Average Sample Research Articles

Average sampling expansions from regular and irregular samples over shift-invariant subspaces on LCA groups

Average sampling expansions from regular and irregular samples over shift-invariant subspaces on LCA groups

Application of Wearable Technologies in Fall Risk Assessment and Improvement in Patients with Peripheral Neuropathy: A Systematic Review

Background. Peripheral neuropathy is regarded as one of the leading causes of fatal and nonfatal falls. Wearable sensors, due to their increasing availability and flexibility of setting and space, are used widely to obtain wearer’s kinematic data to analyze one’s balance capacities for the evaluation of risk of fall. There is yet to have a review study focusing on the application of wearable sensors in the scope of fall risk for patients with peripheral neuropathy. Objective. To investigate the methods by which researchers adopt to assess risk of fall in peripheral neuropathy patients and potentially shed light on future researches. Methods. A systematic review design was used to identify articles on fall risk assessment and balance training using wearable sensors in patients with peripheral neuropathy. The study is aimed at extracting the following information: the type of sensors, the type of signal and data processing employed, the scales and tests used in the study, and the type of application. Results. We identified 351 studies, from which 8 were included. An average sample size of 35.6 patients enrolled the studies. The accelerometer was the most common wearable sensor used. 10‐meter walk test was the preferable procedure for assessing risk of fall. Conclusion. This review examined several key components in studies on assessing and improving the risk of fall using wearable sensors. We identified the preferred functional test (10‐meter walk test), sensor technology (accelerometer), locations (torso and lower legs), and fall risk improvement methods (prostheses). However, due to the limited number of articles specializing in this field of research, a consensus on patient sample size and procedures is not reached. We would recommend future researches to examine more parameters and adopt a fusion sensor setup.

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan (AMDSSP) was designed to inspect products from a continuous manufacturing process under the accelerated life test (ALT) using both double sampling plan (DSP) and multiple dependent state sampling plan (MDSSP) concepts. Under accelerated conditions, the lifetime of a product follows the Weibull distribution with a known shape parameter, while the scale parameter can be determined using the acceleration factor (AF). The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive. An economic design of the proposed sampling plan was also considered for the ALT. A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number (ASN) and total cost of inspection (TC) under both producer’s and consumer’s risks. Numerical results are presented to support the AMDSSP for the ALT, while performance comparisons between the AMDSSP, the MDSSP and a single sampling plan (SSP) for the ALT are discussed. Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions. The AMDSSP also had a higher operating characteristic (OC) curve than both the existing sampling plans. Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

Повышение мясных качеств свиней после дегельминтизации против аскаридоза на фоне применения глицирризиновой кислоты

The present paper describes the use of glycyrrhizic acid contained in licorice roots on the quality of pork produced. Modern research methods are used in the work, such as ion exchange chromatography, inductively coupled plasma mass spectrometry, as well as drying of an average sample of muscle and pressing method. The production part of the experiments was carried out on twenty-one piglets of a large white breed, weighing 14 kilograms. By analogy, piglets are divided into 3 groups. By the method of coprology, ascariasis was detected in piglets of two months of age. After which, all piglets were dewormed with a drug – fenbendazole. The control group had a diet without additional additives. Glycyrrhizic acid was included in the diet of pigs of 2 experimental groups. Studies on live weight gain were carried out for 6 months. Upon reaching 100 kg, pigs of all the studied groups were slaughtered. The inclusion of glycyrrhizic acid in the diet of piglets served as a beneficial effect on the biochemical processes in the piglets' body, as evidenced by the data obtained.

Comparison of Autonomous and Manual UAV Flights in Determining Forest Road Surface Deformations

The deterioration of the surface of forest roads is an important factor affecting the safe navigation of vehicles and traffic safety. In addition to traditional methods, automated methods are also used to determine the deterioration of the road surface. UAV systems, which are among the automated methods, are widely used to determine surface deformations with high accuracy. This study aimed to evaluate the advantages and disadvantages of two different flight modes of UAV, including autonomous flight and manual flight, in mapping road surface deformations. Within the scope of this study, the 50-meter section of the Type B forest road located in Kardüz Forest Management Chief (Düzce/Türkiye), was selected. For this study, first the pros and cons of the autonomous and manual flight data acquisition process were evaluated. Then, the photogrammetric data processing results were compared in terms of data size, with precision and accuracy. In addition, the deformation status on the surface within the selected road was determined using the average Z value differences obtained with two flight methods. The result of the study showed that, the number of images obtained from manual flights was 5.5 times higher than from autonomous flights and the flight time was taken four times longer. The average ground sampling distance of the orthophotos generated from two different light modes indicated that the manual flight mode provided seven times higher resolution than autonomous flight. Moreover, the results from the statistical tests for the two flight modes showed differences. When manual flights and autonomous flights are evaluated in terms of reducing the shadow effect, manual flights can be considered more advantageous. Furthermore, it was found that the dynamic mobility of erosion and accumulation on the road surface continued in time series in both flight methods.

Real-world clinical outcomes and cost estimates of metastatic castration-resistant prostate cancer treatment: does sequencing of taxanes and androgen receptor-targeted agents matter?

ABSTRACT Introduction Health economic outcomes of real-world treatment sequencing of androgen receptor-targeted agents (ARTA) and docetaxel (DOC) remain unclear. Material and Methods Data from the electronic Castration-resistant Prostate cancer Australian Database (ePAD) were analyzed including median overall survival (mOS) and median time-to-treatment failure (mTTF). Mean total costs (mTC) and incremental cost-effectiveness ratios (ICER) of treatment sequences were estimated using the average sample method and Zhao and Tian estimator. Results Of 752 men, 441 received ARTA, 194 DOC, and 175 both sequentially. Of participants treated with both, first-line DOC followed by ARTA was the more common sequence (n = 125, 71%). mOS for first-line ARTA was 8.38 years (95% CI: 3.48, not-estimated) vs. 3.29 years (95% CI: 2.92, 4.02) for DOC. mTTF was 15.7 months (95% CI: 14.2, 23.7) for the ARTA-DOC sequence and 18.2 months (95% CI: 16.2, 23.2) for DOC-ARTA. In first-line, ARTA cost an additional $13,244 per mTTF month compared to DOC. In second-line, ARTA cost $6726 per mTTF month. The DOC-ARTA sequence saved $2139 per mTTF compared to ARTA-DOC, though not statistically significant. Conclusion ICERs show ARTA had improved clinical benefit compared to DOC but at higher cost. There were no significant cost differences between combined sequences.

The use of natural language processing in palliative care research: A scoping review

Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

Optimal predictive probability designs for randomized biomarker-guided oncology trials.

Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of evidence acquired in single-arm phase II clinical trials. And yet, in the absence of randomization, patient prognosis for progression-free survival and overall survival may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used to design and evaluate emerging targeted therapies often arise as population averages, lacking specificity to the targeted genetic or immunophenotypic profile. Thus, historical trial results are inherently limited for inferring the potential "comparative efficacy" of novel targeted therapies. Consequently, randomization may be unavoidable in this setting. Innovations in design methodology are needed, however, to enable efficient implementation of randomized trials for agents that target biomarker subpopulations. This article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. Only designs with type I error between 0.05 and 0.1 and power of at least 0.8 were considered when selecting an optimal efficiency design from among the candidate designs formed by different combinations of posterior and predictive threshold. A simulation study motivated by the results reported in a recent clinical trial studying atezolizumab treatment in patients with locally advanced or metastatic urothelial carcinoma is used to evaluate the operating characteristics of the various designs. Out of a maximum of 300 total patients, we find that the enrichment design has an average total sample size under the null of 101.0 and a total average sample size under the alternative of 218.0, as compared to 144.8 and 213.8 under the null and alternative, respectively, for the stratified control arm design. The pooled control arm design enrolled a total of 113.2 patients under the null and 159.6 under the alternative, out of a maximum of 200. These average sample sizes that are 23-48% smaller under the alternative and 47-64% smaller under the null, as compared to the realized sample size of 310 patients in the phase II study of atezolizumab. Our findings suggest that potentially smaller phase II trials to those used in practice can be designed using randomization and futility stopping to efficiently obtain more information about both the treatment and control groups prior to phase III study.

An unbiased kinship estimation method for genetic data analysis

Accurate estimate of relatedness is important for genetic data analyses, such as heritability estimation and association mapping based on data collected from genome-wide association studies. Inaccurate relatedness estimates may lead to biased heritability estimations and spurious associations. Individual-level genotype data are often used to estimate kinship coefficient between individuals. The commonly used sample correlation-based genomic relationship matrix (scGRM) method estimates kinship coefficient by calculating the average sample correlation coefficient among all single nucleotide polymorphisms (SNPs), where the observed allele frequencies are used to calculate both the expectations and variances of genotypes. Although this method is widely used, a substantial proportion of estimated kinship coefficients are negative, which are difficult to interpret. In this paper, through mathematical derivation, we show that there indeed exists bias in the estimated kinship coefficient using the scGRM method when the observed allele frequencies are regarded as true frequencies. This leads to negative bias for the average estimate of kinship among all individuals, which explains the estimated negative kinship coefficients. Based on this observation, we propose an unbiased estimation method, UKin, which can reduce kinship estimation bias. We justify our improved method with rigorous mathematical proof. We have conducted simulations as well as two real data analyses to compare UKin with scGRM and three other kinship estimating methods: rGRM, tsGRM, and KING. Our results demonstrate that both bias and root mean square error in kinship coefficient estimation could be reduced by using UKin. We further investigated the performance of UKin, KING, and three GRM-based methods in calculating the SNP-based heritability, and show that UKin can improve estimation accuracy for heritability regardless of the scale of SNP panel.

Correlation of Physical Indices with the Subjective Physiological and Neurophysiological Parameters of Health

In our work, we conducted research and analysis of the components of physical health of students of pedagogical specialties. To this end, the vital and strength indices, the Robinson and Rufier indices, and the height-to-weight ratio were determined. The article also confirms the hypothesis that the physical health and the neurophysiological (psychological) state are related and mutually determined, but not in the direct proportion, depending on the type of index and aspect of self-esteem. Specific results were obtained by comparing physical health indices with subjective feelings of psychological or neurophysiological discomfort. Data saturation and their analysis were conducted using the following methods: anthropometry; spirometry; dynamometry; tonometry; functional tests; mathematical and statistical analysis, as well as questionnaires developed by the authors of subjective assessment of their own condition, which were offered remotely. The research was performed on the basis of Khmelnytskyi Humanitarian-Pedagogical Academy at the Department of Physical Culture and Valeology.The experimental part was performed with the participation of students of 1-4 years of three specialties with a total of 118 people, of which 100 were girls and 18 were boys aged 17 to 21 years (region – Ukraine). The average sample values for girls of the Rufier, Robinson, and “strength” indexes were below average; “vital” and “height-to-weight” indices are defined at the average level. Averaged values for the sample for boys were the following: high are “strength” and “height-to-weight” levels; the Robinson index is low, and the Rufier and “vital” indices are below average. The international significance of the article lies in the fact that for the first time the objective and subjective health indices stated by the interviewers have been measured comparatively. The authors' original contribution is determined by unexpected results obtained by correlating physical indices with subjective physiological and neurophysiological health parameters in young boys and girls.

Shear wave velocity measurement of the placenta is not limited by placental location

IntroductionUltrasound elastography shows diagnostic promise via the non-invasive determination of placental elastic properties. A limitation is a potential for inadequate measurements from posterior placentae. This study aimed to analyse placental position's influence on measures of shear wave elastography (SWV). MethodsSWV elastography measurements were obtained via ultrasound at 24, 28 and 36 weeks gestation from 238 pregnancies. . The placental position was labelled as either anterior, posterior or fundal/lateral. Average SWV measurements (m/s) and the corresponding standard deviations (SD) were used for data analysis. ResultsThere was a statistically significant difference between SWV recorded from anterior (1.33 ± 0.19)m/s and posterior (1.39 ± 0.18)m/s placentae (p < 0.001). However, the average sampling depth between these groups was significantly different (3.98 cm vs. 5.38 cm, p < 0.001). There was no statistically significant difference between SWV when measurements were compared at similar depths, regardless of placental location. The addition of placental position to a previously developed mixed-effects model confirmed placental position did not result in improved SWV measurements. In this model, sampling depth remained the best predictor for SWV. ConclusionsThis study showed that placental position does not influence the accuracy or reliability of SWV.

OpenStreetMap-Based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance

OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of "valley" areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road's shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system's robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road's center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians.

First demonstration of a fiber optic bolometer on a tokamak plasma (invited).

A fiber optic bolometer (FOB) was demonstrated observing a fusion plasma for the first time at the DIII-D tokamak. A FOB uses a fiber optics-based interferometric technique that is designed to have a high sensitivity to temperature changes [75 mK/(W/m2) responsivity in high vacuum with 0.38 mK noise level] with a negligible susceptibility to electromagnetic interference (EMI) that can be problematic for resistive bolometers in a tokamak environment. A single-channel test apparatus was installed on DIII-D consisting of a measurement FOB and shielded reference FOB. The single-channel FOB showed a negligible increase in the noise level during typical plasma operations (0.39 mK) compared to the benchtop results (0.38 mK), confirming an insignificant EMI impact to the FOB. Comparisons to DIII-D resistive bolometers showed good agreement with the single-channel FOB, indicating that the FOB is comparable to a resistive bolometer when the impulse calibration is applied. The noise-equivalent power density of the calibrated FOB during a plasma operation was 0.55 W/m2 with an average sampling time of 20ms. The major potential effect of ionizing radiation on the FOB would be the radiation-induced attenuation, which can be efficiently compensated for by adjusting the probing light power.

Comparison of RT-dPCR and RT-qPCR and the effects of freeze–thaw cycle and glycine release buffer for wastewater SARS-CoV-2 analysis

Public health efforts to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic rely on accurate information on the spread of the disease in the community. Acute and surveillance testing has been primarily used to characterize the extent of the disease. However, obtaining a representative sample of the human population is challenging because of limited testing capacity and incomplete testing compliance. Wastewater-based epidemiology is an agnostic alternative to surveillance testing that provides an average sample from the population served by the treatment facility. We compare the performance of reverse transcription quantitative PCR (RT-qPCR) and reverse transcription digital droplet PCR (RT-dPCR) for analysis of SARS-CoV-2 RNA in a regional wastewater treatment facility in northern Indiana, USA from the earliest stages of the pandemic. 1-L grab samples of wastewater were clarified and concentrated. Nucleic acids were extracted from aliquots and analyzed in parallel using the two methods. Synthetic viral nucleic acids were used for method development and generation of add-in standard-curves. Both methods were highly sensitive in detecting SARS-CoV-2 in wastewater, with detection limits as low as 1 copy per 500 mL wastewater. RT-qPCR and RT-dPCR provided essentially identical coefficients of variation (s/overline{mathrm{x} } = 0.15) for triplicate measurements made on wastewater samples taken on 16 days. We also observed a sevenfold decrease in viral load from a grab sample that was frozen at – 80 °C for 92 days compared to results obtained without freezing. Freezing samples before analysis should be discouraged. Finally, we found that treatment with a glycine release buffer resulted in a fourfold inhibition in RT-qPCR signal; treatment with a glycine release buffer also should be discouraged. Despite their prevalence and convenience in wastewater analysis, glycine release and freezing samples severely and additively (~ tenfold) degraded recovery and detection of SARS-CoV-2.

Development of an adaptive sampling system based on a process capability index with flexible switching mechanism

The quick-switch sampling system (QSS) and tightened-normal-tightened sampling system (TSS) are efficient schemes for dispositioning a series of lots. However, the QSS mechanism for switching decision rules is too simple to satisfy the requirements of suppliers and buyers. Conversely, the TSS is more flexible due to its adaptable switching mechanism. The TSS was recently developed based on process capability indices (PCIs) to help practitioners make more reliable and accurate decisions in practice. The existing PCI-based TSSs are the required sample-size type (TSS-n). However, the TSS-n requires a large sample size for the tightened inspection, which is costly and time-consuming. We propose the acceptance-benchmark type TSS (TSS-k) based on the most commonly used PCI, to improve the lot-disposition sampling efficiency. The TSS-k adjusts the acceptance benchmark instead of the sample size to constitute tightened and normal inspections. We investigated combinations of TSS-k switching mechanism parameters and provided managerial suggestions for practitioners. Compared with the existing TSS-n, the proposed TSS-k can reduce the average sample number by more than 60% and has superior discrimination power. Moreover, we developed a cloud-computing programme to calculate the optimal system design online. Finally, we illustrate an industrial case to demonstrate the applicability of the proposed TSS-k.

An innovative passive sampler to reveal the high contribution of biomass burning to black carbon over Indo-China Peninsula: Radiocarbon constraints

Black carbon (BC) is an important component of the brown clouds, contributing to climate and health effects of air pollution. In this work, we developed a new method for measuring BC concentrations and its radiocarbon isotope (14C) using quartz wool disk passive air samplers (Pas-QW). A field calibration study was conducted at two sampling sites (Guangzhou and Yantai), using an improved thermal/optical method to measure BC. Uptake profiles of Pas-QW were linear over the 77 days that the samplers were deployed, and the average sampling rate of BC measured was 1.1 ± 0.2 m3/day. The method was found to be reproducible with coefficients of variation as low as 17% for BC measured in ambient passive samples. Furthermore, passive sampling was carried out at six sites in the Indo-China Peninsula (ICP) from January to April and June to September of 2016. The high BC concentrations and the high fraction of modern carbon (fM) values emphasizes that ICP is still dominated by biomass burning emissions (residential cooking and agricultural burning), both in dry and wet seasons. This study provides a proof concept and methodology for the application of Pas-QW to cost-effectively expand measurements of BC at the global scale.

The Gaia-ESO Survey: Chemical tagging in the thin disk

Context. The chemical makeup of a star provides the fossil information of the environment where it formed. Under this premise, it should be possible to use chemical abundances to tag stars that formed within the same stellar association. This idea – known as chemical tagging – has not produced the expected results, especially within the thin disk where open stellar clusters have chemical patterns that are difficult to disentangle. Aims. The ultimate goal of this study is to probe the feasibility of chemical tagging within the thin disk population using high-quality data from a controlled sample of stars. We also aim at improving the existing techniques of chemical tagging and giving some kind of guidance on different strategies of clustering analysis in the elemental abundance space. Methods. Here we develop the first blind search of open clusters’ members through clustering analysis in the elemental abundance space using the OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate different strategies of analysis (e.g., choice of the algorithm, data preprocessing techniques, metric, space of data clustering), determining which ones are more performing. Second, we apply these methods to a data set including both field stars and open clusters attempting a blind recover of as many open clusters as possible. Results. We show how specific strategies of data analysis can improve the final results. Specifically, we demonstrate that open clusters can be more efficaciously recovered with the Manhattan metric and on a space whose dimensions are carefully selected. Using these (and other) prescriptions we are able to recover open clusters hidden in our data set and find new members of these stellar associations (i.e., escapers, binaries). Conclusions. Our results indicate that there are chances of recovering open clusters’ members via clustering analysis in the elemental abundance space, albeit in a data set that has a very high fraction of cluster members compared to an average field star sample. Presumably, the performances of chemical tagging will further increase with higher quality data and more sophisticated clustering algorithms, which will likely became available in the near future.

Designing of Variables Double Sampling Plan Under Rectifying Inspection for Consumer Protection

Designing of acceptance sampling plans based on operating characteristics fail to prescribe the corrective action for rejected lots although they guarantee the producer’s and the consumer’s protection at their corresponding quality levels. But, rectification sampling inspection plan suggests 100% inspection for rejected lots and hence, the producer’s risk is reduced and also there is a guarantee to the consumer by restricting upper limit of the maximum expected proportion of non-conforming of the shipping lot. Although the variables single sampling plan for rectifying inspection is available, variables double sampling inspection plan under rectifying inspection has not yet been designed. Hence, in this paper, we design a variables double sampling plan for rectifying inspection indexed by acceptable quality level and average outgoing quality limit to meet the needs of the producer as well as the consumer. A non-linear optimization problem is developed to determine the optimal parameters with minimum average sample number at acceptable quality level for both cases of standard deviation are known and unknown. We investigate the validation and limitation of the proposed plan through the simulation study and industrial application of the proposed plan is also given. It can be confirmed from the investigations on the results that in many situations, variables double sampling plan designed under rectifying inspection is more economical than variables single sampling plan designed with the same conditions in providing the same protection to consumer.

RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data.

Deficits in responding to joint attention (RJA) are early symptoms of autism spectrum disorder (ASD). Currently, no automated tools exist for identifying and quantifying RJA behaviors. A few eye tracking studies have investigated RJA in ASD children but have produced conflicting results. In addition, little is known about the trajectory of RJA development through developmental age. Here, a new video was designed including 12 clips of an actor pointing to or looking at an object. Eye tracking technology was used to monitor RJA in three groups: 143 ASD children assessed with the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) (4-7 years old), 113 age- and gender-matched typically developing children (TDC), and 43 typically developing adults (TDA) (19-32 years old). RJAfinder was developed in R and MATLAB to quantify RJA events from the eye tracking data. RJA events were compared among the three groups. Spearman correlation coefficients between total number of RJA events in ASD and the Social Responsiveness Scale (SRS) scores were calculated. A logistic regression model was built using the average valid sampling rate and the total number of RJA events as two predictive variables to classify ASD and TDC groups. ASD children displayed statistically significantly less RJA events than the TDC and TDA groups with medium-to-large-sized effects. ASD and TDC children both displayed more RJA events in response to pointing stimuli than to looking stimuli. Our logistic regression model predicted ASD tendency with 0.76 accuracy in the testing set. RJA ability improved more slowly between the ages of 4-7 years old in the ASD group than in the TDC group. In ASD children, RJA ability showed negative correlation with SRS total T-score as well as the scores of five subdomains. Our study provides an automated tool for quantifying RJA and insights for the study of RJA in ASD children, which may help improve ASD screening, subtyping, and behavior interventions.

Evaluation and optimal designing of a new system of skip-lot reinspection schemes

A new sampling plan by integrating the resampling concept in a skip-lot sampling plan of type SkSP-3 with a single sampling plan as the reference plan is proposed and designated as SkSP-3-R plan. The performance metrics are derived by using the Markov chain formulation. A designing methodology using two points on the operating characteristic curve approach is proposed to determine the plan parameters of the SkSP-3-R plan involving minimum the average sample number. The execution of the proposed SkSP-3-R plan is discussed with an illustrative example. The comparative study shows that the proposed plan has better performance where the inspection is involved in terms of nonconforming items rather than nonconformities. In addition, it demonstrates that the proposed SkSP-3-R plan is more efficient than SkSP-2 plan, SkSP-3 plan, SkSP-2-R plan, and SSP. Based on average run length, the SkSP-3-R plan's characteristic of response-to-change in quality is also investigated.