Statistical Basis Research Articles

Data-dependent early completion of dose-finding trials for drug-combination.

Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression. Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios. The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%. The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.

Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle

Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation.

A probabilistic model for murder weapon identification using stab-marks in human ribs.

The aim of this article is to provide a scientific and statistical basis to identify the murder weapon in stabbing cases from the geometric characteristics of the stab-marks left on human ribs. For this purpose, a quantitative predictive model is developed, based on geometric measurements of the stab-mark and its location along the rib. A general method based on Bayesian inference and probabilities is used for the model development, rather than a deterministic model given its inability in certain occasions to identify the murder weapon. Following the process explained in this article to collect the stab-mark information required, the complete probabilistic model exposed attained a high accuracy in the identification of the murder weapon between two macroscopically identical blades with a microscopic alteration in one of them (more than 90% of correct identification is achieved).

A novel ego dissolution scale: A construct validation study

Ego dissolution (i.e., ego loss, ego disintegration, ego death, or self-loss) is a conscious state marked by a loss or diminution of one’s sense of self and a lack of first-person experience. We developed a novel, valid, and internally consistent ego dissolution scale to both (a) assess trait-like aspects of ego dissolution, which have received scant attention to date, and (b) facilitate future research in a variety of contexts (e.g., personality, psychopathology, substance use/psychedelics, contemplative practices). We determined that the 10-item Ego Dissolution Scale (EDS; Cronbach’s α = 0.80) and its identified subfactors of Ego-Loss (Cronbach’s α = 0.84) and Unity (Cronbach’s α = 0.75) were internally consistent, possessed strong convergent (e.g., depersonalization/derealization, mysticism, unusual experiences) and discriminant validity (e.g., neuroticism, social desirability). We found ego dissolution and dissociation to be empirically related yet discriminable on a statistical basis.

Cognitive Term Frame: Creation of a Dictionary Entry on the Basis of a Specialized Text Corpus

The study deals with testing a specialized text corpus on the example of a number of cognitive linguistic terms with the hypernym frame. The corpus includes a subcorpus of scientific texts and a subcorpus of journalistic texts. The former is represented by 13 journals indexed in the RSCI; the latter one is represented by 10 significant Russian newspapers & magazines. The collected texts were lemmatized and tokenized, as well as automatically marked up using the Universal Dependencies standard. The corpus is used for creating a learner’s cognitive linguistic term dictionary. This lexicographic source includes 60 major terms in the university disciplines within cognitive sciences. The novelty of the approach is due to the thesaurus-encyclopedic type of dictionary which allows scholars to describe the word both as a term (a minimal component of scientific knowledge) and as a unit of scientific text in its various collocations and ontological relations (synonymy, quasi-synonymy, class-subclass; polysemy, etc.). The basis for describing the term systemic relations is corpus statistics: analysis of concordances, collocations, and n-grams. The results of using a specialized text corpus are presented on the example of a terminological field with the dominant frame. With the help of concordance lists, contexts of term usage are revealed and its derivational relations are established. The semantic and grammatical relationships of the term are characterized through n-grams analysis. Synonyms, hyponyms, hyperonyms of the term are described based on the study of collocations. The Dashinimaeva-Wang hypothesis about the links between the terms ‘concept’, ‘frame’, ‘gestalt’, and ‘image’ was also tested. Briefly characterized were the semantic transformations of the scientific term in the media discourse. Besides, systemic relations of the terms identified on the basis of corpus statistics were confirmed by the method of interpretative analysis of the contexts obtained from concordance lists. The data of automatically selected associative measures also largely agree with the results of the associative experiment carried out in the framework of the study.

Dynamic Nomogram for Predicting Long-Term Survival in Terms of Preoperative and Postoperative Radiotherapy Benefits for Advanced Gastric Cancer.

Studies on the prognostic significance of preoperative radiotherapy (PERT) and postoperative radiotherapy (PORT) in patients with advanced gastric cancer (GC) remain elusive. The aim of the study was to evaluate the survival advantage of preoperative and postoperative radiotherapy and construct a dynamic nomogram model to provide customized prediction of the probability of prognostic events for advanced GC patients. We collected clinical records from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database with a specific target for stage II-IV GC patients treated with PERT or PORT. We used the least absolute shrinkage and selection operator (LASSO) regression model to identify factors that contribute to the overall survival (OS) of GC patients. The dynamic nomogram infographic was constructed based on the prognostic factors of tumor-specific survival. Out of the 3215 total patients (2271 [70.6%] male; median age, 61 [SD = 12] years), 1204 were in the PERT group and 2011 in the PORT group. Receiving PORT was associated with a survival advantage over PERT for stage II GC patients (HR = 0.791, 95% CI= 0.712-0.879, p < 0.001). The 1-, 3-, and 5-year OS rates were 89.9%, 63.8%, and 53.8% in the PORT group, whereas the corresponding rates were significantly lower in the PERT group (86.4%, 57.1%, and 44.3%, respectively, all p < 0.05). The survival prediction model demonstrated that patients aged > 65 years, with an advanced cancer development stage and tumor size >3 were independent risk factors for poor prognosis (all HR > 1, p < 0.05). In this study, a dynamic nomogram was established based on the LASSO model to provide a statistical basis for the clinical characteristics and predictive factors of advanced GC in a large population. PORT demonstrated significantly better treatment advantages than PERT for stage II GC patients.

Modification of SqueezeNet for Devices with Limited Computational Resources

In recent years, the computational approach has shifted from a statistical basis to deep neural network architectures which process the input without explicit knowledge that underlies the model. Many models with high accuracy have been proposed by training the datasets using high performance computing devices. However, only a few studies have examined its use on non-high-performance computers. In fact, most users, who are mostly researchers in certain fields (medical, geography, economics, etc.) sometimes need computers with limited computational resources to process datasets, from notebooks, personal computers, to mobile processor-based devices. This study proposes a basic model with good accuracy and can run lightly on the average computer so that it remains lightweight when used as a basis for advanced deep neural networks models, e.g., U-Net, SegNet, PSPNet, DeepLab, etc. Using several well-known basic methods as a baseline (SqueezeNet, ShuffleNet, GoogleNet, MobileNetV2, and ResNet), a model combining SqueezeNet with ResNet, termed Res-SqueezeNet, was formed. Testing results show that the proposed method has accuracy and inference time of 84.59% and 8.46 second, respectively, which has an accuracy of 2% higher than the SqueezeNet (82.53%) and is close to the accuracy of other baseline methods (from 84.93% to 0.88.01%) while still maintaining the inference speed (below nine second). In addition, residual part of the proposed method can be used to avoid vanishing gradient, hence, it can be implemented to solve more advanced problems which need a lot of layers, e.g., semantic segmentation, time-series prediction, etc.&#x0D;

Anomaly detection of I/O behaviours in HEP computing cluster based on unsupervised machine learning

Problematic I/O pattern is the major cause of low efficient HEP jobs. In a cluster with tens of thousands of job slots, locating the source of an anomalous workload is a nontrivial task. Automatic anomaly detection can largely shorten the recovery time of these situations and reduce manpower for problem diagnoses. This paper provides a data-driven approach to solve this problem. We design and implement an anomaly detection system based on Isolation Forest, a very efficient and scalable machine learning algorithm for spatial anomaly detection in high dimension space. Historical monitoring I/O patterns collected from the Lustre file system provides a sufficient statistical basis for model training and updating. Routine model updates and job tagging ensures adaptability and promptness. Web-based visualization and sorting tools facilitate the validation of model prediction. With the detection system, time spent on tracing the source of a problematic workload can be reduced from hours to minutes.

The Handprint Initiative: Identifying learners’ attitudes towards the environment

The Handprint Initiative was born out of the need for a new pedagogical approach to deal with current global challenges. It is founded in the social responsibility of educators and learners to actively participate in the decision-making process of our society. To achieve this, there was the need for a statistical basis to measure learners’ attitudes towards the environment. A 19-item survey was used to determine five key attitudes in high school students in Mexico. With appropriate statistical tools (including PCA, KMO, normality tests and others), it was possible to measure ecocentrism, eco-apathy, naturalism, pessimism, and scientism attitudes and to confirm the data of other studies. The results of this study contribute to assessment of the impact of the way we teach, as well as the promotion of quality education and the implementation of the Handprint Initiative.&#x0D; Keywords: handprint; attitudes towards environment; eco-apathy; sustainability, quantitative study

The Handprint Initiative: Identifying learners’ attitudes towards the environment

The Handprint Initiative was born out of the need for a new pedagogical approach to deal with current global challenges. It is founded in the social responsibility of educators and learners to actively participate in the decision-making process of our society. To achieve this, there was the need for a statistical basis to measure learners’ attitudes towards the environment. A 19-item survey was used to determine five key attitudes in high school students in Mexico. With appropriate statistical tools (including PCA, KMO, normality tests and others), it was possible to measure ecocentrism, eco-apathy, naturalism, pessimism, and scientism attitudes and to confirm the data of other studies. The results of this study contribute to assessment of the impact of the way we teach, as well as the promotion of quality education and the implementation of the Handprint Initiative.&#x0D; Keywords: handprint; attitudes towards environment; eco-apathy; sustainability, quantitative study

Development of a Method for Selecting Bus Rapid Transit Corridors Based on the Economically Viable Passenger Flow Criterion

The creation of bus rapid transit systems requires significant investments in transport infrastructure. It often requires changes in roadway parameters, building boarding platforms, new bus depots, as well as creating a priority passage system at intersections with individual vehicles flows. In world practice, the routing of bus rapid transit (hereinafter—BRT) corridors is often based on the criterion of an opinion of transport experts who assess passenger flows and the location of main attraction points. This article describes an algorithm for building BRT corridors based on the criterion of economically viable passenger flow. The method is based on an iterative algorithm built on the principle of passenger flows redistribution over the transport network in the event of a change in its characteristics. Specifically, what changes is the speed of transit in certain areas due to inclusion of the area in a BRT corridor when the area reaches the threshold of economically viable passenger flow. A threshold value of passenger flow for different cities, grouped by population size, is determined on the basis of passenger flow statistics in globally operated BRT systems. The condition for exiting the iterative algorithm can be either the absence of new network areas where an economically viable passenger flow is achieved, or the achievement of 90% of the labor commuting share in the city during the time specified in the urban planning standard. This method can be used to identify new and extend existing BRT corridors in cities with populations from 100 to 2000 thousand people.

The influence of time on dynamic signature: An exploratory data analysis

Dynamic signatures are a digitalized form of handwritten signatures. Their use has seen a steep increase for important transactions, such as life insurance and telecommunication contracts, sales and banking operations. A dynamic signature can be disputed and a forensic handwriting examiner may be hired to help determine whether it is genuine or not. Such a conflict may emerge years after the questioned signature has been affixed. Since reference material contemporary to the relevant period may not be made available to the expert, it can be questioned whether time influences dynamic signature data, which could affect the expert’s results. This study was designed to explore this possible influence. Dynamic signatures of three participants were collected over a duration of 18 months, during 44 acquisition sessions. Based on this sample, the goals of describing variation of dynamic features over short and longer time periods, defining adequate sample collection strategies and sampling time frames, as well as laying down the foundation for using the time information for comparative analysis of dynamic signatures, were pursued. Both the relative stability and the slow ‘drift’ of signatures over time were illustrated by our results. The findings of this study lead to recommendations for sampling in casework, validate statements previously made by forensic scientist through an empirical investigation on dynamic signatures and strengthen the statistical basis for forensic signature comparison.

Applying an entropy-weighted TOPSIS method to evaluate energy green consumption revolution progressing of China.

The energy green consumption revolution (EGCR) is the highest priority in the Chinese government's energy revolution agenda. The purpose of this study is to provide a comprehensive and objective evaluation of the China's EGCR progressing from 2011 to 2019. In this study, an integrated economic-social-energy-environmental EGCR evaluation framework is built, and the entropy-weighted TOPSIS method with four customized equations is used to calculate and analyze the EGCR index. The study finds that the EGCR index at the national level fluctuates between 0.290 and 0.302, showing a stagnant and regressive trend. At the regional and provincial levels, the EGCR index for eastern China remains at high level, floating above 0.4 and no further growing trend has been indicated. As for the eastern China, Beijing is the only city has high-level EGCR index and is able to maintain positive growth trend. The EGCR index in central, western, and northeastern China is at a low level, fluctuating below 0.4. This result is mainly caused by the fact that the majority of these regions are still constrained by the fossil fuel-dominated social, economy, energy, and environment structures. Therefore, the research findings not only provide supportive evidence for the Chinese government to recognize the progressing of EGCR, but also offer statistical basis over formulating and updating EGCR policies at a timely manner.

The pharmaceutical industry of the world: investment in R &amp; D in the postcovid period

In the article, the authors analyzed the activities of pharmaceutical companies in the world in the field of research and development (R &amp; D) on the basis of international and Russian statistics. The financing of R &amp; D in pharmaceuticals traditionally retains its decisive role in the development of both the industry as a whole and the global economy and the social sphere in the conditions of the outpacing growth of the age qualification of the world's population, which is the relevance of the article. The purpose of the article is to identify new trends in the development of investment and R&amp;D in the pharmaceutical industry. The authors conducted quantitative and qualitative monitoring of the parameters of pharmaceutical companies' activities in the field of research and development in the last decade and identified a number of fundamentally new trends, such as the emergence of new pharmaceutical technologies, evaluation of healthcare technologies by society/the state, digitalization (including digital interaction with customers whose base is becoming more diversified), price growth on drugs and research risks, transformation of the industry's business strategies and more flexible project management due to the impact of COVID-19, including in the field of research, development and investment in the pharmaceutical industry.

Methodological principles of the formation the concept of green economy

Introduction. A “green economy” is an economy that aims to reduce environmental risks and environmental deficits, and that aims at sustainable development without environmental degradation. It is closely related to environmental economics, but has a more politically applied orientation. A green economy is socially inclusive, produces very little carbon and is resource efficient. In this aspect, the problem of forming new ways of development for the restoration of the natural environment, while ensuring a decent standard of living for people, becomes particularly important. Among the important results of such searches are the concept of “green economy” and indicators of “green economy”, their adaptation to the real conditions of the country and regions.The purpose of the article is to research and analyze the methodological foundations of the formation of the concept of green economy, its main principles and indicators.Methods. The methodological basis of the research is theoretical generalization and comparison, analysis and synthesis, carrying out scientific knowledge in order to define the concept of “green economy” and the concept of “green economy”. The statistical and factual basis of the study is the official data of the statistical organization of the European Commission - Eurostat.Conclusions and prospects of further research. The concept of “green economy” is defined, which is based on minimizing the impact on the environment, social justice, ensuring a high level of well-being. The concept of “green economy” has gained special importance in recent years. The goal of the “green economy” is the formation of an effective environment for economic and social progress, based on minimizing the negative impact on the environment and effective use of natural resources while maintaining a decent population balance; reducing waste or ensuring its proper disposal is an important part of the ongoing process of economic development to protect lives and livelihoods and transition to a vibrant green and sustainable economy.Prospects for further research consists in applying the principles and concept of “green economy” for the transition and development of the domestic “green economy”.

Experimental solid–liquid equilibria and solid formation kinetics for carbon dioxide in methane for LNG processing

AbstractIn cryogenic liquefaction processes, CO2 poses a solid‐formation risk even in trace concentrations. We present solid–fluid equilibrium (SFE) data for CO2 in liquid methane at CO2 concentrations from (52 to 500) ppm, extending the available data and indicating that models tuned to existing data over predict the solubility of CO2 at LNG storage temperatures (~112 K) by nearly a factor of 3. The new data are used to improve the SFE model in the ThermoFAST software package. The formation kinetics of CO2 solids in liquid methane are elucidated at conditions relevant to cryogenic gas processing. Repeated, ramped‐temperature formation measurements provide a statistical basis for quantifying solidification risk. Nucleation rates extracted from the ramped‐temperature data, consistent with those measured at fixed temperature, were used to extract parameters describing CO2 solid formation in methane. These results significantly improve the ability to predict CO2 solid formation risk in cryogenic natural gas processing.

The 'Tabes Syphilis Controversy' : A Dispute About the Cause of Tabes Dorsalis and Progressive Paralysis at the End of the 19th Century

For some years now, the incidence of syphilis and neuroluetic clinical pictures has been increasing. As a result, tabes dorsalis and progressive paralysis are once again gaining relevance in neurology. In the last two decades of the 19th century, there was a heated debate in German neurology about the understanding of the aetiology of these conditions, the so-called 'tabes syphilis controversy'. In 1879, Wilhelm Erb had called on the German neurological community in a much-noticed lecture to finally tackle the problem to unequivocally identify the cause of tabes dorsalis. Mainly on the basis of disease statistics he himself postulated a previous syphilitic infection as of key importance. Answering this question was urgent, because the proportion of patients in psychiatric institutions suffering from progressive paralysis, to which tabes was seen in close parallel, was rapidly increasing. The Berlin neurologists Carl Westphal, Ernst Julius Remak, Martin Bernhardt and Ernst Victor von Leyden regarded Erb's thesis as a gauntlet. They saw the causes of tabes and progressive paralysis in social impoverishment such as damp and cold living conditions, in physically one-sided work overloads or in the hardships of soldiers in the army. They assumed that traumatic tabes was caused by concussions or bruises. The Leipzig neurologist Paul Julius Möbius was the first to state between 1890 and 1897 that the only cause of tabes and progressive paralysis was a previous syphilitic infection. He consistently ruled out all other aetiological theories. Above all, bacteriological and microbiological research in the following years proved Möbius right. Thus 40 years after Erb's lecture, in addition to diagnostic, specific therapeutic approaches could be developed and applied.

A rigorous derivation of the Hamiltonian structure for the Vlasov equation

Abstract We consider the Vlasov equation in any spatial dimension, which has long been known [ZI76, Mor80, Gib81, MW82] to be an infinite-dimensional Hamiltonian system whose bracket structure is of Lie–Poisson type. In parallel, it is classical that the Vlasov equation is a mean-field limit for a pairwise interacting Newtonian system. Motivated by this knowledge, we provide a rigorous derivation of the Hamiltonian structure of the Vlasov equation, both the Hamiltonian functional and Poisson bracket, directly from the many-body problem. One may view this work as a classical counterpart to [MNP+20], which provided a rigorous derivation of the Hamiltonian structure of the cubic nonlinear Schrödinger equation from the many-body problem for interacting bosons in a certain infinite particle number limit, the first result of its kind. In particular, our work settles a question of Marsden, Morrison and Weinstein [MMW84] on providing a ‘statistical basis’ for the bracket structure of the Vlasov equation.

Developing an education model to improve Indonesian capital market literation and inclusion

This study attempts to analyse the capital market literacy and inclusion in Indonesia. The total number of respondents was 495 college students in Java, the island with the most significant number of college students having 60.5% of the entire Indonesian college students. Studies have shown that the degree of literacy has had a beneficial influence on the engagement of students in the capital market, whereas the Indonesian student capital market literacy is poor on the basis of statistics. Students' primary weaknesses include practical understanding of the capital market, a material which is extremely important in promoting the engagement of students in the capital market. This research has established a realistic, inclusive, and collaborative model that teaches about the capital market, tackles the challenges, and can be implemented by IDX in its education program.

Screening of neoplastic diseases by statistical analysis of urine fluorescence spectroscopic data. Application of multivariate techniques for enhancing classification.

The composition of human fluids is modified during the course of neoplastic diseases. Urine analysis offers the advantage of being a noninvasive method for which samples are easily and routinely collected from patients. In this work, urine fluorescence spectra recorded upon excitation at 405nm were obtained from healthy volunteers and individuals with different oncologic pathologies. A large number of indexes, i.e., parameters obtained from spectral data which assist spectral features characterization, were developed to classify healthy and pathological populations. The discrimination ability of simple predictive indexes, obtained from spectra pretreated with different normalization procedures and by taking their derivatives, was statistically assessed. In addition, multivariate methods, such as principal component analysis and multivariate curve resolution by alternating least squares, were used to develop more elaborate indexes for distinguishing between healthy and pathological populations. All indexes were systematically evaluated on a statistical basis by in lab-developed routines capable of detecting outliers, judging the normal distribution of the indexes, evaluating variance homogeneity, testing the difference between the means of healthy and pathological populations, as well as performing a receiver operator curve analysis to assess the classification power of each index. Those indexes with the best performances were further combined to perform a linear discriminant analysis, which yielded a powerful classification algorithm with an area under the receiver operator curve of 0.986, a sensitivity of 97.7%, a specificity of 100%, and an overall accuracy of 98.8%. The present study shows that the statistical analysis of urine fluorescence data with a proper combination of multivariate techniques bears a high potential to develop massive screening tests for the early detection of oncologic pathologies.