Accuracy

The issue of spatial database accuracy has been viewed as critical to the successful implementation and long-term viability of the geographic information system (GIS) technology. In order to improve the spatial querying accuracy and quality, the problems associated with the areas of fuzziness and uncertainty are of great common in the spatial databases. In this paper, we are dedicated to develop an approach that can perform fuzzy spatial querying under uncertainty. An inferring strategy under uncertainty is investigated. The study shows that the fuzzy set and the certainty factor can work together to deal with spatial querying. Querying examples implemented by FuzzyClips are also provided. Public reporting burder for this collection of information is estibated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burder to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

Computing constrained shortest paths is fundamental to some important network functions such as QoS routing, which is to find the cheapest path that satisfies certain constraints. In particular, finding the cheapest delay-constrained path is critical for real-time data flows such as voice calls. Because it is NP-complete, much research has been designing heuristic algorithms that solve the ε-approximation of the problem with an adjustable accuracy. A common approach is to discretize (i.e., scale and round) the link delay or link cost, which transforms the original problem to a simpler one solvable in polynomial time. The efficiency of the algorithms directly relates to the magnitude of the errors introduced during discretization. In this paper, we propose two techniques that reduce the discretization errors, which allows faster algorithms to be designed. Reducing the overhead of the costly computation for constrained shortest paths is practically important for the design of a high-throughput QoS router, which is limited by both processing power and memory space. Our simulations show that the new algorithms reduce the execution time by an order of magnitude on power-law topologies with 1000 nodes. The reduction in memory space is similar. When there are multiple constraints, the improvement is more dramatic.

Multiple-input-multiple-output (MIMO) radar makes use of orthogonal signals to obtain the phase delay for each transmitting/receiving antenna pair, and thus increasing the accuracy of direction estimation. The previously proposed MIMO radar assumes narrowband signals that guarantee the waveform orthogonality during the signals' transmission, propagation and reception. However, a narrowband system is unstable in target localisation because of the fluctuation of the target's radar cross section. An MIMO-OFDM radar is proposed for target localisation. It adopts the OFDM technique to simultaneously transmit and receive a set of multiple narrowband orthogonal signals at orthogonal frequencies. A practical model accommodating a physical target is presented to simulate the MIMO-OFDM radar. As an example, a composite target composed of five infinite dielectric cylinders is localised by a four-element uniform linear array. The performance of the MIMO-OFDM radar is investigated by examining the estimation error for different numbers of sub-bands, different signal-to-noise ratios and different target directions. It is demonstrated by simulation that the MIMO-OFDM radar gives more statistically stable estimation by spreading the signal power over a wider spectrum.

Cooling towers are widely used in many industrial and utility plants as a cooling medium, whose thermal performance is of vital importance. Despite the wide interest in cooling tower design, rating and its importance in energy conservation, there are few investigations concerning the integrated analysis of cooling systems. This work presents an approach for the systemic performance analysis of a cooling water system. The approach combines experimental design with mathematical modeling. An experimental investigation was carried out to characterize the mass transfer in the packing of the cooling tower as a function of the liquid and gas flow rates, whose results were within the range of the measurement accuracy. Then, an integrated model was developed that relies on the mass and heat transfer of the cooling tower, as well as on the hydraulic and thermal interactions with a heat exchanger network. The integrated model for the cooling water system was simulated and the temperature results agree with the experimental data of the real operation of the pilot plant. A case study illustrates the interaction in the system and the need for a systemic analysis of cooling water system. The proposed mathematical and experimental analysis should be useful for performance analysis of real-world cooling water systems.

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.

Education institutions and teachers are in desperate need of automated, nonintrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the user's preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.

Scan-derived landmarks locations and surface shapes are more and more used, but there is no commonly accepted protocol for evaluating the accuracy of these measurements. Therefore we propose a protocol for evaluating the accuracy of surface shape and the repeatability of scan-derived landmark locations. According to existing Japanese and German domestic standards, we propose to use an artefact (e.g. sphere with diameter of about 120 mm) calibrated very accurately for evaluating the accuracy of scanner-systems. For evaluating the repeatability of landmark locations, we propose to use an anthropomorphic dummy with landmark locations premarked. These test objects are measured by a 3D body scanner to be evaluated. Evaluation parameters such as trueness, precision, and repeatability are calculated from the measured data. A round-robin test was conducted in six different institutes using 17 body/head/foot scanners produced by eight companies. The purposes of the roundrobin test were to ev...

Body dimensions are based on landmarks of the body, but the magnitude of error in landmark determination is not well known. Therefore, a study was performed in which 40 subjects were marked five times in total by one highly skilled marker and a novice marker. Immediately after marking, a skilled measurer determined 34 body dimensions that were based on the mark locations. Intra-and interobserver errors in landmarking of 35 landmarks, as well as those in 34 body dimensions were quantified. The error in landmarking was defined as the distance between two marks made on the same landmark by the same marker (intra-observer error) or by two different markers (inter-observer error). To make the first mark invisible when the second mark was made, the first mark was made using an invisible ink pen under black light. Landmarks with large intra-observer errors also had large inter-observer errors. Errors in body dimensions were smaller than landmarking errors in 23 measurements, which suggested that the magnitude of landmarking error would be underestimated from errors in body dimensions. In 15 body dimensions, measurements based on marks made by two different markers were not comparable according to the ISO 20685 criterion. Examination of body dimensions and landmarks with large interobserver errors suggested that reducing inter-observer landmarking errors was necessary to reduce inter-observer measurement errors, and that a possible solution was to explicitly define landmarks with large errors in more detail so that anthropometrists can pinpoint them on the skin. Quantitative data on the intra-and inter-observer landmarking errors in the present study may be useful as a reference when evaluating and comparing the performance of software for calculating landmark locations for 3D anthropometry.

Focused assessment with sonography for trauma (FAST) is a method for detecting haemoperitonem in trauma patients on initial assessment in the Emergency Department. The aim of this paper is to present an Australian trauma centre's experience with FAST as a tool to screen for intraabdominal free fluid in patient's sustaining blunt truncal trauma. Method: Over a 63-month period, FAST scans were prospectively studied and compared with findings from a gold-standard investigation, either computed tomography (CT) or laparotomy. Results: 463 FAST results were collected prospectively from 463 patients. 53 scans were excluded due to lack of a corresponding confirmatory gold-standard test. Overall sensitivity, specificity, positive and negative predictive values for FAST in detecting free fluid were 78%, 97%, 91%, 93%, respectively. Analysis of the credentialed operators demonstrated an improvement in accuracy (sensitivity 80%, specificity 100%, positive predictive value 100%, negative predictive value 94%). These findings are comparable with documented international experience. The study demonstrates that the use of non-radiologist performed FAST in the detection of free fluid is safe and accurate within an Australian Trauma Centre.

To compare the diagnostic accuracy of stress computed tomography myocardial perfusion (CTP) for the detection of significant coronary artery disease with visual approach vs. quantitative analysis with transmural perfusion ratio (TPR) in consecutive symptomatic patients scheduled for invasive coronary angiography (ICA) plus invasive fractional flow reserve (FFR).

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC). In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and � 800 s/mm 2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I 2 ) and considering covariables: field strength, lowest bvalue, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84

Objective To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods. Methods Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed. Results Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p < 0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p < 0.001-0.040) by median quantification compared with six (p < 0.001-0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053). Conclusion Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment. • Median pixel-wise quantification of native T1 maps is robust and can be applied regardless of the statistical distribution of data points. • Median quantification is more sensitive to early heart function abnormality compared with mean quantification. • The new method yields significant native T1 value differentiation between the three coronary artery territories.

In this paper, we have investigated the influence of cluster instability on the performance of layered cluster oriented ensemble classifier. The final contents of clusters in some clustering algorithms like k-means depend on the initialization of clustering parameters like cluster centres. Layered cluster oriented ensemble classifier is based on this philosophy where the base classifiers are trained on clusters generated at multiple layers from random initialization of cluster centres. As the data is clustered into multiple layers some patterns move between clusters (unstable patterns). This instability of patterns brings in diversity among the base classifiers that in turn influences the accuracy of the ensemble classifier. There is thus a connection between the instability of the patterns and the accuracy of layered cluster oriented ensemble classifier. The research presented in this paper aims to find this connection by investigating the influence of unstable patterns on the overall ensemble classifier accuracy as well as diversity among the base classifiers. We have provided results from a number of experiments to quantify this influence.

As part of a quality assurance (QA) study for sensor systems, an enclosure approach is applied to assess the accuracy of non-dispersive infrared (NDIR)-based CO 2 sensors. To examine the performance of the sensor system, an enclosure chamber containing six sensor units of the two model types (B-530 and H-500) was equilibrated with calibrated CO 2 standards at varying concentration levels. Initially, the equilibration pattern was analyzed by CO 2 -free gas (0 ppm) at varying flow rates (i.e., 100, 200, 500, and 1000 mL min -1 ). Results of the test yielded a highly predictable and quantifiable empirical relationship as a function of such parameters as CO 2 concentration, flow rate, and equilibration time for the enclosure system. Hence, when the performance of the NDIR-method was evaluated at other concentrations (i.e., 500 and 1000 ppm), all the sensor units showed an excellent compatibility, at least in terms of the correlation coefficients (r > 0.999, p = 0.01). According to our analysis, the NDIR sensor system seems to attain an overall accuracy near the 5% level. The relative performance of the NDIR sensor for CO 2 analysis is hence comparable with (or superior to) other methods previously investigated. The overall results of this study indicate that NDIR sensors can be used to provide highly accurate and precise analyses of CO 2 both in absolute and relative terms.

Unclssified ,_,_, 2a. SECURITY CLASSIFICATION AUTHORITY 3. DISTRIBUTIONIAVAILABIUTY OF REPORT 2b. DECLASSIFICATION / OOWNGRADING SCHEDULE Approved for public release, distribution unlimited. 4. PERFORMING ORGANIZATION REPORT NUMBER(S) S. MONITORING ORGANIZATION REPORT NUMBER(S) B-3 6a. NAME OF PERFORMING ORGANIZATION 6b. OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION 20. OISTRIBUTION/AVAILABIUTY OF ABSTRACT 21 ABSTRACT SECURITY CLASSIFICATION OUNCLASSIFIEUNUMTED 1l SAMEAS ItP . C0 W USERS Unclasslfteg 22a. NAME OF RESPONSIBLE INDIVIDUAL 22b. TtLE VOtElA. Ale Code) U&, OfFICE SYMBOL vOFO 147., _qltyet-9mv (AM , A -10n I M &AJf iI 00FR 43,s m6 APR editmo may be iawd untl exuliattd. ~ nna. e ANt othler edftion ams obsolet. hI'Clfle 18. disposal, disposal area management, management alternatives, water quality evaluation, storage capacity evaluation, consolidation and sedimentation testing.

A quantitative structure-property relationships (QSPR) model, correlating the light sensitivity against theoretical molecular descriptors, was developed for a set of 1,4-dihydropyridine calcium channel antagonist drugs. These compounds are characterized by a high tendency to degradation when exposed to light, furnishing in the most of cases a related oxidation product from aromatization of the dihydropyridinic ring. Photodegradation was forced by exposing the drugs to a Xenon lamp, in accordance with the ICH international rules, and degradation kinetics was monitored by spectrophotometry. The photodegradation rates combined with a series of descriptors related to the chemical structures were computed by Partial Least Squares (PLS) multivariate analysis. An accurate selection of the variables, fitting at the best the PLS model, was performed. Two descriptors related to the substituent information on both the dihydropyridinic and benzenic rings and four molecular descriptors, were selected. The QSPR model was fully cross validated and then optimized with an external set of novel 1,4-dihydropyridine drugs, obtaining very satisfactory statistical results. The good agreement between predicted and measured photodegradation rate (R 2 = 0.8727) demonstrated the high accuracy of the QSPR model in predicting the photosensitivity of the drugs belonging to this class. The model was finally proposed as an effective tool to design new congeneric molecules characterized by high photostability.

A novel analytical technique able to determine the anti-ischemic drug trapidil in human serum and urine is proposed. In order to achieve satisfactory sensitivity and selectivity, an extraction procedure was required to isolate the drug from complex matrixes such as serum and urine. A solid-phase extraction procedure was investigated to both increase the analyte concentration and eliminate the interfering molecules present in large amounts in both matrixes. Optimization of the extraction step was realized by selecting a new polymeric sorbent based on a surfacemodified styrene-divinylbenzene polymer which provided fast and efficient drug extraction. Drug quantification was performed by using the third-order derivative spectra of the SPE eluates. Absorbance specific signals at 3 D 335,316 and 3 D 316 nm for urine and serum, respectively, were demonstrated to be directly proportional to drug concentration and barely affected by residual matrix interferences. Under the optimized experimental conditions the calibration plots were linear over the concentration range 0.2-50 μg mL -1 . The method was validated by analysis of a series of spiked samples. Accuracy (recovery of 95 and 94% for serum and urine, respectively) and precision (RSD below 4%) were good.

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

First, the paper proposes the method for interpolation of any experimentally obtained symmetric hysteresis loop curve (SHLC) with accuracy and computation efficiency at discrete Fourier transformation (DFT) level. Second, the method has been further developed so that, based on the family of the properly chosen and measured SHLCs, it reliably and accurately predicts an arbitrary inner SHLC. Sinusoidal magnetic flux, along with applied zero crossing sampling system, allows for the introduction of the pure linearization approach. The novelty of this approach is a direct transformation of a cosine polynomial (CP) interpolating of one SHLC over the set of equidistant nodes in the electric angle (EA) domain to the algebraic polynomial (AP) interpolating the same SHLC over the set of nonequidistant Chebyshev nodes in the magnetic flux (MF) domain, with the accuracy remaining unchanged. Based on the results of the interpolation error analyses, the SHLC measurement has been proposed for nonequidistant values of magnetic flux at the loop tip, matching the Chebyshev nodes of the second kind. This is the second novelty which enables a successful prediction of an arbitrary inner SHLC.

A novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) is suggested in this paper, with feature selection capabilities, for the classification of an IKONOS image. The structure of the proposed network is developed in a sequential fashion using the group method of data handling (GMDH) algorithm. The node models, regarded as generic classifiers, are represented by fuzzy rulebased systems, combined with a fusion scheme. A data splitting mechanism is incorporated to discriminate between correctly classified and ambiguous pixels. The classifier was tested on the wetland of international importance of Lake Koronia, Greece, and the surrounding agricultural area. To achieve higher classification accuracy, the image was decomposed into two zones, namely, the wetland and the agricultural zone. Apart from the initial bands, additional input features are considered: textural features, IHS and tasseled cap transformation. To assess the quality of the suggested model, SONeFMUC is compared with a maximum likelihood classifier (MLC). The experimental results show that the SONeFMUC exhibited superior performance than MLC, providing lower confusion of the dominant classes in both zones. Especially in the wetland zone, an overall accuracy of 89.5% was attained.

In this work we develop a new multiscale procedure to compute numerically the statistical moments of the stochastic variables which govern single phase flow in heterogeneous porous media. The technique explores the properties of the log-normally distributed hydraulic conductivity, characterized by power-law or exponential covariances, which shows invariance in its statistical structure upon a simultaneous change of the scale of observation and strength of heterogeneity. We construct a family of equivalent stochastic hydrodynamic variables satisfying the same flow equations at different scales and strengths of heterogeneity or correlation lengths. Within the new procedure the governing equations are solved in a scaled geology and the numerical results are mapped onto the original medium at coarser scales by a straightforward rescaling. The new procedure is implemented numerically within the Monte Carlo algorithm and also in conjunction with the discretization of the low-order effective equations derived from perturbation analysis. Numerical results obtained by the finite element method show the accuracy of the new procedure to approximated the two first moments of the pressure and velocity along with its potential in reducing drastically the computational cost involved in the numerical modeling of both power-law and exponential covariance functions.

As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.

As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA's public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.

The precision of mean exposure to pushing was examined in 2 occupational groups using various combinations of the number of workers and measurements per worker. The frequency and duration of pushing of the 2 occupational groups was assessed using onsite observation. All data were divided into successive periods of 30 minutes of observation. The precision of the group mean exposure to pushing was expressed by 90% confidence intervals obtained by bootstrapping. The effect on the confidence interval of varying numbers of workers and numbers of periods per worker was examined. For both occupational groups there was little precision to be gained when >10 workers were observed. Within the maximum number of workers used in the bootstrap simulations, it appeared that, beyond 10 workers, the confidence intervals decreased by <5% for every worker that was added, when each worker was observed at least 8 periods of 30 minutes. If workers were observed exactly 4 periods of 30 minutes per worker, an additional 4 workers were required to compensate for the loss of precision. An unbalanced strategy with approximately 8 periods of 30 minutes per worker hardly decreased the precision of the group mean, however. The precision of the group-based mean exposure to pushing is influenced by the number of workers observed and by the number of repeated measurements per worker. In the planning of measurement strategies, it is advisable to account for possible sources of variance in advance and to assess the exposure variability.

The precision of mean exposure to pushing was examined in 2 occupational groups using various combinations of the number of workers and measurements per worker. The frequency and duration of pushing of the 2 occupational groups was assessed using onsite observation. All data were divided into successive periods of 30 minutes of observation. The precision of the group mean exposure to pushing was expressed by 90% confidence intervals obtained by bootstrapping. The effect on the confidence interval of varying numbers of workers and numbers of periods per worker was examined. For both occupational groups there was little precision to be gained when >10 workers were observed. Within the maximum number of workers used in the bootstrap simulations, it appeared that, beyond 10 workers, the confidence intervals decreased by <5% for every worker that was added, when each worker was observed at least 8 periods of 30 minutes. If workers were observed exactly 4 periods of 30 minutes per worker, an additional 4 workers were required to compensate for the loss of precision. An unbalanced strategy with approximately 8 periods of 30 minutes per worker hardly decreased the precision of the group mean, however. The precision of the group-based mean exposure to pushing is influenced by the number of workers observed and by the number of repeated measurements per worker. In the planning of measurement strategies, it is advisable to account for possible sources of variance in advance and to assess the exposure variability.

Performance evaluation of principal component analysis (PCA) of dynamic F-18-FDG-PET studies of patients with recurrent colorectal cancer. Principal component images (PCI) of 17 iteratively reconstructed data sets were visually and quantitatively evaluated. The F-18-FDG compartment model parameters were estimated using polynomial regression. All structures were present in PCI1. PCI2 was correlated with the vascular component and PCI3 with the tumor. The vessel density in the tumor was estimated with a correlation coefficient equal to 0.834. PCA supports the visual interpretation of dynamic F-18-FDG-PET studies, facilitates the application of compartment modeling and is a promising quantification technique.

Introduction Total parkinsonian motor symptom severity correlates with presynaptic striatal dopamine function in patients with Parkinson's disease. There is a lack of studies that have investigated the associations between parkinsonian motor signs and striatal dopaminergic deficiency in patients with parkinsonism of an unknown origin. Identification of specific motor signs associated with the highest likelihood of striatal dopamine deficiency could aid the differential diagnostics of parkinsonian and tremor syndromes. Methods In this cross-sectional clinical and imaging study, detailed motor examinations were performed for 221 patients with parkinsonism or tremor of an unknown origin immediately before dopamine transporter (DAT) [I-123]FP-CIT SPECT imaging. Region-of-interest and voxel-based methods were used to investigate striatal DAT deficiency in relation to individual motor signs. Results Upper extremity rigidity and facial expression were the only motor signs that differentiated patients with normal and abnormal striatal DAT function. The presence of any upper extremity rigidity showed the highest likelihood of DAT deficiency (OR 4.79, 95% CI 1.56-14.75, P = 0.006) followed by reduced facial expression (OR 2.14, 95% CI 1.14-4.00, P = 0.018). In patients with DAT deficits, reduced facial expression was associated with DAT deficiency specifically in the caudate nucleus, and increased upper extremity rigidity was associated with DAT loss in the dorsal putamen (FWE-corrected P < 0.05). Conclusions Increased upper extremity muscle tone and hypomimia are independently associated with a higher likelihood of striatal hypodopaminergic imaging finding. This information can be used as a factor when the clinical need of auxiliary investigations, such as DAT SPECT, is considered for patients with parkinsonism.

Each of twenty commercial motor vehicle (CMV) drivers participated in a single twenty-hour experimental session, during which they were continuously kept awake, but were allowed to ingest caffeine and use tobacco as they would in real-world conditions. Each participant drove in a fixed-base advanced driving simulator for approximately one hour on four occasions (at 9:00 am, 3:00 pm, 9:00 pm, and 3:00 am). The 59.5-mile (95.8-km) test route was designed with overpasses and intersections and changes in speed limits—to make the driving experience more like real-word driving. After the fourth drive, the participants were driven to the University of Minnesota’s General Clinical Research Center, where they slept for eight hours. The main result was that the steering performance of CMV drivers was impaired when they stayed awake for an extended period: There was a considerable increase in steering instability between the morning drive, at 9:00 am, and the nighttime drive, at 3:00 am—an inc...

The research presented in this paper proposes a novel gender classification approach using face image. The approach extracts features from grayscale face images through Infomax ICA and subsequently selects features using k-means clustering and classifies the clustered features employing PNN. All the experimental evaluations are done on cropped face images from FERET database using 280 faces for training and 120 different faces for testing. The approach, when features are not clustered gives maximum accuracy of 93.33%. However the proposed approach yields 95% accuracy through employing clustering on features, which is significant for gender classification using low resolution (118 × 97) face images.

The quality of data becomes an essential component for the success of an organisation in a world that is largely influenced by data, where data analytics is becoming increasingly popular in the process of informing strategic decisions. The failure to improve the quality of the data can lead to undesirable outcomes such as poor decisions, ineffective strategies, dysfunctional operations, lost commercial prospects, and abrasion of the consumer. In the process of organisations shifting their focus towards transformative methods such as generative artificial intelligence, several use cases may emerge that have the potential to aid the improvement of data quality. Streamlining procedures such as data classification, metadata management, and policy enforcement can be accomplished by the incorporation of generative artificial intelligence into data governance frameworks. This, in turn, reduces the workload of human data stewards and minimises the possibility of human error. In order to ensure compliance with legal standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), generative artificial intelligence may analyse enormous datasets by utilising machine learning algorithms to discover patterns, inconsistencies, and compliance issues.

In news portals, text category information is needed for news presentation. However, for many news stories the category information is unavailable, incorrectly assigned or too generic. This makes the text categorization a necessary tool for news portals. Automated text categorization (ATC) is a multifaceted difficult process that involves decisions regarding tuning of several parameters, term weighting, word stemming, word stopping, and feature selection. In this study we aim to find a categorization setup that will provide highly accurate results in ATC for Turkish news portals. We also examine some other aspects such as the effects of training dataset set size and robustness issues. Two Turkish test collections with different characteristics are created using Bilkent News Portal. Experiments are conducted with four classification methods: C4.5, KNN, Naive Bayes, and SVM (using polynomial and rbf kernels). Our results recommends a text categorization template for Turkish news portals and provides some future research pointers.

The quality of data becomes an essential component for the success of an organisation in a world that is largely influenced by data, where data analytics is becoming increasingly popular in the process of informing strategic decisions. The failure to improve the quality of the data can lead to undesirable outcomes such as poor decisions, ineffective strategies, dysfunctional operations, lost commercial prospects, and abrasion of the consumer. In the process of organisations shifting their focus towards transformative methods such as generative artificial intelligence, several use cases may emerge that have the potential to aid the improvement of data quality. Streamlining procedures such as data classification, metadata management, and policy enforcement can be accomplished by the incorporation of generative artificial intelligence into data governance frameworks. This, in turn, reduces the workload of human data stewards and minimises the possibility of human error. In order to ensure compliance with legal standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), generative artificial intelligence may analyse enormous datasets by utilising machine learning algorithms to discover patterns, inconsistencies, and compliance issues.

New gaming software must undergo a series of tests before its general release. The objective of these tests is to ensure that the simulation is appropriate for its intended audience, plays well, possesses the requisite level of fidelity to the system being modeled, and is free from programming errors. This article first catalogs the design parameters associated with a good beta test. It then compares this ideal against the beta test created for a first-generation online business game released by a major online game publisher. It then examines the actual behaviors and results produced by the study’s beta testers to determine the degree the publisher and authors could be confident that the game met the criteria of targeted audience propriety, playability, model fidelity, and algorithmic accuracy. In this instance, this well-designed beta test could not guarantee the release of error-free software, and the likely reasons for this outcome are identified.

A modification on the SPICE BJT model with the purpose of extending the accuracy range is proposed. The proposed model improves the representation of the Early effect for forward and inverse operating regions. Another main improvement is on the pF-Ic and PR-IE dependences at high-injection levels achieved by introducing one extra model parameter and by modifying the normalised total base-charge expression, The model provides new possibilities in analogue IC design such as accurate simulation

Preliminary results using the SBM-2DH model WL | Delft Hydraulics i Bed variability in the surf zone at the storm-and seasonal time scale, mapped by Argus-video techniques Z3781.b November, 2004 Preliminary results using the SBM-2DH model WL | Delft Hydraulics 1 1 Bed variability in the surf zone at the storm-and seasonal time scale, mapped by Argus-video techniques Z3781.b November, 2004 Preliminary results using the SBM-2DH model WL | Delft Hydraulics 2 SBM-2DH model has recently been upgraded, improvements over the version presented in Roelvink et al. (2003) will be briefly discussed in Chapter 2. The general approach adopted in this analysis is to first calibrate the SBM-2DH model on the annual scale, i.e. to assess suitable parameter settings to obtain realistic bed evolution data over a 1.5 year period. Whether the evolution is realistic is determined by qualitative comparison to 5 traditional bathymetric surveys during the considered period. For the analysis of seasonal-and storm scale bed level variability the calibrated SBM-2DH model will subsequently be ran over the period between the two nearest traditional bathymetric surveys. For the seasonal variability these are the surveys of March 2000 1 and March 2001. For the storm-related bed variability these are the surveys of September 1999 and March 2000. The degree of similarity between Argus-derived bathymetry and surveyed bathymetry at the end of each period provides a measure for the level of detail allowed in the interpretation of the preceding Argus-derived bed level variability. Argus metainformation Tide & wave information Initial bathymetry Video Interpretation Model Bathymetry Assessment Model Video-based bathymetry Time series of video images Evolution of sub-tidal bathymetry Sub-tidal Beach Mapper (SBM-2DH) Sub-model 1 Sub-model 2 Bed variability in the surf zone at the storm-and seasonal time scale, mapped by Argus-video techniques Z3781.b November, 2004 Preliminary results using the SBM-2DH model WL | Delft Hydraulics 4 Bed variability in the surf zone at the storm-and seasonal time scale, mapped by Argus-video techniques Z3781.b

The layout, construction, implementation, and testing of a control bus interface board which is compatible with the INTERDATA 7/32 multiplexer bus is presented. The interface board is independently addressable and universal in design. A complete test procedure for isolating faults is included. Volume II of this report describes a special signal processing technique developed in the Naval Postgraduate School Satellite Communications Laboratory. The doppler phenomenon, as it affects neary geostationary satellites, is discussed and suggestions for future research topics are given. jawai «i!~!?* TABLE OF CONTENTS ^w-SSff"* I.

There have been many queuing analyses for a single server queue fed by an M/G/∞ traffic process, in which G is a Pareto Distribution, that focus on certain limiting conditions. In this paper we enhance the so-called Quasi-Stationary (QS) approximation -a queuing analysis introduced previously that provides an algorithm for computation of an accurate approximation for the stationary queue distribution, applicable to the entire range of system parameters. By numerical evaluation of the QS approximation and the asymptotic approximations (large buffer, many sources, and heavy traffic) over an extremely wide range of parameter values we are able to graphically display consistency of the QS approximation with all the asymptotic results. We demonstrate that the accuracy of the asymptotic approximations is satisfactory only in limited regions of the system parameter space.

Three-dimensional magnetotelluric (MT) inversion is an emerging technique for offshore hydrocarbon exploration. We have developed a new approach to the 3D inversion of MT data, based on the integral equation method. The Tikhonov regularization and physical constraint have been used to obtain a stable and reasonable solution of the inverse problem. The method is implemented in a fully parallel computer code. We have applied the developed method and software for the inversion of marine MT data collected by the Scripps Institution of Oceanography (SIO) in the Gemini prospect, Gulf of Mexico. The inversion domain was discretized into 1.6 million cells. It took nine hours to complete 51 iterations on the 832-processor cluster with a final misfit between the observed and predicted data of 6.2%. The inversion results reveal a resistive salt structure, which is confirmed by a comparison with the seismic data. These inversion results demonstrate that resistive geoelectrical structures like s...

Background: This study was designed to assess the accuracy and repeatability of the Derks Calculation Method for results found in the normal foot during walking. Methods: Measurements were taken from 25 healthy subjects (age 32.0 +/-12.4), 23 females and 2 males, on five separate occasions at seven days, three weeks, three months, and nine months apart by means of a mid-gait method. Values were calculated for internal rotation (IR) and external rotation of the heel (ER), heel valgus/varus (HV), heel length (HL), heel width (HW), width of the midfoot (WM) and the forefoot (WF), and the length of the foot (LF). Results: For all five different occasions in 87.5% of the parameters investigated the coefficient of repeatability (CR, expressed as a percentage of the mean) was less than 5%. One parameter showed a higher CR: heel valgus/varus (HV) was extremely high (>800%). The maximum 95% Confidence Interval (CI) for the five different occasions was no higher than 0.2 cm for IR, ER and HV with a standard error (SE) of 0.01 and >0.01 respectively. The maximum 95% CI for WF was 0.4cm (SE 0.1), and for HW, WM and LF the maximum 95% CI was 0.7cm (SE 0.1 or 0.2). HL showed the highest 95% CI (0.9cm) with an SE of 0.2. The Derks Calculation Method was found to be accurate and repeatable if HV was excluded, which renders this method a viable clinical tool in settings where sophisticated computerised systems are still unavailable.

We propose an algorithm for particle tracking based on Cheng's method [Int. J. Numer. Meth. 39 (1996) 1111-1136]. Velocities in a flow field are known at a series of points and interpolated between them by finite element local functions. Tracking is performed in local coordinates, element by element, using any standard ODE solution method. The exit from an element is found using the polynomials to interpolate between the tracking points. The algorithm was tested and compared to Pollock's and Cheng's method in a series of numerical experiments, in which the Euler, Runge-Kutta 2, Runge-Kutta 5(4) and Runge-Kutta 6(4) ODE solution methods were combined with first-, second-, third-and fifth-order exit polynomials. The known velocities had a random error with standard deviation of 0%, 0.1% and 1% of the velocity. Meaningful results were obtained only when the spatial interpolation error and the error of the tracking method were calculated separately, otherwise some results were misleading. The numerical experiments confirmed that the accuracy of the exit polynomial has to be consistent with the ODE solution method. Quadratic interpolation of velocities on a coarser mesh often gives more accurate path lines and requires less computational time than linear interpolation. Pollock's method for particle tracking is viable only if input data are rather inaccurate and path lines nearly straight. Cheng's method is appropriate for moderately accurate input data, while the proposed algorithm with Runge-Kutta 5(4) or Runge-Kutta 6(4) method and fifth-order exit polynomial has excellent accuracy. Computational time is about 10 times longer than for Cheng's method while the accuracy is increased by several orders of magnitude.

Recently, the decision feedback channel estimation based on the minimum mean square error criterion (DF-MMSE-CE) using a fixed DF filter coefficient has been proposed to improve the channel estimation accuracy for DS-CDMA with frequency-domain equalization (FDE). In this paper, we propose adaptive DF (ADF)-MMSE-CE, in which the DF filter coefficient is adapted to changing channel conditions based on a recursive least square (RLS) algorithm. Furthermore, the channel estimate is phase corrected upon the reception of the periodically inserted pilot chip blocks. The average BER performance of DS-CDMA with MMSE-FDE using ADF-MMSE-CE is evaluated by computer simulation in a frequencyselective Rayleigh fading channel and the simulation results show that our proposed scheme is very robust against fast fading.

Introduction: advisor for the youth group of modern day. The basic terrible effect is "listening is hard". Youngsters are calling an attention to the audio This study aims to bring a awareness in the younger generation and presenting an audiometric outcome of mobile and MP3. Aim: To stimulus Methodology: group. The age group of 20 to 35 years subjects were selected ba criteria. The audiologicaltest battery includes PTA, Speech Audiometry, Impedance and OAE evaluation were carried out before and after a period of continuous exposure of stimulus in both groups. Results: Audiological evaluation shows for all the subjects and when compared it has found from the base that the mobile users has normal hearing threshold where as the mp3 users has towards the higher ran normal tending to minimal hearing loss. though both the users has a threshold shift where mobile users has a mean shift of right ear mean 11 to 24,left ear mean 11 to 25.5 and mp3 users right ear mean 13 to 32,left ear mean 15.5 to 37 was showing a s exposure though results were not very supportive for reflexometry in impedance audiometry but a significant change in OAE's, and in Mp3 users 90% among all the subjects. it was reported subjectively from the mp3 users as presents of low pitched Tinnitus soon exposure, which is tolerable.

Disclosed is a system for the characterization of thin films and interfaces between thin films through measurements of their mechanical and thermal properties. In the system light is absorbed in a thin film or in a structure made up of several thin films, and the change in optical transmission or reflection is measured and analyzed. The change in reflection or transmission is used to give information about the ultrasonic waves that are produced in the structure. The information that is obtained from the use of the measurement methods and apparatus of this invention can include: (a) a determination of the thickness of thin films with a speed and accuracy that is improved compared to earlier methods; (b) a determination of the thermal, elastic, and optical properties of thin films; (c) a determination of the stress in thin films; and (d) a characterization of the properties of interfaces, including the presence of roughness and defects.

Statistical modeling involves building sufficiently complex models to represent the system being investigated. Overly complex models lead to imprecise parameter estimates, increase the subjective role of the modeler, and can distort the perceived characteristics of the system under investigation. One approach for controlling the tendency to increased complexity and subjectivity is to use model selection criteria that account for these factors. The effectiveness of two selection criteria was tested in an application with the stock assessment program known as Stock Synthesis. This program, which is often used on the U.S. west coast to assess the status of exploited marine fish stocks, can handle multiple data sets and mimic highly complex population dynamics. The Akaike information criterion and Schwarz's Bayesian information criterion are criteria that satisfy the fundamental principles of model selection: goodness-of-fit, parsimony, and objectivity. Their ability to select the correct model form and produce accurate estimates was evaluated in Monte Carlo experiments with the Stock Synthesis program. In general, the Akaike information criterion and the Bayesian information criterion had similar performance in selecting the correct model, and they produced comparable levels of accuracy in their estimates of ending stock biomass. Résumé : La modélisation statistique demande la construction de modèles suffisamment complexes pour représenter le système à l'étude. Si les modèles sont trop complexes, les estimations des paramètres manquent de précision, l'influence subjective du modélisateur est accrue et peut distordre les caractéristiques perçues du système à l'étude. Pour limiter la tendance à l'accroissement de la complexité et de la subjectivité, il est possible d'avoir recours à des critères de sélection du modèle qui tiennent compte de ces facteurs. Nous avons testé l'efficacité de deux critères de sélection dans une application à un programme d'évaluation des stocks (Stock Synthesis), souvent utilisé sur la côte ouest des États-Unis pour évaluer l'état des stocks de poissons marins exploités, qui peut manipuler des séries multiples de données et simuler une dynamique de population extrêmement complexe. Le critère d'information d'Akaike et le critère d'information bayésien de Schwarz sont des critères qui respectent les principes fondamentaux du choix d'un modèle : bon ajustement, parcimonie et objectivité. Nous avons évalué leur aptitude à choisir la forme correcte de modèle et à produire des estimations exactes en menant des expériences par la méthode de Monte Carlo sur le programme Stock Synthesis. En général, la performance du critère d'information d'Akaike et du critère d'information bayésien étaient similaires pour ce qui est du choix du modèle correct, et les modèles produisaient des niveaux comparables d'exactitude dans leurs estimations de la biomasse finale du stock.