Mean Square Research Articles

Nonunion scaphoid bone shape prediction using iterative kernel principal polynomial shape analysis.

The scaphoid is an important mechanical stabilizer for both the proximal and distal carpal columns. The precise estimation of the complete scaphoid bone based on partial bone geometric information is a crucial factor in the effective management of scaphoid nonunion. Statistical shape model (SSM) could be utilized to predict the complete scaphoid shape based on the defective scaphoid. However, traditional principal component analysis (PCA) based SSM is limited by its linearity and the inability to adjust the number of modes used for prediction. This study proposes an iterative kernel principal polynomial shape analysis (iKPPSA)-based SSM to predict the pre-morbid shape of the scaphoid, aiming at enhancing the accuracy as well as the robustness of the model. Sixty-five sets of scaphoid images were used to train SSM and nine sets of scaphoid images were used for validation. For each validation image set, three defect types (tubercle, proximal pole, and avascular necrosis) were virtually created. The predicted shapes of the scaphoid by PCA, PPSA, KPCA, and iKPPSA-based SSM were evaluated against the original shape in terms of mean error, Hausdorff distance error, and Dice coefficient. The proposed iKPPSA-based scaphoid SSM demonstrates significant robustness, with a generality of 0.264mm and a specificity of 0.260mm. It accounts for 99% of variability with the first seven principal modes of variation. Compared to the traditional PCA-based model, the iKPPSA-based scaphoid model prediction demonstrated superior performance for the proximal pole type fracture, with significant reductions of 25.2%, 24.7%, and 24.6% in mean error, Hausdorff distance, and root mean square error (RMSE), respectively, and a 0.35% improvement in Dice coefficient. This study showed that the iKPPSA-based SSM exploits the nonlinearity of data features and delivers high reconstruction accuracy. It can be effectively integrated into preoperative planning for scaphoid fracture management or morphology-based biomechanical modeling of the scaphoid.

An Adaptive Radon-Transform-Based Marker Detection and Localization Method for Displacement Measurements Using Unmanned Aerial Vehicles.

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method.

Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R2, root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted.

Leader-following mean square consensus of stochastic switched multi-agent systems with event-triggered control

ABSTRACT In this paper, a novel distributed event-triggered mechanism (ETM) is used to solve the problem of the leader-following mean square consensus of stochastic switched multi-agent systems (SSMASs) with switching dynamics of agents, disturbed by the multiplicative noise. Moreover, the distributed ETM utilises only the state information at the triggering instants of neighbouring agents, which eliminates continuous communication among agents while reducing the frequency of controller updates. A general agent-dependent switching law is developed to achieve leader-following mean square consensus of SSMASs. Subsequently, it is proved that under the distributed ETM, linear SSMASs can achieve leader-following mean square consensus and no Zeno behaviour occurs with the help of stochastic analysis and the average dwell time approach. Finally, the effectiveness of the study is illustrated by a numerical example.

Application of aggregation operators for forecasting [formula omitted] fluctuations: From available Caribbean data sites to unequipped ones

Air pollution is a substantial issue for public health. Predicting the levels of airborne particles, especially those originating from natural phenomena like sand mists from the African dust belt, enables more effective warnings for affected populations. Currently, there are only three islands that continuously measure PM10 concentrations in the Caribbean area. Thus, there is a main issue for the forecast of these particulate pollutants for unequipped sites. In this article, a new approach based on the use of aggregation operators in a non-fuzzy framework allows to predict the quantity of PM10 for certain sites in this geographical area. It is the characteristics of non-linearity and downward reinforcement of the fuzzy operators used (triple Pi and t-norms) which make it possible to model the evolution of a phenomenon as complex as the sand mists from African dust belt. The time series having been normalized, the tests were carried out on two of the three sites (Martinique and Guadeloupe) to predict the values of the third site (Puerto Rico) and assess the quality of the prediction. The mean absolute error (MAE), the mean bias error (MBE), the root mean square error (RMSE) and the index of agreement (IOA) values show that this approach provides significant results. The African dust seasonality strongly impacts these performances metrics. These initial promising findings emphasize that the utilization of aggregation operators is a robust method for forecasting PM10 fluctuations, eliminating the need for complex parameterization or learning phase that would otherwise entail significant computational costs.

Modified time domain randomization technique for multi-shaker non-stationary non-Gaussian random vibration control

Traditional random vibration environmental tests simulate stationary Gaussian random vibration environments. In this work, the generation and closed-loop control of multi-shaker non-stationary non-Gaussian random vibration signals with the modified time domain randomization technique are studied. The amplitude modulation function is introduced to control the non-stationary and non-Gaussian characteristics that the root mean square value of the generated signal changes with time. The kurtosis relationship between the one frame pseudo-random signal and consecutive true random signal is derived theoretically. It is found that the kurtosis of the consecutive non-stationary random signal is determined by the amplitude modulation function and kurtosis of the pseudo-random signal. The standard deviation of the time-varying root mean square reflects the non-stationary characteristics of the generated signal. A control strategy for multi-shaker non-stationary non-Gaussian random vibration environmental test is proposed. The non-stationary and non-Gaussian characteristics are extracted from the measured response signals so that the power spectral densities, kurtoses and non-stationary characteristics can be controlled simultaneously. The results from the simulation examples and multi-shaker vibration experiments prove the feasibility and practicability of the proposed non-stationary non-Gaussian random signal generation method and control strategy.

Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI.

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasetsto fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.

Unravelling the link between SARS-CoV-2 mutation frequencies, patient comorbidities, and structural dynamics.

Genomic surveillance is crucial for tracking emergence and spread of novel variants of pathogens, such as SARS-CoV-2, to inform public health interventions and to enforce control measures. However, in some settings especially in low- and middle- income counties, where sequencing platforms are limited, only certain patients get to be selected for sequencing surveillance. Here, we show that patients with multiple comorbidities potentially harbour SARS-CoV-2 with higher mutation rates and thus deserve more attention for genomic surveillance. The relationship between the patient comorbidities, and type of amino acid mutations was assessed. Correlation analysis showed that there was a significant tendency for mutations to occur within the ORF1a region for patients with higher number of comorbidities. Frequency analysis of the amino acid substitution within ORF1a showed that nsp3 P822L of the PLpro protease was one of the highest occurring mutations. Using molecular dynamics, we simulated that the P822L mutation in PLpro represents a system with lower Root Mean Square Deviation (RMSD) fluctuations, and consistent Radius of gyration (Rg), Solvent Accessible Surface Area (SASA) values-indicate a much stabler protein than the wildtype. The outcome of this study will help determine the relationship between the clinical status of a patient and the mutations of the infecting SARS-CoV-2 virus.

Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy.

Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEISS (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEILS (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.

A Graph-Based Technique for the Automated Control-Oriented Modeling of District Heating Networks

Abstract Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.

DEM-based topographic change detection considering the spatial distribution of errors

ABSTRACT Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

Natural Isatin Derivatives Against Black Fungus: In Silico Studies.

During this coronavirus pandemic, when a lot of people are already severely afflicted with SARS-CoV-19, the dispersion of black fungus is making it worse, especially in the Indian subcontinent. Considering this situation, the idea for an in silico study to identify the potential inhibitor against black fungal infection is envisioned and computational analysis has been conducted with isatin derivatives that exhibit considerable antifungal activity. Through this in silico study, several pharmacokinetics properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) are estimated for various derivatives. Lipinski rules have been used to observe the drug likeliness property, and to study the electronic properties of the molecules, quantum mechanism was analyzed using the density functional theory (DFT). After applying molecular docking of the isatin derivatives with sterol 14-alpha demethylase enzyme of black fungus, a far higher docking affinity score has been observed for the isatin sulfonamide-34 (derivative 1) than the standard fluconazole. Lastly, molecular dynamic (MD) simulation has been performed for 100ns to examine the stability of the proposed drug complex by estimating Root Mean Square Deviation (RMSD), Radius of gyration (Rg), Solvent accessible surface area (SASA), Root Mean Square Fluctuation (RMSF), as well as hydrogen bond. Listed ligands have precisely satisfied every pharmacokinetics requirement for a qualified drug candidate and they are non-toxic, non-carcinogenic, and have high stability. This natural molecule known as isatin derivative 1 has shown the potential of being a drug for fungal treatment. However, the impact of the chemicals on living cells requires more investigation and research.

Asymptotic properties for the parameter estimation in stochastic (functional) differential equations with Hölder drift

In this paper, by taking Zvonkin's transformation, we investigate parameter estimation for a class of multidimensional stochastic differential equations with small perturbation parameters in diffusion coefficients, where the drift coefficients not only have an unknown parameter θ but also are Hölder continuous. These processes may enhance the applicability of our results to considerable practical models. Due to the irregular drift, the primary challenge is dealing with the mean square error between an accurate and numerical solution. Under these settings, we demonstrate the consistency and asymptotic normality of error concerning the least squares estimator in probability when stepsize δ → 0 and small parameter ε → 0 simultaneously. Moreover, we extend the results to the case of stochastic functional differential equations.

Predicting hydrogen segregation energy distributions in strained regions of tungsten using artificial neural network

Employing the smooth overlap of atomic position (SOAP) descriptors, we established an artificial neural network (ANN) model with the ability to effectively and accurately predict the segregation energy Eseg distributions of hydrogen (H) atoms in various strained regions of tungsten (W). The model is verified to have comparable accuracy with the molecular statics (MS) relaxations, as quantitively evidenced by the root mean square error (RMSE) values of 0.018 eV for the homogeneously strained regions, averagely 0.025 eV for regions near different dislocations, and 0.032 eV for regions surrounding an interstitial H cluster. Besides, the predictions yield no systematic bias at both high and low Eseg extremes, which is a common issue in the elastic theory (ET) calculations. Moreover, Eseg values are precisely predicted with the RMSE value of 0.041 eV surrounding an interstitial H cluster that is not encountered during the learning phase of the model, demonstrating the model’s robust generalization capability. Considering its markedly enhanced computation speed, this accurate ANN model holds promising prospects in circumstances that require massive Eseg calculations.

Assessment of Unmanned Aerial System Flight Plans for Data Acquisition from Erosional Terrain

Accurate data mapping and visualization are of crucial importance for the detection and monitoring of slope morphodynamics, including erosion processes and studying small erosional landforms (rills and gullies). The purpose of the current research is to examine how the flight geometry of unmanned aerial systems (UASs) could affect the accuracy of photogrammetric processing products, concerning small erosion landforms that are a result of slope wash and temporary small streams formed by rain. In October 2021, three UAS flights with a different geometry were carried out in a hilly to a low-mountain area with an average altitude of about 650 m where erosion processes are observed. UAS imagery processing was carried out using structure-from-motion (SfM) photogrammetry. High-resolution products such as photogrammetric-based point clouds, digital surface models (DSMs) and orthophotos were generated. The obtained data were compared and evaluated by the root mean square error (RMSE), length measurement, cloud-to-cloud comparison, and 3D spatial GIS analysis of DSMs. The results show small differences between the considered photogrammetric products generated by nadir-viewing and oblique-viewing (45°—single strip and 60°—cross strips) geometry. The complex analysis of the obtained photogrammetric products gives an advantage to the 60°—cross strips imagery, in studying erosional terrains with slow slope morphodynamics.

Correlation between weight variability and the risk of diabetic nephropathy in patients with type 2 diabetes mellitus

Objective: To evaluate the relationship between different indexes of weight variability and the risk of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods: A retrospective cohort study. The clinical data of 2 180 T2DM patients without DKD who underwent case management at Lee's United Clinic in Taiwan, China from 2002 to 2018 were retrospectively analyzed, including 1 103 females and 1 077 males, with an average age of (64.8±12.4) years. Regular follow-up was conducted for patients for at least 2 years, and their metabolic indexes were monitored annually. BMI variability independent of the mean (BMI-VIM), average yearly mean square successive difference (BMI-ASV), coefficient of variation (BMI-CV) and standard deviation (BMI-SD) were calculated,based on the body mass index (BMI) recorded annually by the patients. Patients were divided into four groups (Q1-Q4) based on the quartiles of the four weight variability indexes. DKD group and non-DKN group(NDKD group) were defined based on the occurrence of DKD at the end of the follow-up. Cox proportional hazards regression models were used to analyze the relationship between the four weight variability indicators and the incidence of DKD. Subgroup analysis was performed by categorizing patients into non-obesity (BMI<28 kg/m2) and obesity groups (BMI≥28 kg/m2) to investigate the impact of the four weight variability indicators on the risk of DKD. Results: After a follow-up of (4.55±2.13) years, 904 patients developed DKD. Compared with the NDKD group, patients in the DKD group had a higher proportion of females, older age, longer duration of diabetes, more insulin users, higher waist-to-hip ratio, higher levels of BMI-VIM, BMI-ASV, BMI-CV, BMI-SD, systolic blood pressure, diastolic blood pressure, and urine albumin-creatinine ratio, a lower proportion of hypoglycemic drugs, estimated glomerular filtration rate, and high-density lipoprotein cholesterol level, with statistically significant differences between the two groups(all P<0.05). Cox proportional hazards regression analysis results revealed that the risk of DKD in T2DM patients increased with the increase in BMI-SD, BMI-CV, BMI-VIM, and BMI-ASV after correcting a series of influencing factors. In the BMI-VIM subgroup, compared with the Q1 group, the risk of DKD in the Q4 group increased by 22.4% [HR=1.224 (95%CI:1.008-1.487), P=0.041]. In the BMI-ASV group, compared with the Q1 group, the risk of DKD in the Q4 group increased by 51.1% [HR=1.511 (95%CI:1.240-1.841), P<0.01]. In the BMI-CV group, compared with the Q1 group, the risk of DKD in the Q4 group increased by 22.2% [HR=1.222 (95%CI:1.006-1.485), P=0.044]. In the BMI-SD subgroup, compared with the Q1 group, the risk of DKD in the Q4 group increased by 22.2% [HR=1.222 (95%CI:1.002-1.490), P=0.048]. Sub-group analysis showed that when the non-obesity group was grouped by BMI-ASV, after correcting a series of influencing factors, compared with the Q1 group, the highest risk of DKD occurred in the Q4 group [HR=1.551 (95%CI:1.228-1.958), P<0.001];when the obesity group was grouped by BMI-ASV, after correcting a series of influencing factors, compared with the Q1 group, the highest risk of DKD occurred in the Q4 group [HR=1.703 (95%CI:1.168-2.485), P=0.006]. Conclusion: Increases in BMI-VIM, BMI-ASV, BMI-CV, and BMI-SD are associated with an increased risk of DKD in T2DM patients.

Automatic diagnosis of depression based on attention mechanism and feature pyramid model.

Currently, most diagnoses of depression are evaluated by medical professionals, with the results of these evaluations influenced by the subjective judgment of physicians. Physiological studies have shown that depressed patients display facial movements, head posture, and gaze direction disorders. To accurately diagnose the degree of depression of patients, this paper proposes a comprehensive framework, Cross-Channel Attentional Depression Detection Network, which can automatically diagnose the degree of depression of patients by inputting information from the facial images of depressed patients. Specifically, the comprehensive framework is composed of three main modules: (1) Face key point detection and cropping for video images based on Multi-Task Convolutional Neural Network. (2) The improved Feature Pyramid Networks model can fuse shallow features and deep features in video images and reduce the loss of miniscule features. (3) A proposed Cross-Channel Attention Convolutional Neural Network can enhance the interaction between tensor channel layers. Compared to other methods for automatic depression identification, a superior method was obtained by conducting extensive experiments on the depression dataset AVEC 2014, where the Root Mean Square Error and the Mean Absolute Error were 8.65 and 6.66, respectively.

Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology.

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

The relationship between fear of old age, loneliness and death anxiety in adults.

The increase in the number of elderly people in the world, individuals' perspectives on older adults, and false beliefs and ideas about old age negatively affect adults in terms of ageing. This study was conducted to determine the relationship between fear of old age, loneliness and death anxiety in adults. This study, which was designed in a correlational cross-sectional descriptive model, was conducted with 1074 adult individuals living in one province in eastern Turkey. Data were collected using Personal Information Form, Fear of Old Age Scale, UCLA Loneliness Scale and Turkish Death Anxiety Scale. Data were analyzed using SPSS 25.0, AMOS 24.0, G*Power 3.1 statistical package programs. In our study, it was determined that the model created in line with the hypotheses was compatible and the model fit indices were within the desired limits as χ2 /df = 4.737, root mean square error of approximation = 0.05, comparative fit index = 0.93, goodness-of-fit index = 0.92, adjusted goodness-of-fit index = 0.90, IFI = 0.93. There is a significant relationship between loneliness and death anxiety (P < 0.05). There is a significant relationship between loneliness and fear of old age (P < 0.05). There is a significant relationship between death anxiety and fear of old age (P < 0.05). It was determined that death anxiety has a mediating role in the effect of loneliness on fear of old age (95% confidence interval: 0.112-0.226; P < 0.05). As the level of loneliness increases, the level of death anxiety and fear of old age increases. Fear of old age also increases in the mediating role of death anxiety. It is recommended to conduct intervention studies to reduce fear of old age. Longitudinal study on fear of old age is recommended.

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10