Analysis Package Research Articles

The Contribution of Sustainable Packaging to the Circular Food Supply Chain

ABSTRACTAs food supply chains pursue sustainability, numerous questions arise from their agents, focusing on the role of reverse logistics in sustainable packaging design and its effective contribution to waste reduction and the conservation of natural resources. With the goal of developing a new multi‐criteria decision‐making framework, this article examines how several multi‐criteria decision‐making approaches can be used to facilitate the selection of sustainable product‐package designs. Using a case study, the multi‐criteria decision‐making method analyses package design, helping decision‐makers to determine and minimize the percentage of food losses at each step of the supply chain. Furthermore, the design of both the product and its packaging must encompass the entire life cycle, including materials sourcing, production, transportation, use and disposal. Achieving sustainability requires a collaborative effort among all supply chain agents, from producers to consumers. Consequently, this study aims to explore the challenges and opportunities of attaining sustainable product and packaging design in the food supply chain, emphasizing the need for a long‐term, collaborative approach to sustainability. This study concludes that sustainable packaging is highly relevant in the circular food supply chain due to its direct contributions to reducing food waste, conserving resources, mitigating carbon emissions, and meeting consumer preferences for eco‐friendly products. Given the consideration of relevant criteria, the multicriteria method appears to be suitable for analysing food supply chains, thanks to its systematic approach in managing the application of sustainable packaging throughout the entire process, including the production, processing, distribution, and consumption of food products.

Building trauma informed schools in non-western contexts: Teachers’ perceived competence in identification and trauma-informed practices in schools

ABSTRACT The Substance Abuse and Mental Health Services Administration trauma model was used as conceptual framework in guiding the development of the Teacher Trauma Identification and Management Scale completed by 284 teachers in inclusive schools in Egypt. In addition, the Statistical Package for the Social Sciences Analysis of Moment Structures (SPSS AMOS) and Andrew Haye’s moderation model were used to estimate the relationship between perceived knowledge of trauma identification and practices among teachers. The result supported the hypothesized relationship between trauma identification and trauma-informed practice. The study concludes with recommendations for developing a homegrown trauma policy to guide schools and teachers.

Identification and validation of IRF6 related to ovarian cancer and biological function and prognostic value

BackgroundOvarian cancer (OC) is a severe gynecological malignancy with significant diagnostic and therapeutic challenges. The discovery of reliable cancer biomarkers can be used to adjust diagnosis and improve patient care. However, serous OC lacks effective biomarkers. We aimed to identify novel biomarkers for OC and their pathogenic causes.MethodsThe present study used the differentially expressed genes (DEGs) obtained from the “Limma” package and WGCNA modules for intersection analysis to obtain DEGs in OC. Three hub genes were identified—claudin 3 (CLDN3), interferon regulatory factor 6 (IRF6), and prostasin (PRSS8)—by searching for hub genes through the PPI network and verifying them in GSE14407, GSE18520, GSE66957, and TCGA + GTEx databases. The correlation between IRF6 and the prognosis of OC patients was further confirmed in Kaplan-Miller Plotter. RT-qPCR and IHC confirmed the RNA and protein levels of IRF6 in the OC samples. The effect of IRF6 on OC was explored using transwell invasion and scratch wound assays. Finally, we constructed a ceRNA network of hub genes and used bioinformatics tools to predict drug sensitivity.ResultsThe joint analysis results of TCGA, GTEx, and GEO databases indicated that IRF6 RNA and protein levels were significantly upregulated in serous OC and were associated with OS and PFS. Cell function experiments revealed that IRF6 knockdown inhibited SKOV3 cell proliferation, migration and invasion.ConclusionIRF6 is closely correlated with OC development and progression and could be considered a novel biomarker and therapeutic target for OC patients.

Exploring the molecular mechanisms of asthma across multiple datasets.

Asthma, a prevalent chronic respiratory disorder, remains enigmatic, notwithstanding considerable advancements in our comprehension. Continuous efforts are crucial for discovering novel molecular targets and gaining a comprehensive understanding of its pathogenesis. In this study, we analyzed gene expression data from 212 individuals, including asthma patients and healthy controls, to identify 267 differentially expressed genes, among which C1orf64 and C7orf26 emerged as potential key genes in asthma pathogenesis. Various bioinformatics tools, including differential gene expression analysis, pathway enrichment, drug target prediction, and single-cell analysis, were employed to explore the potential roles of the genes. Quantitative PCR demonstrated differential expression of C1orf64 and C7orf26 in the asthmatic airway epithelial tissue, implying their potential involvement in asthma pathogenesis. GSEA enrichment analysis revealed significant enrichment of these genes in signaling pathways associated with asthma progression, such as ABC transporters, cell cycle, CAMs, DNA replication, and the Notch signaling pathway. Drug target prediction, based on upregulated and downregulated differential expression, highlighted potential asthma treatments, including Tyrphostin-AG-126, Cephalin, Verrucarin-a, and Emetine. The selection of these drugs was based on their significance in the analysis and their established anti-inflammatory and antiviral invasion properties. Utilizing Seurat and Celldex packages for single-cell sequencing analysis unveiled disease-specific gene expression patterns and cell types. Expression of C1orf64 and C7orf26 in T cells, NK cells, and B cells, instrumental in promoting hallmark features of asthma, was observed, suggesting their potential influence on asthma development and progression. This study uncovers novel genetic aspects of asthma, highlighting potential therapeutic pathways. It exemplifies the power of integrative bioinformatics in decoding complex disease patterns. However, these findings require further validation, and the precise roles of C1orf64 and C7orf26 in asthma warrant additional investigation to validate their therapeutic potential.

Phybers: a package for brain tractography analysis.

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.

Potential diagnostic value of serum microRNAs for 19 cancer types: a meta-analysis of bioinformatics data

Cancer is the second most common cause of mortality worldwide, accounting for almost 10 million deaths in 2020. These deaths were partly due to delayed diagnosis that led to deferred treatment. Therefore, new diagnostic methods are necessary to enhance the accuracy of noninvasive cancer detection. The present study developed a microRNA (miRNA)-based serum diagnostic marker for detecting a wide range of cancers. The study involved 61,019 serum samples from 19 different cancer types. A miRNA prediction model was established through bioinformatics analysis of serum samples from various cancer pathologies and qRT-PCR results from studies in PubMed aligned to the analysis criteria. R software v.4.1.1 with the limma data analysis package was used for single gene expression series data series, and batchNormalize and robustRankAggreg were used to predict the changes in miRNA expression in multiple datasets. GO and KEGG analyses showed that these miRNAs play a role in cancer-related biological signaling pathways. Finally, the diagnostic capability of these miRNA biomarkers was assessed using area under the curve analysis. The study predicted that 7 miRNAs were upregulated and 10 miRNAs were downregulated in 19 different types of cancer. Some miRNAs showed significant differential expression in a specific cancer type. Additionally, downstream genes regulated by miRNAs focused on many cancer-related molecular signaling pathways. In this review, we summarize the current understanding of miRNAs in various cancers, with a particular focus on their potential as future noninvasive diagnostic biomarkers. The emphasis is on their capacity for achieving high accuracy and cost savings compared to conventional biomarkers. Communicated by Ramaswamy H. Sarma

Objective comparison of commonly used computed tomography body composition analysis software

ObjectivesSarcopenia is defined as an age-related, involuntary loss of skeletal muscle mass and strength. This condition is increasingly gaining clinical attention, as it has proved a predictor of complications and unfavorable outcomes in several diseases. For analysis of body composition on computed tomography images, several different software packages are used. Extensive research is being conducted globally to establish general cutoff values for different patient groups by combining the results of different studies with meta-analysis. Therefore, it is important that the measurements are independent of the software used. However, clinical software comparisons suggest there are differences between analysis packages, which would complicate establishment of cutoff values. For this study, we compared the eight most used analysis software programs in an objective manner, using a phantom image, to assess if their results can be readily compared. MethodsEight software packages (sliceOmatic, OsiriX, ImageJ/Fiji, Mimics, CoreSlicer, SarcoMeas, 3D Slicer, and Aquarius iNtuition) were objectively evaluated, by performing measurements in a standardized synthetic image, containing fixed muscle and fat compartments with homogeneous radiodensities. For all programs, the measured areas and radiodensities of the regions of interest were assessed. ResultsFor sliceOmatic, OsiriX, ImageJ/Fiji, Mimics, CoreSlicer, SarcoMeas, and 3D Slicer, identical results were found, all reporting correct values for muscle and fat areas as well as correct radiodensity values, whereas values reported by Aquarius iNtuition deviated ≤ 5% for area measurements and had slight variation in radiodensity measurements. ConclusionsSeven of eight software packages (sliceOmatic, OsiriX, ImageJ/Fiji, Mimics, CoreSlicer, SarcoMeas, and 3D Slicer) perform identically, so their results can be readily compared and combined when assessing body composition in computed tomography images. Area measurements acquired with Aquarius iNtuition may differ slightly (≤ 5%) from the other packages.

Research trends of computational thinking in mathematics learning: A bibliometric analysis from 2009 to 2023

This study presents a comprehensive overview of computational thinking (CT) research trends in mathematics learning from 2009 to 2023. To reach this aim, a bibliometric approach was used in this study to analyze the publication distribution pattern on CT focused on the following categories: research development, the most productive journals and countries, highly cited references, topic network, and thematic evolution map. A total of 276 articles retrieved from the Scopus database were analyzed and visualized through the Bibliometrix analysis package from R and VOSviewer software. The finding shows that since 2009, CT has been the subject of mathematics learning research, which has grown significantly since 2013. Regarding total publication in CT, Education and Information Technologies contributes as the most productive journal, and the United States places first among all countries. The article ‘computational thinking’ appears as the most widely referenced source. Moreover, the frequent topics network with CT are the integration of CT with programming, STEM, and coding. This result is analyzed further by the thematic evolution map showing CT research in STEM education, including mathematics, exhibits promising prospects for future development.

Innovative prognostic modeling in ESCC: leveraging scRNA-seq and bulk-RNA for dendritic cell heterogeneity analysis.

Globally, esophageal squamous cell carcinoma (ESCC) stands out as a common cancer type, characterized by its notably high rates of occurrence and mortality. Recent advancements in treatment methods, including immunotherapy, have shown promise, yet the prognosis remains poor. In the context of tumor development and treatment outcomes, the tumor microenvironment (TME), especially the function of dendritic cells (DCs), is significantly influential. Our study aims to delve deeper into the heterogeneity of DCs in ESCC using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis. In the scRNA-seq analysis, we utilized the SCP package for result visualization and functional enrichment analysis of cell subpopulations. CellChat was employed to identify potential oncogenic mechanisms in DCs, while Monocle 2 traced the evolutionary trajectory of the three DC subtypes. CopyKAT assessed the benign or malignant nature of cells, and SCENIC conducted transcription factor regulatory network analysis, offering a preliminary exploration of DC heterogeneity. In Bulk-RNA analysis, we constructed a prognostic model for ESCC prognosis and immunotherapy response, based on DC marker genes. This model was validated through quantitative PCR (qPCR) and immunohistochemistry (IHC), confirming the gene expression levels. In this study, through intercellular communication analysis, we identified GALECTIN and MHC-I signaling pathways as potential oncogenic mechanisms within dendritic cells. We categorized DCs into three subtypes: plasmacytoid (pDC), conventional (cDC), and tolerogenic (tDC). Our findings revealed that pDCs exhibited an increased proportion of cells in the G2/M and S phases, indicating enhanced cellular activity. Pseudotime trajectory analysis demonstrated that cDCs were in early stages of differentiation, whereas tDCs were in more advanced stages, with pDCs distributed across both early and late differentiation phases. Prognostic analysis highlighted a significant correlation between pDCs and tDCs with the prognosis of ESCC (P< 0.05), while no significant correlation was observed between cDCs and ESCC prognosis (P = 0.31). The analysis of cell malignancy showed the lowest proportion of malignant cells in cDCs (17%), followed by pDCs (29%), and the highest in tDCs (48%), with these results being statistically significant (P< 0.05). We developed a robust ESCC prognostic model based on marker genes of pDCs and tDCs in the GSE53624 cohort (n = 119), which was validated in the TCGA-ESCC cohort (n = 139) and the IMvigor210 immunotherapy cohort (n = 298) (P< 0.05). Additionally, we supplemented the study with a novel nomogram that integrates clinical features and risk assessments. Finally, the expression levels of genes involved in the model were validated using qPCR (n = 8) and IHC (n = 16), thereby confirming the accuracy of our analysis. This study enhances the understanding of dendritic cell heterogeneity in ESCC and its impact on patient prognosis. The insights gained from scRNA-seq and Bulk-RNA analysis contribute to the development of novel biomarkers and therapeutic targets. Our prognostic models based on DC-related gene signatures hold promise for improving ESCC patient stratification and guiding treatment decisions.

Comparing multi-class classifier performance by multi-class ROC analysis: A nonparametric approach

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. Real world applications often require classification into multiple (more than two) classes. For multi-class classifiers that produce class membership scores, a popular multi-class AUC (MAUC) variant is to average the pairwise AUC values (Hand and Till, 2001). Due to the complicated correlation patterns, the variance of MAUC is often estimated numerically using resampling techniques. This work is a generalization of DeLong’s nonparameteric approach for binary AUC analysis (DeLong et al., 1988) to MAUC. We first derive the closed-form expression of the covariance matrix of the pairwise AUCs within a single MAUC. Then by dropping higher order terms, we obtain an approximate covariance matrix with a compact, matrix factorization form, which then serves as the basis for variance estimation of a single MAUC. We further extend this approach to estimate the covariance of correlated MAUCs that arise from multiple competing classifiers. For the special case of binary correlated AUCs, our results coincide with that of DeLong. Our numerical studies confirm the accuracy of the variance and covariance estimates. We provide the source code of the proposed covariance estimation of correlated MAUCs on GitHub (https://tinyurl.com/euj6wvsz) for its easy adoption by machine learning and statistical analysis packages to quantify and compare multi-class classifiers.

Accurate Determination of the Average Rate Constant of Pyrene Excimer Formation for Pyrene-Labeled Macromolecules from the Analysis of Individual Fluorescence Decays with Sums of Exponentials.

The average rate constant (⟨k⟩) for pyrene excimer formation (PEF) between an excited and a ground-state pyrenyl label covalently attached to a pyrene-labeled macromolecule (PyLM) has been found to be an ideal parameter to probe macromolecular conformations due to its proportionality to the local concentration ([Py]loc) of pyrenyl labels in PyLM. To date, ⟨k⟩ has only been determined with the model-free analysis (MFA) involving the global analysis of the pyrene monomer and excimer fluorescence decays of PyLM. Unfortunately, the MFA is computationally demanding which prevents its widespread use. To circumvent this complication, a methodology is introduced that involves the analysis of individual fluorescence decays with sums of exponentials (SoE), which are commonly used in the analysis packages of commercial time-resolved fluorometers. The individual fluorescence decays of the pyrene monomer acquired with 286 PyLM were analyzed with a SoE to yield ⟨kSoE-M⟩. The strong correlation between ⟨kSoE-M⟩ and ⟨kMF⟩ obtained from the global MFA indicated that ⟨kSoE-M⟩ was a good representation of ⟨k⟩. Furthermore, the AE-/AE+ ratio, equal to the ratio of the sum of the negative pre-exponential factors over the sum of the positive pre-exponential factors, was determined by fitting the individual pyrene excimer fluorescence decays of the 286 PyLM with a SoE. AE-/AE+ was found to take a value between -1.0 and -0.8, indicating that the pyrenyl labels were not aggregated. This result indicated that [Py]loc was well described by ⟨kSoE-M⟩, so that ⟨kSoE-M⟩ could be used to describe the conformation of macromolecules in the same manner as ⟨kMF⟩. Consequently, the methodology based on the analysis of individual fluorescence decays with sums of exponentials to determine ⟨kSoE-M⟩ and AE-/AE+ provides a robust alternative to the use of the MFA for the study of PyLM to many scientists interested in the characterization of macromolecular conformations.

PyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.

Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously. Python package, code and examples are available at (https://m2aia.github.io/m2aia).

Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients

BackgroundLiver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC. MethodsSingleR's annotations combined with Seurat was used to automatically annotate the single-cell clustering results of the LIHC dataset GSE166635 downloaded from the Gene Expression Omnibus (GEO) database and to identify clusters related to exhausted T cells. Patients were classified using ConsensusClusterPlus package. Next, weighted gene co-expression network analysis (WGCNA) package was employed to distinguish key gene module, based on which least absolute shrinkage and selection operator (Lasso) and multi/univariate cox analysis were performed to construct a RiskScore system. Kaplan-Meier (KM) analysis and receiver operating characteristic curve (ROC) were employed to evaluate the efficacy of the model. To further optimize the risk model, a nomogram capable of predicting immune infiltration and immunotherapy sensitivity in different risk groups was developed. Expressions of genes were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and immunofluorescence and Cell Counting Kit-8 (CCK-8) were performed for analyzing cell functions. ResultsWe obtained 18,413 cells and clustered them into 7 immune and non-immune cell subpopulations. Based on highly variable genes among T cell exhaustion clusters, 3 molecular subtypes (C1, C2 and C3) of LIHC were defined, with C3 subtype showing the highest score of exhausted T cells and a poor prognosis. The Lasso and multivariate cox analysis selected 7 risk genes from the green module, which were closely associated with the C3 subtype. All the patients were divided into low- and high-risk groups based on the medium value of RiskScore, and we found that high-risk patients had higher immune infiltration and immune escape and poorer prognosis. The nomogram exhibited a strong performance for predicting long-term LIHC prognosis. In vitro experiments revealed that the 7 risk genes all had a higher expression in HCC cells, and that both liver HCC cell numbers and cell viability were reduced by knocking down MMP-9. ConclusionWe developed a RiskScore model for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent prognostic factor could improve the clinical treatment for LIHC patients.

فاعلية استراتيجية خلايا التعلم في تحصيل طالبات الصف الرابع العلمي في مادة الفيزياء

The current research aims to identify (the effectiveness of the strategy of learning cells in the achievement of physics among the students of the fourth scientific grade), and therefore the researchers formulated the hypothesis There is no statistically significant difference at the level of significance (0.05) between the mean scores of the students of the experimental group who will study using the learning cells strategy and the average scores of the students of the control group who will study in the usual way in the physics achievement test The current research community is determined by the students of the scientific fourth in secondary and preparatory governmental daytime schools affiliated to the Directorate of Education of Al-Qadisiyah, and the subject of physics (semester six, chapter seven, chapter eight, and chapter nine) from the curriculum of the physics book scheduled for the second semester (second course) for the academic year (2022-2023) for the grade The fourth scientific, and the two researchers chose the experimental design with partial control (the experimental group and the control group) with the post-test for achievement. Fadak Secondary School for Girls was selected from among (36) schools by simple random method to represent the sample of the current research. The fourth scientific grade students, who are (60) students, were divided into two groups, with (30) students for the experimental group and (30) students for the control group. . The researchers equalized the two groups of the current research in the variables (chronological age calculated in months, intelligence test, previous physical information and enlightened thinking, previous achievement test), and the researchers also prepared the research tool, which is the achievement test tool, which is in its final form of (40) test items. objectivity The two researchers applied the experiment in the second semester of the academic year (2022-2023), and the experiment lasted for (8) weeks, and the data was analyzed and processed using the statistical analysis package (SPSS) and (Microsoft Excel), by using the second test for two independent samples and a correlation coefficient Pearson, and the effect size equation.The two researchers reached the following results 1- The students of the experimental group who studied using the learning cells strategy outperformed the students of the control group who studied using the traditional method in the physics achievement test Keywords: learning cells strategy, academic achievement

Novel Halbach Permanent Magnet Solenoid Design using Finite Element Method

To generate the high axial field usually Electromagnetic Coils solenoid is used but it consumes large DC power supplies and cooling system and is not compact. The PM (Permanent Magnet) solenoid on the contrary can give fixed axial field profiles that are very compact and do not require any energy source as they are themselves energy sources. PM solenoids are usually designed using large radial and axial magnetized magnets. The drawback of such a design is using large radial magnets which are often unavailable with assembly problems. The paper discusses the novel Halbach solenoid configuration design and uses ref1 solenoid results to verify it. The finite element magneto static method is used to analyze the two models. The FEM magneto static analysis was done in Magnet Infolytica FEM magneto static analysis package.

(فاعلية توظيف برنامج (windows movie maker ) في تحصيل مادة العلوم والاستكشاف المتنوع لدى طالبات الصف الاول المتوسط )

The current study is limited to first stage intermediate female students in public schools, morning study, affiliated with the directorate of Education in Al-Qadisiyah, and the science subject (the last four chapters) of the first intermediate stage science textbook curriculum for the academic year 2022/2023. The researcher chose the quasi-experimental design with partial control (experimental and control groups) with a post-test for achievement and a varied exploration scale. Baghdad School for Girls was intentionally selected to represent the current study sample, and two sections of the first intermediate stage were randomly selected, including (85) female students, section (A) included (43) female students representing the experimental group, while section (C) included (42) female students representing the control group. The researcher equivalence the two research groups in variables (age, intelligence test, and mid-year scores). The researcher also prepared two tools; The first was the achievement test which consisted in its final version (40) multiple-choice objective test items with four alternatives. The apparent validity, content validity and reliability of this tool were verified. The difficulty, discrimination and effectiveness of the wrong alternatives for the objective test items were extracted. As for the second tool, it consisted of preparing the varied exploration scale, which consists in its final version (40) items. The apparent validity, construct validity was verified, and the reliability coefficient was found using the (split-half method and Richardson-20) equation. The researcher applied the experiment in the second semester of the academic year 2022-2023, and the experiment lasted (eight) weeks. The data were analyzed and processed using the Statistical Analysis Package for Social Sciences (SPSS) and the (Microsoft Excel) program, by using the t-test for two independent samples, the Pearson correlation coefficient, and the effect size. The researcher concluded the following results: 1- The result related to the first hypothesis regarding the achievement test results indicated that there is a statistically significant difference between the mean scores of female students of the experimental group who studied science according to the Windows Movie Maker program, and the mean scores of female students of the control group who studied the same material in the usual way in the achievement variable in favor of the female students of the experimental group. 2- The result related to the second hypothesis regarding the results of the varied exploration scale indicated that there are statistically significant differences between the mean scores of female students of the experimental group who studied science using the Windows Movie Maker program, and the mean scores of female students of the control group who studied the same material in the usual way in the varied exploration variable in favor of the female students of the experimental group. In light of the results, the researcher came out with a set of conclusions, recommendations and suggestions. Keywords :- Windows Movie Maker Program , achievement , diverse exploration

A clinical-pharmaceutical medication reconciliation with patient interview for a medication review to identify drug-related problems in elective patients during hospital admission.

Background and aim: Drug-related problems (DRPs), e.g.drug-drug interactions (DDI), can lead to adversedrug reactions (ADRs) and thus complications during hospitalization. For this reason, such DRP, DDI and ADR should be identified and characterized as early as possible during hospital admission. We aimed to perform a clinical-pharmaceutical medication reconciliation in which patient-related information was collected and compared to drug-related information in a medication review. Investigations: During a 24-week-period, we consecutively invited patients electively admitted to Urology, Otolaryngology, Oral and Maxillofacial Surgery, General and Visceral Surgery, and Oncology Departments of a 300-bed hospital. A clinical pharmacist performed a patient interview asking for medication, ADR, and adherence. The medication reconciliation considered packages for a brown-bag analysis, medication lists, and data from the clinical information-system (CIS). In a medication review, we matched patient-related information to drug-related information from the drug label, guidelines, drug-databases and websites to identify DRPs. Results: In the study, 356 patients (median age: 58 years) taking 1,712 drugs participated. Of all patients, 7.3% reported ADR and 10.7% missing adherence. 5.3% brought packages that enabled a brown-bag analysis and 21.1% a medication list. In 76.7% of patients, information from CIS was incomplete or not up-to-date. Among the most frequently identified DRPs were "Medication without diagnosis" (31.2%) and "Inappropriate timing of administration" (11.5%). The proportion of patients affected by severe DDI ranged from 0.8%-16.6%, depending on the drug information source. Conclusions: Incomplete patient data, frequently identified DRPs and inconsistent drug-based information make pharmaceutical involvement in medication reconciliation on admission a necessity.

Simulation of exchange processes in multi-component environments with account of data uncertainty

The goal of the work. Proposals for methods of solving systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients that defined in interval form and intended for modeling exchange processes in multicomponent environments. Research subject: systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients defined in interval form. Research method: interval analysis. The obtained results. Systems of linear homogeneous and non-homogeneous differential equations, which are used in modeling exchange processes in multicomponent environments, are considered. Such systems can be considered, for example, in problems of chemical kinetics, materials science, and the theory of Markov processes. To obtain the solution of these equations, specialized calculators of analytical transformations were used and tested. The Matlab system (ode15s solver) was used for numerical analysis of systems of differential equations. It is shown that the application of interval methods of numerical analysis at the initial stage of system modeling has some advantages over probabilistic methods because they do not require knowledge of the laws of distribution of the results of the system state parameter measurements and their errors. It is shown that existing methods of solving systems of linear differential equations can be divided into two groups. Common to these groups is the use of interval expansion of classical methods for solving differential equations given in interval form. The difference between these two groups of methods is as follows. The methods of the first group can be used for all types of differential equations but require the creation of special software. The peculiarity of the methods of the second group is that they can be used to solve equations analytically or using numerical analysis packages. The application of the methods of the second group is shown on the example of solving a system of differential equations, the coefficients of which are determined in interval form. The system of these equations is intended for modeling the processes of exchange with the external environment of the elements of the model of a specific physical system. In the case when the coefficients of these equations are variables, their piecewise-constant approximation is applied and a criterion that determines the possibility of its application is given. The technique proposed in the paper can be applied to solve systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients if they are given by slowly varying functions. In the case when the coefficients of the equations are determined in the interval form, the technique allows obtaining their solution also in the interval form and does not require the creation of special software.

Prediction of the burden of road traffic injuries in Iran by 2030: Prevalence, death, and disability-adjusted life years

PurposeRoad traffic accidents pose a global challenge with substantial human and economic costs. Iran experiences a high incidence of road traffic injuries, leading to a significant burden on society. This study aims to predict the future burden of road traffic injuries in Iran until 2030, providing valuable insights for policy-making and interventions to improve road safety and reduce the associated human and economic costs. MethodsThis analytical study utilized time series models, specifically autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs), to predict the burden of road traffic accidents by analyzing past data to identify patterns and trends in Iran until 2030. The required data related to prevalence, death, and disability-adjusted life years (DALYs) rates were collected from the Institute for Health Metrics and Evaluation database and analyzed using R software and relevant modeling and statistical analysis packages. ResultsBoth prediction models, ARIMA and ANNs indicate that the prevalence rates (per 100,000) of all road traffic injuries, except for motorcyclist road injuries which have an almost flat trend, remaining at around 430, increase by 2030. Based on estimations of both models, the rates of death and DALYs due to motor vehicle and pedestrian road traffic injuries decrease. For motor vehicle road injuries, estimated trends decrease to approximately 520 DALYs and 10 deaths. Also, for pedestrian road injuries these rates reached approximately 300 DALYs and 6 deaths, according to the models. For cyclists and other road traffic injuries, the predicted DALY rates by the ANN model increase to almost 50 and 8, while predictions conducted by the ARIMA model show a static trend, remaining at 40 and approximately 6.5. Moreover, these rates for the prediction of death rate by the ANN model increased to 0.6 and 0.1, while predictions conducted by the ARIMA model show a static trend, remaining at 0.43 and 0.07. According to the ANN model, the predicted rates of DALY and death for motorcyclists decrease to 100 and approximately 2.7, respectively. On the other hand, predictions made by the ARIMA model show a static trend, with rates remaining at 200 and approximately 3.2, respectively. ConclusionThe prevalence of road traffic injuries is predicted to increase, while the death and DALY rates of road traffic injuries show different patterns. Effective intervention programs and safety measures are necessary to prevent and reduce road traffic accidents. Different interventions should be designed and implemented specifically for different groups of pedestrians, cyclists, motorcyclists, and motor vehicle drivers.

A Comparative Analysis of 3D Software for Modeling Fatigue Crack Growth: A Review

Fatigue crack growth modeling is critical for assessing structural integrity in various engineering applications. Researchers and engineers rely on 3D software tools to predict crack propagation accurately. However, choosing the right software can be challenging due to the plethora of available options. This study aimed to systematically compare and evaluate the suitability of seven prominent 3D modeling software packages for fatigue crack growth analysis in specific applications. The selected software tools, namely ABAQUS, FRANC3D, ZENCRACK, LYNX, FEMFAT, COMSOL Multiphysics, and ANSYS, were subjected to a comprehensive analysis to assess their effectiveness in accurately predicting crack propagation. Additionally, this study aimed to highlight the distinctive features and limitations associated with each software package. By conducting this systematic comparison, researchers and engineers can gain valuable insights into the strengths and weaknesses of these software tools, enabling them to make informed decisions when choosing the most appropriate software for their fatigue crack growth analysis needs. Such evaluations contribute to advancing the field by enhancing the understanding and utilization of these 3D modeling software packages, ultimately improving the accuracy and reliability of structural integrity assessments in relevant applications.