over young and older adults without T2DM), returning to baseline 60 min into recovery. These indices remained unchanged in older adults with T2DM after HIIE and MICE. Older adults with T2DM had lower resting Ln-RMSSD and BRS than aged-matched controls (Ln-RMSSD, d = − 0.71, 95% CI − 1.16 to − 0.262 ms, p = 0.001; BRS d = − 3.83 ms/mmHg), 95% CI − 6.90 to − 0.76, p = 0.01). Conclusions: Cardiovagal modulation following acute aerobic exercise is intensity-dependent only in adults without T2DM, and appears age-independent. These findings provide evidence of cardiac autonomic impairments in older adults with T2DM at rest and following aerobic exercise. [ABSTRACT FROM AUTHOR]