Higher Mean Error Research Articles

Pareto optimization of SPECT acquisition and reconstruction settings for 177Lu activity quantification

BackgroundThe aim was to investigate the noise and bias properties of quantitative 177Lu-SPECT with respect to the number of projection angles, and the number of subsets and iterations in the OS-EM reconstruction, for different total acquisition times.MethodsExperimental SPECT acquisition of six spheres in a NEMA body phantom filled with 177Lu was performed, using medium-energy collimators and 120 projections with 180 s per projection. Bootstrapping was applied to generate data sets representing acquisitions with 20 to 120 projections for 10 min, 20 min, and 40 min, with 32 noise realizations per setting. Monte Carlo simulations were performed of 177Lu-DOTA-TATE in an anthropomorphic computer phantom with three tumours (2.8 mL to 40.0 mL). Projections representing 24 h and 168 h post administration were simulated, each with 32 noise realizations. Images were reconstructed using OS-EM with compensation for attenuation, scatter, and distance-dependent resolution. The number of subsets and iterations were varied within a constrained range of the product number of iterations ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} number of projections ≤2400\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le 2400$$\\end{document}. Volumes-of-interest were defined following the physical size of the spheres and tumours, the mean activity-concentrations estimated, and the absolute mean relative error and coefficient of variation (CV) over noise realizations calculated. Pareto fronts were established by analysis of CV versus mean relative error.ResultsPoints at the Pareto fronts with low CV and high mean error resulted from using a low number of subsets, whilst points at the Pareto fronts associated with high CV but low mean error resulted from reconstructions with a high number of subsets. The number of projection angles had limited impact.ConclusionsFor accurate estimation of the 177Lu activity-concentration from SPECT images, the number of projection angles has limited importance, whilst the total acquisition time and the number of subsets and iterations are parameters of importance.

Management and Sales Forecasting of an E-commerce Information System Using Data Mining and Convolutional Neural Networks

The exponential development of e-commerce in recent decades has enhanced convenience for individuals. Compared to the conventional business environment, e-commerce is characterized by increased dynamism and complexity, resulting in several obstacles. Data mining assists individuals in effectively addressing these difficulties. Traditional data mining cannot efficiently use big data in the power provider industry. It heavily relies on time-consuming and labor-intensive feature engineering, and the resulting model could be more easily scalable. Convolutional Neural Networks (CNN) can efficiently use vast amounts of data and autonomously extract valuable elements from the original input, resulting in increased effectiveness. This article utilizes a CNN to extract valuable insights from e-commerce information to forecast commodities sales accurately and proposes a CNN-based Sales Forecasting Model (CNN-SFM). The findings indicate that using data mining and CNN yields a high level of precision in forecasting forthcoming people buying capacity data. The correlation variable between actual usage information and projected usage information was 0.98, and the highest mean error was just 1.78%. Data mining can effectively extract hidden relevant information and forecast future consumption habits for e-commerce systems. CNN demonstrates proficiency in accurately predicting forthcoming consumption power and trends.

An eddy current sensor for end-point detection in chemical mechanical planarization using composition signal filtration and a digital potentiometer

End-point detection (EPD) is important for controlling the chemical mechanical polishing process to prevent underpolishing and overpolishing. One EPD method using the relative current density on a Cu wafer involves an eddy current, which has advantages of noncontact and a fast response speed. However, the eddy current method has a large mean error at a low relative current density. To overcome the disadvantage, composition signal filtration uses a high mean error and long delay time using a moving average and 1-D Kalman filtration. After applying composition filtration, the mean error improved by approximately 94.7 %. Still, it is difficult to measure a thickness of 4000 Å or less with existing systems. A digital potentiometer was developed that changed the resistance in real time and improved the detection range. The step number for changing the resistance was 120 steps in the resistance range of 100–5000 Ω at approximately 40 Ω/step. When a digital potentiometer was applied, the EPD detection range improved by 85.8 % over the existing system. In other words, the developed system could measure a Cu wafer thickness of 586 Å.

A Study to Improve the Reliability of High-Strength Concrete Strength Evaluation Using an Ultrasonic Velocity Method.

The ultrasonic pulse velocity (UPV) technique, which is an efficient technique for concrete quality evaluation, can be affected by several factors. Many studies have proposed compressive-strength prediction models based on UPV in concrete; however, few studies have investigated the factors resulting in statistically different UPV results for different models. This study examined the difference between compressive strengths of various concrete specimens calculated by age-dependent and temperature-dependent UPV-based prediction models. Furthermore, a statistical analysis was conducted to evaluate the influence of aggregates and water/cement ratio (design compressive strength), which are said to affect UPV, on the compressive-strength prediction models. The experimental results revealed that the residual compressive strength of concrete after high-temperature exposure was about 9.5 to 24.8% higher than the age-dependent compressive strength. By contrast, after high-temperature exposure, UPV tended to be about 34.5% lower. The compressive strengths and UPVs were significantly different with respect to high temperature, aggregate density, and design compressive strength. The compressive-strength prediction model derived from the regression analysis showed a high R2 (average 0.91) and mean error converged to zero compared to the compressive-strength prediction model without considering these factors. Finally, the differences between the age- and temperature-based compressive-strength prediction models were analyzed according to the corresponding microstructures.

PSMA-Hornet: Fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images.

Prostate-specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor-intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials. In this work we develop and test the PSMA Healthy organ segmentation network (PSMA-Hornet), a fully-automated deep neural net for simultaneous segmentation of 14 healthy organs representing the normal biodistribution of [18 F]DCFPyL on PET/CT images. We also propose a modified U-net architecture, a self-supervised pre-training method for PET/CT images, a multi-target Dice loss, and multi-target batch balancing to effectively train PSMA-Hornet and similar networks. The study used manually-segmented [18 F]DCFPyL PET/CT images from 100 subjects, and 526 similar images without segmentations. The unsegmented images were used for self-supervised model pretraining. For supervised training, Monte-Carlo cross-validation was used to evaluate the network performance, with 85 subjects in each trial reserved for model training, 5 for validation, and 10 for testing. Image segmentation and quantification metrics were evaluated on the test folds with respect to manual segmentations by a nuclear medicine physician, and compared to inter-rater agreement. The model's segmentation performance was also evaluated on a separate set of 19 images with high tumor load. With our best model, the lowest mean Dice coefficient on the test set was 0.826 for the sublingual gland, and the highest was 0.964 for liver. The highest mean error in tracer uptake quantification was 13.9% in the sublingual gland. Self-supervised pretraining improved training convergence, train-to-test generalization, and segmentation quality. In addition, we found that a multi-target network produced significantly higher segmentation accuracy than single-organ networks. The developed network can be used to automatically obtain high-quality organ segmentations for PSMA image analysis tasks. It can be used to reproducibly extract imaging data, and holds promise for clinical applications such as personalized radiation dosimetry and improved radioligand therapy.

Fiducial Markers Allow Accurate and Reproducible Delivery of Liver Stereotactic Body Radiation Therapy

Fiducial markers are utilized for image guided radiotherapy (IGRT) alignment during the delivery of liver stereotactic body radiosurgery (SBRT). There are limited data demonstrating the impact of matching fiducials on the accuracy of liver SBRT. This study quantifies the benefit of fiducial-based alignment and improvements in inter-observer reliability. Nineteen patients with 24 liver lesions were treated with SBRT. Target localization was performed using fiducial markers on cone-beam computed tomography (CBCT). Each CBCT procedure was retrospectively realigned to match both the liver edge and fiducial markers. The shifts were recorded by seven independent observers. Inter-observer variability was analyzed by calculating the mean error and uncertainty for the set-up. The mean absolute Cartesian error observed from fiducial and liver edge-based alignment was 1.5 mm and 5.3 mm, respectively. The mean uncertainty from fiducial and liver edge-based alignment was 1.8 mm and 4.5 mm, respectively. An error of 5 mm or greater was observed 50% of the time when aligning to the liver surface versus 5% of the time when aligning to fiducial markers. Aligning to the liver edge significantly increased the error, resulting in increased shifts when compared to alignment to fiducials. Tumors of 3 cm or farther from the liver dome had higher mean errors when aligned without fiducials (4.8 cm vs. 4.4 cm, p = 0.003). Our data support the use of fiducial markers for safer and more accurate liver SBRT.

Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

Analysis of draught discomfort prediction models

One of the common local thermal discomforts that happen in an indoor environment is draught. Because of the importance of draught, many studies have been carried out to develop equations to calculate the draught rate. But because these equations are obtained from experimental tests in narrow experimental windows, their accuracy under wider environmental conditions should be analyzed. In this paper, the accuracy of equations obtained from the literature were investigated. Each equation predicted the draught rate with high accuracy and mean error of 5.3, 6.8, and 2.2% under the thermal conditions in which the equation was obtained. When applying the equations to different thermal conditions, the mean and maximum errors significantly increased and showed errors in predicting the draught rate with a maximum deviation of 63.5, 51.6, and 49.7%. These equations in some cases, even could not predict the draught rate of the reported draught discomfort percentage. Based on the results, each equation had its limitations and none of the studied equations could accurately predict the draught rate in all experimental conditions. An index that considers all relevant parameters in predicting draught discomfort can lead to a better draught rate prediction.

Structure and properties of alkali aluminosilicate glasses and melts: Insights from deep learning

Aluminosilicate glasses and melts are of paramount importance for geo- and materials sciences. They include most magmas, and are used to produce a wide variety of everyday materials, from windows to smartphone displays. Despite this importance, no general model exists with which to predict the atomic structure, thermodynamic and viscous properties of aluminosilicate melts. To address this, we introduce a deep learning framework, ‘i-Melt’, which combines a deep artificial neural network with thermodynamic equations. It is trained to predict 18 different latent and observed properties of melts and glasses in the K2O-Na2O-Al2O3-SiO2 system, including configurational entropy, viscosity, optical refractive index, density, and Raman signals. Viscosity can be predicted in the 100–1015 log10 Pa·s range using five different theoretical frameworks (Adam-Gibbs, Free Volume, MYEGA, VFT, Avramov-Milchev), with a precision equal to, or better than, 0.4 log10 Pa·s on unseen data. Density and optical refractive index (through the Sellmeier equation) can be predicted with errors equal or lower than 0.02 and 0.006, respectively. Raman spectra for K2O-Na2O-Al2O3-SiO2 glasses are also predicted, with a relatively high mean error of ∼25% due to the limited data set available for training. Latent variables can also be predicted with good precisions. For example, the glass transition temperature, Tg, can be predicted to within 19 K, while the melt configurational entropy at the glass transition, Sconf(Tg), can be predicted to within 0.8 J mol−1 K−1.Applied to rhyolite compositions, i-Melt shows that the rheological threshold separating explosive and effusive eruptions correlates with an increase in the fraction of non-bridging oxygens in rhyolite melts as their alkali/Al ratio becomes larger than 1. Exploring further the effect of the K/(K + Na) ratio on the properties of alkali aluminosilicate melts with compositions varying along a simplified alkali magmatic series trend, we observe that K-rich melts have systematically different structures and higher viscosities compared to Na-rich melts. Combined with the effects of the K/(K + Na) ratio on other parameters, such as the solubility, solution mechanisms and speciation of volatile elements, this could ultimately influence the eruptive dynamics of volcanic systems emitting Na-rich or K-rich alkali magmas.

Estimativa da altura da camada limite planetária no Centro de Lançamento de Alcântara

Sea and land breeze circulation and the internal boundary layer are some aspects that make it difficult to determine the coastal Planetary Boundary Layer (PBL) height (h). This paper evaluates the h estimation for the Alcantara Launch Center (CLA) in Maranhão state using 14 months of remote measurements obtained by a ceilometer and by the ERA5 reanalysis. This response depends on the concentration of aerosols and atmospheric humidity present. Mean results indicated that dry months (September to November, with = 637 488 m) tend to have less hourly and seasonal variability of h compared to wet months (March to May, with = 770 912 m). A higher mean error in the wet season was obtained with ERA5 PBL h (h = 708 53 m over the land; e h = 648 46 m over the ocean) than in the dry season (h = 18 89 m; e h = 46 77 m). The greater amount of atmospheric humidity during the rainy season increases the estimation uncertainty. This condition was more frequent at night due to typical rainfall in the place.

Reliable Breathing Tracking with Wearable Mask Device.

Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.

Accuracy of the measurements from multiplanar and sagittal reconstructions of CBCT.

The purpose of this study was to assess the accuracy of linear measurements performed in multiplanar reconstructions (MPR) and sagittal reconstructions (SR) of the left hemiface obtained from cone-beam computed tomography (CBCT) with 3D® Dolphin Imaging software by comparing them with the same measurements made on lateral cephalograms and on dry skulls. Lateral cephalograms and CBCT (with voxels of 0.25, 0.3 and 0.4mm) were taken of 10 dry skulls. Linear distances were measured using the software's electronic rulers. Measurements performed on dry skulls using a digital caliper were considered the gold standard. The measurements were performed twice by two evaluators. No significant difference was found in the measurements performed with the different imaging modalities and on the dry skulls. The highest mean error was observed in the lateral cephalograms, followed by MPR and SR. Cephalometric measurements performed on multiplanar and sagittal reconstructions from CBCT, with different spatial resolutions, are accurate when compared with the measurements obtained in lateral cephalograms.

Comparison and evaluation of gridded precipitation datasets for streamflow simulation in data scarce watersheds of Ethiopia

The objective of this study was to evaluate the hydrologic performance of gridded precipitation datasets for streamflow simulation in Ethiopia. Four products are prominent in the literature, namely Climate Forecast System Reanalysis (CFSR), Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission (TRMM) 3B42 Version 7 (3B42V7). The datasets from these products were compared statistically with the gauge observation dataset. Furthermore, the hydrologic performance of the products was evaluated using the Soil and Water Assessment Tool (SWAT) hydrologic model in two watersheds of the Lake Ziway basin. Four statistical and three contingency indices were used to compare the precipitation products statistically, and Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) and the ratio of mean square error (RSR) were used to compare streamflow simulations. All the satellite-based precipitation products (CHIRPS, PERSIANN-CDR, and TRMM) performed well for monthly streamflow simulations for both watersheds. The reanalysis product CFSR performed the worst with high mean error (ME) and relative bias ratio (BIAS). The high ME and BIAS values of CFSR adversely affected the hydrologic performance and resulted in unsatisfactory streamflow simulation. Performance of the CHIRPS precipitation product in capturing daily and monthly streamflows showed that it can provide valuable precipitation estimates for use as input in hydrologic models in data-sparse regions of developing countries such as Ethiopia.

Assessing the Correlation between Skeletal and Corresponding Soft-Tissue Equivalents to Determine the Relationship between CBCT Skeletal/Dental Dimensions and 3D Radiographic Soft-Tissue Equivalents.

Objective Compare measurements of skeletal and dental areas on the CBCT to the corresponding soft-tissue measures taken from a 3D Facial Scanner. Methods 30 patients with CBCT and 3D Facial scanner photos were selected from the orthodontic program database. 30 different distance measurements were obtained from CBCT and facial scan. OrthoInsight software was used to obtain the measurements from the facial scan images, and AVIZO software was used for corresponding CBCT landmarks. The Euclidean distance formula was used to determine the distances for the corresponding x, y, and z coordinates of the CBCT. Reliability for CBCT and Facial Scanner was completed by calculating 30 distances for 10 patients, 3 times. Once reliability was determined, all 30 distances were calculated once for CBCT and facial scanner on each patient and descriptive statistics and paired t-test were applied. Results All distances measured presented excellent reliability, the lowest one being the left eye width for the facial scanner (ICC 0.847). The landmark with the highest mean error on the CBCT was 2.0 ± 1.6 mm on the z-axis for the spinal level landmark. The Facial Scanner's largest mean measurement error was 1.5 ± 0.9 mm for the distance of the left corner of the mouth to gonion. All data except width between outer eye corners were statistically significant (p < 0.05). The average differences between facial scan and CBCT measurements ranged between 0.77 mm (left canine to cheekbone) to 26.94 mm (left subnasale to gonion) and are thus comparable. All measurements show a reasonable standard deviation between 2.57 mm (left eye width) to 9.91 mm (left gnathion to EAM). Conclusion Distances obtained from CBCT and facial scan present mild differences giving the perspective of a relationship between them. Understanding this difference and relationship can make it plausible to expect certain underlying skeletal distances under soft-tissue structures.

Vocal dosimeter devices and their uncertainty

Vocal dosimeters may be used to characterize long-term voice production in typical daily activities. The reliability of four vocal dosimeter devices in the estimation of Sound Pressure Level (SPL) and fundamental frequency (Fo) was assessed. The devices evaluated were the Ambulatory Phonation Monitor (KayPENTAX), VoxLog (Sonvox), VocaLog (Griffin Laboratories), and Voice-Care (PR.O.VOICE). Twenty subjects were recorded in a sound booth by means of a head-mounted microphone while using each dosimeter. Subjects were asked to produce a sustained /a/ vowel and to read a text in three different voice styles (relaxed, normal, and raised). On the basis of the difference in the estimation of the parameters between the values acquired from the HMM and the dosimeters, the mean error, and the combined uncertainty were calculated. For the SPL, the dosimeter with the highest mean error was the APM, followed by the VoxLog, the VocaLog, and the Voice-Care. For Fo, the dosimeter with the highest mean error was the VoxLog, followed by the Voice-Care, and the APM (the VocaLog does not monitor Fo).

Assessment of ınverse distance weighting (ıdw) ınterpolation on spatial variability of selected soil properties in the Cukurova plain

This study attempts to evaluate interpolation technique for mapping spatial distribution of some soil characteristics at the Lower Seyhan River Basin in Cukurova (Turkey). These soil characteristics may help to improve agricultural land management practices. In the study area, 7 parallel transects each having 150 m of length were selected at 5 m intervals, and 104 soil samples were collected. In these samples, calcium carbonate, organic matter, cation exchange capacity and clay content (from particle size distribution) were determined. Inverse distance weighting (IDW) interpolation and employing of GIS technology were applied on the results. Calcium carbonate, organic matter, cation exchange capacity and clay content values derived from IDW interpolation were consistent with the results of the soil analysis. The verity of the interpolation technique was tested by employing cross validation. Interpolation of organic matter values showed a high mean error in 30-60 cm depth (2.82%) while this high deviation was not the case with the other parameters studied.

Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm.

To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Inter-observer ICC for each landmark was found to be excellent ([Formula: see text]) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63mm ([Formula: see text] distance) and [Formula: see text] ([Formula: see text]-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and [Formula: see text]). Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification.

Precision and accuracy of suggested maxillary and mandibular landmarks with cone-beam computed tomography for regional superimpositions: An in vitro study.

Our objective was to identify and evaluate the accuracy and precision (intrarater and interrater reliabilities) of various anatomic landmarks for use in 3-dimensional maxillary and mandibular regional superimpositions. We used cone-beam computed tomography reconstructions of 10 human dried skulls to locate 10 landmarks in the maxilla and the mandible. Precision and accuracy were assessed with intrarater and interrater readings. Three examiners located these landmarks in the cone-beam computed tomography images 3 times with readings scheduled at 1-week intervals. Three-dimensional coordinates were determined (x, y, and z coordinates), and the intraclass correlation coefficient was computed to determine intrarater and interrater reliabilities, as well as the mean error difference and confidence intervals for each measurement. Bilateral mental foramina, bilateral infraorbital foramina, anterior nasal spine, incisive canal, and nasion showed the highest precision and accuracy in both intrarater and interrater reliabilities. Subspinale and bilateral lingulae had the lowest precision and accuracy in both intrarater and interrater reliabilities. When choosing the most accurate and precise landmarks for 3-dimensional cephalometric analysis or plane-derived maxillary and mandibular superimpositions, bilateral mental and infraorbital foramina, landmarks in the anterior region of the maxilla, and nasion appeared to be the best options of the analyzed landmarks. Caution is needed when using subspinale and bilateral lingulae because of their higher mean errors in location.

The effect of powered prosthesis control signals on trial-by-trial adaptation to visual perturbations.

Powered prostheses have the potential to restore abilities lost to amputation; however, many users report dissatisfaction with the control of their devices. The high variability of the EMG signals used to control powered devices likely burdens amputees with high movement uncertainty. In able-bodied subjects uncertainty affects adaptation, control, and feedback processing, which are often modeled using Bayesian statistics. Understanding the role of uncertainty for amputees might thus be important for the design and control of prosthetic devices. Here we quantified the role of uncertainty using a visual trial-by-trial adaptation approach. We compared adaptation behavior with two control interfaces meant to mimic able-bodied and prosthesis control: torque control and EMG control. In both control interfaces, adaptation rate decreased with high feedback uncertainty and increased with high mean error. However, we did observe different patterns of learning as the experiment progressed. For torque control, subjects improved and consequently adapted slower as the experiment progressed, while no such improvements were made for EMG control. Thus, EMG control resulted in overall adaptation behavior that supports Bayesian models, but with altered learning patterns and higher errors. These findings encourage further studies of adaptation with powered prostheses. A better understanding of the factors that alter learning patterns and errors will help design prosthesis control systems that optimize learning and performance for the prosthesis user.

Spatial variability of some soil properties: a case study of the Lower Seyhan river basin inTurkey

The aim of this study was to evaluate the performance of a closed drainage system in one area of the Lower Seyhan river basin in Cukurova, Turkey by measuring spatial variations in soil characteristics. Seven parallel transects which had a 150 m length were selected at 5 m intervals within the study area, and 104 soil samples were collected and analysed for soil pH, electrical conductivity (EC), exchangeable sodium (Na) content and exchangeable sodium percentages (ESP). Results were subjected to inverse distance weighting (IDW) interpolation. EC, pH and ESP estimates obtained from IDW interpolation were compatible with the results of soil analysis. Cross validation was performed to determine the accuracy of the interpolation technique. Interpolation of EC values showed a high mean error in 120–150 cm depth (9.010%), which was attributed to poor drainage conditions. Minimum sample number, maximum sample number, maximum radius and power were found to have an influence on the accuracy of IDW interpolation. ESP, exchangeable Na, EC values were generally low for shallow soil horizons, with values increasing with increases in depth. Data sets were analyzed with two-way ANOVA (the factors were six pipe regions (1–6) and five soil depths (30, 60, 90, 120, 150 cm)) and means compared with Duncan’s multiple range test. It was found that the efficiency of drainage pipe, in regions 1, 2, 3 and 4 can diminish rather than the other pipe regions.