Average Correlation Coefficient Value Research Articles

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles.

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

The development of ecosystem misconception diagnostic test

<span lang="EN-US">Misconception is a problem that is often found in learning biology concepts. Ecosystem concept is one of the biology concepts in which misconceptions are found a lot. As a learning problem, misconceptions need to be addressed as soon as possible. The purpose of this research is to produce a valid and reliable diagnostic test instrument for misconceptions in the form of a three-tier multiple choice test which is presented through Google Form platform. This development research applied the research and development method with a 4D development model. The developed diagnostic test instrument was validated by two expert validators and obtained a mean score of 3.27 (very feasible). Through the results of item validation, from 40 questions there are 30 valid items with an average correlation coefficient value of 0.67 and 10 invalid items with an average correlation coefficient value of 0.10. The diagnostic test instrument was also tested for readability to students and got an average value of 3.41 (very feasible) and was tested for readability to teachers and got an average value of 3.74 (very feasible). This shows that the developed product is very valid and reliable to be used as a diagnostic test instrument for misconceptions.</span>

Spatio-temporal changes in fractional vegetation cover and the driving forces during 2001-2020 in the northern slopes of the Tianshan Mountains, China.

This study aims to understand the factors and mechanisms influencing the spatio-temporal changes of fractional vegetation cover (FVC) in the northern slopes of the Tianshan Mountains. The MOD13Q1 product data between June and September (peak of plants growing) during the 2001-2020 period was incorporated into the pixel dichotomy model to calculate the vegetation cover changes. Then, the principal component analysis method was used to identify the primary driving factors affecting the change in vegetation cover from the natural, human, and economic perspectives. Finally, the partial correlation coefficients of FVC with temperature and precipitation were further calculated based on the pixel scale. The findings indicate that (1) FVC in the northern slopes of theTianshan Mountains ranged from 0.37 to 0.47 during the 2001-2020 period, with an obvious inter-annual variation and an overall upward trend of about 0.4484/10 a. Although the vegetation cover had some changes over time, it was generally stable, and the area of strong variation only accounted for 0.58% of the total. (2) The five grades of vegetation cover were distributed spatially similarly, but the area-weighted gravity center for each vegetation class shifted significantly. The FVC under different land use/land cover types and elevations was obviously different, and as elevation increased, vegetation coverage presented a trend of a "∩"-shape change. (3) According to the results of principal component analysis, human activities, economic growth, and natural climate were the main driving factors that caused the changes in vegetation cover, and the cumulative contribution of the three reached 89.278%. In addition, when it came to climatic factors, precipitation had a greater driving force on the vegetation cover change, followed by temperature and sunshine hours. (4) Overall, precipitation and temperature were correlated positively with FVC, with the average correlation coefficient values of 0.089 and 0.135, respectively. Locally, the correlations vary greatly under different LULC and altitudes. This research can provide some scientific basis and reference for the vegetation evolution pattern and ecological civilization construction in the region.

Mathematical Modelling of Dried Carica papaya Leaves

Background: Papaya leaves botanically classified as Carica papaya and are used medicinally in teas to aid in digestion. The Carica Papaya leaves are often considered as wastes, but they are actually beneficial for health. The study was aimed to achieve the suitable drying parameters of operation that makes the drying process to complete in less time without effecting the keeping quality of the product. Methods: The study was conducted using 3 drying methods which consisted of tray drying at temperatures 50°C, 60°C and 70°C, microwave drying at power levels 20% (180 Watts), 30% (270 Watts) and 40% (360 Watts), microwave vacuum drying at pressures of vacuum 400 mmHg, 500 mmHg and 600 mmHg and power levels 20% (180 Watts), 30% (270 Watts) and 40% (360 Watts). Twelve different mathematical drying models were compared based on their correlation coefficient, root mean square error and reduced chi-square to estimate the drying curves. Result: Experimental results showed that page model and weibulls distribution gave the best results, evidenced by the highest average correlation coefficient value of 0.9796, lowest average RMSE (Root mean square error) value of 0.0286 lowest average SSE value of 0.014 and the reduced average c² value of 0.0019. It is concluded that page model and weibulls distribution could sufficiently describe the tray drying, microwave and microwave vacuum drying respectively of carica papaya leaves.

Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions.

Pixel-to-pixel Translation of Solar Extreme-ultraviolet Images for DEMs by Fully Connected Networks

In this study, we suggest a pixel-to-pixel image translation method among similar types of filtergrams such as solar extreme-ultraviolet (EUV) images. For this, we consider a deep-learning model based on a fully connected network in which all pixels of solar EUV images are independent of one another. We use six-EUV-channel data from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), of which three channels (17.1, 19.3, and 21.1 nm) are used as the input data and the remaining three channels (9.4, 13.1, and 33.5 nm) as the target data. We apply our model to representative solar structures (coronal loops inside of the solar disk and above the limb, coronal bright point, and coronal hole) in SDO/AIA data and then determine differential emission measures (DEMs). Our results from this study are as follows. First, our model generates three EUV channels (9.4, 13.1, and 33.5 nm) with average correlation coefficient values of 0.78, 0.89, and 0.85, respectively. Second, our model generates the solar EUV data with no boundary effects and clearer identification of small structures when compared to a convolutional neural network–based deep-learning model. Third, the estimated DEMs from AI-generated data by our model are consistent with those using only SDO/AIA channel data. Fourth, for a region in the coronal hole, the estimated DEMs from AI-generated data by our model are more consistent with those from the 50 frames stacked SDO/AIA data than those from the single-frame SDO/AIA data.

Establishment of discrete element flexible model of the tiller taro plant and clamping and pulling experiment.

The taro harvesting process is affected by a complex system composed of particle mechanics system and multi-body dynamics system. The discrete element method(DEM) can effectively solve the nonlinear problem of the interaction between harvesting components and working materials. Therefore, the discrete element model of taro tiller plants is of great importance for taro harvesting. This paper proposes a simulation method to establish a discrete element flexible plant model and dynamic clamping and pulling process of taro tiller plant. Discrete Element models of taro corm and flexible tiller petiole and leaf were established using DEM method, and the discrete element flexible model of the taro plant was established. Taro clamping and pulling force testing platform was designed and built. The single factor and Plackett-Burman experiments were used to determine the simulation parameters and optimize the taro plant model by taking the correlation coefficient of clamping force and correlation coefficient of pulling force collected from the simulation and the bench experiment as the experiment index. The parameter calibration results of discrete element model of taro plant are as follows: petiole-petiole method/tangential contact stiffness was 8.15×109 N·m-3, and normal/tangential critical stress was 6.65×106 Pa. The contact stiffness of pseudostem- corm method was 1.22×109 N·m-3, the critical stress of normal/tangential was 1.18×105 Pa, and the energy of soil surface was 4.15×106J·m-3. When the pulling speed is 0.1, 0.2, 0.3, 0.4 and 0.5 m·s-1, the correlation coefficients between the simulation experiment and the bench experiment are 0.812, 0.850, 0.770, 0.697 and 0.652, respectively. The average value of correlation coefficient is 0.756, indicating that the simulated discrete element plant model is close to the real plant model. The discrete element model of taro plant established in this paper has high reliability. The final purpose of this paper is to provide a model reference for the design and optimization of taro harvester by discrete element method.

A CNN-Attention Network for Continuous Estimation of Finger Kinematics from Surface Electromyography

Dexterous control of robotic hand driven by human motor intent has drawn a lot of attention in both industrial and rehabilitation scenarios. Providing simultaneous and proportional control has become a prevailing solution recently. Towards improving the finger kinematics estimation precision and reducing its computational cost, a convolution model with attention mechanism (CNN-Attention) was proposed in this study. For comparison purpose, two previously used deep learning models, the long short-term memory (LSTM) and the Sparse Pseudo-input Gaussian processes (SPGP) were also included. By using surface electromyography (sEMG) and kinematic signals corresponding to six hand grasp movements, the estimation performance of each of the three models was evaluated with three measures, Pearson Correlation Coefficient (CC), Root Mean Square Error (RMSE), and coefficient of determination (R2) between real and estimated joint angles. The results demonstrated that the proposed CNN-Attention model outperformed LSTM and SPGP significantly ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -value<0.05), with an average value of CC, RMSE, and R2, 0.87, 9.65 degrees, and 0.73, respectively. Also, the CNN-Attention model is more stable and versatile over various subjects and joint angles in comparison to other models. Additionally, the computational time to build a CNN-Attention was obviously shorter than that to train a LSTM model (43.00 ± 4.25 min vs. 73.40 ± 5.81 min). These findings suggest that the CNN-Attention would be a promising model for continuous estimation of hand movements in the human-machine interaction and cooperation.

A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand

Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify significant factors affecting PM10 concentrations in rural landscapes and PM2.5 in urban landscapes; (2) to predict spatiotemporal PM10 and PM2.5 concentrations using geographically weighted regression (GWR) and mixed-effect model (MEM), and (3) to evaluate a suitable spatiotemporal model for PM10 and PM2.5 concentration prediction and validation. The research methodology consisted of four stages: data collection and preparation, the identification of significant spatiotemporal factors affecting PM concentrations, the prediction of spatiotemporal PM concentrations, and a suitable spatiotemporal model for PM concentration prediction and validation. As a result, the predicted PM10 concentrations using the GWR model varied from 50.53 to 85.79 µg/m3 and from 36.92 to 51.32 µg/m3 in winter and summer, while the predicted PM10 concentrations using the MEM model varied from 50.68 to 84.59 µg/m3 and from 37.08 to 50.81 µg/m3 in both seasons. Likewise, the PM2.5 concentration prediction using the GWR model varied from 25.33 to 44.37 µg/m3 and from 16.69 to 24.04 µg/m3 in winter and summer, and the PM2.5 concentration prediction using the MEM model varied from 25.45 to 44.36 µg/m3 and from 16.68 and 23.75 µg/m3 during the two seasons. Meanwhile, according to Thailand and U.S. EPA standards, the monthly air quality index (AQI) classifications of the GWR and MEM were similar. Nevertheless, the derived average corrected Akaike Information Criterion (AICc) values of the GWR model for PM10 and PM2.5 predictions during both seasons were lower than that of the MEM model. Therefore, the GWR model was chosen as a suitable model for spatiotemporal PM10 and PM2.5 concentration predictions. Furthermore, the result of spatial correlation analysis for GWR model validation based on a new dataset provided average correlation coefficient values for PM10 and PM2.5 concentration predictions with a higher than the expected value of 0.5. Subsequently, the GWR model with significant monthly and seasonal factors could predict spatiotemporal PM 10 and PM2.5 concentrations in rural and urban landscapes in Thailand.

Efficient Loads Surrogates for Waked Turbines in an Array

Accurately and efficiently predicting wind turbine structural loading is a crucial step in wind farm design. Without considering structural loading, wind farm optimization could negatively impact turbine fatigue and ultimate loads, especially for waked and partially waked turbines, which could result in higher maintenance costs and reduced turbine lifetime. However, predicting turbine loads throughout an array is a costly step, as these quantities require time-accurate results across long time histories, which is often intractable for large array optimization. Therefore, surrogate models that link array spacing to load outputs are often used, but the surrogates are then unique to the inflow conditions and array configurations in the training library. This work develops surrogate models for many wind turbine load outputs based solely on rotor plane velocity measurements, with no required input about array configuration or freestream inflow parameters. Surrogate models were constructed for many turbine quantities of interest (QoI), considering mean, standard deviation, ultimate, and fatigue loads. In general, most QoI statistics were accurately captured, as measured by predicted vs. actual correlation coefficient, confirming the suitability of the approach. Temporal mean values of the QoI required only temporal mean measurements of the rotor plane inflow velocity. However, accurate prediction of temporal standard deviation, ultimate, and fatigue values of QoI also required temporal standard deviations of the rotor plane velocity field. Poor surrogate performance was observed when too many correlated inputs were used, such as multiple velocity components. If the fewest inflow parameters are used to construct the surrogates, the average correlation coefficient value for all output QoI statistics is 0.89. Surrogates for standard deviations and damage equivalent loads (DELs) of turbine QoIs generally had lower accuracy and tower-base and shaft load channels posed the most difficult to capture accurately. The results suggest that these surrogates could be easily paired with analytic wake models, which are frequently used for pre-construction wind farm array optimization, to account for turbine loading in addition to power production. By including the optimal inflow conditions, the surrogate accuracy can improve to an average correlation coefficient value for all output QoI statistics of 0.92. This work has established the ability to build accurate surrogates for mean, standard deviation, ultimate load, and DEL turbine QoI values based on the rotor plane inflow velocity, and identified which inflow conditions lead to greater surrogate accuracy.

Detecting drought events over Iran during 1983–2017 using satellite and ground-based precipitation observations

Timely and accurate detection of drought events using reliable data with continuous spatial coverage is of great importance to adapt efficient mitigation strategies. Despite several studies that have been carried out to validate satellite precipitation products in Iran, the performance of satellite precipitation products in precipitation amount estimation and drought events detection has not been assessed yet simultaneously. It is unclear if good estimates of mean precipitation values by satellites can also provide good performance in detecting drought events. Here, we used a comprehensive framework to evaluate the accuracy of the satellite-based precipitation and their performance in detecting drought events against ground-based data. We evaluated three satellite precipitation products, namely, TRMM TMPA 3B43 v7, CHIRPS, and PERSIANN-CDR using multiple evaluation metrics, and three different drought indices (Standardized Precipitation Index-SPI, Standardized Precipitation Evapotranspiration Index-SPEI, and Multivariate Standardized Precipitation Index-MSPI) over different climate zones of Iran during 1983–2017. Our results indicated that TRMM and PERSIANN-CDR had mainly overestimation while CHIRPS had mostly underestimation in precipitation over the different climates of Iran. The precipitation overestimation (underestimation) in TRMM, PERSIANN-CDR, and CHIRPS were observed in 80.0% (20.0%), 71.4% (28.6%), and 45.7% (54.3%) of stations, respectively. The most overestimation in satellite products was found to be in the arid-related climate regions, while the most underestimation was detected in humid-related climate regions. The average correlation coefficient values of PERSIANN-CDR (0.6) and TRMM (0.6) products against ground observations were higher than CHIRPS (0.4) dataset. Further, we found a lower correlation coefficient (0.3) between monthly satellite-based and ground-based precipitation in arid-related regions (with Root Mean Square Error, RMSE, ~ 10 mm per month) of the country but a high correlation coefficient (0.6) in humid-related climates (with RMSE ~100 mm per month). The results of the drought assessment indicated that the most severe meteorological drought events identified by SPI, SPEI, and MSPI were in 1998–2001, 2008–2009, 2010–2012, and 2016–2017 in all climate zones. The most intense drought events were in arid and semi-arid climate zones. Overall, in terms of the statistical metrics at multiple timescales, TRMM and PERSIANN-CDR outperformed CHIRPS, when compared against ground-based observations, for drought events detection. Also, TRMM and PERSIANN-CDR with higher scores for Probability of Detection (POD) and Critical Success Index (CSI) and lower scores for False Alarm Ratio (FAR) showed a good ability to detect drought events in arid and semi-arid climates compared to other climate zones. Although the CHIRPS estimated precipitation with acceptable accuracy, it did not detect drought events as the TRMM and PERSIANN-CDR did.

The use of X-ray diffraction peak profile analysis to determine the structural parameters of cobalt ferrite nanoparticles using Debye-Scherrer, Williamson-Hall, Halder-Wagner and Size-strain plot: Different precipitating agent approach

Cobalt ferrite (CoFe2O4) nanoparticles have been developed by co-precipitation technique using four distinct precipitating agents, e.g., mixture of sodium hydroxide (NaOH) and sodium carbonate (Na2CO3), sodium hydroxide (NaOH), ammonium hydroxide (NH4OH), potassium hydroxide (KOH). The prepared systems had been investigated by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), Nanoparticle analyzer and Transmission electron microscopy (TEM). X-ray peak profile analysis (XPPA) had been used to estimate the physical parameters such as crystallite size and lattice strain by Debye-Scherrer (D-S) method, Williamson-Hall (W-H) plot, Halder-Wagner (H-W) technique and Size-Strain plot (SSP) technique. Different precipitating agents influence strongly the structure, size distribution, morphology and stability of the nanoparticles and on the basis of these parameters an optimum sample had been selected. The crystallite size calculated from XRD and particle size calculated from SEM shows a narrow size distribution with averages between 26.6–50.4 nm and 54–98 nm respectively, for all the samples. The particle size obtained from TEM shows high compatibility with XRD results with averages between 20 and 50 nm. The average lattice strain, dislocation density, lattice constant, cell volume, zeta potential was between 0.00094 and 0.0015, (4.34–14.13) × 1014 (lines/m2), 8.36870–8.38558 Å, 586.10–589.65 Å3, −87.8–27.7 mV respectively. The results estimated from the D-S method, W-H plot, H-W plot, SSP technique, SEM, Nanoparticle Analyzer and TEM regarding the structural and morphological parameters of the CoFe2O4 nanoparticles had been highly intercorrelated. Among the XPPA methods, SSP method is the most compatible one since the data points more accurately fits in this method with average correlation coefficient value (R2) of 0.99 that has been reinforced from TEM results as well. The synthesized CoFe2O4 nanoparticles can be applicable as liquid black coloring agent on ceramic surface after mixing with suitable solvent.

Statistical models to provide meaningful information to GNSS users in the presence of ionospheric scintillation

Ionospheric scintillation is one of the most challenging problems in Global Navigation Satellite Systems (GNSS) positioning and navigation. Scintillation occurrence can not only lead to an increase in the probability of losing the GNSS signal lock but also reduce the precision of the pseudorange and carrier phase measurements, thus leading to positioning accuracy degradation. Statistical models developed to estimate the probability of loss of lock and Geometric Dilution of Precision normalized 3D positioning errors as a function of scintillation levels are presented. The models were developed following the statistical approach of nonlinear regression on data recorded by Ionospheric Scintillation Monitoring Receivers operational at high and low latitudes. The validation of the probability of loss of lock models indicated average correlation coefficient values above 0.7 and average Root Mean Squared Error (RMSE) values below 0.35. The validation of the positioning error models indicated average RMSE values below 10 cm. The good performance of the developed models indicates that these can provide GNSS users with information on the satellite loss of lock probability and the error in the 3D position under scintillation.

English

Variations of equatorial electrojet (EEJ) in six Magnetic Data Acquisition System (MAGDAS) stations (four stations within the magnetic equator region and two stations outside the region) on the 210oMagnetic Meridian (MM) chain were examined. Data of hourly values of horizontal component of magnetic field, H in the year 2007 for Cooktown (CKT), Amami-oh-Shima (AMA), Davao (DAV), Langkawi (LKW), Yap- Island (YAP) and Cebu (CEB) were used in the study. The electrojet current was studied using both the estimation method and Fambitakoye simulation model to determine the correlation between their results. The strength of equatorial electrojet in the stations were estimated with respect to Cooktown (CKT; 15.48°N, 145.25°E), a station located outside the electrojet region. The maximum electrojet strength was observed in the month of March in Davao (DAV), Cebu (CEB) and Langkawi (LKW) with values of 107.5, 88.5 and 64.08 nT, respectively. The model simulation of EEJ current presents maximum values of 237.9, 207 and 160 nT in DAV, CEB and LKW, respectively in the same month. This result establishes a similarity in the performance of the methods but with higher values in the model performance. A linear correlation analysis between them showed average correlation coefficient values of 0.81 in Yap-Island (YAP), 0.84 in LKW, 0.6 in DAV and 0.54 in CEB. The data also show variability in EEJ, with results showing higher EEJ current magnitude for stations closer to the dip equator. The Root Mean Square Error (RMSE) was carried out to determine the suitability of the model for in the four seasons for each station. Result shows that the model performed better for March equinox in Cebu (3.58) and Yap-Island (5.97), December solstice (8.96) and September equinox in Davao and Langkawi (6.55), respectively. Key words: Equatorial electrojet, dip equator, magnetic data acquisition system (MAGDAS), Fambitakoye model, E-region, solar quiet.

Evaluation of the corrosion inhibition effect of the combined admixture of rosemary and cinnamon cassia oil on mild steel in weak acid electrolyte

The corrosion resistance of mild steel (MS) in 0.5 M H2SO4 and HCl solution with specific concentrations of the intermixture of rosemary and cinanamon cassia essential oil extracts (RCC) was studied by potentiodynamic polarization method, open circuit potential analysis (OCP) and optical macroscopy. Information obtained showed RCC effectively inhibited MS corrosion from the lowest to the highest concentrations in both acids with lowest inhibition result of 95.30% and 94.19%, and highest inhibition efficiency of 99.21% and 97.69%. RCC exhibited dominant cathodic inhibition effect in H2SO4 due to suppression of the reduction reactions while in HCl RCC displayed mixed inhibition properties due to surface coverage effect in the presence of Cl− anions. OCP showed RCC increased the thermodynamic tendency of MS to corrosion in H2SO4 solution despite effective inhibition performance with significant electronegative shift in corrosion potential. In HCl significant electropositive potential displacement was observed relative to the potential of the control MS due to adsorbed protonated RCC molecules unto the steel. Adsorption of RCC molecules on MS aligned with Langmuir, Frumkin and Freundlich isotherm models through chemisorption mechanism in both acids with average correlation coefficient value above 0.9. Optical images of MS in both acids without RCC showed severe surface degradation with the degree of degradation being higher from H2SO4 solution. Optical images from RCC inhibited steel depict effective surface protection.

A Secure Framework for Authentication and Encryption Using Improved ECC for IoT-Based Medical Sensor Data

Mobile users are increasing exponentially to adopt ubiquitous services offered by various sectors. This has attracted attention for a secure communication framework to access e-health data on mobile devices. The wearable sensor device is attached to the patient's body which monitors the blood pressure, body temperature, serum cholesterol, glucose level, etc. In the proposed secure framework, first, the task starts with the patient authentication, after that the sensors device linked to the patient is activated and the sensor values of the patient are transmitted to the cloud server. The patient's biometrics information has been added as a parameter in addition to the user name and password. The authentication scheme is coined with the SHA-512 algorithm that ensures integrity. To securely send the sensor information, the method follows two kinds of encryption: Substitution-Ceaser cipher and improved Elliptical Curve Cryptography (IECC). Whereas in improved ECC, an additional key (secret key) is generated to enhance the system's security. In this way, the intricacy of the two phases is augmented. The computational cost of the scheme in the proposed framework is 4H + Ec + Dc which is less than the existing schemes. The average correlation coefficient value is about 0.045 which is close to zero shows the strength of the algorithm. The obtained encryption and decryption time are 1.032 μs and 1.004μs respectively. The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest-Shamir-Adleman (RSA)and ECC algorithms.

Non-rigid alignment pipeline applied to human gait signals acquired with optical motion capture systems and inertial sensors

An accurate gait characterization is fundamental for diagnosis and treatment in both clinical and sportive fields. Although several devices allow such measurements, the performance comparison between the acquired signals may be a challenging task.A novel pipeline for the accurate non-rigid alignment of gait signals is proposed. In this paper, the measurements of Inertial Measurement Units (IMU) and Optical Motion Capture Systems (OMCAP) are aligned using a modified version of the Dynamic Time Warping (DTW) algorithm. The differences between the two acquisitions are evaluated using both global (RMSE, Correlation Coefficient (CC)) and local (Statistical Parametric Mapping (SPM)) metrics.The method is applied to a data-set obtained measuring the gait of ten healthy subjects walking on a treadmill at three different gait paces. Results show a global bias between the signal acquisition of 0.05°.Regarding the global metrics, a mean RMSE value of 2.65° (0.73°) and an average CC value of 0.99 (0.01) were obtained. The SPM profile shows, in each gait cycle phase, the percentage of cases when two curves are statistically identical and reaches an average of 48% (22%).

A new correlation between global solar radiation and the quality of sunshine duration in China

Accurate and reliable solar radiation models are important in the building energy conservation, solar thermal and photovoltaic utilization, agricultural production and ecological environment, in which the global solar radiation model based on sunshine percentage is the most common model. Sunshine duration only reflects the time accumulation of solar radiation, but cannot reflect its intensity accumulation. Therefore, it is difficult to improve the accuracy of such models. To solve this problem, a new quality of sunshine duration model (QSD model) is proposed, which takes into account both the influence of sunshine duration on solar radiation and the effect of solar radiation intensity on the accumulation of day. The QSD model is simplified from the energy balance equation which is established based on theoretical analysis of the heat transfer process among the Earth's surface layer, air layer and atmospheric layer. Then Meteorological data of 24 stations are utilized to analyze the accuracy of QSD model. The results show that the QSD model is more accurate, and the accuracy of QSD model is best during sunny days, followed by cloudy days, whereas overcast days provide the worst results. Meanwhile, a QSD model is obtained based on data from different regions. Statistical analysis shows that a QSD model has universal applicability in China, and the average value of correlation coefficient (R) is 0.929, Nash-Sutcliffe equation (NSE) is 0.864, and root mean squared error (RMSE) is 0.088.

Busy hour traffic congestion analysis in mobile macrocells

Traffic congestion during busy hour (BH) deteriorates the overall performance of cellular network and may become unmanageable unless effective and efficient methods of congestion control are developed through real live traffic data measurement and analysis. In this work, real live traffic data from integrated GSM/GPRS network was used for traffic congestion analysis. The analysis was carried out on 10 congesting cells using network management system (NMS) statistics data span for three years period. Correlation test showed that traffic channel (TCH) congestion depend only on call setup success rate (CSSR) and BH traffic at cell level. An average correlation coefficient value of 0.9 was observed between TCH congestion and CSSR while 0.6 was observed between TCH congestion and BH traffic. The correlation test is important when selecting input for congestion prediction modeling. http://dx.doi.org/10.4314/njt.v36i4.37

Nutrient content monitoring of potato plant based on spectrum device

The nutrient estimation is an important basis for water and fertilizer management in the field. In order to achieve crop nutrient content for variable-rate fertilization, a spectrum device based on the Ocean Optics STS-VIS sensor was developed to measure the spectral reflectance at 350-800 nm. The spectral reflectance data could be measured by optical sensor, data transmission module and the controller. The control software was developed with functions such as acquisition parameters, acquisition control and data management. Before the field application, the calibration experiments were conducted. The correlation was analyzed between the developed device and ASD Field Spec Hand Held 2. The result showed that the average correlation coefficient value was 0.94. It was used to detect the chlorophyll and moisture content of potato plant. The SPAD regression model was established and the determinant coefficient R2 was 0.484, moisture content regression model was established and R2 was 0.735. Results showed that the device has a certain ability to predict chlorophyll content and moisture content of potato leaf.