Information File Research Articles

Special audit examination of the coactiva courts of the central bank of Ecuador, period 2013 - 2014

Auditing is a fundamental tool in the management of entities, ensuring transparency and efficiency through adequate controls. This study analyzes the "Special Audit Review of the Coactiva Courts of the Central Bank of Ecuador" conducted by the Superintendency of Banks during the period 2013-2014. The objective is to evaluate the effectiveness of internal controls and administrative processes of the coactiva court. The results reveal significant deficiencies, including a lack of coordination between the areas of the Recovery and Liquidation Department, inconsistencies in the records of insolvency and bankruptcy judgments, and informal management of portfolio recovery. These deficiencies represent a high risk for the bank's operations, affecting approximately 70% of the processes reviewed. A set of practical recommendations are proposed, such as the implementation of an information filing and systematization system, the updating of the lawsuit registry and the creation of an annual operational audit plan. These measures are aligned with international best practices and seek to improve efficiency, effectiveness and transparency in the management of public resources.

Development of a software solution to an applied mechanics problem based on numerical methods

The present work delves into the significance of material selection in the manufacturing of tools for milling operations and determines their suitability through a detailed analysis of strength and behavior during material processing. To enhance longevity and optimize production, the utilization of mathematical models and numerical methods, notably the least squares method and the solution of systems of linear algebraic equations (SLAE) employing the Gaussian elimination method with pivot selection, is proposed. These methods are applied for the approximation of experimental data and the analysis of material characteristics, ensuring precision in the evaluation of its properties. Situations where only partial data is known are investigated, along with simplifications in the computation of known functions. The study encompasses software for numerical computation and visualization of various problem types, effectively addressed through the aforementioned methods. The software algorithm for data approximation involves storing information in a text file, user input of variables, and selection of the quantity and type of basis functions. After the user inputs of parameters, the program formulates a system of equations based on selected functions, determines approximation coefficients, and generates a graph for an objective assessment of results. With a user-friendly interface, users can easily interact with the program, input values, and analyze results through graphical representation, streamlining the workflow and facilitating the comprehension of obtained data. The approximation of functions using numerical methods proves to be efficiently applicable in diverse fields for addressing applied mechanics problems.

Machine Learning-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extremes of Intermetallic Complexity.

Intermetallic phases represent a domain of emergent behavior, in which atoms with packing and electronic preferences can combine into complex geometrical arrangements whose long-range order involves repeat patterns containing thousands of atoms or is incompatible with a 3D unit cell. The formation of such arrangements points to unexplained driving forces within these systems that, if understood, could be harnessed in the design of new metallic materials. DFT-chemical pressure (CP) analysis has emerged as an approach to visualize how atomic packing tensions within simpler crystal structures can drive this complexity and create potential functionality. However, the applications of this method have hitherto been limited in scope by its dependence on resource-intensive electronic structure calculations. In this Article, we develop machine learning (ML)-based implementation of the CP approach, drawing on the collection of DFT-CP schemes in the Intermetallic Reactivity Database. We illustrate the method with comparisons of ML-CP and DFT-CP schemes for a series of examples, before demonstrating its application with an exploration of one of the quintessential instances of complexity in intermetallic chemistry, Mg2Al3, whose high-temperature unit cell is a 2.8 nm cube containing 1227 atoms. An analysis of its ML-CP-derived interatomic pressures traces the origins of the structure to simple matching rules for the assembly of Frank-Kasper polyhedra. The ML-CP model can be immediately employed on other intermetallic systems, through either its web interface or a command-line version, with just a crystallographic information file.

Elucidating the Impact of Mn-Fe Substitution on The Crystal Structure of LiMn(x)Fe(1-x)PO4 Solid Solutions: A Theoretical Study

Abstract This study generated crystallography information files (CIF) to synthesize LiMn(x)Fe(1-x)PO4 solid solutions, with the Mn to Fe substitution ratio (x) ranging from 0 to 1 in 0.1 increments, guided by Vegard’s law, utilizing crystallography software. The investigation focused on how this substitution influences key structural parameters and properties within the Mn-Fe Olivine system. Specifically, it examined variations in lattice parameters, unit cell volume, cell density, and Theoretical capacity attributable to the substitution factor. Furthermore, X-ray diffraction (XRD) patterns, predicted using mathematical models through the same software, provided insights into structural changes. Notably, the XRD analysis revealed a progressive shift in peak positions and an increase in peak intensities corresponding to the substitution level. These observations were linked to the altered lattice parameters resulting from the introduction of ions with varying ionic radii, and an enhanced electron density stemming from the increased presence of Fe. This comprehensive analysis underscores the significant impact of Mn-Fe replacement on the microstructural characteristics and electron density of the olivine system, offering valuable insights into material design and optimization in the context of energy storage materials.

In silico studies of thiazole derivative towards its potential use against SARS-CoV-2: An intuition from an experimental and computational approach

The N-(4-methoxyphenyl)-4-phenylthiazole-2-carboxamide (TT7) obtained via cyclization of methyl 2-((4-methoxyphenyl)amino)-2-oxoethanedithioate with ɑ-haloketone and then characterized by 1H-NMR and 13C-NMR spectroscopy, and single crystal X-ray diffraction method. Weak intermolecular interactions like C-H...O, C-H...π, C-O...π, and π...π interactions help to stabilize the compound TT7. The weak interactions formed in the solid structure of the compound TT7 were meticulously investigated using theoretical approach like Hirshfeld surface analysis using crystallographic information file (.cif). Further, the density functional theory calculations have been performed to obtain the optimized geometry of the structure, and to explore the electronic properties of the molecule. The charge distribution on the molecular surface is analyzed by the molecular electrostatic potential (MEP) map. QTAIM and RDG analyses were done to explore the weak interactions (intramolecular) in the compound TT7 in the gaseous phase. The compound TT7 displayed good in silico results against SARS-CoV-2 main protease. Molecular docking study on SARS-CoV-2 virus major protease (PDB IDs: 6LZE and 6LU7) shows higher docking scores. Molecular dynamics simulations confirmed the inhibitory action of the newly synthesized compound (TT7). The binding free energy and contributed energies were determined using the MM-GBSA technique. The 6LU7-ligand complex has the highest binding free energy, with considerable contributions from covalent and van der Waals interactions.

Peat profile database from peatlands in Canada.

Peatlands cover approximately 12% of the Canadian landscape and play an important role in the carbon cycle through their centennial- to millennial-scale storage of carbon under waterlogged and anoxic conditions. In recognizing the potential of these ecosystems as natural climate solutions and therefore the need to include them in national greenhouse gas inventories, the Canadian Model for Peatlands module (CaMP v. 2.0) was developed by the Canadian Forest Service. Model parameterization included compiling peat profiles across Canada to calibrate peat decomposition rates from different peatland types, to define typical bulk density profiles, and to describe the hydrological (i.e., water table) response of peatlands to climatic changes. A total of 1217 sites were included in the dataset from published and unpublished sources. The CORESITES table contains site location and summary data for each profile, as well as an estimate of total carbon mass per unit area (in megagrams of C per hectare). Total carbon mass per unit area at each location was calculated using bulk density and carbon content through each profile. The PROFILES table contains data for depth (in centimeters), bulk density (in grams per cubic meter), ash and carbon content (in percentage), and material descriptions for contiguous samples through each peat profile. Data gaps for bulk density and C content were filled using interpolation, regression trees, and assigned values based on material description and/or soil classification to allow for the estimation of total carbon mass per unit area. A subset of the sites (N = 374) also have pH and pore water trace-elemental geochemistry data and are found in the WATER table. The REFERENCES table contains the full citation of each source of the data and is linked to each core location through the SOURCEDATA table. The LOOKUP table defines codes in the database that required more space that what was sufficient in the metadata tables. The data can be accessed on Open Government Canada and will be useful for future work on carbon stock mapping and ecosystem modeling. All metadata and data are provided © Her Majesty the Queen in Right of Canada, 2023 and information contained in this publication may be reproduced for personal or public noncommercial purposes with attribution, whereas commercial reproduction and distribution are prohibited except with written permission from NRCan; complete details are noted in the Supporting Information file Metadata S1 (see Class III.B.3: Copyright restrictions).

Potential tumor-specific antigens and immune landscapes identification for mRNA vaccine in thyroid cancer.

Tumor mRNA vaccines have been identified as a promising technology for cancer therapy in multiple cancer types, while their efficacy in thyroid cancer (THCA) is unclear. Immunotyping is strongly associated with the immune microenvironment and immune status in cancer, thus it is important in vaccination and therapeutic response. This study is to identify potential valuable antigens and novel immune subtypes of THCA for immune landscape construction, thus screening patients suitable for mRNA vaccination. The clinical information and gene expression files of 568 THCA cases were obtained from the TCGA dataset. The DNA copy number variation and the somatic mutation of THCA were visualized by the cBioPortal database. TIMER was used to investigate the immune infiltrating correlation with candidate antigens. Consensus clustering analysis was conducted to cluster data using the ConsensusClusterPlus package. The immune landscapes of THCA patients were visualized using the Monocle package. The critical hub genes for THCA mRNA vaccines were identified by WGCNA package. To validate, the immunohistochemistry and real-time quantitative PCR (RT-qPCR) were performed to detect the expression level of potential antigen for mRNA vaccine in tissue and cell lines in THCA. Thymidine kinase 1 (TK1) was identified as a potential biomarker of mRNA vaccine against THCA. It was confirmed to be significantly upregulated in THCA tissues and cells lines. Moreover, three novel immune subtypes of THCA were obtained based on the expression consistency of immune-associated genes. The S2 subtype was characterized as an immunological "cold" phenotype with a high expression of immunogenic cell death modulators. S1 and S3 subtypes were immunological "hot" phenotypes with immune checkpoints upregulation. Further, the immune landscape of THCA patients was visualized and ten hub genes for mRNA vaccines were identified. TK1 was a tumor-specific antigen of mRNA vaccines. The patients belonging to the S2 subtype ("cold" tumor) were suitable for mRNA vaccine therapy in THCA. Notably, ten hub genes were conducted as potential biomarkers for identifying suitable patients for mRNA vaccination. These findings provided novel insights into mRNA vaccine development against THCA.

Visualization-based comprehensive feature representation with improved EfficientNet for malicious file and variant recognition

Malicious file attacks seriously affect network and data security, and recognizing malicious files and variants is crucial for preventing network attacks. Faced with the challenge of traditional methods in quickly, effectively, and efficiently recognizing malicious files or variants, visualization-based feature representation methods have shown promising results. However, practical applications encounter issues such as loss of crucial information, high spatiotemporal overhead, and the need for model performance improvement. Therefore, this paper introduces a novel recognition framework focusing on feature representation and model performance. The framework uses the proposed visualization-based comprehensive feature representation method (VCFR) to extract file information into the Gray-Level Co-occurrence Matrix (GLCM), 2-gram frequency matrix, and interval 2-gram frequency matrix, followed by feature fusion to generate the three-channel RGB images. Subsequently, the proposed lightweight model is applied for recognizing those files, which utilizes ideas such as group convolution, channel shuffle, and attention mechanisms to improve model performance while significantly reducing model parameters, size, and FLOPs. In summary, through a series of experiments conducted on manually collected malicious file dataset (MFD) and public dataset MMCC, the proposed framework significantly outperformed other state-of-the-art technologies and has F1-Score as high as 94.10% and 98.58%, respectively, further verifying its outstanding effectiveness and efficiency.

The impact of antenatal cluster management on maternal delivery and postpartum rehabilitation

ObjectivePregnancy care can improve maternal pregnancy outcomes. Cluster nursing, an evidence-based, patient-centered model, enhances pregnancy care, can provide patients with high-quality nursing services, has been widely used in clinical practice in recent years. However, most previous studies evaluated cluster nursing program only for a single clinical scenario. In this study, we developed and implemented a antenatal cluster care program for various prenatal issues faced by puerpera to analyze its application effect.MethodsThis is a historical before and after control study. 89 expectant mothers who had their prenatal information files registered in the outpatient department of a grade III, level A hospital from June 2020 to September 2021 were finally enrolled in observation group, and received prenatal cluster management. Another set of 89 expectant mothers from January 2019 to December 2019 were included in the control group and received traditional routine prenatal management. The effect of cluster nursing management on maternal delivery and postpartum rehabilitation was evaluated and compared between the two groups.ResultsCompared with the control group, the observation group had a significantly higher natural delivery rate, better neonatal prognosis, higher rates of exclusive breastfeeding, lower incidence of postpartum complications, shorter postpartum hospital stay, better postpartum health status, and higher satisfaction with nursing services. Compared with before intervention, the SAS and SDS scores of the observation group showed significant improvement after intervention.ConclusionAntenatal cluster care is beneficial to improve maternal and neonatal outcomes, and can have positive effects on natural pregnancy and breastfeeding, while improving the multimedia health education ability of medical care and emphasizing the importance of social support.

Smoking, Obesity, and Metabolic Syndrome in Two High Security Settings

Forensic psychiatric patients pose a vulnerable population due to their unbalanced diet, overweight, smoking, adverse effects of medication, and sedentary lifestyle within secure settings. These lifestyle traits have been correlated with a higher incidence of morbidity and a lower life expectancy. Nonetheless, empirical data regarding these topics remains limited. In the current study we examined obesity, metabolic syndrome and smoking patterns in two high-security settings in Flanders. We also examined the influence of psychiatric diagnoses and the usage of antipsychotic medication to these characteristics. Results indicated that obesity, smoking, and metabolic syndrome were highly prevalent across different diagnoses. In contrast to previous findings, there was no association between the presence of metabolic syndrome and the use of (second generation) antipsychotics. Missing information in the electronic files further indicated that there remains room for improvement in the detection and monitoring of physical health. It is recommended that forensic institutions pay more attention to the provision of care programs to manage weight and smoking.

Nursing Home Oversight Trends During COVID-19 and the Current Survey Backlog in the United States

ABSTRACT Since the Nursing Home Reform Act of 1987, regular oversight of United States nursing home activities has been a key strategy to ensure minimum levels of care quality for residents. Oversight activities have included “standard” survey visits – that is, annual unannounced visits by state survey agencies (SSAs) that directly observe resident care and interview nursing home residents and staff. This study provides an overview of these activities, focusing on oversight delays arising from policy changes brought on by the pandemic. Data from the Centers for Medicare and Medicaid Service’s (CMS) Quality, Certification and Oversight Reports, Survey Summary Files, and Provider Information Files were used to measure delays in survey completion across SSAs. Study findings reveal delays in inspection activities, which have resulted in a large backlog of uncompleted standard surveys far exceeding regulatory requirements. These delays exist across nursing homes with high and low levels of quality. As SSAs work through the backlog of surveys, they may prioritize the completion of surveys based on prior performance. This precedent may be expanded as CMS explores opportunities to produce processes that target the completion of surveys in the poorest performing nursing homes.

GUANIN: an all-in-one GUi-driven analyzer for NanoString interactive normalization.

Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from an endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the data preprocessing step without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI). GUANIN can be installed with pip install GUANIN and it is available at https://pypi.org/project/guanin/. Source code, documentation, and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license.

Expert System for Extracting Hidden Information from Electronic Documents during Outgoing Control

For confidential and sensitive electronic documents within enterprises and organizations, failure to conduct proper checks before sending can easily lead to incidents such as security degradation. Sensitive information transmission has become one of the main ways of internal data leakage. However, existing methods or systems cannot extract hidden data and do not support mining the binary structure of hidden information in files. In this paper, an expert system for mining electronic document hidden information is designed for various office documents, compressed files, and image files. This system can quickly mine various forms of concealment in more than 40 common types of electronic documents and extract hidden information, such as file type tampering, encryption concealment, structure concealment, redundant data concealment, etc. Additionally, feature information in the binary structure of the document is extracted to form a feature information base. Subsequently, an expert knowledge base is constructed. Finally, a hidden information mining engine is designed using the knowledge base to realize the security control of corresponding outgoing files with good expansibility and integration. By controlling the exit of documents through scanning for sensitive information contained within them, the security level contents can be obtained effectively, avoiding data leakage by technical means while also facilitating forensics. The actual test result proves that this system can quickly mine various means used to conceal information, extract their respective information, and provide a fast, practical diagnostic way for outgoing control over these electronic documents.

Current Quality Level of Referral Letters and Feedback Reports in the First Health Cluster in Riyadh Health Cluster Primary Healthcare Centers

Background: Effective communication between primary healthcare centers (PHCCs) and referral hospitals is critical for ensuring the continuity and quality of patient care. Referral letters and feedback reports are essential for this communication, yet their quality is often inconsistent, potentially impacting patient outcomes. Study Aim: To assess the standard of feedback reports from referral hospitals and the quality of referral letters from PHCCs within the first health cluster in Riyadh. Methodology: This cross-sectional study randomly selected 360 referral letters and feedback reports from nine PHCCs in the first health cluster in Riyadh. Systematic sampling was employed to select approximately 55 documents from each center. Each document was evaluated based on 16 key components as per the Quality Assurance Manual of the Ministry of Health, using an author-developed scoring system. Results: The study included 360 referral letters and feedback reports, with an average quality score of 13.2 ± 1.5 out of 16. A majority of the documents (253, 70.3%) scored 13 or higher. Key components such as general information and patient file numbers were present in all documents (100%). Vital signs were documented in 351 cases (97.5%), and the reason for referral in 327 cases (90.8%). However, investigation results and current treatment details were included in only 142 (39.4%) and 150 (41.7%) of the documents, respectively. Clear handwriting was observed in 262 reports (72.8%), while 98 (27.2%) had legibility issues. Conclusion: The overall quality of referral letters and feedback reports in the first health cluster in Riyadh is relatively high. However, significant gaps were identified in the documentation of investigation results, current treatment details, and clinical examination findings. Addressing these gaps through targeted training, standardized documentation practices, and the adoption of electronic health records can enhance the quality of patient referrals and improve care continuity and outcomes.

A database of high-pressure crystal structures from hydrogen to lanthanum

This paper introduces the HEX (High-pressure Elemental Xstals) database, a complete database of the ground-state crystal structures of the first 57 elements of the periodic table, from H to La, at 0, 100, 200 and 300 GPa. HEX aims to provide a unified reference for high-pressure research, by compiling all available experimental information on elements at high pressure, and complementing it with the results of accurate evolutionary crystal structure prediction runs based on Density Functional Theory. Besides offering a much-needed reference, our work also serves as a benchmark of the accuracy of current ab-initio methods for crystal structure prediction. We find that, in 98% of the cases in which experimental information is available, ab-initio crystal structure prediction yields structures which either coincide or are degenerate in enthalpy to within 300 K with experimental ones. The main manuscript contains synthetic tables and figures, while the Crystallographic Information File (cif) for all structures can be downloaded from the related figshare online repository.

Determining the frequency of thyroid involvement in chest CT scans of COVID-19 patients and its correlation with the severity of lung involvement and survival of patients in 2020.

This study aimed to determine the frequency of thyroid gland involvement in chest CT scans of patients with COVID-19 admitted to university-affiliated hospitals and assess its relationship with the severity of lung involvement and patient survival in 2020. In this retrospective cross-sectional study, 1000 PCR-positive patients with COVID-19 who were referred to University-affiliated Hospital in 2020 and had chest CT performed within 72 hours of admission to the hospital were examined. The data was collected by patient file information and CT findings recorded in the PACS system, including thyroid involvement, the severity of lung involvement, and findings related to the death and recovery of patients. The mean age of the examined patients was 56 years. 525 people (52.5%) were men, and 475 (47.5%) were women. 14% had severe pulmonary involvement, and 9.3% had very severe involvement. Moreover, 15.9 percent of them had deceased. 19.7% had focal thyroid involvement, 14% had diffuse involvement, and 66.3% were healthy subjects. Male gender and older age showed a significant relationship with thyroid gland involvement. The severity of lung involvement, the death rate in patients, and hospitalization in ICU were also significantly related to thyroid gland involvement in patients with COVID. This study highlights the importance of considering thyroid-gland involvement in the comprehensive management of COVID-19 patients. Routine screening and monitoring of thyroid-function may facilitate earlier detection and appropriate management of thyroid-related complications, potentially improving clinical outcomes. This study suggests that in COVID-19 infection the monitoring of thyroid function is prudent, particularly in cases of more serious disease.

DENNIS: a design and analysis tool for dynamic material x-ray diffraction experiments

We present DENNIS (Diffraction Experiment desigN and aNalysiS): a graphical software tool useful for the design and analysis of dynamic x-ray diffraction experiments, such as those performed on the Z Pulsed Power Facility, Thor Pulsed Power Generator, and Dynamic Compression Sector (DCS) of the Advanced Photon Source. DENNIS provides rapid powder and single-crystal diffraction pattern predictions and powder diffraction pattern image integration in three-dimensional geometries. Additional features include crystallographic information file reading, image processing, and synthetic diffraction pattern image generation. We overview the software's capabilities, detail the prediction and integration methodologies, and provide example implementations on Z and DCS experiments.

Visualization and Segmentation of Lung Tissue in DICOM-format CT Images

Abstract In this project, two tools were developed as a preliminary step towards the creation of future Computer-Aided Diagnosis systems for the automatic detection of lung diseases. The first methodology aimed to extract information from DICOM-format files using Python and the Jupyter Notebook programming environment. The implemented process enables the generation of images in various anatomical planes, using the pixel matrix in Hounsfield units. Additionally, a custom tool was developed for anonymizing and visualizing file information. The techniques employed are independent of the grayscale image generated and are not reliant on the type of window used. The second tool introduces a lung segmentation technique for chest CT images. The segmentation uses the pixel matrix in Hounsfield units to initially identify lung tissue based on its linear X-ray attenuation. Subsequently, the Watershed algorithm based on markers is employed as the primary foundation of the technique. Various morphological operations and transformations on axial slices are applied, resulting in successful lung segmentation.

Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.