Maximum Correlation Research Articles

The modified model for hyperspectral estimation of soil organic matter using positive and inverse grey relational degree

PurposeIn order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.Design/methodology/approachBased on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R2) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.Social implicationsThe model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.Originality/valueThe paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Twisted-wind effect on the aerodynamic force acting on varying side-ratios tall buildings

The influence of side ratio on the aerodynamic performance of tall buildings is investigated by pressure measurement experiments in a mimicked twisted wind field (TWF) with the maximum twisted angle of 30° and its corresponding conventional wind field (CWF). The global wind loads and distribution of local wind forces on the buildings with different side ratios are analyzed in TWF and compared with those in CWF. Additionally, the effect of side ratio on the spatial correlations of aerodynamic forces in temporal and frequency domains and their spectral features are discussed in the two fields. The results show that the aerodynamic forces are largely affected by the side ratio of tall buildings in TWF. The correlation coefficients of local lateral wind force in the TWF are decreased by 0.1–0.4 compared with those in CWF. The trajectory envelopes of the base moment components in lateral and torsional directions are positively inclined, and the building with B/D = 2 takes the maximum correlation coefficient. The spectral peak of local torsional forces is diminished with the increasing side ratio in TWF and CWF, and the peak frequency in TWF deviates from that in CWF even though the building is under the equivalent wind direction at some specific elevations. This study aims to provide some useful insights into the twisted wind effect on tall buildings with varying side ratios.

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.

Quantum machine learning assisted lung cancer telemedicine

The rapidly increasing human population, the high cost of medical equipment, and the spread of multifactor diseases have transformed the entire healthcare industry into an electronic consulting, telediagnosis, delivery, and treatment model to avoid the physical personalization of patients. Despite this, we face many challenges, including low sensitivity of medical instruments, signal losses, and security of medical records. This study aims to develop a lung cancer telemedicine platform that utilizes classical and quantum computing models to classify lung cancer subtypes and stages and develop effective telemedicine techniques. By using optoplasmonic biosensors, the biosignals (that show lung cancer) were extracted and correlated with cloud datasets (containing patient information) to determine the dose of laser interstitial thermal therapy (LITT). Then, applying quantum teleportation, the biosignals and doses of LITT were teleported between two healthcare stations. From the numerical result, we observed the maximum sensitivity (10 421 nm/RIU) of the proposed biosensor, a minimum degree of the loss function and maximum correlation of data from the quantum machine learning model, and high teleportation fidelity (96% transmission fidelity for biosignals and 98% teleportation fidelity for doses LITT). This shows the proposed telemedicine schemes anticipated solutions for the long-distance faithful lung cancer telemedicine.

Fabrication of PCL/CMARX/GO Composite Nanofibrous Mats for Dye Adsorption: Wastewater Treatment.

The effluents of industrial wastewater contain several toxic organic and inorganic pollutants that may contaminate clean and freshwater sources if untreated or poorly treated. These toxic pollutants include colors; hazardous compounds; surfactants; cosmetics; agrochemicals; pharmaceutical by-products; and agricultural, pharmaceutical, and medical contaminants. Treating wastewater has become a global problem. Many projects have been started in the last two decades to treat wastewater, resultant water pollution, and associated waste management problems. Adsorbants based on graphene oxide (GO) are viable wastewater treatment materials due to their adaptability, photocatalytic action, and capacity for self-assembly. Here, we report the fabrication of nanofibrous mats from polycaprolactone (PCL), carboxymethyl arabinoxylan (CMARX), and carboxyl-functionalized-graphene oxide using an electrospinning technique. The silver nanoparticles were loaded onto the mat to enhance their photocatalytic activity. These mats were characterized using different techniques, including Fourier transform infrared (FTIR), scanning electron microscope (SEM), and transmission electron microscope (TEM). The water contact angles were used to study their hydrophilic and hydrophobic behavior. The Langmuir isotherm model and adsorption kinetics were studied to evaluate their adsorption capabilities against methylene blue (MB). Sample 2 followed the Langmuir isotherm model (R2 = 0.9939). Adsorption kinetics exhibited pseudo-second order behavior (R2 = 0.9978) due to their maximum correlation coefficient values. MB has excellent adsorption at room temperature and the formation of the monolayer at the surface of the adsorption mat. An enhanced PO43- and MB adsorption was observed, providing recyclability up to 4-5 times. Hence, the fabricated nanofibrous mat would be a potential candidate for more effective wastewater treatment applications.

Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height

The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach.

Development of a Raman Temperature LiDAR with Low Energy and Small Aperture by Parameter Optimization

The range of detection and accuracy of currently available Raman temperature LiDAR systems are primarily improved by increasing the energy or the aperture of the receiving telescope. However, this does not lead to a corresponding linear increase in the distance of detection and accuracy of the system. In this paper, the authors construct a simulation model and optimize its parameters to develop a Raman temperature LiDAR with low energy and a small aperture that has a maximum distance of detection of over 5 km during the day and over 10 km at night. The profile of the atmospheric temperature obtained through field tests was in good agreement with the results of a radiosonde. The maximum correlation between the Raman temperature LiDAR and the radiosonde was 0.94 at night and 0.81 during the day. The results showed that the proposed Raman temperature LiDAR, with low energy and a small aperture, can provide reliable data on the temperature in the troposphere throughout the day.

Atmospheric processes control the stable isotopic variability of precipitation in the middle–lower reaches of the Yangtze River Basin, East Asian monsoon region

Paleoclimatic records in the East Asian monsoon region (EAM) depend on precipitation isotopes as proxies for past hydroclimatic variability. However, the potential mechanisms controlling precipitation isotopic variability in this region remain poorly understood. This study collected daily precipitation isotopes in the middle-lower reaches of the Yangtze River Basin from 2017 to 2020 to explore how convective behaviors and moisture sources determining the variability of precipitation stable isotopes. Daily precipitation δ18O presented a weak inverse relationship with local precipitation amount and air temperature. Results of correlation analysis showed that the maximum correlation zones of precipitation δ18O between different cloud levels and outgoing long wave radiation were identified in the upstream regions such as the Indian Peninsula, BoB, and Indo-China Peninsula during the rainy season. Back-trajectory analysis showed that substantial water vapor from maritine sources in the Indian Ocean and the Bay of Bengal (BoB) largely contributed to the lower δ18O values during the rainy season (−7.5 ‰) because of intense rainout processes in the upstream sourced regions, demonstrated by significant regional correlation between precipitation δ18O and regional precipitation amount. The regional correlations were stronger in the upstream regions than the local influences, indicating that precipitation isotopes in this region are more influenced by regional convective processes in the upstream region during the rainy season. Using the modified Stewart model, the mean subcloud evaporation fraction estimated was greater in rainy season (24.1 % vs 36.1 % for HK and HZ, respectively) than that in dry season (15.1 % vs 19.9 % for HK and HZ, respectively). These findings highlight the importance of upstream processes in controlling precipitation isotopes in the EAM region and provide reliable evidence on the interpretation of monsoonal climate variability.

Unravelling the Release Kinetics of Exchangeable Magnesium in Acid Soil of Nilgiris

Magnesium deficiency is a pervasive and recurrent factor that significantly restricts crop production, primarily attributable to the low levels of exchangeable magnesium (ex-Mg) present in acidic soil conditions. This deficiency exerts a pronounced negative influence on the sustainability and progress of agricultural development. Hence the current study aspired at modeling the kinetics of Exchangeable Magnesium release from 3 fertilizer sources i.e., Epsom salt (MgSO4·7H2O), Magnesite (MgCO3) and Dolomite [CaMg(CO3)2] in the acidic soil of the Nilgiris district in Tamil Nadu, India. Four mathematical models were verified—Power function, parabolic diffusion, Simple-Elovich, and first-order to explain cumulative Mg2+ release. Power function was noticed to be an outstanding empirical equation finely fitted to the experimental data. The intensity, as well as the modality of the release pattern, was predicted by the numerical parameters. The power function as well as Parabolic Diffusion portrayed the Mg2+ release kinetics best as verified by the maximum correlation coefficients (r2). The parabolic diffusion model also designated the data as suitable, signifying diffusion-controlled exchange. From the derived dissolution rates, it was conceivable to agree Epsom salt (MgSO4·7H2O) from which the release was faster than the other two magnesium sources. In conclusion, these outcomes provided an insight into the temporal dynamics of magnesium availability in acidic soil, highlighting the importance of understanding its release kinetics for sustainable agriculture development. The findings contribute to the broader knowledge of magnesium management strategies, aiding in the development of targeted interventions to alleviate magnesium deficiency and optimize crop productivity in acidic soil environments.

Investigation of Multilayer Perceptron Regression-based Models to Forecast Reference Evapotranspiration (ETo)

Reference evapotranspiration (ETo) is a valuable factor in the hydrological process and its estimation is a sophisticated and nonlinear problem. In this study, the utility of multilayer perceptron regression is investigated to estimate ETo of Jodhpur city, India which has a hot arid climate. Four different multilayer perceptron regression-based models are created and compared in this study. Multilayer perceptron regression is a popular tool used to predict the results of sophisticated problems. Each created model has a different architecture, in which the size (neurons) of the input and hidden layers is decided by the maximal correlation relationship between meteorological attributes and observed ETo using the Food Agriculture Organization Penman-Monteith method (FAO-PM56). This study found that model with meteorology inputs (namely both high and low temperatures, solar radiation, wind speed at 2 m, and humidity) and nine neurons at the hidden layer achieved high predictive accuracy with mean absolute error (MAE) of 0.08, mean squared error (MSE) of 0.01, root mean squared error (RMSE) of 0.10, Pearson correlation (r) of 0.99, and coefficient of determination (r2) of 0.99. The finding of this study is that the multilayer perceptron regression-based models with at least three meteorological inputs (temperature, solar radiation, and wind speed) can effectively utilize to estimate ETo and may receive attention from agriculturists, engineers, and researchers for irrigation scheduling, water resource handling, crop production enhancement, draught area prediction, etc.

A New Combination Model for Air Pollutant Concentration Prediction: A Case Study of Xi’an, China

As energy demand continues to increase, the environmental pollution problem is becoming more severe. Governments and researchers have made great efforts to avoid and reduce air pollution. The prediction of PM2.5, as an important index affecting air quality, has great significance. However, PM2.5 concentration has a complex change process that makes its prediction challenging. By calculating both PM2.5 concentration and that of other pollutants in the atmosphere and meteorological factors, it is evident that the variation in PM2.5 concentration is influenced by multiple factors, and that relevant features also influence each other. To reduce the calculated loss, with full consideration given to the influencing factors, we used the maximum correlation and minimum redundancy (MRMR) algorithm to calculate the correlation and redundancy between features. In addition, it is known from the Brock–Dechert–Scheinman (BDS) statistical results that the change in PM2.5 is nonlinear. Due to the outstanding performance of bidirectional long short-term memory (BiLSTM) neural networks in nonlinear prediction, we constructed an encoder–decoder model based on BiLSTM, named ED-BiLSTM, to predict the PM2.5 concentration at monitoring stations. For areas without monitoring sites, due to the lack of historical data, the application of neural networks is limited. To obtain the pollutant concentration distribution in the study area, we divided the study area into a 1 km × 1 km grid and combined the ED-BiLSTM model via the use of the inverse distance weighting (IDW) algorithm to obtain the PM2.5 concentration values in a region without monitoring stations. Finally, ArcGIS was used to visualize the results. The data for the case study were obtained from Xi’an. The results show that, compared with the standard long short-term memory (LSTM) model, the RMSE, MAE, and MAPE of our proposed model were reduced by 24.06%, 24.93%, and 22.9%, respectively. The proposed model has a low error for PM2.5 prediction and can provide a theoretical basis for the formulation of environmental protection policies.

A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems

This manuscript aims to develop a new multivariate composite index for monitoring agricultural drought. To achieve this, the AVHRR, VIIRS, CHIRPS data series over a period of 40 years, rainfall and crop yield data as references were used. Variables include parameters for vegetative stress (SVCI, PV, SMN), water stress (PCI, RDI, NRDI), and heat stress (SMT, TCI, STCI), and a new variable related to environmental conditions was calculated through a normalized rainfall efficiency index. Then, random forest algorithm was used to determine the weights of each component of the model by considering interannual fluctuations in cereal yields as an impact variable. The multivariate composite model was compared to the VHI, NVSWI and SPI-12 indices for validation. The results show a large spatiotemporal concordance between the MDCI and the validation indices with a maximum correlation of 0.95 with the VHI and a highly significant p value (< 2.2e-16). Validation of the MDCI model by SPI-12 shows a significantly higher statistically significant relationship than that observed between SPI and VHI and NVSWI. P value range from 3.531e-05 to 6.137e-06 with correlations that vary between 0.6 and 0.64 depending on the station. It is also highly correlated with the Palmer drought severity index (PDSI) and climatic water deficit index (CWDI), with R = 0.85 and p value < 5.8e-10 and R = 0.72 and p value < 1.9e-6, respectively. Finally, the study provides a new direction for multivariate modeling of agricultural drought that should be further explored under various agroclimatic conditions.

Time-lagged and cumulative effects of drought and anthropogenic activities on China's vegetation greening from 1990 to 2018

ABSTRACT Previous studies have confirmed the time-lagged and cumulative effects of drought and anthropogenic activities on vegetation growth, but these studies focus on the time-lagged effect of drought and are poorly known how vegetation productivity responds to anthropogenic activities. Here, based on the reconstructed Normalized Difference Vegetation Index, the Standardized Precipitation Evapotranspiration Index and land use degree comprehensive index, we diagnosed the spatiotemporal pattern of vegetation and drought, investigated time-lagged and cumulative effects of drought and anthropogenic activities over China through the month where the maximum correlation coefficient occurred. It revealed that the browning trend of 32.21% of vegetated lands was covered by overall greening, especially in northwestern China. Drought intensified with a rate of 0.0014/year. 66.41% and 54.57% of the vegetated lands had time-lagged and cumulative response to drought, with a shorter timescales of 1–4 months, indicating the higher sensitivity of vegetation growth to drought. There was a U-shaped relationship between moisture conditions and vegetation response time. 49.9% of China's vegetation showed time-lagged effects to anthropogenic activities, with a longer timescales of 6–10 years, demonstrating that anthropogenic activities triggered ecological changes but vegetation ecosystems cannot keep pace. The accumulated and time-lagged years declined with increased land use intensity.

The relative area score for sublingual varices reliability measurement: a diagnostic study

BackgroundSublingual varices (SV) and their predictive potential for other clinical parameters is a much studied topic in oral medicine. SVs have been well studied as predictive markers for many common diseases such as arterial hypertension, cardiovascular disease, smoking, type 2 diabetes mellitus and age. Despite many prevalence studies, it is still unclear how the reliability of SV inspection affects its predictive power. The aim of this study was to quantify the inspection reliability of SV.MethodsIn a diagnostic study, the clinical inspection of 78 patients by 23 clinicians was examined for the diagnosis of SV. Digital images of the underside of the tongue were taken from each patient. The physicians were then asked to rate them for the presence of sublingual varices (0/1) in an online inspection experiment. Statistical analysis for inter-item and inter-rater reliability was performed in a τ-equivalent measurement model with Cronbach's alpha and Fleiss κ.ResultsThe interrater reliability for sublingual varices was relatively low with κ = 0.397. The internal consistency of image findings for SV was relatively high with α≈ 0.937. This shows that although SV inspection is possible in principle, it has a low reliability R. This means that the inspection finding (0/1) of individual images often cannot be reproduced stably. Therefore, SV inspection is a difficult task of clinical investigation. The reliability R of SV inspection also limits the maximum linear correlation {r}_{max} of SV with an arbitrary other parameter Y. The reliability of SV inspection R = 0.847 limits the maximum correlation to {r}_{max} (SV, Y) = 0,920—a 100% correlation was a priori not achievable in our sample. To overcome the problem of low reliability in SV inspection, we propose the RA (relative area) score as a continuous classification system for SV, which normalises the area of visible sublingual veins to the square of the length of the tongue, providing a dimensionless measure of SV.ConclusionsThe reliability of the SV inspection is relatively low. This limits the maximum possible correlation of SV with other (clinical) parameters. SV inspection reliability is an important indicator for the quality of SV as a predictive marker. This should be taken into account when interpreting previous studies on SV and has implications for future studies. The RA score could help to objectify the SV examination and thus increase its reliability.

Introduction of a new synchronization index by correlating Leg Movements and overall activity distinguishes subjects with normal cognitive functioning from those with MCI: Preliminary results

AbstractBackgroundExploratory data analysis was performed, looking after correlations of Leg Movement (LMs) against the 22 full‐EEG polysomnography during sleep. The EEG signals were recorded with the 19 derivations from the traditional International 10/20 electrode placement, chin electromyography (EMG), and electrooculogram (EOG). Nine subjects were considered: six in the Mild Cognitive Impairment (MCI) group, and three controls (CTRL). In line with the hypothesis about phasic activity reorganizing cerebral processing, lower correlations were expected for MCI, during REM sleep.MethodFor each subject, the first rotation (PC1) of the Principal Component Analysis (PCA) decomposition of the data (22 channels) was generated. This accounts for the direction of the maximal variance. The proportion of variance explained by PC1 was very high (Table 2). In view of this, pairing the LMs and the PC1 time series allowed for reliable comparisons. As expected, there was no linear relation between the LMs and each of the channels: out of 198 pairs (22 for each subject), the maximal correlation coefficient was 0.0194. Therefore, it was natural to look for nonlinear relationships. The metric considered, was the total variation, at 5‐second intervals. The total variation corresponds with the arclength of the graph and is a measure of variability of a time series. For each subject, correlation coefficients were calculated for the total variations for LM versus PC1.ResultThe mean coefficients for the CTRL group were higher than those for the MCI group for all the sleep epochs (Table 1). The difference was highest for REM sleep. In Table 2, the coefficients of the PC1 for each subject and variable (PC1) are presented (variations of one subject, Fig. 1).ConclusionDue to the small size of the sample, it is not possible to draw any actual conclusions. However, anecdotal evidence was found for differences in synchronic activity between groups. This was most notorious for REM sleep, with decreased correlations for the MCI group. It would be of interest to replicate this study with a much larger sample.

ИНДЕКСЫ СУРРОГАТНОЙ ОЦЕНКИ ИНСУЛИНОРЕЗИСТЕНТНОСТИ У ДЕТЕЙ И ПОДРОСТКОВ С ОЖИРЕНИЕМ

Obesity is the most common reason for the insulin resistance (IR) though obesity in children is not always associated with IR. The markers based on fasting glucose and/or insulin levels and alternative parameters with conventional lipid-based indexes are used in assessing the IR in clinical practice. The purpose of the research was to assess the relationship between alternative IR surrogate biomarkers as follows: triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C), TG/HDL-C ratio; single-point insulin sensitivity estimator (SPISE) index; triglyceride-glucose index (TyG), with generally accepted indices based on the insulin/glucose ratios, such as HOMA-IR, QUICKI, Caro, in a cohort of obese children and adolescents. Materials and methods used: a single-stage multicenter comparative study was conducted involving 127 patients aged 6 to 17 y/o with simple obesity of varying degrees. The following indices were calculated: HOMA-IR = fasting insulin (mU/ml) x fasting plasma glucose (FPG) (mmol/L) / 22.5; QUICKI = 1/Log (Fasting Insulin, µU/ml) + Log (Fasting Glucose, mg/dl); Caro = fasting plasma glucose (mmol/l) / fasting insulin (μU/ml); TG/HDL-C = TG (mmol/L) / HDL-C (mmol/L); triglyceride-glucose index TyG = Ln [fasting TG (mg/dl) x fasting plasma glucose (mg/dl)/2], where Ln is the logarithm; SPISE = 600 × HDL-C0.185/(TG0.2 x BMI1.338). Results: 75 (59%) boys and 52 (41%) girls were included in the study; the median age was 12.9 [10.4; 14.9] y/o. Pre-pubertal development was recorded in 25 (19.7%), pubertal - in 102 (80.3%). The prevalence of impaired glucose tolerance (IGT) was 5.5%. In children with IGT, the SPISE index was lower than in children with normal glucose tolerance (3.69 [3.49; 4.37] vs. 4.8 [3.99; 5.55], p=0.021) with no statistically significant differences between these groups in other markers. In the IGT subgroup, a high negative correlation was found between SDS BMI and SPISE (R=-0.97). In the general SDS group, BMI correlated with insulin (R=+0.24), TG (R=+0.2), HOMA (R=+0.24), Caro (R=-0.26), TG/HDL-C (R=+0.28) and SPISE (R=-0.65). The SPISE index was lower in girls than in boys (4.94 [3.63; 5.73] vs. 4.07 [3.59; 5.16], p = 0.045). No statistically significant differences were recorded for other gender-related markers. A negative correlation was also found between the obesity degree and SPISE in the subgroups of girls (R=-0.49) and boys (R=-0.70) as well as among pre-pubertal children of both genders (R=-0.61). The maximum correlation between SDS BMI and SPISE was recorded in the subgroup of teenagers of both genders (R=-0.88). In the general group, TyG was weakly correlated with HOMA-IR (R=+0.26) and QUICKI (R=-0.24), while SPISE showed a weak association with all studied insulin indices; no statistically significant correlation between insulin indices and TG/HDL-C was found. In the subgroup of girls, no significant relationships between the evaluated markers were found, in the subgroup of boys, TyG weakly correlated with HOMA-IR (R=+0.29) and QUICKI (R=-0.32). Stronger correlations were recorded in boys between SPISE and all studied insulin indices. In the subgroup of prepubertal patients, a correlation was found between TyG and QUICKI (R=-0.44), SPISE and Caro (R=+0.56), SPISE and HOMA-IR (R=-0.50). The TG/HDL-C ratio did not statistically significantly correlate with traditional IR markers. TyG showed weak correlations with HOMA-IR and QUICKI in the general group and the boys’ subgroup. In the prepubertal period, TyG only weakly correlated with QUICKI and did not show strong associations with insulin-based indices in pubertal children. The SPISE index showed correlations with insulin markers both in the main group and in individual subgroups. Conclusion: the SPISE index can be considered as a relatively simple and accessible method for assessing IR in obese children and adolescents.

Bioinformatics-based analysis of mechanistic differences in vascular endothelial injury ischemic stroke induced by atrial fibrillation and atherosclerosis.

Studies of the intracranial vasculature in patients with ischemic stroke caused by atherosclerosis (AS) and cardiac embolism have revealed significantly different degrees of AS, plaque, and vascular stenosis. And the endothelium has a great influence on the vasculature throughout the circulatory system, especially in the brain. This study aimed to investigate the mechanistic differences in endothelial injury between atrial fibrillation (AF)- and AS-induced ischemic stroke. All target genes of AF, AS, and the vascular endothelial cell (VC) were obtained from the GeneCards database; the differential genes of AF and AS separately associated with the VC were established by a Venn diagram. A protein-protein interaction network was created, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used to perform genomic enrichment and functional enrichment analysis. Hub genes were selected by Maximal Clique Centrality algorithm ranking and correlation linkage in the STRING database, and then, clinical serum samples were used to verify the quantitative expressions in the AF, AF stroke, AS, and AS stroke groups. Fifty-five AF-VC-related genes and ninety-three AS-VC-related genes were screened, which differed in biological function, cellular composition, and molecular function. The genes correlation between AF and vascular endothelial cells (VCs) was KRAS and PTPN11, and those correlation between AS and VCs was IL-4, IFNG, IL-17A, and CSF-2. IL-4 and CSF-2 may be relevant proteins involved in the differences in stroke mechanisms between AF and AS, and they may act by further influencing the function of their downstream cells. This study provides a preliminary theoretical basis for investigating the differences in mechanisms of endothelial injury between AF- and AS-induced ischemic stroke.

Fault Traveling Wave Detection Method based on ICEEMDAN-MCKD

To address the challenge of calibrating the traveling wave-front for fault location in high noise environments, we propose an improved method that combines the ICEEMDAN and maximum correlation kurtosis deconvolution (MCKD). Our approach first decomposes the fault traveling wave signal into modal components using the ICEEMDAN algorithm. Next, the MCKD algorithm enhances the energy amplitude of the decomposed signal, suppresses strong background noise interference, and accurately calibrates the fault traveling wave-front. Simulation results demonstrate that ICEEMDAN-MCKD achieves precise calibration of the wave-front, exhibits strong denoising ability, and performs better than existing methods in terms of detecting traveling waves and minimizing wave-front determination time errors in noisy environments.

AoA-Based Low Complexity Beamforming for Aerial RIS Assisted Communications at mmWaves

Reconfigurable intelligent surface (RIS) mounted on mobile devices (aerial RIS), such as unmanned aerial vehicles, is envisioned to increase coverage for future millimeter wave (mmWave) communications. In literature, RIS beamformer is designed using channel state information (CSI) or by scanning through gNodeB and user codebooks over different training RIS weight matrices. Both of these methods incur significant training overhead. Also, the traditional subspace-based AoA estimators require several power-intensive radio frequency (RF) chains, which is not practically affordable to users. As a low-power and low-complexity alternative, we propose to design RIS beamforming weights based on angle-of-arrival (AoA) from gNodeB-to-RIS and from RIS-to-user with a single RF chain coupled to an antenna array. The proposed estimator is a combination of maximum likelihood and maximum correlation estimator and uses a single RIS training matrix. The simulation results show that, though a slightly larger number of antenna elements is required for the same spectral efficiency, the proposed approach offers a significantly energy- and computationally-efficient beamforming alternative compared to the existing subspace-based AoA estimator and CSI based techniques.

Strong Dark Matter Self-interactions Diversify Halo Populations within and surrounding the Milky Way

We perform a high-resolution cosmological zoom-in simulation of a Milky Way (MW)–like system, which includes a realistic Large Magellanic Cloud analog, using a large differential elastic dark matter self-interaction cross section that reaches ≈100 cm2 g−1 at relative velocities of ≈10 km s−1, motivated by the diverse and orbitally dependent central densities of dwarf galaxies within and surrounding the MW. We explore the effects of dark matter self-interactions on satellite, splashback, and isolated halos through their abundance, central densities, maximum circular velocities, orbital parameters, and correlations between these variables. We use an effective constant cross section model to analytically predict the stages of our simulated halos’ gravothermal evolution, demonstrating that deviations from the collisionless – relation can be used to select deeply core-collapsed halos, where is a halo’s maximum circular velocity, and is the radius at which it occurs. We predict that a sizable fraction (≈20%) of subhalos with masses down to ≈108 M ⊙ is deeply core collapsed in our SIDM model. Core-collapsed systems form ≈10% of the isolated halo population down to the same mass; these isolated, core-collapsed halos would host faint dwarf field galaxies with extremely steep central density profiles. Finally, most halos with masses above ≈109 M ⊙ are core-forming in our simulation. Our study thus demonstrates how self-interactions diversify halo populations in an environmentally dependent fashion within and surrounding MW-mass hosts, providing a compelling avenue to address the diverse dark matter distributions of observed dwarf galaxies.