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Background and objective: The number of people suffering from dementia is increasing worldwide and so is the need for reliable and economical diagnostic instruments. Therefore, the aim of this study was to compare the processing times of the neuropsychological tests Trail Making Tests A and B (TMT-A/B) and Color-Word Interference Test (CWIT), which were performed in both digital and paper versions. Methods: The pilot study was conducted among 50 healthy participants (age 65–83 years) using a randomized crossover design. The correlations and differences in the individual processing times of the two test versions were statistically analyzed. Further research questions concerned the influence of the individual usage of technology and the technology commitment of participants as well as the influence of the assessed usability on participants' performance. Results: Between the two versions (paper-based vs. digital) statistically significant correlations were found in all tests, e.g., TMT-A r(48) = 0.63, p < 0.01; TMT-B rs(48) = 0.77, p < 0.001). The mean value comparison showed statistically significant differences, e.g., interference table (CWIT) t(49) = 11.24, p < 0.01). Correlations with medium effect were found between the differences in processing times and the individual usage of computer (e.g., rs(48) = − 0.31) and smartphone (rs(48) = − 0.29) and between the processing times of the TMT-B and the usability (rs(48) = 0.29). Conclusions: The high correlations between the test procedures appear promising. However, the differences found in the processing times of the two test versions require validation and standardization of digitized test procedures before they can be used in practice. [ABSTRACT FROM AUTHOR]

We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included. [ABSTRACT FROM AUTHOR]

This study examined the measurement equivalence of the original paper-based vertical format of the EQ-5D visual analog scale (EQ VAS) with a touch-screen computer-based horizontal format. A total of 314 subjects were administered two modes of the EQ VAS in a randomized crossover design. One mode was the original paper-based 20 cm vertical EQ VAS; the other mode was touch-screen-based. Measurement equivalence was assessed by testing the 95% confidence interval of the mean differences from an equivalence threshold of −3 to +3 points on the VAS and evaluating the intraclass correlation coefficient (ICC). The adjusted mean (SE) EQ VAS score was 80.96 (0.87) on the paper and 79.59 (0.85) on the touch-screen. The mean (CI) difference between scores on the two formats was 1.37 with a confidence interval of 0.175–2.559, wholly contained within the equivalence interval. The ICC was 0.75, indicating acceptable agreement between the two modes. Almost a third (30.1%) of the respondents reported identical scores on both formats. These results provide evidence for the measurement equivalence of this EQ VAS touch-screen administration mode with the original paper mode. [ABSTRACT FROM AUTHOR]

Purpose: This study compared a tablet PC questionnaire with a paper method for reliability and patient preferences in the acquisition of patient-reported outcomes (PROs) for patients treated with radiotherapy. By comparing the two modes of PRO administration, we aimed to evaluate the adequacy of using tablet PC questionnaires in future clinical use.Methods: Patients were randomized in a crossover study design using two different methods for PRO entry. A group of 89 patients answered a paper questionnaire followed by the tablet PC version, whereas 89 patients in another group completed the tablet PC questionnaire followed by the paper version. Surveys were performed four times per patient throughout the course of the radiotherapy. The Korean versions of the M.D. Anderson Symptom Inventory (MDASI-K) and the Brief Fatigue Inventory (BFI-K) were used. The primary endpoint of our current study was an assessment of patient preference for the survey method. The proportions of patients preferring each mode of questionnaire were evaluated.Results: The proportion of patients who preferred the tablet PC version, paper form, or who had no preference was 52.2, 22.1, and 25.7 %, respectively. More than half of the patients preferred the tablet PC to the paper version in all four surveys. Age, gender, educational status, prior experience of using a tablet PC, and the order of paper to tablet PC administration did not impact patient preferences. Inter-class correlation coefficients (ICCs) between the modes were 0.92 for MDASI-K and 0.94 for BFI-K and ranged from 0.91 to 0.96 on both instruments during the four surveys.Conclusions: A tablet PC-based PRO is an acceptable and reliable method compared with paper-based data collection for Korean patients receiving radiotherapy. [ABSTRACT FROM AUTHOR]

Due to sampling restrictions in the analysis of cultural heritage materials, non-destructive approaches are intensively sought for. While NIR spectrometry has rarely been used for this purpose due to the complexity of the spectra, chemometric methods can be used to extract the necessary information. For the purpose of determination of mechanical properties of historical paper, partial least squares approach was used and it is shown that tensile strength, and tensile strength after folding, can be estimated based on NIR spectra. As the mechanical properties of paper-based objects define their accessibility, a new dispersive portable instrument was built, which will enable us to rapidly survey the condition of library and archival collections. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

This paper seeks to examine the questions of what criteria definitions of Artificial Intelligence (AI) use to define AI, what the disagreements that revolve around the term AI are based on, and what correlations can be drawn to other parameters. Framed as a problem of classification, a random sample of 45 definitions from various text sources was subjected to a qualitative content analysis. The criteria found are concluded in five dimensions, namely (1) learning ability, (2) human likeness, (3) state of "mind", (4) complexity of the problem, and (5) successfulness. Further, the results support the view that there is no consensus neither on which of these criteria are crucial to define AI nor on how these criteria must be fulfilled. By opposing the frequencies of the dimensions found with the metadata collected, it can be seen that most of these, e.g., country, scientific field, or gender of the author, are statistically independent of content variables, while the medium in which the definition was published shows a strong correlation. Since different mediums target different purposes and different readers, it must be taken into account that writing a definition of AI is to be seen in the context of its distribution area and its goal. [ABSTRACT FROM AUTHOR]

In this paper, we study the frequency of different types of arithmetic word problems (AWP) in Lithuanian textbooks. The results show the lack of variety among types of AWP. We propose the framework for analysis of the frequency of types of AWP in a textbook and apply it to a particular set of primary school textbooks. We use a statistical method to compare the sample from the textbook rather than from the entire textbook. Also, we compare the proportions of types of AWP in Lithuanian textbooks with those in Singaporean and Spanish textbooks. The approach adopted in the paper can be used to analyze other textbooks from different countries. [ABSTRACT FROM AUTHOR]

Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of Things malicious traffic classification problems. However, the employment difficulty of these models may be immense due to their high resource consumption and inadequate interpretability. Fortunately, the effectiveness of sampling methods based on the statistical principles in imbalance data distribution indicates the path. In this paper, we address these challenges by proposing a hybrid sampling method, termed HSS, which integrates undersampling and oversampling techniques. Our approach not only mitigates the imbalance in malicious traffic but also fine-tunes the sampling threshold to optimize performance, as substantiated through validation tests. Employed across three distinct classification tasks, this method furnishes simplified yet representative samples, enhancing the baseline models' classification capabilities by a minimum of 6.02% and a maximum of 182.66%. Moreover, it notably reduces resource consumption, with sample numbers diminishing to a ratio of at least 83.53%. This investigation serves as a foundation, demonstrating the efficacy of HSS in bolstering security measures in IoT networks, potentially guiding the development of more adept and resource-efficient solutions. [ABSTRACT FROM AUTHOR]

In this paper we aim to propose a new method for improving the design effect of household surveys based on a two-stage design in which the first stage clusters, or Primary Selection Units (PSUs), are stratified along administrative boundaries. Improvement of the design effect can result in more precise survey estimates (smaller standard errors and confidence intervals) or in the reduction of the necessary sample size, i.e. a reduction in the budget needed for a survey. The proposed method is based on the availability of a previously conducted poverty maps, i.e. spatial descriptions of the distribution of per capita consumption expenditures, that are finely disaggregated in small geographic units, such as cities, municipalities, districts or other administrative partitions of a country that are directly linked to PSUs. Such information is then used to select PSUs with systematic sampling by introducing further implicit stratification in the survey design, so as to maximise the improvement of the design effect. Since per capita consumption expenditures estimated at PSU level from the poverty mapping are affected by (small) standard errors, in the paper we also perform a simulation study in order to take into account this addition variability. [ABSTRACT FROM AUTHOR]

Interactive computer simulations are commonly used as pedagogical tools to support students' statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks—dual processes and grounded cognition—in the context of people's conceptions about statistical sampling, setting the stage for the potential benefits of simulations in learning such conceptions. Then, we continue with reviewing the educational literature on statistical sampling simulations. Our review tentatively suggests benefits of the simulations for building statistical habits of mind. However, challenges seem to persist when more specific concepts and skills are investigated. With and without simulations, students have difficulty forming an aggregate view of data, interpreting sampling distributions, showing a process-based understanding of the law of large numbers, making statistical inferences, and context-independent reasoning. We propose that grounded cognition offers a framework for understanding these findings, highlighting the bidirectional relationship between perception and conception, perceptual design features, and guided perceptual routines for supporting students' meaning making from simulations. Finally, we propose testable instructional strategies for using simulations in statistics education. Significance: Interactive computer simulations are popularly used to teach statistical sampling and inference. A substantial body of classroom-based design research has emerged over the last two decades on this topic, paralleling the interest of cognitive psychologists in statistical reasoning. This review bridges the gap by synthesizing diverse literature, from laboratory-based cognitive research to classroom-based design research, to investigate people's reasoning about statistical sampling with interactive computer simulations. We organize the commonly occurring findings from these studies under a grounded cognition framework. Using this framework, we also identify instructional design strategies that future empirical researchers can test and statistics and data science practitioners can adopt. First, we highlight the importance of repeated exposure to simulations in a way that fosters creating perception–action routines aligned with mathematical principles. Second, we argue that intuitive representations ground students' meaning making from simulations, and idealized representations help generalize learning. Third, we recommend that visual routines be guided during activities with simulations. Fourth, we note the separate affordances of simulations and verbal materials. Lastly, we propose that statistical processes depicted in the simulations should be reified as foundations for more advanced concepts and practices. Overall, the paper contributes to the learning theories and instructional design in the context of simulation-based learning in statistics. [ABSTRACT FROM AUTHOR]

In survey statistics, estimating and reducing population variation is crucial. These variations can occur in any sampling design, including stratified random sampling, where stratum weights may increase the variance of estimators. Calibration techniques, which use additional auxiliary information, can help mitigate this issue. This paper examines three calibration-based estimators—calibration variance, calibration ratio, and calibration exponential ratio estimators—within the framework of stratified random sampling. The study generates data from normal, gamma, and exponential distributions to test these estimators. Results demonstrate that the proposed calibration estimators offer more accurate estimates of population variance and outperform existing methods in estimating population variance under stratified random sampling, providing more accurate and reliable estimates. [ABSTRACT FROM AUTHOR]

Deep neural networks are increasingly used in medical imaging for tasks such as pathological classification, but they face challenges due to the scarcity of high-quality, expert-labeled training data. Recent efforts have utilized pre-trained contrastive image-text models like CLIP, adapting them for medical use by fine-tuning the model with chest X-ray images and corresponding reports for zero-shot pathology classification, thus eliminating the need for pathology-specific annotations. However, most studies continue to use the same contrastive learning objectives as in the general domain, overlooking the multi-labeled nature of medical image-report pairs. In this paper, we propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. We aim to improve the performance of zero-shot pathology classification without relying on external knowledge. Our method can be applied to any pre-trained contrastive image-text encoder and easily transferred to out-of-domain datasets without further training, as it does not use external data. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models, with an average macro AUROC increase of 4.3%. Additionally, our method outperforms the state-of-the-art and marginally surpasses board-certified radiologists in zero-shot classification for the five competition pathologies in the CheXpert dataset. [ABSTRACT FROM AUTHOR]

In this paper, we obtain the asymptotic form of the joint distribution of maxima and minima of independent observations, when the sample size is a random variable. We also discuss the asymptotic distribution of the Range. [ABSTRACT FROM AUTHOR]

This paper reports the results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support the implementation of a digital medicine intervention for persons with mild-to-moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (SM; n = 185), peer-supported use (PS; n = 186), or a clinically integrated model CI; (n = 187). Across all three groups, the percentage of self-reported heavy drinking days dropped from 38.4% at baseline (95% CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health and quality of life compared to the self-monitoring group (p = 0.011). However, higher attrition rates in the clinically integrated group warrant consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction. This study was prospectively registered at clinicaltrials.gov on 7/03/2019 (NCT04011644). [ABSTRACT FROM AUTHOR]

This paper offers a novel approach to formulate efficient ratio estimator of the population variance using a transformed auxiliary variable. The impact of transformation on auxiliary information has also been discussed. It is observed that incorporating a transformed auxiliary variable result in a high gain in efficiency. Theoretical properties of the newly developed estimators have been derived. The empirical and simulation studies show that the suggested estimators outperformed the existing estimators. [ABSTRACT FROM AUTHOR]

The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model's predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties. An active learning model, muTOX-AL, has been proposed for the task of molecular mutagenicity prediction. muTOX-AL is capable of rapidly enhancing its performance with a small number of labeled samples, thereby reducing experimental costs. [ABSTRACT FROM AUTHOR]

Purpose: Although batterers' intervention programs (BIPs) constitute an important part of the coordinated response to intimate partner violence (IPV) and contribute to increasing the perpetrators' accountability and keeping victims safe, the effectiveness of these programs is still debated. Only recently has the focus of these debates shifted away from researching outcomes by measuring overall program effectiveness toward specific qualities of interventions across program models that may be effective for the distinct client (Babcock et al., Clinical Psychology Review,23(8), 1023–1053, 2004; Zarling et al., Psychology of Violence,9(3), 257–266, 2019). The discussion on the practical implementation of BIPs was supplemented by various significant concepts emerging from new empirical findings, such as differential treatment, motivational interviewing, and effective facilitator–client alliances (Hamel et al., Partner Abuse,11(4), 387–414, 2020; Holtrop et al., Journal of Interpersonal Violence,32(8), 1267–1290, 2017). This paper aimed to address some of the abovementioned concepts by identifying challenges that arise during the implementation of BIPs in Lithuania. Methods: Using qualitative data from semi-structured interviews and focus group discussions with BIPs facilitators, BIPs attendees, and various stakeholders, the paper discusses how to contribute to more effective BIPs outcomes. Results: According to this study, the process of implementing BIPs in Lithuania should be systematized by incorporating existing instruments, such as motivational interviewing, risk-based assessment, and differential treatment, evidence-based programs focused on different theoretical approaches, and forms of implementation. Conclusion: The study found that the consistency and integrity of the entire BIP implementation process should be prioritized, and institutional cooperation in Lithuania needs to be improved to achieve a successful coordinated response to intimate partner violence. [ABSTRACT FROM AUTHOR]

Geostatistics is concerned with the estimation and prediction of spatially continuous phenomena using data obtained at a discrete set of locations. In geostatistics, preferential sampling occurs when these locations are not independent of the latent spatial field, and common modeling approaches that do not account for such a dependence structure might yield wrong inferences. To overcome this issue, some methods have been proposed to model data collected under preferential sampling. However, while these methods assume a constant degree of preferentiality, real data may present a degree of preferentiality that varies over space. For that reason, we propose a new model that accounts for preferential sampling by including a spatially varying coefficient that describes the dependence strength between the process that models the sampling locations and the latent field. To do so, we approximate the preferentiality component by a set of basis functions with the corresponding coefficients being estimated using the integrated nested Laplace approximation (INLA) method. By doing that, we allow the degree of preferentiality to vary over the domain with low computational burden. We assess our model performance by means of a simulation study and use it to analyze the average PM 2.5 levels in the USA in 2022. We conclude that, given enough observed events, our model, along with the implemented inference routine, retrieves well the latent field itself and the spatially varying preferentiality surface, even under misspecified scenarios. Also, we offer guidelines for the specification and size of the set of basis functions. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study. [ABSTRACT FROM AUTHOR]

Uniform sampling in the permutation space is very important for solving permutation problems with NP-hard nature. However, due to the complexity of this space, there is no uniform sampling method for it up to now. In this paper, the description of permutation space and a review of uniform sampling in other space are given. After that, the limitation of the random method for uniform sampling is analyzed, and a k-means clustering algorithm with an improved Borda's method is introduced for sampling based on the above analysis. An extended Latin matrix is defined, and a sampling method based on this matrix that can only solve for a fixed number of sampling is presented. The properties of this method are explored and demonstrated. A uniform sampling method is then proposed for an arbitrary number of sampling points. Experiments are implemented under different sizes of permutation spaces and the results show that the method proposed in this paper has superior performance, which is more than 100 times better than the random method. [ABSTRACT FROM AUTHOR]

The area under a receiver operating characteristic (ROC) curve is frequently used in medical studies to evaluate the effectiveness of a continuous diagnostic biomarker, with values closer to one indicating better classification. Unfortunately, the standard statistical procedures based on simple random sampling (SRS) and ranked set sampling (RSS) techniques tend to be less efficient when the values of the area under a ROC curve (AUC) get closer to one. Thus, developing some statistical procedures for efficiently estimating the AUC when it is close to one is very important. In this paper, some estimators are developed using nomination sampling to assess AUC. The proposed AUC estimators are compared with their counterparts in SRS and RSS using Monte Carlo simulation. The results show that some of the estimators developed in this study considerably improve the efficiency of the AUC estimation when it is close to one. This substantially reduces the cost and time for the sample size needed to obtain the desired precision. [ABSTRACT FROM AUTHOR]

In this paper, a new improved calibration estimator for estimating the population mean in the stratified random sampling using standard deviation of the auxiliary variable is proposed. A simulation study was conducted to assess the performance of the estimators in symmetric and skewed populations using absolute relative bias, mean square error and percentage relative efficiency. The results showed that the proposed estimator is more efficient compared to the existing estimators considered in this work. [ABSTRACT FROM AUTHOR]

A method commonly used in health care research is the waiting room (WR) survey. While patients are waiting for their appointment, they are asked to complete a questionnaire measuring their attitudes, behaviors and other characteristics. In this paper we synthesize practical guidelines for WR surveys by comparing the method with two similar approaches: public intercept (PI) surveys and drop-off-pick-up (DOPU) surveys. In this comparison we use the Total Survey Error approach Groves (Survey Methodology, Wiley, New York, 2004); (Groves in Public Opinion Quarterly 74(5): 849-879, 2010) and apply it to three case examples in which one of the three surveys is used. We take into account measurement (validity, measurement- and processing error) and representation (coverage-, sampling- and nonresponse error). From our review, we conclude that waiting room surveys, though limited to patients and their caregivers, can provide useful information on patients' perspective on health care. Response rates in waiting rooms are usually high, but often not even reported. We recommend adjustment for sampling bias by taking into account the number of visits to the hospital per respondent and sample times proportionate to the number of sample members expected on a particular time. These surveys also allow for collection of para-data; i.e., relevant information in the circumstances of a request to participate in survey research, and behavior of surveyors can easily be controlled, or investigated in an experimental design. [ABSTRACT FROM AUTHOR]

Feasible region detection is a critical problem in the field of intelligent driving. To solve the problem that the feasible region of vehicles is difficult to identify in the unstructured environment, a detection method based on the improved Random Sample Consensus (RANSAC) algorithm and Pyramidal Stereo Matching Network (PSMNet) is proposed in this paper. To overcome the influence of the complex environment and weather on the detection results, the 3D point cloud of the detection region is established to restore the actual scene. In contrast to the traditional stereo matching algorithm, the disparity map is obtained by the deep learning method PSMNet. Therefore, the 3D reconstruction of the detection region can be realized more accurately. Subsequently, the method of point cloud selection in RANSAC is redefined by constructing the k-dimensional tree (kd-tree) and using radius space density. Finally, the detection results of the feasible region are obtained by the improved RANSAC. It is tested in a variety of scenarios in complex environments and weather. The results demonstrate the high environmental adaptability and stability of the detection method. And compared with another method Segformer, the comparison results indicate that the method is effective in detecting road scenes under multiple weather conditions. [ABSTRACT FROM AUTHOR]

Modern particle size statistics uses many different statistical distributions, but these distributions are empirical approximations for theoretically unknown relationships. This also holds true for the famous RRSB (Rosin-Rammler-Sperling-Bennett) distribution. Based on the compound Poisson process, this paper introduces a simple stochastic model that leads to a general product form of particle mass distributions. The beauty of this product form is that its two factors characterize separately the two main components of samples of particles, namely, individual particle masses and total particle number. The RRSB distribution belongs to the class of distributions following the new model. Its simple product form can be a starting point for developing new particle mass distributions. The model is applied to the statistical analysis of samples of blast-produced fragments measured by hand, which enables a precise investigation of the mass-size relationship. This model-based analysis leads to plausible estimates of the mass and size factors and helps to understand the influence of blasting conditions on fragment-mass distributions. [ABSTRACT FROM AUTHOR]

We study mentorship in scientific collaborations, where a junior scientist is supported by potentially multiple senior collaborators, without them necessarily having formal supervisory roles. We identify 3 million mentor–protégé pairs and survey a random sample, verifying that their relationship involved some form of mentorship. We find that mentorship quality predicts the scientific impact of the papers written by protégés post mentorship without their mentors. We also find that increasing the proportion of female mentors is associated not only with a reduction in post-mentorship impact of female protégés, but also a reduction in the gain of female mentors. While current diversity policies encourage same-gender mentorships to retain women in academia, our findings raise the possibility that opposite-gender mentorship may actually increase the impact of women who pursue a scientific career. These findings add a new perspective to the policy debate on how to best elevate the status of women in science. Here, the authors study mentorship in scientific collaborations, and find that mentorship quality predicts the scientific impact of protégés post mentorship. Moreover, female protégés collaborating with male mentors become more impactful post mentorship than those who collaborate with female mentors. [ABSTRACT FROM AUTHOR]

This paper provides new evidence on the geography of literacy rates in pre-unitary Italy. I provide direct estimates based on a novel and balanced random sample of marriage certificates in 1815. The new figures are disaggregated by gender, area, and skill levels, and are compared to the relevant alternative estimates available. Literacy rates are generally low, and North versus South difference in 1815 literacy was as low as 13.7 percentage points, with the Southern literacy rate of about 50% that of Northern Italy. The North–South gap is much smaller for women than for men, and the average female literacy rate across Italy is a lower 9%. Literacy rates in Central Italy were almost identical to that of Southern Italy, arguably because the schooling systems of Central and Southern Italy were more elitist than the Northern Italian one. This evidence suggests that, although partially present also before 1815, the wide magnitude of North–South gaps in literacy which characterized the country on the eve of the political unification (1861) originated after the Napoleonic period. Primary school centralization reforms might have helped women to rapidly improve literacy rates, leading to a first, regionally unequal, 'Silent Revolution' (Cappelli and Vasta in Cliometrica 15:1–27, 2020a). [ABSTRACT FROM AUTHOR]

The log-normal and the log-logistic distributions are two of the most commonly used distributions for studying positively skewed lifetime data. Both the distributions share number of interesting properties, and for a certain range of parameters their cumulative and hazard functions can also be similar in nature. However, selecting a more appropriate distribution and discriminating among them for a given data to best fit is an important issue. Further, when the data are observed in the presence of some censoring scheme the problem becomes more challenging. In this paper, we address the problem of selecting a more appropriate distribution by discriminating based on the random samples drawn in the presence of type-II censoring. We consider the difference of the maximized log-likelihood functions, and compute the asymptotic distribution of the discrimination statistic. We further propose a modified discriminating approach, and compute the probabilities of correct selection to check the performance of the discrimination procedure. Finally, simulation study is conducted, and two real data sets are analysed for the illustration purpose. [ABSTRACT FROM AUTHOR]

With the development of 3D matching technology, point cloud registration (PCR) based on corresponding points has received increasing attention in the field of computer vision. Unfortunately, 3D keypoint technology inevitably produces a large number of outliers. To solve the problems of poor stability, low efficiency and the high number of iterations required to calculate the accepted solution of random sampling consistency (RANSAC) and its variants under a high outlier rate, a streamlined progressive sample consensus (SPROSAC) algorithm is proposed in this paper. SPROSAC is an improved estimator of progressive sample consensus that guides the sampling process by increasing the use of 3D point cloud surface information and optimizes the model verification process based on registration error decision acceptance. Compared to classic RANSAC-family algorithms, SPROSAC has a greater probability of obtaining an accepted solution more quickly. The experiments demonstrate that SPROSAC achieves significantly smaller and more stable registration errors with fewer iterations across three datasets. In the performance experiments based on evaluation metrics such as recall, 1-precision, and F1 score for inlier classification, SPROSAC demonstrates the best performance across the three datasets, with outlier rates exceeding 95%. Furthermore, we propose a coarse–fine PCR algorithm based on SPROSAC and ICP to address the issues of high initialization requirements, susceptibility to local optima, and low efficiency in traditional ICP algorithms. The experimental results of coarse–fine registration show that our algorithm provides initial values for the ICP, which can reduce the number of iterations of the ICP by 50%, 64.4%, and 57.4%. [ABSTRACT FROM AUTHOR]

Direct observation, through surveys, underpins nearly all aspects of environmental sciences. Survey design theory has evolved to make sure that sampling is as efficient as possible whilst remaining robust and fit-for-purpose. However, these methods frequently focus on theoretical aspects and often increase the logistical difficulty of performing the survey. Usually, the survey design process will place individual sampling locations one-by-one throughout the sampling area (e.g. random sampling). A consequence of these approaches is that there is usually a large cost in travel time between locations. This can be a huge problem for surveys that are large in spatial scale or are in inhospitable environments where travel is difficult and/or costly. Our solution is to constrain the sampling process so that the sample consists of spatially clustered observations, with all sites within a cluster lying within a predefined distance. The spatial clustering is achieved by a two-stage sampling process: first cluster centres are sampled and then sites within clusters are sampled. A novelty of our approach is that these clusters are allowed to overlap and we present the necessary calculations required to adjust the specified inclusion probabilities so that they are respected in the clustered sample. The process is illustrated with a real and on-going large-scale ecological survey. We also present simulation results to assess the methods performance. Spatially clustered survey design provides a formal statistical framework for grouping sample sites in space whilst maintaining multiple levels of spatial-balance. These designs reduce the logistical burden placed on field workers by decreasing total travel time and logistical overheads.Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large errors. In this paper, we propose a random subspace sampling method, RSS, by sampling missing items from the corresponding feature histogram distributions in random subspaces, which is effective and efficient at different levels of missing data. Unlike most established approaches, RSS does not train on fixed imputed datasets. Instead, we design a dynamic training strategy where the filled values change dynamically by resampling during training. Moreover, thanks to the sampling strategy, we design an ensemble testing strategy where we combine the results of multiple runs of a single model, which is more efficient and resource-saving than previous ensemble methods. Finally, we combine these two strategies with the random subspace method, which makes our estimations more robust and accurate. The effectiveness of the proposed RSS method is well validated by experimental studies. [ABSTRACT FROM AUTHOR]

Innovation and quality management are two fundamental business orientations that complement each other in improving performance and are important drivers of long-term economic growth. These themes have generated widespread attention in the literature; however, most of these studies mainly focused on a narrow area and only in a short term. No systematic effort has been made to build an extended bibliometric database regarding these research areas, which can be immediately used to conduct literature reviews. This study presents a complete (from 1975–2021), up-to-date, preprocessed and geocoded bibliometric database combining published articles of the two themes. The data collection was performed following the PRISMA methodology. The database consists of seven data tables, including one core dataset with 59,231 records and six citation network-related tables, including latitude and longitude values of the affiliations. These data will benefit researchers conducting comparative and in-depth analyses, such as gaining an overview of relevant existing studies, identifying relevant trends and gaining opportunities for a variety of geographic analyses. Measurement(s) Scientific Publication Technology Type(s) Systematic Review Sample Characteristic - Environment Innovation and Quality Management Sample Characteristic - Location Global [ABSTRACT FROM AUTHOR]

It is hard to come up with a strong learning algorithm with high cross-media retrieval accuracy, but finding a weak learning algorithm with slightly higher accuracy than random prediction is simple. This paper proposes an innovative Bagging-based cross-media retrieval algorithm (called BCMR) based on this concept. First, we use bootstrap sampling to take a random sample from the original set. The amount of sample abstracted by bootstrapping is set to be the same as the amount of sample abstracted by the original dataset. Secondly, 50 bootstrap replicates are used to train 50 weak classifiers. In our experiments, we used homogeneous individual classifiers and eight different baseline methods. Last but not least, we generate the final strong classifier from 50 weak classifiers using the sample voting integration strategy. Using collective wisdom, we can get rid of bad decisions, giving the integrated model a much better generalization ability. Extensive experiments on three datasets show that BCMR can significantly improve cross-media retrieval accuracy. [ABSTRACT FROM AUTHOR]

Background: Goals generated by Goal Attainment Scaling (GAS) can be used as an outcome measure to promote person-centred research and care. There are no training packages which support its use outside of the rehabilitation discipline. This paper describes the development and evaluation of a training package to support the implementation of GAS as an outcome measure in healthcare research. The training package consisted of classroom teaching, a training manual for self-directed learning, one-on-one simulation and hot reviews. It was developed for the GOAL Trial, a randomised controlled trial assessing a Comprehensive Geriatric Assessment's effectiveness in enabling frail older people living with chronic kidney disease to attain their goals. Training participants were invited to complete pre- and post-training online evaluation surveys. Results: Forty-two healthcare professionals attended an initial online classroom teaching, with 27 proceeding to administer GAS to GOAL Trial patients. Response rates for the online pre- and post-training surveys were 95% and 72%, respectively. Prior to training, only 15% of participants reported being able to appropriately scale and troubleshoot GAS goals. Post-training this was 92%. There was 100% participant satisfaction for the training manual, one-on-one simulation, and hot reviews. Conclusions: This training package helps ensure healthcare professionals administering GAS have adequate knowledge and skills. It has the potential for adoption as a guide to support the implementation of GAS by other researchers seeking to embrace persont-centred principles in their work. [ABSTRACT FROM AUTHOR]

In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study. [ABSTRACT FROM AUTHOR]

Background: Decisions regarding maintenance therapy in patients with multiple myeloma should be based on both treatment efficacy and health-related quality of life (HRQL) consequences. In the CARFI trial, patients with first relapse of multiple myeloma underwent salvage autologous stem cell transplantation (salvage ASCT) before randomization to carfilzomib-dexamethasone maintenance therapy (Kd) or observation. The primary clinical endpoint was time to progression, which was extended by 8 months by Kd. The aim of this paper is to present the all HRQL endpoints of the CARFI trial including the HRQL effect of Kd maintenance therapy relative to observation. The primary HRQL endpoint was assessed by EORTC QLQ-C30 Summary score (QLQ-C30-sum) at 8 months follow-up. A key secondary HRQL endpoint was quality-adjusted progression-free-survival (QAPFS). Methods: HRQL was assessed with EORTC QLQ-C30, EORTC QLQ-MY20 and FACT/GOG-Ntx at randomization and every second month during follow-up. HRQL data were analyzed with linear mixed effect models until 8 months follow-up. QAPFS per individual was calculated by multiplying progression-free survival (PFS) by two quality-adjustment metrics, the QLQ-C30-sum and EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The QAPFS per treatment group was estimated with the Kaplan-Meier method. P < 0.05 was used for statistical significance, and a between-group minimal important difference of 10 points was interpreted as clinically relevant for the QLQ-C30-sum. Results: 168 patients were randomized. HRQL questionnaire compliance was 93%. For the QLQ-C30-sum, the difference of 4.62 points (95% confidence interval (CI) -8.9: -0.4, p = 0.032) was not clinically relevant. PFS was 19.3 months for the Kd maintenance group and 16.8 months for the observation group; difference = 2.5 months (95% CI 0.5; 4.5). QAPFS based on the QLQ-C30-sum for the Kd maintenance group was 18.0 months (95% CI 16.4; 19.6) and for the observation group 15.0 months (95% CI 13.5; 16.5); difference = 3.0 months (95% CI 0.8–5.3). QAPFS based on the QLU-C10D for the Kd maintenance group was 17.5 months (95% CI 15.9; 19.2) and 14.0 months (95% CI 12.4; 15.5) for the observation group; difference = 3.5 months (95% CI 1.1–5.9). Conclusions: Kd maintenance therapy after salvage ASCT did not adversely affect overall HRQL, but adjustment for HRQL reduced the PFS compared to unadjusted PFS. PFS of maintenance therapy should be quality-adjusted to balance the benefits and HRQL impact. [ABSTRACT FROM AUTHOR]

The problem of optimal estimation of location and scale parameters of absolutely continuous distributions, by means of two-dimensional confidence regions based on L-statistics, is considered. The case, when the sample size is random and tends to infinity, is studied. The paper can be considered as a supplement to Zaigraev and Alama-Bućko (Metrika 81:283–305, 2018) in case of samples of random size. [ABSTRACT FROM AUTHOR]

In this paper, we present a new proposal on how to measure quark/gluon jet properties at the LHC. The measurement strategy takes advantage of the fact that the LHC has collected data at different energies. Measurements at two or more energies can be combined to yield distributions of any jet property separated into quark and gluon jet samples on a statistical basis, without the need for an independent event-by-event tag. We illustrate our method with a variety of different angularity observables, and discuss how to narrow down the search for the most useful observables. [ABSTRACT FROM AUTHOR]

One popular approach to access the importance/influence of a group of nodes in a network is based on the notion of centrality. For a given group, its group betweenness centrality is computed, first, by evaluating a ratio of shortest paths between each node pair in a network that are "covered" by at least one node in the considered group, and then summing all these ratios for all node pairs. In this paper we study the problem of finding the most influential (or central) group of nodes (of some predefined size) in a network based on the concept of betweenness centrality. One known approach to solve this problem exactly relies on using a linear mixed-integer programming (linear MIP) model. However, the size of this MIP model (with respect to the number of variables and constraints) is exponential in the worst case as it requires computing all (or almost all) shortest paths in the network. We address this limitation by considering randomized approaches that solve a single linear MIP (or a series of linear MIPs) of a much smaller size(s) by sampling a sufficiently large number of shortest paths. Some probabilistic estimates of the solution quality provided by our approaches are also discussed. Finally, we illustrate the performance of our methods in a computational study. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been extensively utilized in feature selection tasks because they can obtain the global optimal solution. However, the meta-heuristic algorithm will take too much time in the face of a large number of samples. Although most of the studies compromise to approximate optimal solutions for avoiding time-consuming problems, a new problem with reduced classification performance, especially classification stability, is then generated. Aiming to above problems, this paper proposes a new feature selection framework. First, this framework exploits a voting ensemble strategy to improve classification stability by reducing the impact of misclassified labels on the overall classification results. Second, the framework uses a data perturbation strategy to enhance classification accuracy. In particular, the data perturbation strategy is able to generate more neighborhood relationships in the dataset, which could reveal the distribution of various features of the samples. A voting ensemble of different feature distributions is capable of extracting more information from the dataset, then the initially misclassified samples are more likely to be returned to the correct classification. Third, the framework takes a random sampling accelerator into account to solve the problem of excessive time consumption by reducing the size of the search sample space. Finally, for the sake of verifying the effectiveness of the proposed framework, four meta-heuristic feature selection methods based on a neighborhood rough set are compared on 20 datasets. The experimental results indicate that our framework could improve classification performance and accelerate feature selection, particularly in confronting large sample sizes. [ABSTRACT FROM AUTHOR]

In recent years, Hunza has seen a surge of suicide cases among youth. This paper attempts to decipher the multiple layers of meanings entailed in suicide cases. The dominant perception among the natives is the attribution of influence to supernatural beings. This paper attempts to provide insights into the supernatural cosmology and its relationship to youth suicide among the Burushaski speakers of Hunza. Using qualitative research methodology, this paper describes the local perception of supernatural beings, and their classifications as well as the role of local shamans, known in Burushaski as bitan. In this scenario, suicide is not seen as a self-destruction, but is rather a punishment by the supernatural beings owing to the violation of the sacred supernatural social order imposed by the spirits connected to the supernatural world. For the natives, both the terrestrial and supernatural spheres share common habitats and mountain ecology; however, the latter is perceived to have more powers than the former. Therefore, breach of this order is highly discouraged and bears dire consequences. What appears as mere supernatural and human conflict actually carries deeper references to social and ecological disruptions. [ABSTRACT FROM AUTHOR]

Sampling design plays a critical role in farm-level digital soil mapping (DSM). In many cases, a soil mapping model may not have been decided upon at the sample design stage. Design-based sampling may be more appropriate than model-based sampling because it is independent of subsequent soil mapping models. However, existing sampling methods optimize the sample size and locations in geographical space or feature space without considering the impacts of environmental similarity in local geographical space. In this paper, a novel sampling design method based on local environmental similarity was developed. Image segmentation was introduced into the sampling design by partitioning agricultural soil into subregions with good spatial continuity, within-region homogeneity, and between-region heterogeneity to determine the optimal sample size and locations. First, the environmental similarity between adjacent soils was calculated. Second, the merging process was iteratively conducted, and a series of segmentations was generated. Finally, the optimal sample size and locations were determined based on the optimal segmentation results. To validate the proposed method, it was compared with stratified random sampling, k-means sampling, and spatially balanced sampling methods. Two mapping models, ordinary kriging and sandwich estimation, were employed to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus, and available potassium. These comparative experiments showed that the proposed method had better potential to generate farm-level muti-soil property mapping results with good accuracy than the competing sampling methods. In conclusion, consideration of local environmental similarity and the use of image segmentation for soil sampling were helpful in determining the optimal sample size and key sample locations. [ABSTRACT FROM AUTHOR]

The slow convergence rate and large cost of the initial solution limit the performance of rapidly exploring random tree star (RRT*). To address this issue, this paper proposes a modified RRT* algorithm (defined as FF-RRT*) that creates an optimal initial solution with a fast convergence rate. An improved hybrid sampling method is proposed to speed up the convergence rate by decreasing the iterations and overcoming the application limitation of the original hybrid sampling method towards concave cavity obstacle. The improved hybrid sampling method combines the goal bias sampling strategy and random sampling strategy, which requires a few searching time, resulting in a faster convergence rate than the existing method. Then, a parent node is created for the sampling node to optimize the path. Finally, the performance of FF-RRT* is validated in four simulation environments and compared with the other algorithms. The FF-RRT* shortens 32% of the convergence time in complex maze environment and 25% of the convergence time in simple maze environment compared to F-RRT*. And in a complex maze with a concave cavity obstacle, the average convergence time of Fast-RRT* in this environment is 134% more than the complex maze environment compared to 12% with F-RRT* and 34% with FF-RRT*. The simulation results show that FF-RRT* possesses superior performance compared to the other algorithms, and also fits with a much more complex environment. [ABSTRACT FROM AUTHOR]

In this paper, we contend that urban middle-class older Indians engaged in "serious leisure" as a way to reimagine and reconfigure the structure of everyday life during the pandemic-led epochal downtime. In particular, we heuristically show that leisure activity patterns and constraint negotiation strategies among older Indians followed conceptual semblances with the dominant leisure-based typology of Serious Leisure Perspective. By thematically analysing household surveys (n = 71), time-use diaries and in-depth interviews (n = 15) of middle to upper middle-class individuals (55–80 years), we show how both men and women distinguished between serious leisure that is marked by motivation, agency and perseverance with that of unstructured, routinized free-time (or causal leisure). Time-use diaries suggested that despite the changed realities of heightened domestic time available to both genders due to the pandemic, women recorded higher proportion of their daily hours in household management and caregiving. Although women were governed by moral-cultural self-descriptions in their engagement with leisure, it was often associated with an enhanced sense of self-actualisation, self-management and identity. Overall, we show how the social codes of age and gender were inextricably linked with the practice of leisure during the pandemic. [ABSTRACT FROM AUTHOR]

This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators. [ABSTRACT FROM AUTHOR]

This paper presents findings of a second trial evaluating telephone-based motivational enhancement therapy (MET) to motivate untreated and unadjudicated men who abuse their intimate partners to explore treatment options. Participants' perceptions of how their abuse is negatively affecting them personally are a highlight of the paper. One hundred forty-one adult men were recruited through social marketing and randomly assigned to the intervention (MET) or comparison (Mail) group. The MET condition consisted of two feedback sessions guided by a personalized feedback report on participants' intimate partner violence (IPV) and substance use. The Mail condition included a mailed educational brochure on IPV and substance use. Results supported the likely effectiveness of MET in short-term reduction of IPV behavior, marijuana use, and increasing motivation for treatment seeking particularly for participants who reported more adverse consequences of IPV to themselves. Findings emphasize the importance of including a focus in interventions on IPV's impact on the abusers themselves. The study's virtual participation and success in reaching and retaining a diverse population of male abusers can contribute to transformative justice and communities looking for alternative early interventions for men of color prior to encountering the criminal justice system. [ABSTRACT FROM AUTHOR]

Background: This paper presents the baseline characteristics and their moderators in the Healthy Learning Mind (HLM)– school-based cluster-randomized controlled trial. Objectives: The paper evaluates the state of various measures of well-being, their moderators and how these results compare to national and global norms/population studies. Methods: Data were collected from all participants prior to the intervention and further analyzed by gender, grade and perceived socioeconomic status, including standardized measures for resilience, depressive symptoms and socioemotional functioning; health-related quality of life, dispositional mindfulness, satisfaction with life, compassion/self-kindness, self-rated health and morning tiredness. Results: Participating 2793 students (1425 girls, 1368 boys), ages 12 to 15 years, filled in the questionnaires. The outcomes were in line with previous research, demonstrating gender differentiation and lower wellbeing among older children and adolescents. Conclusions: All outcomes were associated with perceived socioeconomic status, suggesting that perceived low socioeconomic status should be addressed as a serious risk factor and included as a moderator in similar trials. [ABSTRACT FROM AUTHOR]