Comparison Of Approaches Research Articles

Methods Analysis: Comparing Competitive Team-Based Learning with other Instructional Methods and Approaches

Having introduced Competetive Team Based Learning (CTBL), my innovative approach to teaching, this paper presents a cogent and critical analysis and comparison of CTBL with other popular methods /approaches in the arena of Education in general and Language Teaching in particular, in terms of their distinguishing features and characteristics. Among such methods and approaches are Communicative Language Teaching (CLT), Collaborative Learning (ColL), Interactive Learning (IntL), and Cooperative Learning (CL) methods which are appreciated particularly in the U.S. and in the West. A synthesis of the distinguishing drawbacks of the comparison methods and approaches is part of the article. The chapter also explicates how CTBL, my educational innovation, that has been formulated based on my edu-political theories (Hosseini, 2023) is, in the last analysis, an approach to human security and prosperity and world peace. I hope this chapter would contribute to making a sound decision on implementing CTBL in the arena of (Language) Education for the ultimate goal of peace making and more compassionate civilizations building.

Развитие цифрового сегмента российского предпринимательства в условиях пандемии

Digitalization has become inevitable as the demand for digital services increases. The purpose of the article is to study the development dynamics of Russian digital segment during and after the pandemic and to predict the vector of its long-term development. The authors provide evidence of dualism: on one hand, the pandemic led to new restrictions in the development of entrepreneurial activity; on the other hand, the change in demand and the high adaptability of business structures made it possible to expand the digital segment of Russian small and medium-sized businesses. The authors use methods of systematization, comparison, economic analysis, graphical method and systematic approach. The development of the Russian digital entrepreneurship sector in the context of digitalization of internal business processes, consumer communication processes, and external relations is analyzed. The authors conclude that digital technologies have high potential for the development of Russian small and medium-sized businesses and provide recommendations on the implementation of state policy measures aimed at optimizing the development of digital entrepreneurship in Russia.

Organization of psychosocial factors associated with worry about acquiring SARS-CoV-2 among women undergoing cancer treatment: an empirical network comparison approach

Objectives: Pandemic-induced anxiety can have adverse mental and somatic health consequences on cancer patients (CP). This study aimed to (1) explore the intricate relationships between worry related to potential SARS-CoV-2 infection, COVID-19 perception, sociodemographic factors, and the perceived effectiveness of lockdown measures and (2) investigate if these relationships differ between cancer patients and individuals without a history of cancer (IWHC). Methods: We conducted a cross-sectional quantitative study in France between December 1 and 14, 2020. Network analysis was employed on a sample of 1889 women, including 282 cancer patients and 1607 noncancer individuals. Results: Our findings indicate that CP were 20% more likely to express worry than IWHC. Anxiety is embedded within a complex network involving sociodemographic, cognitive, and emotional factors. The emotional components related to COVID-19 perception were found to play a crucial role. The networks for both groups were observed to be identical. Conclusions: Our study underscores the heightened vulnerability of cancer patients to pandemic-induced anxiety, emphasizing the crucial role of emotional components related to COVID-19 perception. The observed similarities in the anxiety network between cancer patients and those without a history of cancer suggest that universal approaches might be effective across groups. Implications: Utilizing the Causal Attitude Network Model, we propose potential methods for managing and reducing individual anxiety levels.

Criminal Responsibilities Or Political Partners As Corporations In Corruption Criminal Action And Money Laundering Criminal Acts

The existence of political parties plays a strategic role in the development of democracy and the nation is faced with a number of issues that create a negative impression on these organs, some of which include; first, the problem of involvement of political parties in various cases of corruption and money laundering crimes that have an impact on the fall of public image and trust in the organization; second, the problem of pro and contra accountability of political parties involved in cases of corruption and criminal acts of money laundering before the law; and third, the issue of imposing criminal sanctions on political parties as the subject of corporate offenses known in corruption and money laundering. This research belongs to the typology of juridical normative legal research with secondary data types in the form of primary, secondary and tertiary legal materials. The approach taken includes the comparison, conceptual, statutory, and philosophical approaches, and analyzed descriptively qualitatively through literature. The results of the study show that; first, the justification of political parties as corporations in criminal acts of corruption and criminal acts of money laundering can be seen in five basic perspectives, namely philosophical foundation, juridical basis, historical basis, sociological basis, and political foundation of criminal law; second, the ideal concept of political party punishment as a corporation is carried out with a reconceptualization of the subject of corporate delictions in the affirmation of two types of corporations, namely corporations in the form of private and special corporations or in the form of public with different provisions on punishment arrangements. The need for an amendment to some provisions of the Corruption Law and the TPPU Law related to corporations in accommodating the two types of corporations into a forum for the implementation of ideas, of course issuing a law that contains specific provisions regarding special corporate punishment can be the answer to a myriad of issues regarding criminalization

An agricultural community's perspectives on COVID-19 testing to support safe school reopening.

School-based COVID-19 testing may be an effective strategy for reducing transmission in schools and keeping schools open. The study objective was to examine community perspectives on school-based COVID-19 testing as a mitigation strategy to support safe school reopening. We conducted a qualitative study in Yakima County, an agricultural region of Washington state, where over half of residents are Hispanic/Latino. From June to July 2021, we interviewed 18 students (13 years old, on average) and 19 school employees, and conducted four focus groups (2 in Spanish, 2 in English) with 26 parents. We audio-recorded the semi-structured interviews and focus group discussions which were then transcribed. We used an inductive, constant comparison approach to code the transcripts and conducted a thematic analysis to generate themes. We identified four main themes. Students, parents, and school employees desired a return to in-person learning (Theme 1). Schools implemented numerous COVID-19 mitigation strategies (e.g., masking) to facilitate a safe return to school but felt that adding testing would not be feasible due to a lack of resources and overworked staff (Theme 2). Parents and school employees' familiarity with COVID-19 testing procedures influenced their support for testing (Theme 3). Parents and school employees felt there were inadequate resources for individuals who test positive for COVID-19 (Theme 4). Schools require adequate resources and medical personnel to implement COVID-19 testing. Individuals also need resources after testing positive, including physical space to isolate, financial resources for those without paid time off, and delivery of food and other necessities to households in rural communities.

Uncovering the dimensionality of authorial voice in the integrated writing task: A confirmatory factor analysis study

Research has long noted the importance of establishing an authorial voice as a hallmark of academic writing. However, despite purported connections between academic writing and integrated writing tasks (Gebril & Plakans, 2014; Huang & Hung, 2013), little attention has been paid to examine the role and effect of authorial voice on writing quality in a test task that involves use of source texts. The primary intent of this study is to shed light on the internal structure of authorial voice in an integrated reading-to-write task by way of Confirmatory Factor Analysis (CFA). The participants of this study included 315 Chinese college-level EFL students who wrote for a task addressing the topic of “eating for professional situations”. Based on an analytic rubric developed by the researchers with indicators informed by Hyland's (2005) interactional framework of authorial voice, the collected essays were rated on a scale of 1–5 based on the quality as it pertains to each of the indicators under the rubric. The results of a model comparison approach to CFA suggested that the current data are best characterized as a unidimensional construct; it represents one single factor. Furthermore, the Structural Equation Modeling (SEM) analysis indicated verbatim source use is inversely associated with quality of authorial voice, corroborating our hypothesis that improper source copying negatively influences the rater's perception of a test-taker's authorial voice. Practical implications regarding rubric use and instruction are discussed.

Stability of Nonlinear Impulsive Systems: Self-Triggered Comparison System Approach

This paper presents a new approach to the problem of globally asymptotical stability for nonlinear impulsive systems by utilizing self-triggered impulsive control (STIC), where impulse instants are determined by a well-designed self-triggered mechanism (STM) which is based on the comparison approach. Different from event-triggered impulsive control (ETIC) strategy in which the triggering conditions need to be continuously or periodically monitored to determine whether an impulse should be generated, our proposed STIC strategy does not require those monitoring because it can utilize the measurable information to predict the next impulse instant. Based on the proposed STIC strategy, some Lyapunov-based sufficient conditions for globally asymptotical stability are derived by adopting the comparison approach. Moreover, we prove that the Zeno behavior can be excluded from our results. Specifically, we apply the theoretical results to resolve the STIC problem for a class of nonlinear systems, where two different cases are fully considered, that is, state-based STIC and output-based STIC. For each case, the design method of STIC strategy is given in terms of linear matrix inequalities (LMIs). The effectiveness of the proposed methods is verified by two numerical examples and their simulations.

Hydra: Deadline-Aware and Efficiency-Oriented Scheduling for Deep Learning Jobs on Heterogeneous GPUs

With the rapid proliferation of deep learning (DL) jobs running on heterogeneous GPUs, scheduling DL jobs to meet various scheduling requirements, such as meeting deadlines and reducing job completion time (JCT), is critical. Unfortunately, existing efficiency-oriented and deadline-aware efforts are still rudimentary. They lack the capability of scheduling jobs to meet deadline requirements while reducing total JCT, especially when the jobs have various execution times on heterogeneous GPUs. Therefore, we present Hydra, a novel quantitative cost comparison approach, to address this scheduling issue. Here, the cost represents the total JCT plus a dynamic penalty calculated from the total tardiness (i.e., the delay time of exceeding the deadline) of all jobs. Hydra adopts a sampling approach that exploits the inherent iterative periodicity of DL jobs to estimate job execution times accurately on heterogeneous GPUs. Then, Hydra considers various combinations of job sequences and GPUs to obtain the minimized cost by leveraging an efficient branch-and-bound algorithm. Finally, the results of evaluation experiments on Alibaba traces show that Hydra can reduce total tardiness by 85.8% while reducing total JCT as much as possible, compared with state-of-the-art efforts.

Directional and stabilizing selection shaped morphological, reproductive, and physiological traits of the invader Solidago canadensis.

Trait evolution in invasive plant species is important because it can impact demographic parameters key to invasion success. Invasive plant species often show phenotypic clines along geographic and climatic gradients. However, the relative contributions of natural selection and neutral evolutionary processes to phenotypic trait variation among populations of invasive plants remain unclear. A common method to assess whether a trait has been shaped by natural selection or neutral evolutionary processes is to compare the geographical pattern for the trait of interest to the divergence in neutral genetic loci (i.e., Q ST -F ST comparisons). Subsequently, a redundancy analysis (RDA) can facilitate identification of putative agents of natural selection on the trait. Here, we employed both a Q ST -F ST comparisons approach and RDA to infer whether natural selection shaped traits of invasive populations of Solidago canadensis in China and identify the potential environmental drivers of natural selection. We addressed two questions: (1) Did natural selection drive phenotypic trait variation among S. canadensis populations? (2) Did climatic, latitudinal, longitudinal, and altitudinal gradients drive patterns of genetic variation among S. canadensis populations? We found significant directional selection for several morphological and reproductive traits (i.e., Q ST > F ST) and stabilizing selection for physiological traits (i.e., Q ST < F ST). The RDA showed that stem biomass of S. canadensis was strongly positively correlated with longitude, while leaf width ratio and specific leaf area were significantly positively correlated with the mean diurnal range. Stem biomass had a strong negative correlation with annual precipitation. Moreover, height of S. canadensis individuals was strongly positively correlated with altitude and precipitation of the wettest quarter. A longitudinal shift in precipitation seasonality likely selected for larger stem biomass in S. canadensis. Overall, these results suggest that longitudinal and altitudinal clines in climate exerted strong selection pressures that shaped the phenotypic traits of S. canadensis.

Contrastive adversarial learning in text classification tasks

In recent years, contrastive learning has been very successful in unsupervised tasks of representation learning and has received a lot of attention in supervised tasks. In supervised tasks, the discrete nature of natural language makes the construction of sample pairs difficult and the models are poorly robust to adversarial samples, so it remains a challenge to make contrastive learning effective for text classification tasks and to guarantee the robustness of the models. This paper presents a contrastive adversarial learning framework built using data augmentation with labeled insertion data. Specifically,By adding perturbation to the word-embedding matrix, adversarial samples are generated as positive examples of contrastive learning, and external semantic information is introduced to construct negative examples. Contrastive learning is used to improve the sensitivity and generalization ability of the model, and adversarial training is used to improve robustness, thereby improving the classification accuracy. In addition, the momentum contrast from unsupervised tasks is also introduced into the text classification task to increase the number of sample pairs. Experimental results on several datasets show that the proposed approach outperforms the baseline comparison approach, and in addition some experiments are conducted to verify the effectiveness of the proposed framework under low-resource conditions.

ARGLRR: A Sparse Low-Rank Representation Single-Cell RNA-Sequencing Data Clustering Method Combined with a New Graph Regularization.

The development of single-cell transcriptome sequencing technologies has opened new ways to study biological phenomena at the cellular level. A key application of such technologies involves the employment of single-cell RNA sequencing (scRNA-seq) data to identify distinct cell types through clustering, which in turn provides evidence for revealing heterogeneity. Despite the promise of this approach, the inherent characteristics of scRNA-seq data, such as higher noise levels and lower coverage, pose major challenges to existing clustering methods and compromise their accuracy. In this study, we propose a method called Adjusted Random walk Graph regularization Sparse Low-Rank Representation (ARGLRR), a practical sparse subspace clustering method, to identify cell types. The fundamental low-rank representation (LRR) model is concerned with the global structure of data. To address the limited ability of the LRR method to capture local structure, we introduced adjusted random walk graph regularization in its framework. ARGLRR allows for the capture of both local and global structures in scRNA-seq data. Additionally, the imposition of similarity constraints into the LRR framework further improves the ability of the proposed model to estimate cell-to-cell similarity and capture global structural relationships between cells. ARGLRR surpasses other advanced comparison approaches on nine known scRNA-seq data sets judging by the results. In the normalized mutual information and Adjusted Rand Index metrics on the scRNA-seq data sets clustering experiments, ARGLRR outperforms the best-performing comparative method by 6.99% and 5.85%, respectively. In addition, we visualize the result using Uniform Manifold Approximation and Projection. Visualization results show that the usage of ARGLRR enhances the separation of different cell types within the similarity matrix.

Impact of Reservoirs on Local Precipitation‐Temperature Coupling Relationships

AbstractReservoirs are an essential part of modern society, serving a variety of critical functions while exerting an uncertain and debatable impact on the surrounding climate and environment. The precipitation‐temperature coupling relationship and the associated compound extreme events are critical climatic factors for humans and ecosystems, but how reservoirs would affect these factors is unknown. Here, we found that sites near reservoirs had higher sensitivity of precipitation extremes to temperature increases, as well as an increased frequency of compound precipitation‐temperature extreme events, based on a global‐scale analysis of reservoir and meteorological observations and a paired comparison approach. Additionally, these reservoir climate effects are closely correlated with reservoir size and are more pronounced in warm‐dry climates. It highlights the potential climate risks associated with reservoirs which have important implications for strengthening a community's resilience in the face of these challenges.

The trend of oral cancer screening in Bangladesh, perceptions, and risk factors: a model comparison approach

Background: Oral cancer (OC) is a leading cause of death among cancer patients and a major public health concern in Bangladesh. Timely OC screening can help in the early detection of OC and could reduce the mortality rate in Bangladesh. We aimed to assess the tend of OC screening, perception and risk factors among dental patients. Methods: This was a facility-based cross-sectional study among 423 participants with dental problems using a convenience sampling technique. The collected data were analyzed by IBM SPSS 25 and various statistics, such as the chi-squared test, Poisson regression, logistic regression, etc., were used to attain the objectives. Results: Only 2.4% of the study participants underwent OC screening before the study period. The use of smokeless tobacco and smoking were significantly associated with the tend of OC screening. According to the Poisson model, previous smokeless tobacco users were 5 times (odds ratio (OR): 5.403, P&lt;0.05) more likely than non-users to go for OC screening, which was 8 times (OR: 7.729, P=0.033) for the binary logistic regression. Having a family history of OC increased the odds of receiving an OC screening by nine times (OR: 9.544, P&lt;0.002) compared to individuals without a family history of OC; it was 16 times (OR: 16.438, P=0.001) for the binary logistic regression. Conclusions: This study shows former smokeless tobacco users and past smokers are more likely to uptake OC screening than non-users. In addition, patients having a family history of OC increased the odds of receiving OC screening than subjects without a family history of OC. The lack of OC screening in Bangladesh highlights the need to implement a national-level screening program to help with the early diagnosis of OC and its prevention.

Contaminants of emerging concern: Silylation procedures, evaluation of the stability of silyl derivatives and associated measurement uncertainty

Analyte range of gas chromatography–mass spectrometry (GC–MS), widely used in environmental analysis, can be significantly broadened by derivatization. Silyl derivatives have improved volatility and thermal stability, chromatographic and mass spectrometric behaviors, and thus detection, structural elucidation and quantification. However, silylation use is often hindered by the stability of generated derivatives and the need to optimize silylation conditions. In this study, we optimized the derivatization conditions for 70 selected contaminants of emerging concern (CEC) using chemometrics approaches. N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), N, O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) and BSTFA + 1 % trimethylchlorosilane (TMCS) were investigated, among which the latter gave the best yield. CEC were grouped in three derivatization protocols: 60 °C/45 min, 70 °C/90 min, and 70 °C/45 min. The short- and long-term stability of the CEC-trimethylsilyl (TMS) derivatives, i.e. for 28 days and up to 20 weeks were examined in a solvent and artificial wastewater (AWW) extract at 25 °C, 4 °C and − 18 °C, and during repeated five freeze-thaw (F/T) cycles, at two concentration levels: 100 μg/L and 1000 μg/L. Except for TMS derivatives of shikimic acid (SHA), quinic acid (QA) and sulfanilamide (SFA), the remaining derivatized compounds were stable in solvent (EtAc) for 28 days. In AWW extract, TMS derivatives of citric acid (CA), 17β-estradiol (E2), estriol (E3) and 17α-ethinyl estradiol (EE2) were unstable at 25 °C and 4 °C. Within up to 20 weeks, only the TMS derivatives of CA, meso-erythritol (ERY) and bisphenol BP (BPBP) were unstable. The most significant hydrolytic breakdown was observed during repeated F/T cycles. After three cycles, ≤ 20 % of the initial concentration of six and nine CEC-TMS derivatives had degraded in solvent and AWW extracts, respectively. According to the deep statistical comparison (DSC) approach, the most prominent degradation was observed for TMS derivatives of E2, CA, 9-hydroxyfluorene (9-HF), estrone (E1) and trans-3′-hydroxycotinine (T3HC) in solvent; E2, CA, 9-HF, E3 and E1 in AWW extracts and ERY, E2, CA, 9-HF and E1 in both matrices. Finally, the sample concentration of CEC accounted for most of the measurement uncertainty (MU).Based on our findings, we recommend the derivatized samples to be stored at −18 °C for up to 20 weeks to ensure the stability of their TMS derivatives. Sample freezing and thawing of not more than twice is allowed to maintain ≥80 % of the initial CEC-TMS concentration.

Transcriptome analysis reveals key regulatory genes for root growth related to potassium utilization efficiency in rapeseed (Brassica napus L.).

Root system architecture (RSA) is the primary predictor of nutrient intake and significantly influences potassium utilization efficiency (KUE). Uncertainty persists regarding the genetic factors governing root growth in rapeseed. The root transcriptome analysis reveals the genetic basis driving crop root growth. In this study, RNA-seq was used to profile the overall transcriptome in the root tissue of 20 Brassica napus accessions with high and low KUE. 71,437 genes in the roots displayed variable expression profiles between the two contrasting genotype groups. The 212 genes that had varied expression levels between the high and low KUE lines were found using a pairwise comparison approach. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional classification analysis revealed that the DEGs implicated in hormone and signaling pathways, as well as glucose, lipid, and amino acid metabolism, were all differently regulated in the rapeseed root system. Additionally, we discovered 33 transcription factors (TFs) that control root development were differentially expressed. By combining differential expression analysis, weighted gene co-expression network analysis (WGCNA), and recent genome-wide association study (GWAS) results, four candidate genes were identified as essential hub genes. These potential genes were located fewer than 100 kb from the peak SNPs of QTL clusters, and it was hypothesized that they regulated the formation of the root system. Three of the four hub genes' homologs-BnaC04G0560400ZS, BnaC04G0560400ZS, and BnaA03G0073500ZS-have been shown to control root development in earlier research. The information produced by our transcriptome profiling could be useful in revealing the molecular processes involved in the growth of rapeseed roots in response to KUE.

Revenue reflection in the auditor’s report on accounting statements

Organizations whose securities are traded with the organizers of trading on the securities market, which include public joint-stock companies, are subject to mandatory audit. In the audit reports, according to the requirements of the international auditing standard 701 issued to such organizations, the key audit issues should be disclosed. Very often, auditors opt for such a key issue as “revenue recognition”. The article deals with situations when the choice of the subject of a key question on revenue seems insufficiently justified, and the wording of the key questions is as identical as if their text were standardized, while the adoption of standard 701 had the opposite goal — information transparency, specificity and reflection of the characteristics of the activities of each audited entity. The article uses examples to reveal various approaches to the disclosure of revenue information in key audit issues. During the research, such methods as analysis and synthesis, comparison, grouping, analogy, logical and systematic approaches were used. The research results obtained in this article can be applied in the practical work of modern organizations, in the educational process of higher educational institutions and in the development and reform of regulatory documents on accounting, reporting and auditing. The results presented in this paper will also be useful to a wide range of readers interested in modern problems of accounting, reporting and auditing at the domestic and international level.

TOWARDS POVERTY ALLEVIATION FOR THE BASE OF PYRAMID: SOCIAL BUSINESS MODEL IN URBAN LOW-COST HOUSINGS

Purpose – this study investigates alternative a small-scale social business model that potentially generate sustainable income for households at the base of the pyramid. A comparison of two low-cost housings that have a different geographical condition and local characteristics is examined. Research methodology – this research adopted two-stage approach to address the research objective. A pairwise comparison was employed to evaluate alternatives based on selected criteria for decision-making. In the second stage, the proposed business model was assessed by taking into account investment, processing cost, and revenue. Findings – the findings of this research suggest suitable business model that combine profit orientation and facilitates social mission in urban settings. The business model offers attractive financial feasibility from the investor viewpoint and simultaneously engages low-income households to improve their prosperity level leaving the base of the pyramid (BOP) status. Research limitations – this paper is not involving division of responsibility between stakeholders in low-cost housing and BOP sector. This study also not discussed how social entrepreneurs play a role in the social business model. There is a need to further investigate how the impact of social entrepreneurs on this model and engage collaboration with interest parties to engage community development. Practical implications – the findings recommend strategies that can be used by policy-makers and other related stakeholders to scale-up the business model, empower more low-income households, and create new job opportunities for urban poor. The findings of this research also indicate social business model that enables households at the BOP to earn sustainable income and release their current poverty status. Originality/Value – the research is one of the few studies that explored alternatives to social business models available for urban poor by taking into account project feasibility. No previous research has been attempted to consider both pairwise comparison and life cycle cost approach in the development of social business models. This research can be found useful for those with similar issues not only in emerging economies but also in developed countries.

Adaptive scatter kernel deconvolution modeling for cone-beam CT scatter correction via deep reinforcement learning.

Scattering photons can seriously contaminate cone-beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality-related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q-network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U-net based scatter estimation approach for comparison. The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal-to-noise ratio (PSNR) is higher than 23.90dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32dB. In the measurement study, we adopt a hardware-based beam stop array algorithm to obtain the scatter-free projections as a comparison baseline, and our method can achieve superior performance with MAPE<17.79% and PSNR>16.34dB. In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality.

A Method for Identifying the Dominant Meteorological Factors of Atmospheric Evaporative Demand in Mid‐Long Term

AbstractTo better understand the changes in the atmospheric evaporative demand (AED) in the context of global warming, the anomaly contribution analysis is presented to estimate mid‐long term contributions of meteorological factors to the AED. The Pearson correlation coefficient (RP) between the total contribution (ψ) of meteorological factors and the relative variation (ϕ) of the AED, and the Sen's trend slope (βS) of (ϕ, ψ) scatters are used to evaluate the applicability of the method. The smaller the values of |1 − RP| and |1 − βS|, the more applicable the method is. To validate the method, the reference crop evapotranspiration is employed as a proxy for the AED. The multi‐year contribution analysis is used as a comparison approach, which can only investigate the dominant meteorological factors of the AED in long term. Moreover, the Huaihe River basin of China is taken as a case study. Results show that (a) the values of |1 − RP| and |1 − βS| in mid‐long term are less than 0.1 in most cases when applying the anomaly contribution analysis, and the mid‐long term contribution processes of meteorological factors to the AED are clearly demonstrated; and (b) the wind speed and sunshine hours are the two most dominant factors (the total absolute contribution exceeds 60%) in long term, but they are not always the dominant factors in mid‐long term (e.g., wind speed in summer, and sunshine hours in winter). Therefore, the anomaly contribution analysis is a reasonable and effective method, which can help to gain insights into the changes in the AED.

Evaluation of the Causal Association of Risk Factors for Death From COVID-19 Patients Admitted to Golestan Hospitals by Propensity Score Estimation Method

Background and Purpose: Evaluating the causal association effect of risk factors is essential in estimating the mortality rate among COVID-19 patients. Much research has been conducted to assess the impact of COVID-19 on death in various countries worldwide. However, few studies have addressed the effect of causal association of risk factors. This study aims to address this gap by estimating the impact of COVID-19 on death by evaluating the causal association with the risk factors. Materials and Methods: The research population included all inpatients with initial COVID-19 symptoms, confirmed by their PCR test results. They were admitted to hospitals affiliated with Golestan University of Medical Science, Golestan, Iran, in 2020. We employed the propensity score method, an effective statistical technique for evaluating the causal association effect of risk factors in observational studies. We also used the student and chi-squared tests to compare the differences between the two study groups. Results: We used the propensity score matching estimation approach and logistic regression analysis for comparison. Of 6379 inpatients, 5581 (87.5%) were discharged or recovered, and 798 (12.5%) died. The causal association between treatment results (discharged vs died) and variables of PCR test, SpO2, gender, age, and hospitalization duration in ICU were statistically significant. Conclusion: The propensity score matching estimation method revealed a high risk of death in patients with PCR+ test diagnosis. Specifically, using this approach, the above-measured risk factors increased the chance of death in patients with PCR+ to 72%. However, the traditional multiple logistic regression model estimated the risk of death at 46%, suggesting potential underestimation. This disparity might be due to better control of the effect of the above-measured risk factors by the propensity score matching. Therefore, the former estimating approach is more effective in assessing the impact of COVID-19 on death.