Mean Coefficient Research Articles

Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis.

Background: We used continuous glucose monitoring (CGM) data to investigate glycemic outcomes in a real-world population with type 1 diabetes (T1D) from South Korea, where the widespread use of CGM and the nationwide education program began almost simultaneously. Methods: Data from Dexcom G6 users with T1D in South Korea were collected between January 2019 and January 2023. Users were included if they provided at least 90 days of glucose data and used CGM at least 70% of the days in the investigational period. The relationship between CGM utilization and glycemic metrics, including the percentage of time in range (TIR), time below range (TBR), and time above range (TAR), was assessed. The study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2023-05-030). Results: A total of 2288 users were included. Mean age was 41.5 years (57% female), with average uploads of 428 days. Mean TIR was 62.4% ± 18.5%, mean TBR <70 mg/dL was 2.6% ± 2.8%, mean TAR >180 mg/dL was 35.0% ± 19.3%, mean glucose was 168.1 ± 35.8 mg/dL, mean glucose management indicator was 7.2% ± 0.9%, and mean coefficient of variation was 36.7% ± 6.0%. Users with higher CGM utilization had higher TIR (67.8% vs. 52.7%), and lower TBR <70 mg/dL (2.3% vs. 4.7%) and TAR >180 mg/dL (30.0% vs. 42.6%) than those with low CGM utilization (P < 0.001 for all). Users whose data were shared with others had higher TIR than those who did not (63.3% vs. 60.8%, P = 0.001). Conclusions: In this South Korean population, higher CGM utilization was associated with a favorably higher mean TIR, which was close to the internationally recommended target. Using its remote data-sharing feature showed beneficial impact on TIR.

Relationship between the number of public health nurses and the total number of people receiving home-visit nursing services for mental health/intractable diseases, and those numbers' regional differences

Objectives The primary aim of this study was to clarify the relationship between the number of public health nurses (PHNs) and the total number of people who received home-visit nursing services for mental health or intractable diseases. The secondary aim was to clarify the extent of regional differences in the number of PNHs and mental health or intractable diseases.Methods This study used the total number of people who received home-visit nursing services for mental health or intractable diseases in 2019 from the Portal Site of Official Statistics of Japan (e-Stat) and population and area data in January 2020. Single and multiple regression analyses (covariates: population and area) were performed on the relationship between the number of PHNs per 100,000 population (abbreviated as "ratios of PHNs") and the total number of people who received home-visit nursing services for mental health or intractable diseases per 100,000 population (abbreviated as "mental health/intractable disease achievements"). Regional differences in ratios of PHNs and mental health/intractable disease achievement were examined using mean, standard deviation, maximum/minimum values, and Gini coefficients. Analyses were performed for each of the five units: the prefectures as a whole, prefectural public health centers, municipalities within the jurisdiction of prefectural public health centers, and cities in which public health centers are established (including or not including special wards).Results Regression analyses indicated a positive relationship between the ratios of PHNs and mental health/intractable disease achievements. Multiple regression analysis indicated that both achievements were positively associated with population size and negatively associated with area size. The largest regression coefficients between the ratios of PHNs and achievements were 34.07 and 5.48 regarding mental health achievements and intractable disease achievements, respectively. For regional differences, the smallest Gini coefficient was the ratios of PHNs, and the largest was intractable disease achievements. The smallest and largest coefficient of the prefectures as a whole was 0.15 and 0.34, respectively. The maximum/minimum values of the prefectures as a whole also indicated that the smallest was 3.8 in the ratio of PHNs and the largest was 30.0 in intractable disease achievement.Conclusions Increasing number of PHNs is needed to provide more home-visit nursing services for mental health and intractable diseases. It is particularly important to fill up the larger number of PHNs in smaller populations or larger area prefectures. Due to regional differences in the home-visit nursing service, it is important to promote the increase in the level of these activities.

Unraveling the Intraday Variations in the Tear Fluid Proteome.

Tear fluid is a complex and dynamic biological fluid that plays essential roles in maintaining ocular homeostasis and protecting against the external environment. Owing to the small sample volume, studying the tear proteome is challenging. However, advances in high-resolution mass spectrometry have expanded tear proteome profiling, revealing >500 unique proteins. Tears are emerging as a noninvasive source of biomarkers for both ocular and systemic diseases; nevertheless, intraday variability of proteins in tear fluid remains questionable. This study investigates intraday variations in the tear fluid proteome to identify stable proteins that could act as candidate biomarkers. Tear samples from 15 individuals at four time points (10 am, 12 pm, 2 pm, and 4 pm) were analyzed using mass spectrometry to evaluate protein variation during these intervals. Technical variation was assessed by analyzing pooled samples and was subtracted from the total variation to isolate biological variability. Owing to high technical variation, low-abundant proteins were filtered, and only 115 proteins met the criteria for further analysis. These criteria include being detected at all four time points in at least eight subjects, having a mean peptide-spectrum match count greater than 5, and having a technical variation less than 0.10. Lactotransferrin, lipocalin-1, and several immunoglobulins were among the 51 stable proteins (mean biological coefficient of variation < 0.10). Additionally, 43 proteins displayed significant slopes across the 4 time points, with 17 increasing and 26 decreasing over time. These findings contribute to the understanding of tear fluid dynamics and further expand our knowledge of the tear proteome.

Cognitive Function and Variability in Antipsychotic Drug–Naive Patients With First-Episode Psychosis

Cognitive impairment contributes significantly to clinical outcome and level of function in individuals with psychotic disorders. These impairments are present already at psychosis onset at a group level; however, the question of heterogeneity in cognitive function among patients has not been systematically investigated. To provide an updated quantification of cognitive impairment at psychosis onset before patients receive potentially confounding antipsychotic treatment, and to investigate variability in cognitive function compared with healthy controls. In this systematic review and meta-analysis, PubMed articles were searched up to September 15, 2022. Original studies reporting data on cognitive function in antipsychotic drug-naive patients with first-episode psychosis (FEP) were included. Data were independently extracted by 2 researchers. Cognitive tasks were clustered according to 6 domains of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery and the domain of executive function. Random-effects model meta-analyses of mean differences and coefficient of variation ratios (CVRs) were performed, as well as meta-regressions, assessment of study quality, and publication bias. The main outcome measure was Hedges g for mean differences in cognition and CVR for within-group variability. Fifty studies were included in the analysis with a total of 2625 individuals with FEP (mean [SD] age, 25.2 [3.6] years, 60% male; 40% female) and 2917 healthy controls (mean [SD] age, 26.0 [4.6]; 55% male; 45% female). In all cognitive domains, the FEP group displayed significant impairment compared with controls (speed of processing: Hedges g = -1.16; 95% CI, -1.35 to -0.98; verbal learning: Hedges g = -1.08; 95% CI, -1.28 to -0.88; visual learning: Hedges g = -1.05; 95% CI, -1.27 to -0.82; working memory: Hedges g = -1.04; 95% CI, -1.35 to -0.73; attention: Hedges g = -1.03; 95% CI, -1.24 to -0.82; reasoning/problem solving: Hedges g = -0.90; 95% CI, -1.12 to -0.68; executive function: Hedges g = -0.88; 95% CI, -1.07 to -0.69). Individuals with FEP also exhibited a larger variability across all domains (CVR range, 1.34-1.92). Results of this systematic review and meta-analysis identified cognitive impairment in FEP before the initiation of antipsychotic treatment, with large effect sizes. The high variability within the FEP group suggests the need to identify those individuals with more severe cognitive problems who risk worse outcomes and could benefit the most from cognitive remediation.

Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches

Abstract QSPR is a powerful tool for elucidating the correlation between chemical structure and property for both natural and synthesized compounds. In the present work, the half-wave reduction potential for a set of aziridinylquinones (Anticancer Agents [AA]) is modelled using a quantitative structure-electrochemistry relationship (QSER) based on multilinear regression (MLR) and artificial neural network (ANN). Molecular descriptors introduced in this work were computed using the Dragon software (V5). Before the model’s generation, using the Kennard and Stone algorithm, the data set of 84 aziridinylquinones was divided into training and prediction sets consisting of 70 % and 30 % of data points. Quantitative Structure Electrochemistry Relationship (QSER) models were developed using the Genetic Algorithm Multiple Linear Regressions (GA-MLR) and an Artificial Neural Network (ANN). The coefficient of determination (R 2) and Root Mean Squared Error of prediction (RMSE) were mentioned to demonstrate the QSER model’s prediction abilities. Calculated R 2 and RMSEval values for the MLR model were 0.858 and 0.054, respectively. The R 2 and RMSEval values for the ANN training set were calculated to be 0.914 and 0.050, respectively. Findings show that GA is a powerful tool for selecting variables in QSER analysis. Comparing the two employed regression methods showed that ANN is superior to MLR in predictive ability.

Health policy competencies in regional organizations: a retrospective analysis for 76 regional organizations from 1945 to 2015

BackgroundHealth policy competencies of regional organizations include mandates to create regional health laws and policies, as well as authorities that allow member states to undertake collective actions in the health field. The examination of the health policy competencies of regional organizations is essential, as it constitutes an important prerequisite for regional organizations to govern regional health. This study aims to map the development trajectory of health policy competencies in regional organizations worldwide and investigate their potential correlates. This will contribute to the enhanced promotion of both existing and new regional health cooperation.MethodsThis retrospective analysis utilized the health policy competencies of the 76 regional organizations worldwide from 1945 to 2015, as investigated in the Regional Organizations Competencies Database. By aggregating member state data from various sources such as the IHME Global Burden of Disease 2019, the World Bank, and the World Trade Organization, we extracted the mean values and coefficients of variation for the covariates in regional organization characteristics, socioeconomic and demographic factors, health status and health-system capacity. The correlation between changes in the health policy scope of regional organizations and independent variables was analyzed using Poisson pseudo-likelihood regression with multiple levels of fixed effects.ResultsFrom 1945 to 2015, the number of regional organizations with health policy competencies experienced a slow growth stage before 1991 and an explosive growth stage post-1991. By 2015, 48 out of the 71 existing regional organizations had developed their health policy competencies, yet 26 (54.2%) of these organizations possessed only 1–2 health policy competencies. An enhancement in the health policy scope of a regional organization correlated with its founding year, a greater number of policy fields, higher under-five mortality, and larger disparities in trade and healthcare access and quality indexes among member states. In contrast, larger disparities in population, under-five mortality and health worker density among member states, along with more hospital beds per capita, were negatively correlated with the expansion of a regional organization’s health policy scope.ConclusionSince 1991, there has been a surge of interest in health among regional organizations, although health remains a secondary priority for them. The health policy competencies of regional organizations are pivotal for promoting social equity within regional communities. Its establishment is also closely linked to the level and disparities among member states in aspects such as trade, population, child mortality rates, and health system capacity.

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.

Regional choroidal thickness estimation from color fundus images based on convolutional neural networks

PurposeThis study aims to estimate the regional choroidal thickness from color fundus images from convolutional neural networks in different network structures and task learning models. Method1276 color fundus photos and their corresponding choroidal thickness values from healthy subjects were obtained from the Topcon DRI Triton optical coherence tomography machine. Initially, ten commonly used convolutional neural networks were deployed to identify the most accurate model, which was subsequently selected for further training. This selected model was then employed in combination with single-, multiple-, and auxiliary-task training models to predict the average and sub-region choroidal thickness in both ETDRS (Early Treatment Diabetic Retinopathy Study) grids and 100-grid subregions. The values of mean absolute error and coefficient of determination (R2) were involved to evaluate the models' performance. ResultsEfficientnet-b0 network outperformed other networks with the lowest mean absolute error value (25.61 μm) and highest R2 (0.7817) in average choroidal thickness. Incorporating diopter spherical, anterior chamber depth, and lens thickness as auxiliary tasks improved predicted accuracy (p-value = 6.39×10−44, 2.72×10−38, 1.15×10−36 respectively). For ETDRS regional choroidal thickness estimation, multi-task model achieved better results than single task model (lowest mean absolute error = 31.10 μm vs. 33.20 μm). The multi-task training also can simultaneously predict the choroidal thickness of 100 grids with a minimum mean absolute error of 33.86 μm. ConclusionsEfficientnet-b0, in combination with multi-task and auxiliary task models, achieve high accuracy in estimating average and regional macular choroidal thickness directly from color fundus photographs.

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

Agreement between a markerless and a marker-based motion capture systems for balance related quantities

Balance studies usually focus on quantities describing the global body motion. Assessing such quantities using classical marker-based approach can be tedious and modify the participant’s behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. This work compared dynamic balance related quantities obtained with markers and videos. Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The centre of mass position (CoM) was computed, and the associated extrapolated centre of mass position (XCoM) and whole-body angular momentum (WBAM) were derived. Bland-Altman analysis was performed and root mean square difference (RMSD) and coefficient of correlation were computed to compare the results obtained with marker-based and markerless methods. Bias remained of the magnitude of a few mm for CoM and XCoM positions, and RMSD of CoM and XCoM was around 1 cm. RMSD of the WBAM was less than 10 % of the total amplitude in any direction, and bias was less than 1 %. Results suggest that outcomes of balance studies will be similar whether marker-based or markerless motion capture system are used. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest differences (both bias and RMSD), although it is unclear whether these differences are due to errors in markerless or marker-based motion capture system.

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Brain disorders pose a substantial global health challenge, persisting as a leading cause of mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain disorders, but it can be challenging for medical practitioners to interpret complex EEG signals and make accurate diagnoses. To address this, our study focuses on visualizing complex EEG signals in a format easily understandable by medical professionals and deep learning algorithms. We propose a novel time–frequency (TF) transform called the Forward–Backward Fourier transform (FBFT) and utilize convolutional neural networks (CNNs) to extract meaningful features from TF images and classify brain disorders. We introduce the concept of eye-naked classification, which integrates domain-specific knowledge and clinical expertise into the classification process. Our study demonstrates the effectiveness of the FBFT method, achieving impressive accuracies across multiple brain disorders using CNN-based classification. Specifically, we achieve accuracies of 99.82% for epilepsy, 95.91% for Alzheimer’s disease (AD), 85.1% for murmur, and 100% for mental stress using CNN-based classification. Furthermore, in the context of naked-eye classification, we achieve accuracies of 78.6%, 71.9%, 82.7%, and 91.0% for epilepsy, AD, murmur, and mental stress, respectively. Additionally, we incorporate a mean correlation coefficient (mCC) based channel selection method to enhance the accuracy of our classification further. By combining these innovative approaches, our study enhances the visualization of EEG signals, providing medical professionals with a deeper understanding of TF medical images. This research has the potential to bridge the gap between image classification and visual medical interpretation, leading to better disease detection and improved patient care in the field of neuroscience.

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation.

Amidst escalating environmental concerns, short natural-fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load, and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% with regard to the impact load, absorbed energy, and tensile strength, respectively.

Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms

With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.

Predicting the distribution coefficient of cesium in solid phase groups using machine learning

The migration and retention of radioactive contaminants such as 137Cesium (137Cs) in various environmental media pose significant long-term storage challenges for nuclear waste. The distribution coefficient (Kd) is a critical parameter for assessing the mobility of radioactive contaminants and is influenced by various environmental conditions. This study presents machine-learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict the Kd values for Cs in solid phase groups. We used three different machine learning models: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The models were trained on 14 input variables from the JAEA-SDB, including factors such as the Cs concentration, solid-phase properties, and solution conditions, which were preprocessed by normalization and log-transformation. The performances of the models were evaluated using the coefficient of determination (R2) and root mean squared error (RMSE). The RF, ANN, and CNN models achieved R2 values greater than 0.97, 0.86, and 0.88, respectively. We also analyzed the variable importance of RF using an out-of-bag (OOB) and a CNN with an attention module. Our results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. Our models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and long-term risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

Relation between innovative work behavior and ethical climate perceptions among nursing personnel

BackgroundGlobalization and innovative technologies forced organizations to adopt innovative approaches and innovations for gaining a sustainable competitive advantage. Innovative Work Behavior (IWB) is related to the employees, ability, and enthusiasm to create innovative ideas. It exhibits a dynamic framework that is easier to be impacted by the ethical climate.MethodsDescriptive correlational design was applied and the study was performed at different inpatient units in Kafrelsheikh Governorate General Hospital. Two hundred twenty-two staff nurses and 45 head nurses from the aforementioned setting were chosen as a purposive sample. Two instruments were utilized to obtain the data; Innovative Work Behavior questionnaire and Ethical Climate Questionnaire. The significance of the acquired data was evaluated at the 5% level. Number and percentage were used to describe qualitative data and Range frequency, mean, standard deviation, and Pearson coefficient were used to characterize quantitative data.ResultsMore than half of staff nurses had a positive perception of innovative work behavior and more than three quarters of them had a negative Ethical climate perception.ConclusionThe study proved a significant relation between Efficiency dimension and the overall innovative work behavior perception p = 0.044, and the economic affairs and innovation dimension and the overall ethical climate perception p = 0.033.

A novel WaveNet-GRU deep learning model for PEM fuel cells degradation prediction based on transfer learning

Precise prediction of Remaining Useful Life (RUL) within the transportation industry is essential for cost reduction and enhanced energy efficiency, focusing on extending the operational lifespan of proton exchange membrane fuel cells (PEMFCs). In pursuit of this objective, this study employs data-driven prediction methodologies centered on deep neural networks and transfer learning. The fundamental premise is that these approaches hinge on the compatibility of functional conditions across diverse datasets. Multiple strategies, amalgamating transfer learning, and deep neural networks, are introduced to forecast the PEMFC stack's behavior and its associated RUL. Network hyperparameters are optimized through Bayesian optimization, targeting root-mean-square error (RMSE) minimization in voltage predictions. The efficacy of these prediction techniques is evaluated through essential performance metrics, including the mean absolute percentage error (MAPE), RMSE, and coefficient of determination (R2), applied to both voltage predictions and RUL estimations. For the first time, a WaveNet-GRU model has been developed. Comparative assessment of models trained on 50% of the dataset underscores its supremacy. This model attains R2, RMSE, and MAPE scores of 99.1, 2.16E-4, and 0.166E-1, respectively, in predicting stack voltage. Also, RUL has increased by 21% compared to the best contemporary research. The WaveNet-GRU model demonstrates exceptional transfer learning capabilities when applied to stacks influenced by current ripples. In this context, it achieves optimal R2, RMSE, and MAPE values of 99.69, 1.37E-4, and 0.31E-1, respectively.

Analysis and future projections of the electricity demands of the Jordanian household sector using artificial neural networks

Purpose The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector. Design/methodology/approach Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R2). Findings The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade. Originality/value The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

Teaching Styles and Pupils’ Learning Styles: Their Relationship with Pupils’ Academic Performance

The concept of teaching and learning styles suggests that every individual is unique. Every teacher teaches in his way, and every learner learns according to his preference. This study determines the relationship between the teachers’ teaching styles and the learning styles of the pupils and their academic performance. A total of (27) Grade 6 teachers and (208) Grade 6 pupils of public elementary schools of Lopez Jaena District, Division of Misamis Occidental participated in the study. Frequency counts, percentages, mean, and spearman rho correlation coefficient were the statistical tools used. Findings revealed that teachers teaching styles in the classroom, with expert being the most preferred style, followed by formal authority, facilitator, personal model and delegator respectively. The pupils used a variety of learning styles, with auditory being the most common, followed by visual and kinesthetics. Pupils demonstrated very satisfactory academic performance. Further, a significant relationship between teachers’ teaching styles and pupils’ learning styles, as well as a significant relationship between teachers’ teaching styles, pupils’ learning styles, and pupils’ academic performance shown. These suggest that teachers should consider adapting their teaching styles to match the learning styles of their pupils, which could potentially enhance their academic performance.