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Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

The success of a scientist depends on their production of scientific papers and the impact factor of the journal in which they publish. Because most major scientific journals are published in English, success is related to publishing in this language. Currently, 98% of publications in science are written in English, including researchers from English as a Foreign Language (EFL) countries. Colombia is among the countries with the lowest English proficiency in the world. Thus, understanding the disadvantages that Colombians face in publishing is crucial to reducing global inequality in science. This paper quantifies the disadvantages that result from the language hegemony in scientific publishing by examining the additional costs that communicating in English creates in the production of articles. It was identified that more than 90% of the scientific articles published by Colombian researchers are in English, and that publishing in a second language creates additional financial costs to Colombian doctoral students and results in problems with reading comprehension, writing ease and time, and anxiety. Rejection or revision of their articles because of the English grammar was reported by 43.5% of the doctoral students, and 33% elected not to attend international conferences and meetings due to the mandatory use of English in oral presentations. Finally, among the translation/editing services reviewed, the cost per article is between one-quarter and one-half of a doctoral monthly salary in Colombia. Of particular note, we identified a positive correlation between English proficiency and higher socioeconomic origin of the researcher. Overall, this study exhibits the negative consequences of hegemony of English that preserves the global gap in science. Although having a common language is important for science communication, generating multilinguistic alternatives would promote diversity while conserving a communication channel. Such an effort should come from different actors and should not fall solely on EFL researchers.

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics - by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments) - using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts.

Background There is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way. Methods A checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument. Results The scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%. Conclusions A reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review.

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs.

Scientific and reasonable treatment of dredged silt can not only protect the ecological environment but also play an essential role in the utilization of silt resources. Due to high water content, low permeability and high organic matter content of the silt, a large amount of bacteria and harmful gases are often produced during the process of silt sedimentation. Thermal drying has been taken as a technically attractive method for harmless treatment of contaminated dredged silt. In this study, ultrasound technology is introduced to shorten the time needed for silt drying. A preliminary laboratory study is carried out to assess the effectiveness of ultrasound on thermal drying. A series of thermal drying tests, with and without ultrasound, were conducted on kaolin soil specimens that were prepared by settling and self-weight consolidation. The test results show that the length of drying time can be shortened by increasing temperature and ultrasound power. The drying time plays a dominant role in the determination of the total energy consumption. This is because reduction of drying time leads to significant decrease in energy consumption for thermal drying, and the energy consumption for additional ultrasound is relatively marginal. For thermal drying at temperatures 60 and 100°C, when combined with 100 W ultrasound, the length of drying time was shortened by 44.19% and 45.16%, and the energy consumption was saved by 30.07% and 38.16%, respectively; when combined with 60 W ultrasound, the length of drying time was shortened by 4.65% and 6.45%, but the energy consumption was increased by 9.79% and 0.48%, respectively. The combination of thermal drying and 100 W ultrasound is found to be optimal in terms of drying rate and energy consumption for silt drying.

Previous studies have demonstrated that skin vibration is an important factor affecting the roughness perception of fine textures. For coarse textures, the determining physical factor is much less clear and there are indications that this might be participant-dependent. In this paper, we focused on roughness perception of both coarse and fine textures of different materials (glass particle surfaces and sandpapers). We investigated the relationship between subjective roughness ratings and three physical parameters (skin vibration, friction coefficient, and particle size) within a group of 30 participants. Results of the glass particle surfaces showed both spatial information (particle size) and temporal information (skin vibration) had a high correlation with subjective roughness ratings. The former correlation was slightly but significantly higher than the latter. The results also indicated different weights of temporal information and spatial information for roughness ratings among participants. Roughness ratings of a different material (sandpaper versus glass particles) could be either larger, similar or smaller, indicating differences among individuals. The best way to describe our results is that in their perceptual evaluation of roughness, different individuals weight temporal information, spatial information, and other mechanical properties differently.

In this paper, a two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter is proposed. The first stage firstly detects the noise corrupted pixels by combining characteristic difference parameter and gray level extreme, then develops an improved adaptive median filter to firstly restore them. The second stage introduces a restoration scheme to further restore the noise corrupted pixels, which firstly divides them into two types and then applies different restoration skills for the pixels based on the classification result. One type of pixels is restored by the mean filter and the other type of pixels is restored by the proposed adaptive directional mean filter. The new filter firstly adaptively selects the optimal filtering window and direction template, then replaces the gray level of noise corrupted pixel by the mean value of pixels on the optimal template. Experimental results show that the proposed filter outperforms many existing main filters in terms of noise suppression and detail preservation.

Non-native bark and ambrosia beetles represent a threat to forests worldwide. Their invasion patterns are, however, still unclear. Here we investigated first, if the spread of non-native bark and ambrosia beetles is a gradual or a discontinuous process; second, which are the main correlates of their community structure; third, whether those correlates correspond to those of native species. We used data on species distribution of non-native and native scolytines in the continental 48 USA states. These data were analyzed through a beta-diversity index, partitioned into species richness differences and species replacement, using Mantel correlograms and non-metric multidimensional scaling (NMDS) ordination for identifying spatial patterns, and regression on distance matrices to test the association of climate (temperature, rainfall), forest (cover area, composition), geographical (distance), and human-related (import) variables with β-diversity components. For both non-native bark and ambrosia beetles, β-diversity was mainly composed of species richness difference than species replacement. For non-native bark beetles, a discontinuous invasion process composed of long distance jumps or multiple introduction events was apparent. Species richness differences were primarily correlated with differences in import values while temperature was the main correlate of species replacement. For non-native ambrosia beetles, a more continuous invasion process was apparent, with the pool of non-native species arriving in the coastal areas that tended to be filtered as they spread to interior portions of the continental USA. Species richness differences were mainly correlated with differences in rainfall among states, while rainfall and temperature were the main correlates of species replacement. Our study suggests that the different ecology of bark and ambrosia beetles influences their invasion process in new environments. The lower dependency that bark beetles have on climate allowed them to potentially colonize more areas within the USA, while non-native ambrosia beetles, being dependent on rainfall, are typically filtered by the environment.

In general, in composting facilities the active, or intensive, stage of the process is done separately from the maturation stage, using a specific technology and time. The pre-composted material to be matured can contain enough biodegradable substrates to cause microbial proliferation, which in turn can cause temperatures to increase. Therefore, not controlling the maturation period during waste management at an industrial level can result in undesired outcomes. The main hypothesis of this study is that controlling the maturation stage through turning provides one with an optimized process when compared to the static approach. The waste used was sludge from a seafood-processing plant, mixed with shredded wood (1:2, v/v). The composting system consists of an intensive stage in a 600L static reactor, followed by maturation in triplicate in 200L boxes for 112 days. Two tests were carried out with the same process in reactor and different treatments in boxes: static maturation and turning during maturation when the temperature went above 55°C. PLFAs, organic matter, pH, electrical conductivity, forms of nitrogen and carbon, hydrolytic enzymes and respiratory activity were periodically measured. Turning significantly increased the duration of the thermophilic phase and consequently increased the organic-matter degradation. PCA differentiated significantly the two treatments in function of tracking parameters, especially pH, total carbon, forms of nitrogen and C/N ratio. So, stability and maturity optimum values for compost were achieved in less time with turnings. Whereas turning resulted in microbial-group stabilization and a low mono/sat ratio, static treatment produced greater variability in microbial groups and a high mono/sat ratio, the presence of more degradable substrates causes changes in microbial communities and their study during maturation gives an approach of the state of organic-matter degradation. Obtaining quality compost and optimizing the composting process requires using turning as a control mechanism during maturation.

PLoS one 11(1), e0147725 (2016). doi:10.1371/journal.pone.0147725, Published by PLoS, Lawrence, Kan.

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint–peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape., Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review This study analyzes the full text content of the bioRxiv preprint repository, identifying field-specific patterns and changes that occur during publication, and providing a search tool that can identify the published papers that are most similar to a given bioRxiv or medRxiv preprint.

The prevalence of dyslexia identification has increased significantly over the last two decades. Yet there is debate over whether there are distinct biological and cognitive differences between those with literacy difficulties and the subgroup of people identified as dyslexic. This is the first paper that provides evidence for this ongoing debate by investigating the socio-demographic factors, outside biology and cognition, that predict whether a child is identified as dyslexic in the UK. Using secondary data from the UK’s Millennium Cohort Study, this paper examines the socio-demographic factors that predict whether a child’s teacher identifies them as dyslexic at age 11. Gender, season of birth, socio-economic class and parental income are found to be significant predictors of the dyslexia label. Therefore, factors seemingly unrelated to the clinical aspects of dyslexia influence whether a child is identified as dyslexic in England and Wales. This suggests that label may not be evenly distributed across a population; furthermore, it may also indicate that resources for support may not be fairly allocated. The findings further support the argument that a ‘dyslexic sub-group’ within poor readers is created due to the impact of environmental factors. The results from this national-scale study thus questions the reliability, validity and moral integrity of the allocation of the dyslexia label across current education systems in the UK.

This paper introduces anenhanced MSM(Mutual Subspace Method) methodology for gait recognition, to provide robustness to variations in walking speed. Theenhanced MSM (eMSM)methodology expands and adapts the MSM, commonly used for face recognition, which is a static/physiological biometric, to gait recognition, which is a dynamic/behavioral biometrics. To address the loss of accuracy during calculation of the covariance matrix in the PCA step of MSM, we use a 2D PCA-based mutual subspace. Furhtermore, to enhance the discrimination capability, we rotate images over a number of angles, which enables us to extract richer gait features to then be fused by a boosting method. The eMSM methodology is evaluated on existing data sets which provide variable walking speed, i.e. CASIA-C and OU-ISIR gait databases, and it is shown to outperform state-of-the art methods. While the enhancement to MSM discussed in this paper uses combinations of 2D-PCA, rotation, boosting, other combinations of operations may also be advantageous.

Background To establish successful strategies and increasing the utilization of preventive services, there is a need to explore the extent to which the general female population is aware and use the service for cervical cancer-screening among women infected with HIV in Africa. Available evidences in this regard are controversial and non-conclusive on this potential issue and therefore, we estimated the pooled effect of the proportion of knowledge, attitude and practice of HIV infected African women towards cervical cancer screening to generate evidence for improved prevention strategies. Methods We applied a systematic review and meta-analysis of studies conducted in Africa and reported the proportion of knowledge, attitude and practice towards cervical cancer screening. We searched electronic databases: PubMed/Medline, SCOPUS, ScienceDirect, Web of science, Cumulative Index of Nursing and allied Health Sciences (CINAHL) and Google scholar databases to retrieve papers published in English language till August 2020. We used random-effects model to estimate the pooled effect, and funnel plot to assess publication bias. The registration number of this review study protocol is CRD42020210879. Results In this review, we included eight published papers comprising 2,186 participants. The estimated pooled proportion of knowledge of the participants was 43.0% (95%CI:23.0–64.0) while the pooled estimates of attitudes and practices were 38.0% (95%CI: 1.0–77.0) and 41.0% (95%CI: 4.0–77.0), respectively. The proportion of the outcome variables were extremely heterogeneous across the studies with I2> 98%). Conclusion The pooled estimates of knowledge, attitude and practice were lower than other middle income countries calls for further activities to enhance the uptake of the services and establish successful strategies.

In this paper, we use choice architecture techniques to activate both social and personal norms, seeking to increase pro-environmental choices and to better understand the effect of such norm types on post-choice emotional responses. In four experiments, we make different social or personal norms salient by aligning choice environments with psychosocial mechanisms that activate different types of norms. We use different choice architecture techniques to change information, alter product sets, and generate the social consequences of choices. The target behavior, purchasing a recycled paper notebook, is captured through direct purchase behaviors or willingness to pay commitments. We find that choice architecture activates personal but not social norms, and that associated positive and negative emotions (guilt, shame, regret and pride) are elicited by choices but not by willingness to pay. Moreover, manipulating choice environment moderates the relationship between choice and norm-related emotions, such that positive emotional responses seem to be stronger than negative ones. The results suggest that choice architecture interventions can activate individual level beliefs about sustainability and help reduce the attitude-behavior gap.

Breast cancer is the cancer with the highest incidence of malignant tumors in women, which seriously endangers women’s health. With the help of computer vision technology, it has important application value to automatically classify pathological tissue images to assist doctors in rapid and accurate diagnosis. Breast pathological tissue images have complex and diverse characteristics, and the medical data set of breast pathological tissue images is small, which makes it difficult to automatically classify breast pathological tissues. In recent years, most of the researches have focused on the simple binary classification of benign and malignant, which cannot meet the actual needs for classification of pathological tissues. Therefore, based on deep convolutional neural network, model ensembleing, transfer learning, feature fusion technology, this paper designs an eight-class classification breast pathology diagnosis model BCDnet. A user inputs the patient’s breast pathological tissue image, and the model can automatically determine what the disease is (Adenosis, Fibroadenoma, Tubular Adenoma, Phyllodes Tumor, Ductal Carcinoma, Lobular Carcinoma, Mucinous Carcinoma or Papillary Carcinoma). The model uses the VGG16 convolution base and Resnet50 convolution base as the parallel convolution base of the model. Two convolutional bases (VGG16 convolutional base and Resnet50 convolutional base) obtain breast tissue image features from different fields of view. After the information output by the fully connected layer of the two convolutional bases is fused, it is classified and output by the SoftMax function. The model experiment uses the publicly available BreaKHis data set. The number of samples of each class in the data set is extremely unevenly distributed. Compared with the binary classification, the number of samples in each class of the eight-class classification is also smaller. Therefore, the image segmentation method is used to expand the data set and the non-repeated random cropping method is used to balance the data set. Based on the balanced data set and the unbalanced data set, the BCDnet model, the pre-trained model Resnet50+ fine-tuning, and the pre-trained model VGG16+ fine-tuning are used for multiple comparison experiments. In the comparison experiment, the BCDnet model performed outstandingly, and the correct recognition rate of the eight-class classification model is higher than 98%. The results show that the model proposed in this paper and the method of improving the data set are reasonable and effective.

Due to the nascency of bacteriophage-based pathogen detection technologies, several practical hurdles stand in the way between providing promising proof-of-concept data and development of robust detection platforms. One such hurdle, and the focus of this work, is the development of methods for transitioning laboratory stocks of bacteriophage into functional, consistent, and shelf-stable delivery methods in commercial detection kits. Research described here was undertaken to evaluate two methods for their ability to store the bacteriophage ɸV10 at ambient temperature without aqueous storage solutions while limiting loss of viability. ɸV10 is a temperate bacteriophage which solely infects the zero-tolerance food adulterant Escherichia coli O157:H7 and has been genetically modified to generate a detectable phenotype in host cells. In order to integrate this reporter bacteriophage into food-borne pathogen detection methodologies, two methods of processing phage suspensions for long-term, ambient storage were evaluated: printing solutions onto pieces of dissolvable paper and lyophilizing suspensions with sucrose. Applying phage to dissolvable paper yielded key attributes to consider when addressing phage viability, however, optimized methodology still resulted in an approximate five-log reduction in titer of viable phage. Lyophilization of ɸV10 with various concentrations of the cryoprotectant molecule, sucrose, yielded losses of approximately 0.3-log after 120 days of storage at 23°C. Liquid storage buffer samples with and without sucrose saw a reduction of viable phage of at least 3.9-log in the same period. Additionally, the ability for ɸV10 to form lysogens in an E. coli O157:H7 host was not negatively affected by lyophilization. Drying ɸV10 at ambient temperature drastically reduces the viability of the phage. However, lyophilizing ɸV10 in the presence of sucrose is an effective method for dehydration and storage of the phage in ambient environmental conditions for an extended time lending to commercial application and integration into foodborne pathogen detection methodologies.

Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.

Gautret and colleagues reported results of a non-randomised open-label case series which examined the effects of hydroxychloroquine and azithromycin on viral load in the upper respiratory tract of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients. The authors report that hydroxychloroquine (HCQ) had significant virus reducing effects, and that dual treatment of both HCQ and azithromycin further enhanced virus reduction. These data have triggered speculation whether these drugs should be considered as candidates for the treatment of severe COVID-19. However, questions have been raised regarding the study’s data integrity, statistical analyses, and experimental design. We therefore reanalysed the original data to interrogate the main claims of the paper. Here we apply Bayesian statistics to assess the robustness of the original paper’s claims by testing four variants of the data: 1) The original data; 2) Data including patients who deteriorated; 3) Data including patients who deteriorated with exclusion of untested patients in the comparison group; 4) Data that includes patients who deteriorated with the assumption that untested patients were negative. To ask if HCQ monotherapy is effective, we performed an A/B test for a model which assumes a positive effect, compared to a model of no effect. We find that the statistical evidence is highly sensitive to these data variants. Statistical evidence for the positive effect model ranged from strong for the original data (BF+0 ∼11), to moderate when including patients who deteriorated (BF+0 ∼4.35), to anecdotal when excluding untested patients (BF+0 ∼2), and to anecdotal negative evidence if untested patients were assumed positive (BF+0 ∼0.6). To assess whether HCQ is more effective when combined with AZ, we performed the same tests, and found only anecdotal evidence for the positive effect model for the original data (BF+0 ∼2.8), and moderate evidence for all other variants of the data (BF+0 ∼5.6). Our analyses only explore the effects of different assumptions about excluded and untested patients. These assumptions are not adequately reported, nor are they justified in the original paper, and we find that varying them causes substantive changes to the evidential support for the main claims of the original paper. This statistical uncertainty is exacerbated by the fact that the treatments were not randomised, and subject to several confounding variables including the patients consent to treatment, different care centres, and clinical decision-making. Furthermore, while the viral load measurements were noisy, showing multiple reversals between test outcomes, there is greater certainty around other clinical outcomes such as the 4 patients who seriously deteriorated. The fact that all of these belonged to the HCQ group should be assigned greater weight when evaluating the potential clinical efficacy of HCQ. Randomised controlled trials are currently underway, and will be critical in resolving this uncertainty as to whether HCQ and AZ are effective as a treatment for COVID-19.WarningThere have been reports of people self-administering chloroquine phosphate (intended for treatment of disease in aquarium fish), which has led to at least one death and one serious illness. We state that under no circumstances should people self-administer hydroxychloroquine, chloroquine phosphate, azithromycin, or anything similar-sounding, or indeed any other drug, unless approved by a medical doctor. The FDA has issued a specific warning: https://www.fda.gov/animal-veterinary/product-safety-information/fda-letter-stakeholders-do-not-use-chloroquine-phosphate-intended-fish-treatment-covid-19-humans

This paper presents regulation of an asynchronous induction motor so as to create a stable vacuum milk pump using Variable Frequency Drive (VFD). Contribution includes providing information about the usage of the VFD, which regulates the activity of an asynchronous induction motor so that the vacuum pump milking machine creates stable vacuum. The paper describes the functional and time dependence of input values and output parameters of frequency converters at changing electric motor speed. For simulation and verification the milking process a mathematical model of the milking machine was created. The simulation was verified in Matlab/Simulink software. The constructed mathematical model showed symmetric regulation. Control model symmetry was verified at the laboratory of milking machine. The possibility to remove the control valve from milking equipment was proven using the measured data. It was found that constant vacuum values can be maintained. A constant vacuum can be maintained by changing vacuum pump speed. This control is of an accepted standard (ISO 5707: 2007). The power saving control values (on the milking equipment) of the VFD were positive throughout the measuring range. The performance of the milking vacuum pump is normally designed from the maximum air consumption of the milking machine at nominal vacuum (50 kPa), and a performance reserve is added to this. This means that the pump is operated between the ranges 7.53 and 15.06 dm3 s−1. By using a vacuum pump controlled by a VFD, power savings can be achieved from 32.50% to 54.02% compared to a control valve.

Purpose The purpose of this paper is to investigate the utilization of Lean & Six Sigma quality initiatives in healthcare sector in India. Methodology The survey questionnaires were sent to 454 hospitals through registered postal in all the states of India. The survey questionnaire was designed to assess different quality initiatives; currently implemented in Indian hospitals, factors align with organization’s objectives, reasons for not implementing Lean & Six Sigma and contribution of Lean & Six Sigma projects in healthcare improvement projects etc. A separate section in the questionnaire provides the feedback on implementation of Lean & Six Sigma in various hospitals. The relationships between Lean & Six Sigma and healthcare performance have also been established in this paper. Findings It is found that 15 Nos. of hospitals have implemented the Lean tools while 14 Nos. have implemented the Six Sigma tools out of 109 collected responses. This shows the utilization of Lean & Six Sigma in Indian healthcare sector. The ‘Lack of knowledge’ and ‘Availability of resources’ are the major reasons for not implementing Lean & Six Sigma. It is also observed that 22% running projects were related to Lean & Six Sigma out of various improvement projects running in various hospitals. Originality There is lack of evidences of similar studies that determines the utilization of Lean & Six Sigma in Indian healthcare sector at the national level. This paper will provide important breakthrough to academicians and healthcare practitioners, who are involved in Lean & Six Sigma research. Social implications The present study will create awareness among healthcare practitioners across India for utilization of quality tools that will provide direct benefits to the society.

Recent relation extraction models’ architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic information in the sequence or the distance dependence problem, the internal semantic information may contain the useful knowledge which can help relation classification. Focus on these problems, this paper proposed a BERT-based relation classification method. Compare with the existing Bert-based architecture, the proposed model can obtain the internal semantic information between entity pair and solve the distance semantic dependence better. The pre-trained BERT model after fine tuning is used in this paper to abstract the semantic representation of sequence, then adopt the piecewise convolution to obtain semantic information which influence the extraction results. Compare with the existing methods, the proposed method can achieve a better accuracy on relational extraction task because of the internal semantic information extracted in the sequence. While, the generalization ability is still a problem that cannot be ignored, and the numbers of the relationships are difference between different categories. In this paper, the focal loss function is adopted to solve this problem by assigning a heavy weight to less number or hard classify categories. Finally, comparing with the existing methods, the F1 metric of the proposed method can reach a superior result 89.95% on the SemEval-2010 Task 8 dataset.

Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We propose a two-branch neural network where mixed layers, a combination of the TGW and convolutional layers, make up the building block of our deep neural network. We test our method on twoo challenging publicly available datasets and compare our results with state of the art.

BackgroundThe Penn Alcohol Craving Scale (PACS) is an instrument with good psychometric properties that is widely used to assess alcohol craving. Based on the assumption that the experience of craving is independent of substance type, the Polish version of the PACS was modified to measure drug craving, thus creating the Penn Drug Craving Scale (PDCS). The analyses presented in the paper aim to verify the hypothesis that the PDCS has a unidimensional structure, is highly reliable and features longitudinal measurement invariance.MethodsThe research was conducted in 14 inpatient and 13 outpatient randomly selected facilities that provide psychosocial therapy to people with substance use disorder (SUD) in Poland, during June 2018 –July 2019. The data used for the analyses came from 282 patients diagnosed on the basis of ICD-10 criteria (F11.2-F19.2). The paper presents analyses with the application of: [1] confirmatory factor analysis (CFA) conducted on the basis of a polychoric correlation matrix and the WLSMV estimator; [2] a reliability estimate using Cronbach’s alpha and coefficient omega; [3] verification of longitudinal measurement invariance between the beginning and end of therapy; [4] evaluation of criterion validity; [5] normalisation of the raw scores.ResultsThe CFA results confirmed a unidimensional PDCS structure (RMSEA = 0.047, 95% CI: 0.000–0.103; CFI = 0.999; TLI = 0.999) and a high reliability of the scale (ω = 0.93). Moreover, a strict longitudinal measurement invariance of the instrument was confirmed.ConclusionsAccurate assessment of craving is possible only with valid and reliable instruments. Therefore, the psychometric properties of the PDCS were verified based on the latest statistical approaches. The scale is a valid and highly reliable tool featuring longitudinal measurement invariance and can be usefully used for research and clinical purposes. Thus, the Polish version of the PACS has been modified and successfully applied to the population of people with SUD.

Some concepts in nuclear radiation physics are abstract and intellectually demanding. In the present paper, an “MCHP platform” (MCHP was an acronym for Monte Carlo simulations + Human Phantoms) was proposed to provide assistance to the students through visualization. The platform involved Monte Carlo simulations of interactions between ionizing radiations and the Oak Ridge National Laboratory (ORNL) adult male human phantom. As an example to demonstrate the benefits of the proposed MCHP platform, the present paper investigated the variation of the absorbed photon dose per photon from a 137Cs source in three selected organs, namely, brain, spine and thyroid of an adult male for concrete and lead shields with varying thicknesses. The results were interesting but not readily comprehensible without direct visualization. Graphical visualization snapshots as well as video clips of real time interactions between the photons and the human phantom were presented for the involved cases, and the results were explained with the help of such snapshots and video clips. It is envisaged that, if the platform is found useful and effective by the readers, the readers can also propose examples to be gradually added onto this platform in future, with the ultimate goal of enhancing students’ understanding and learning the concepts in an undergraduate nuclear radiation physics course or a related course.

At present, people are demanding better indoor air quality during the COVID-19 pandemic. In addition to maintaining the basic functions, new air-conditioning should also add air purification functions to improve indoor air quality and reduce the possibility of virus transmission. Nowadays, there is lack of research results on the innovation of air-conditioning. The aim of this study is to present a two-stage mathematical model for identifying critical manufacturing factors in the innovation process of air conditioning. In this paper, Kano and quality function deployment (QFD) are used to analyze the critical factors affecting air-conditioning innovation. Some studies have proposed using Kano-QFD model to analyze product innovation, but the study only studies one stage, which loses the analysis of the subsequent stages of product innovation. Based on this, this paper studies the priority method of two-stage critical factors for air-conditioning innovation. Firstly, the questionnaire survey and fuzzy sets are used to collect demand information of multi-agent (customers and professional technicians). Secondly, the Kano model is used to classify and calculate satisfaction of multi-agent. Then, QFD is used to transform multi-agent demands into engineering property indexes (first stage) and technical property indexes (second stage) and calculate the weight of each index. Finally, the applicability and superiority of this method is illustrated by taking the central air-conditioning as an example.

Background and objectives With the increase in the number of COVID-19 infections, the global health apparatus is facing insufficient resources. The main objective of the current study is to provide additional data regarding the clinical characteristics of the patients diagnosed with COVID-19, and in particular to analyze the factors associated with disease severity, lack of improvement, and mortality. Methods 102 studies were included in the present meta-analysis, all of which were published before September 24, 2020. The studies were found by searching a number of databases, including Scopus, MEDLINE, Web of Science, and Embase. We performed a thorough search from early February until September 24. The selected papers were evaluated and analyzed using Stata software application version 14. Results Ultimately, 102 papers were selected for this meta- analysis, covering 121,437 infected patients. The mean age of the patients was 58.42 years. The results indicate a prevalence of 79.26% for fever (95% CI: 74.98–83.26; I2 = 97.35%), 60.70% for cough (95% CI: 56.91–64.43; I2 = 94.98%), 33.21% for fatigue or myalgia (95% CI: 28.86–37.70; I2 = 96.12%), 31.30% for dyspnea (95% CI: 26.14–36.69; I2 = 97.67%), and 10.65% for diarrhea (95% CI: 8.26–13.27; I2 = 94.20%). The prevalence for the most common comorbidities was 28.30% for hypertension (95% CI: 23.66–33.18; I2 = 99.58%), 14.29% for diabetes (95% CI: 11.88–16.87; I2 = 99.10%), 12.30% for cardiovascular diseases (95% CI: 9.59–15.27; I2 = 99.33%), and 5.19% for chronic kidney disease (95% CI: 3.95–6.58; I2 = 96.42%). Conclusions We evaluated the prevalence of some of the most important comorbidities in COVID-19 patients, indicating that some underlying disorders, including hypertension, diabetes, cardiovascular diseases, and chronic kidney disease, can be considered as risk factors for patients with COVID-19 infection. Furthermore, the results show that an elderly male with underlying diseases is more likely to have severe COVID-19.

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.

The spread of COVID-19 implied a large and fast increase of demand for intensive care services. To face this increase in demand, health care systems need to adapt their response by increasing hospital beds, intensive care unit (ICU) capacity and by (re-)deploying doctors and other personnel. This paper proposes a forecast approach based on the Vector Error Correction model for the daily counts of hospitalized patients with symptoms and of patients in ICU, using publicly available data on the current COVID-19 outbreak in Italy, Switzerland and Spain. The level of analysis is the local government managing the health care system response, which corresponds to regions for Italy. The one-week-ahead forecasts are validated with out-of-sample data over successive weeks; they are found to provide timely and robust prediction of ICU capacity needs in Lombardy, the most-affected Italian region, starting from the sample of the first 2 weeks of data. The same methodology is successfully validated on other Italian regions, Switzerland and Spain. This approach may be used in other countries/regions/provinces to help adapt the health care system response to COVID-19 (or other similar disease); for this purpose, the open-source software code to produce the forecasts is provided with the paper.

Drawing on a recent online survey combined with city-level data, this paper examines the impact of the COVID-19 on consumers' online food purchase behavior in the short term. To address the potential endogeneity issues, we adopt an instrumental variable (IV) strategy, using the distance from the surveyed city to Wuhan as the instrumental variable. We show that our IV method is effective in minimizing potential bias. It is found that the share of confirmed COVID-19 cases increases the possibility of consumers purchasing food online. This is more likely to be the case for young people having a lower perceived risk of online purchases and living in large cities. Despite some limitations, this paper has policy implications for China and other countries that have been influenced by the COVID-19 epidemic. Specifically, government support and regulation should focus on (i) ensuring the safety of food sold on the internet, (ii) protecting the carrier from becoming infected, and (iii) providing financial support to the poor since they may have difficulties in obtaining access to food living in small cities. Moreover, how to help those who are unable to purchase food online because of their technical skills (e.g., the elderly who are not familiar with smart phones or the internet) also deserves more attention for the government and the public.

Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks., Author summary Biological disciplines face the challenge of increasingly large and complex data. One necessary approach toward eliciting information is data visualization. Newer visualization tools incorporate interactive capabilities that allow scientists to extract information more efficiently than static counterparts. In this paper, we introduce technology that allows multiple independent layers of interactive visualization written in open-source code. This technology can be repurposed across various biological problems. Here, we apply this technology to RNA-sequencing data, a popular next-generation sequencing approach that provides snapshots of RNA quantity in biological samples at given moments in time. It can be used to investigate cellular differences between health and disease, cellular changes in response to external stimuli, and additional biological inquiries. RNA-sequencing data is large, noisy, and biased. It requires sophisticated normalization. The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools. This is despite sound evidence that RNA-sequencing data is most effectively explored using graphical and numerical approaches in a complementary fashion. The software we introduce can make it easier for researchers to use models and visuals in an integrated fashion during RNA-sequencing data analysis.

The aims of this study were to achieve a quantitative assessment of the severity of accidents involving roadside trees on highways and to propose corresponding safety measures to reduce accident losses. This paper used the acceleration severity index (ASI), head injury criteria (HIC) and chest resultant acceleration (CRA) as indicators of occupant injuries and horizontal radii, vehicle departure speeds, tree diameters and roadside tree spacing as research variables to carry out bias collision tests between cars, trucks and trees by constructing a vehicle rigid body system and an occupant multibody system in PC-crash 10.0® simulation software. A total of 2,256 data points were collected. For straight and curved segments of highways, the occupant injury evaluation models of cars were fitted based on the CRA, and occupant injury evaluation models of trucks and cars were fitted based on the ASI. According to the Fisher optimal segmentation method, reasonable classification standards of severities of accidents involving roadside trees and the corresponding ASI and CRA thresholds were determined, and severity assessment methods for accidents involving roadside trees based on the CRA and ASI were provided. Additionally, a new index by which to evaluate the accuracy of the accident severity classification and the degree of misclassification was built and applied for the validity verification of the proposed severity assessment methods. A proportion of trucks was introduced to further improve the ASI evaluation model. For the same simulation conditions, the results show that driver chest injuries are more serious than driver head injuries and that the average ASI of cars is greater than that of trucks. The CRA and ASI have a positive linear correlation with the departure speed and a logarithmic correlation with the roadside tree diameters. The larger the spacing of roadside trees is and the smaller the horizontal radius is, the smaller the chance that a vehicle will experience a second collision and the lower the risk of occupant injury. In method validation, the evaluation results from two proposed severity assessment methods based on the CRA and ASI are consistent, and the degrees of misclassification are 4.65% and 4.26%, respectively, which verifies the accuracy of the methods proposed in this paper and confirms that the ASI can be employed as an effective index for evaluating occupant injuries in accidents involving roadside trees.

The cluster innovation network is an important part of regional economic development. In addition, the fairness preference of internal innovators in the processes of investment and benefit distribution are particularly important for curbing "free riding" and other speculative behaviors and for creating a good cooperation environment. Therefore, taking a cluster innovation network constructed by the weighted evolutionary BBV model as the research subject, this paper constructs an evolutionary game model of a cluster innovation network based on a spatial public goods game and the theory of fairness preferences, which involves the processes of investment and payoff allocation. Using simulation analysis, this paper studies the evolution of innovators' cooperative behaviors and benefits in cluster innovation network under the conditions of a fairness preference and a return intensity. The results show that an increase in the weight coefficient, gain coefficient and degree of differentiation between the previous income and current investment can effectively promote improvements in the level of enterprise cooperation. Indeed, the greater the weight coefficient, the gain coefficient and the degree of differentiation are, the more substantial the improvement in the level of enterprise cooperation will be. Moreover, an improvement in the differentiation of the breadth and depth of enterprise cooperation has an inhibitory effect on enterprise cooperation. Furthermore, whereas increases in regulation and gain coefficients can effectively promote enterprise cooperation. However, the increase in the weight coefficient has a different effect on enterprise benefit in terms of the breadth and depth of cooperation. Finally, we hope to improve the overall cooperation level and cooperation income of the network by deeply understanding the fair preferences of innovators in the processes of investment and benefit distribution, which is helpful for promoting the evolution and development of cluster innovation networks.

As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver’s willingness to receive, and receiver’s capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.

Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented a very powerful tracker based on the kernelized correlation filter tracker (KCF). Firstly, we employ an intelligent multi-part tracking algorithm to improve the overall capability of correlation filter based tracker, especially in partial-occlusion challenges. Secondly, to cope with the problem of scale variation, we employ an effective scale adaptive scheme, which divided the target into four patches and computed the scale factor by finding the maximum response position of each patch via kernelized correlation filter. With this method, the scale computation was transformed into locating the centers of the patches. Thirdly, because the small deviation of the central function value will bring the problem of location ambiguity. To solve this problem, the new Gaussian kernel functions are introduced in this paper. Experiments on the default 51 video sequences in Visual Tracker Benchmark demonstrate that our proposed tracker provides significant improvement compared with the state-of-art trackers.

The Global Navigation Satellite System is vulnerable to interference signals that can potentially disable the system, because the signal strength tends to be very weak. Interference such as jamming, which disables the receiver via excessively high signal strength in the satellite navigation frequency band, and spoofing, which induces the receiver to output erroneous position and time data via signals similar to actual navigation signals, disrupt satellite navigation systems. As the threat of interference is increasing, considerable research effort has been expended in an attempt to deal with it in various ways. Spoofing attacks are especially difficult to detect. This paper deals with a technique to detect a spoofing signal and to mitigate attacks on satellite navigation systems. The satellite navigation signal is influenced by the navigation satellite itself and errors due to environmental factors, and spoofing signal detection should be well reflected in the navigation signal. Especially, in the case of mobile receivers, it is not easy to detect a spoofing signal because the exact position of the receiver cannot be known. To detect a spoofing signal, additional hardware may be required; in some cases, heterogeneous sensors, such as inertial sensors, may be used. The technique introduced in this paper effectively discriminates spoofing signals based only on receiver measurements, without the need for additional devices. It generates test statistics based on the pseudorange, which is the measured value of the receiver position, and detects spoofing signals by setting the monitoring interval according to a “sliding window”. Because the proposed method uses output data and measurements obtained from the receiver, it can be applied to general receivers at low cost.

The gently inclined medium-thick orebody is generally viewed as difficult to extract. This paper presents a sublevel open stoping using long-hole with back filling method, particularly for inclined medium-thick orebodies. This method not only changes the temporal sequence of ore stoping and realizes transportation using gravity and trackless devices, but also improves production capacity and efficiency. Nonetheless, this method also has some disadvantages; for example, the orebody has more contact with country rock, and the method has a more complex loss and dilution process. This paper starts with the analysis of the results of physical simulation experiments that examine the interactive relationship among the lower stope footwall dip angle, the footwall surface roughness, draw point interval and production blast ring and concludes their influence on the ore-recovery ratio in each production cycle. Secondly, based on physical simulation results, the multivariate linear fit was carried out on the SPSS using the dimensional analysis method. Thus a statistical model was developed for investigating the influence of gently inclined medium thick orebody, the footwall dip angle, the footwall roughness and interval of draw points on the recovery ratio, which can accurately forecast the ore-recovery ratio under different parameters in the physical simulation process. The optimal structural parameters obtained from physical simulation and statistical analysis was then applied to industrial experiments. Based on the 3D laser scanning during in-suit experiments, it was found that the lower stope had an 82% recovery ratio and an 18% dilution ratio, while the upper stope had a recovery ratio of 85% and a dilution ratio of 12%. Moreover, the production capacity could be up to 600t/d. The physical simulation and industrial experiments both demonstrate that the new mining method can be adopted for the safe and efficient mining of gently inclined medium-thick orebodies.

Compared to the integer-order modeling, the fractional-order modeling can achieve higher accuracy for designing and analyzing the DC-DC power converters. However, its applications in pulse width modulation (PWM) converters are limited due to the computational complexities. In this paper, a modified fractional-order modeling methodology for DC-DC converters is proposed, and its effectiveness is verified on the fractional-order positive Luo converters. Instead of using fractional-order calculus, the proposed methodology analyzes the harmonic components of the PWM converters by utilizing the non-linear vector differential equations of the periodically time-variant system. The final solution of the state variables is composed of two parts: the steady-state solution and the transient solution. The approximate steady state solution can be obtained by using the equivalent small parameter (ESP) method and the harmonic balance theory, while the main part of the transient solution can be obtained according to the explicit Grunwald-Letnikov (GL) approximation. In addition, the influence of the fractional orders on the performance of the DC-DC converters, and on the dynamic behaviors of the fractional-orders systems are also discussed in this paper. Compared to the conventional fractional-order numerical models, the proposed model is able to present the time-domain information more precisely, which helps to better reveal and analyze the non-linear behaviors of the DC-DC converters. The effectiveness of the work is demonstrated by the simulation and experimental results of the equivalent circuits built with fractional-order components.

The rapid increase in the usage of the mobile internet has led to a great expansion of cellular data networks in order to provide better quality of service. However, the cost to expand the cellular network is high. One of the solutions to provide affordable wireless connectivity is the deployment of a WiFi access point to offload users’ data usage. Nevertheless, the frequent and inefficient handover process between the WiFi AP and cellular network, especially when the mobile device is on the go, may degrade the network performance. Mobile devices do not have the intelligence to select the optimal network to enhance the quality of service (QoS). This paper presents an enhanced handover mechanism using mobility prediction (eHMP) to assist mobile devices in the handover process so that users can experience seamless connectivity. eHMP is tested in two wireless architectures, homogeneous and heterogeneous networks. The network performance significantly improved when eHMP is used in a homogeneous network, where the network throughput increases by 106% and the rate of retransmission decreases by 85%. When eHMP is used in a heterogeneous network, the network throughput increases by 55% and the retransmission rate decreases by 75%. The findings presented in this paper reveal that mobility prediction coupled with the multipath protocol can improve the QoS for mobile devices. These results will contribute to a better understanding of how the network service provider can offload traffic to the WiFi network without experiencing performance degradation.

Magnitude-based inference (MBI) is a controversial statistical method that has been used in hundreds of papers in sports science despite criticism from statisticians. To better understand how this method has been applied in practice, we systematically reviewed 232 papers that used MBI. We extracted data on study design, sample size, and choice of MBI settings and parameters. Median sample size was 10 per group (interquartile range, IQR: 8-15) for multi-group studies and 14 (IQR: 10-24) for single-group studies; few studies reported a priori sample size calculations (15%). Authors predominantly applied MBI's default settings and chose "mechanistic/non-clinical" rather than "clinical" MBI even when testing clinical interventions (only 16 studies out of 232 used clinical MBI). Using these data, we can estimate the Type I error rates for the typical MBI study. Authors frequently made dichotomous claims about effects based on the MBI criterion of a "likely" effect and sometimes based on the MBI criterion of a "possible" effect. When the sample size is n = 8 to 15 per group, these inferences have Type I error rates of 12%-22% and 22%-45%, respectively. High Type I error rates were compounded by multiple testing: Authors reported results from a median of 30 tests related to outcomes; and few studies specified a primary outcome (14%). We conclude that MBI has promoted small studies, promulgated a "black box" approach to statistics, and led to numerous papers where the conclusions are not supported by the data. Amidst debates over the role of p-values and significance testing in science, MBI also provides an important natural experiment: we find no evidence that moving researchers away from p-values or null hypothesis significance testing makes them less prone to dichotomization or over-interpretation of findings.

Investor sentiment is a research focus in behavior finance. This paper chooses five proxy variables according to China’s reality and uses a two-step principal component analysis to construct an investor sentiment index. The five proxy variables are the number of new stock accounts, turnover ratio, margin balance, net active purchasing amount, and investor attention. In the final part of this study, using the price data from the Shanghai and Shenzhen Security Exchange, this paper investigates the dynamic relationship between investor sentiment and stock market realized volatility based on the thermal optimal path. The empirical results show that when the market fluctuates severely, investor sentiment leads stock market realized volatility over one or two steps. The prediction power is also checked. The results indicate that investor sentiment indeed forecasts the realized volatility. This research supports regulators and financial institutions in taking advanced measures.

In the global context, health and the quality of life of people are adversely affected by either one or more types of chronic diseases. This paper investigates the differences in the level of income and expenditure between chronically-ill people and non-chronic population. Data were gathered from a national level survey conducted namely, the Household Income and Expenditure Survey (HIES) by the Department of Census and Statistics (DCS) of Sri Lanka. These data were statistically analysed with one-way and two-way ANOVA, to identify the factors that cause the differences among different groups. For the first time, this study makes an attempt using survey data, to examine the differences in the level of income and expenditure among chronically-ill people in Sri Lanka. Accordingly, the study discovered that married females who do not engage in any type of economic activity (being unemployed due to the disability associated with the respective chronic illness), in the age category of 40–65, having an educational level of tertiary education or below and living in the urban sector have a higher likelihood of suffering from chronic diseases. If workforce population is compelled to lose jobs, it can lead to income insecurity and impair their quality of lives. Under above findings, it is reasonable to assume that most health care expenses are out of pocket. Furthermore, the study infers that chronic illnesses have a statistically proven significant differences towards the income and expenditure level. This has caused due to the interaction of demographic and socio-economic characteristics associated with chronic illnesses. Considering private-public sector partnerships that enable affordable access to health care services for all as well as implementation of commercial insurance and community-based mutual services that help ease burden to the public, are vital when formulating effective policies and strategies related to the healthcare sector. Sri Lanka is making strong efforts to support its healthcare sector and public, which was affected by the coronavirus (COVID-19) in early 2020. Therefore, findings of this paper will be useful to gain insights on the differences of chronic illnesses towards the income and expenditure of chronically-ill patients in Sri Lanka.

What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few informative features. This paper opens avenues for developing more advanced features combined with more sophisticated feature selection methods. It also provides helpful insight to market makers and traders in general by providing useful results that can be used to gain an information edge in trading.

This paper employs a solution to the agent-guidance problem in an environment with obstacles, whose avoidance techniques have been extensively used in the last years. There is still a gap between the solution times required to obtain a trajectory and those demanded by real world applications. These usually face a tradeoff between the limited on-board processing performance and the high volume of computing operations demanded by those real-time applications. In this paper we propose a deferred decision-based technique that produces clusters used for obstacle avoidance as the agent moves in the environment, like a driver that, at night, enlightens the road ahead as her/his car moves along a highway. By considering the spatial and temporal relevance of each obstacle throughout the planning process and pruning areas that belong to the constrained domain, one may relieve the inherent computational burden of avoidance. This strategy reduces the number of operations required and increases it on demand, since a computationally heavier problem is tackled only if the simpler ones are not feasible. It consists in an improvement based solely on problem modeling, which, by example, may offer processing times in the same order of magnitude than the lower-bound given by the relaxed form of the problem.

This paper provides a general framework for controlling quality characteristics related to control variables and limited to the intervals (0, 1], [0, 1), or [0, 1]. The proposed control chart is based on the inflated beta regression model considering a reparametrization of the inflated beta distribution indexed by the response mean, which is useful for modeling fractions and proportions. The contribution of the paper is twofold. First, we extend the inflated beta regression model by allowing a regression structure for the precision parameter. We also present closed-form expressions for the score vector and Fisher's information matrix. Second, based on the proposed regression model, we introduce a new model-based control chart. The control limits are obtained considering the estimates of the inflated beta regression model parameters. We conduct a Monte Carlo simulation study to evaluate the performance of the proposed regression model estimators, and the performance of the proposed control chart is evaluated in terms of run length distribution. Finally, we present and discuss an empirical application to show the applicability of the proposed regression control chart.

A well-designed self-reported scale is highly applicable to current clinical and research practices. However, the problems with the scale method, such as quantitative analysis of content validity and test-retest reliability analysis of state-like variables are yet to be resolved. The main purpose of this paper is to propose an operational method for solving these problems. Additionally, it aims to enhance understanding of the research paradigm for the scale method (excluding criterion-related validity). This paper used a study that involved screening of high-risk groups for OCD (Obsessive-Compulsive Disorder), conducted 5 rounds of tests, and developed scales, reliability, and validity analysis (using sample sizes of 496, 610, 600, 600 and 990). The operational method we propose is practical, feasible, and can be used to develop and validate a scale.

Background Psychiatric disorders are reported to be present in 80% to 90% of suicide deaths in high-income countries (HIC), but this association is less clear in low- and middle-income countries (LMIC). There has been no previous systematic review of this issue in LMIC. The current study aims to estimate the prevalence of psychiatric morbidity in individuals with suicidal behaviour in LMIC. Methods and findings PubMed, PsycINFO, and EMBASE searches were conducted to identify quantitative research papers (any language) between 1990 and 2018 from LMIC that reported on the prevalence of psychiatric morbidity in suicidal behaviour. We used meta-analytic techniques to generate pooled estimates for any psychiatric disorder and specific diagnosis based on International classification of disease (ICD-10) criteria. A total of 112 studies (154 papers) from 26 LMIC (India: 25%, China: 15%, and other LMIC: 60%) were identified, including 18 non-English articles. They included 30,030 individuals with nonfatal suicidal behaviour and 4,996 individuals who had died by suicide. Of the 15 studies (5 LMIC) that scored highly on our quality assessment, prevalence estimates for psychiatric disorders ranged between 30% and 80% in suicide deaths and between 3% and 86% in those who engaged in nonfatal suicidal behaviour. There was substantial heterogeneity between study estimates. Fifty-eight percent (95% CI 46%–71%) of those who died by suicide and 45% (95% CI 30%–61%) of those who engaged in nonfatal suicidal behaviour had a psychiatric disorder. The most prevalent disorder in both fatal and nonfatal suicidal behaviour was mood disorder (25% and 21%, respectively). Schizophrenia and related disorders were identified in 8% (4%–12%) of those who died by suicide and 7% (3%–11%) of those who engaged in nonfatal suicidal behaviour. In nonfatal suicidal behaviour, anxiety disorders, and substance misuse were identified in 19% (1%–36%) and 11% (7%–16%) of individuals, respectively. This systematic review was limited by the low number of high-quality studies and restricting our searches to databases that mainly indexed English language journals. Conclusions Our findings suggest a possible lower prevalence of psychiatric disorders in suicidal behaviour in LMIC. We found very few high-quality studies and high levels of heterogeneity in pooled estimates of psychiatric disorder, which could reflect differing study methods or real differences. There is a clear need for more robust evidence in order for LMIC to strike the right balance between community-based and mental health focussed interventions., Duleeka Knipe and co-workers study associations between psychiatric disorders and suicidal behaviour in low- and middle-income countries., Author summary Why was the study done? Approximately 800,000 people die by suicide each year, with 75% of these deaths occurring in low- and middle-income countries (LMIC). Psychiatric disorder is thought to be associated with 80% to 90% of those who engage in suicidal behaviour in high-income countries (HIC), but this association is less clear in LMIC. A better understanding is needed of this association in LMIC to ensure effective and appropriate allocation of limited resources. What did the researchers do and find? We searched the existing literature for studies conducted in LMIC that estimated the proportion of individuals who engaged in suicidal behaviour with psychiatric disorders and found 112 studies from 26 countries (mainly from China and India). Even in the higher quality studies, there was wide variability in the proportion of psychiatric disorders in fatal (30%–80%) and nonfatal (3%–86%) suicidal behaviour, but on average, 58% of those who died by suicide and 45% of those who engaged in nonfatal suicidal behaviour had a psychiatric disorder. Mood disorders were the most prevalent disorder identified in both fatal and nonfatal suicidal behaviour. What do these findings mean? There were considerable differences in the estimates of psychiatric disorder between studies. This may reflect differences in the way studies were carried out or real differences, but there were too few high-quality studies to estimate the prevalence by individual country. More high-quality research is needed. Psychiatric disorders may be less common in suicidal behaviour in LMIC than in HIC. Although treating psychiatric illness is important, the treatment and prevention of suicidal behaviour should include a wider range of activities.

This paper presents ECOTOOL, a new free MATLAB toolbox that embodies several routines for identification, validation and forecasting of dynamic models. The toolbox includes a wide range of exploratory, descriptive and diagnostic statistical tools with visual support, designed in easy-to-use Graphical User Interfaces. It also incorporates complex automatic procedures for identification, exact maximum likelihood estimation and outlier detection for many types of models available in the literature (like multi-seasonal ARIMA models, transfer functions, Exponential Smoothing, Unobserved Components, VARX). ECOTOOL is the outcome of a long period of programming effort with the aim of producing a user friendly toolkit such that, just a few lines of code written in MATLAB are able to perform a comprehensive analysis of time series. The toolbox is supplied with an in-depth documentation system and online help and is available on the internet. The paper describes the main functionalities of the toolbox, and its power is shown working on several real examples.