Computer-based Approach Research Articles

Supply chain impact from order lead time volatility: a computer-based simulation approach

Supply chain impact from order lead time volatility: a computer-based simulation approach

A model-based approach to addressing energy demand in sustainable urban systems

Sustainable cities address their citizens’ environmental, social, economic, cultural, and institutional needs in an integrated manner. In order to successfully transform urban systems in this context, key enabling technologies must be investigated that can facilitate such changes. In addition, computer-based modeling approaches for analyzing the benefits of such technologies for urban organizations and activities are highly desirable. In this paper, we propose a model-based approach to address sustainable urban environments that employs green edge computing technology to meet the energy and digital service requirements of urban systems. By utilizing the solar energy potential of urban roofs, this technology can be adapted to meet the crucial demands for power arising from digital activity in various urban contexts. As a result of its unique features, the proposed modeling framework is a valuable analysis tool for assisting decision-making to improve the quality of urban systems.

Supply chain impact from order lead time volatility: a computer-based simulation approach

Supply chain impact from order lead time volatility: a computer-based simulation approach

Digital Scholarship in the Field of Church History

Digital Scholarship in the Field of Church History Laura Morreale, Jennifer Paxton, Joel Kalvesmaki, Jeanne-Nicole Mellon Saint-Laurent, Miri Ribin, and Stephen M. Koeth C.S.C. HMML Authority File. Hill Museum and Manuscript Library. HMML Authority File. 2002. https://haf.vhmml.org. Julia Hillner, Dirk Rohmann, et al. The Migration of Faith: Clerical Exile in Late Antiquity. 2018. https://www.clericalexile.org. Rowan Dorin. Corpus Synodalium: Local Ecclesiastical Legislation in Medieval Europe. 2021. https://www.corpus-synodalium.com. Maria Mazzenga. The American Catholic History Research Center. The Catholic University of America. American Catholic History Classroom. 2022. https://cuomeka.wrlc.org. As we enter the third decade of the twenty-first century, the economy of knowledge exchange is now securely situated in the online medium. As the landscape of academic knowledge sharing continues to shift, scholars now rely upon the online medium in their everyday working lives, even if they do not consider themselves digital scholars. Every time a scholar searches through a database or internet-based catalogue from the library, he or she is entering into a computer-based approach to knowledge acquisition and production. The powers afforded to us by digital tools in turn shape our expectations about how we work; likewise, the more born-digital material we consume, the more likely it is that we will expand upon and express our scholarship in digitally-inflected formats. Online academia has changed how we receive, reuse, and cite scholarship in our own subfields. Nowadays, we are more likely to follow up on a footnote by searching for pdfs in an online library than to spend hours in the periodical reading room of the brick-and-mortar one, searching out those same materials. Additionally, we also rely upon born-digital finding aids, curated online corpora, and even projects that explore a particular research question that might open the door to other avenues of scholarly activity. Electronic communication has transformed how we share knowledge because it is both instantaneous and remains open to ongoing modification. No matter how analogue one’s practice may be, scholarly ideas are now shared, refined, and expressed in a digital world. Like colleagues in all scholarly communities, historians of the Church have witnessed the proliferation of computer-based work, and many have come to depend upon resources and research outputs that are available only in online formats. Although this scholarship does indeed trace its origins to traditional forms of research and learning and shares many of its same motivations, digital work is distinctive in a number of ways. With the power to organize and present large amounts of data, it is also interactive, communal, and team based, and it promotes accessible and open knowledge sharing. Given the importance of digital scholarship, it should [End Page 132] be more fully incorporated into systems of rewarding scholarly productivity, and those who produce it should be granted due recognition. With this in mind, the editorial board of the Catholic Historical Review is adding a review section for born-digital scholarship that addresses the journal’s purview, that is, “the history of the universal Church.” What is digital scholarship?1 Since peer review remains fundamental to all serious intellectual endeavors, the CHR section dedicated to born-digital scholarship both recognizes these newer expressions of knowledge and integrates them within the more traditional format of the scholarly journal review. Digital work is not an either/or proposition. It does not replace previous forms of scholarship, but rather accompanies and relies upon them even as it ushers in and facilitates new ways of considering, aggregating, and analyzing the materials that serve as witness to our collective pasts. Digital scholarship transcends mere digitization or database construction; instead, scholars are now using online tools and resources to study previously unknown or inaccessible materials and to use older sources in ways that were previously unimagined. Like many approaches to scholarship, machine-aided historical analysis has its own backstory. An AHA-sponsored blog post from the early 2000s put forth an early definition of digital history with an emphasis on the analytical possibilities of the medium as well as its power to disseminate information.2 Over time, the origin story of digital history has...

Interpretable cardiac anatomy modeling using variational mesh autoencoders.

Cardiac anatomy and function vary considerably across the human population with important implications for clinical diagnosis and treatment planning. Consequently, many computer-based approaches have been developed to capture this variability for a wide range of applications, including explainable cardiac disease detection and prediction, dimensionality reduction, cardiac shape analysis, and the generation of virtual heart populations. In this work, we propose a variational mesh autoencoder (mesh VAE) as a novel geometric deep learning approach to model such population-wide variations in cardiac shapes. It embeds multi-scale graph convolutions and mesh pooling layers in a hierarchical VAE framework to enable direct processing of surface mesh representations of the cardiac anatomy in an efficient manner. The proposed mesh VAE achieves low reconstruction errors on a dataset of 3D cardiac meshes from over 1,000 patients with acute myocardial infarction, with mean surface distances between input and reconstructed meshes below the underlying image resolution. We also find that it outperforms a voxelgrid-based deep learning benchmark in terms of both mean surface distance and Hausdorff distance while requiring considerably less memory. Furthermore, we explore the quality and interpretability of the mesh VAE's latent space and showcase its ability to improve the prediction of major adverse cardiac events over a clinical benchmark. Finally, we investigate the method's ability to generate realistic virtual populations of cardiac anatomies and find good alignment between the synthesized and gold standard mesh populations in terms of multiple clinical metrics.

Concatenated 16S rRNA sequence analysis improves bacterial taxonomy

Background: Microscopic, biochemical, molecular, and computer-based approaches are extensively used to identify and classify bacterial populations. Advances in DNA sequencing and bioinformatics workflows have facilitated sophisticated genome-based methods for microbial taxonomy although sequencing of the 16S rRNA gene is widely employed to identify and classify the bacterial community as a cost-effective and single-gene approach. However, the 16S rRNA sequence-based species identification accuracy is limited by multiple copies of the gene and their higher sequence identity between closely related species. The availability of a large volume of bacterial whole-genome data provided an opportunity to develop comprehensive species-specific 16S rRNA reference libraries. Methods: The 16S rRNA copies were retrieved from the whole genomes in the complete stage at the Genome database. With defined rules, four 16S rRNA gene copy variants were concatenated to develop a species-specific reference library. The sequence similarity search was performed with a web-based BLAST program, and MEGA software was used to construct the phylogenetic tree. Results: Using this approach, species-specific 16S rRNA gene libraries were developed for four closely related Streptococcus species (S. gordonii, S. mitis, S. oralis, and S. pneumoniae). Sequence similarity and phylogenetic analysis using concatenated 16S rRNA copies yielded better resolution than single gene copy approaches. Conclusions: The approach is very effective in classifying genetically related species and may reduce misclassification of bacterial species and genome assemblies.

Innovative Technologies in the Neurorehabilitation of Traumatic Brain Injury: A Systematic Review.

Motor and cognitive rehabilitation in individuals with traumatic brain injury (TBI) is a growing field of clinical and research interest. In fact, novel rehabilitative approaches allow a very early verticalization and gait training through robotic devices and other innovative tools boosting neuroplasticity, thanks to the high-intensity, repetitive and task-oriented training. In the same way, cognitive rehabilitation is also evolving towards advanced interventions using virtual reality (VR), computer-based approaches, telerehabilitation and neuromodulation devices. This review aimed to systematically investigate the existing evidence concerning the role of innovative technologies in the motor and cognitive neurorehabilitation of TBI patients. We searched and reviewed the studies published in the Cochrane Library, PEDro, PubMed and Scopus between January 2012 and September 2022. After an accurate screening, only 29 papers were included in this review. This systematic review has demonstrated the beneficial role of innovative technologies when applied to cognitive rehabilitation in patients with TBI, while evidence of their effect on motor rehabilitation in this patient population is poor and still controversial.

A Transformer Based Approach for Abuse Detection in Code Mixed Indic Languages.

The advancement in the number of online social media platforms has entailed active participation from the web users globally. This has also lead to subsequent increase in the cyberbullying cases online. Such incidents diminish an individual’s reputation or defame a community, also posing a threat to the privacy of users in cyberspace. Traditionally, manual checks and handling mechanisms have been used to deal with such textual content. However, an automatic computer-based approach would provide far better solutions to this problem. Existing approaches to automate this task majorly involves classical machine learning models which tend to perform poorly on low resource languages. Owing to the varied background and language of web users, the cyberspace witnesses the presence of multilingual text. An integrated approach to accommodate multilingual text could be the appropriate solution. This paper explores various methods to detect abusive content in 13 Indic code-mixed languages. Firstly, baseline classical machine learning models are compared with Transformer based architecture. Secondly, the paper presents the experimental analysis of four state-of-the-art transformer-based models vis à vis XLM-RoBERTa, indic-BERT, MurilBert and mBERT, out of which XLM Roberta with BiGRU outperforms. Thirdly, the experimental setup of the best performing model XLM-RoBERTa is fed with emoji embeddings that leads to further enhancement of overall performance of the employed model. Finally, the model is trained with the combined dataset of 13 Indic languages, to compare its performance with those of individual language models. The performance of combined model surpassed those of the individual models in terms of F1 score and accuracy, supporting the fact that combined model fits the data better possibly due to its code-mixed nature. This model reports a F1 score of 0.88 on test data while rendering a training loss of 0.28, validation loss of 0.31 and an AUC score of 0.94 for both training and validation.

THE ROLE OF INFORMATION SYSTEMS IN MAXIMIZING COMPANY PROFIT

It is undeniable that every company can be sure that in carrying out its business always aims to get maximum profit or high return, indeed there are types of companies or business entities that are formed to achieve certain targets that are not related to profit achievement, for example non-profit companies. Non-profit companies whose activities focus on, among others, for purposes related to humanity, nature conservation, and others. Specifically related to companies that are generally engaged in selling services and or goods, it can be said that their orientation is to achieve the maximum profit in running their business. When the number of transactions or buying and selling activities is not too much, it is certainly not a big problem for the company to record transactions manually where the data can also be accessed manually to find out the company's performance and profits for a certain period, but if the company has become large, where the number of goods and or services traded has reached a high enough number and at the same time the company's activities are increasingly complex and complicated, so at that time of course the way of recording transactions or company activities manually would no longer be reliable for serve the needs of the company in monitoring the development of the company's performance including recording company profits for a certain period. Holding a better recording process by using a computer-based information system management approach to record, manage, monitor and move the company so that from time to time it can improve company performance and at the same time increase company profits, this is an option that is not an option. undeniable and need to be taken by the company.

From Child to Adult Victims and Witnesses: Ways of Improving the Quality of Investigative Interviews

Witness statements are important piece of evidence in criminal proceedings. Investigative interviews with witnesses are conducted in various stages of the investigative process, and the person conducting the investigative interview must take into account internal and external factors, that influence the quality of witnesses’ statements. The paper gives a systematic overview of the ways of improving the quality of investigative interviews of adult witnesses carried out by investigators, on the basis of a survey of the literature available from continental Europe but also Common Law countries (such as the UK, the US, and Australia). Even though detailed instructions and in-depth training programmes are available, today’s training consists largely of theoretical classroom lectures of a short-term nature that feature minimal practical tasks. Teaching methods of this type are effective in increasing knowledge but fail to transfer the knowledge into practice. Accordingly, the authors found that the training should be more practically oriented, have a longer duration, and incorporate personalised feedback in order to situate the knowledge in practice. In addition, several new computer-based approaches have been developed to help overcome the limitations identified. These provide a more personalised and flexible approach to learning, yet room still exists for further development. The paper concludes by addressing how work in the field of interviewing child and adult witnesses could develop in the future.

MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.

Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently. In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs. https://github.com/pxystudy/MHADTI.

Repurposing FDA-approved anti-diabetic drug to target H. pylori peptidyl deformylase using computer-based drug discovery approach

ABSTRACT Peptide deformylase (PDF), a metalloenzyme is an important and attractive target in antibacterial drug discovery. It removes the N-formyl group from the nascent peptide and generates a mature N-terminal end of the protein molecule. Acarbose is an FDA-approved microbial origin anti-diabetic drug and is known to alter the gut microbiota in clinical studies. The present study first time identifies the binding efficacy of acarbose isolated from a natural source against PDF. We performed molecular docking and molecular dynamics (MD) simulation studies to check the binding efficacy of acarbose with the catalytic site of Helicobacter pylori PDF. Molecular docking results of acarbose complexed with PDF showed a good docking score (−12.55 kcal/mole) in comparison to standard drug bind with PDF (−8.99 kcal/mole). Interaction with the amino acids (Gly95, Glu139, His138, Gly46 and Glu940) and metal ion present at the catalytic site of the test protein was found to be common in PDF during interaction with the acarbose and actinonin. Various parameters such as RMSD, RMSF, Rg, SASA, Hydrogen-bond formation, energy landscape and principal component analysis showed that the acarbose form stable and energetically stable complex with PDF in comparison to actinonin. Taken together, our study concludes that the acarbose possesses significant efficacy in binding at the catalytic site of H. pylori PDF. Acarbose is a FDA-approved anti-diabetic drug thus its antibacterial efficacy may be directly studied in clinical trials. Further, the in vitro and in vivo studies are needed to study the antibacterial mode of action of acarbose in physiological conditions.

Peptide Microarrays for Studying Autoantibodies in Neurological Disease.

Antibody-mediated neurological diseases constitute an emerging clinical entity that remains to be fully explored. Recent studies identified autoantibodies that directly confer pathogenicity, and it was shown that in these cases immunotherapies can result in profound positive patient responses. These advances highlight the urgent need for improved means to effectively screen patient samples for novel autoantibodies (aAbs) and their subsequent characterization. Here, we discuss challenges and opportunities for peptide microarrays to contribute to the identification, mapping, and characterization of the underlying monospecific disease-defining binding surfaces. We outlinecontrol experiments,workflow modifications and bioinformatic filtering methods thatenhance the predictive power of array-based studies.Further, we highlight experimental and computer-based display approaches that have the potential to expand the use of synthetic microarrays over the detection of discontinuous epitopes. Knowledge over the autoantibody epitopes in neurological disease will enhance our understanding of the pathological mechanisms and thereby potentially contribute to novel diagnostic approaches or even innovative antigen-specific treatments that avoid the serious adverse effects seen with currently used immunosuppressive therapies.

4D reconstruction of murine developmental trajectories using spherical harmonics.

SummaryNormal organogenesis cannot be recapitulated in vitro for mammalian organs, unlike in species including Drosophila and zebrafish. Available 3D data in the form of ex vivo images only provide discrete snapshots of the development of an organ morphology. Here, we propose a computer-based approach to recreate its continuous evolution in time and space from a set of 3D volumetric images. Our method is based on the remapping of shape data into the space of the coefficients of a spherical harmonics expansion where a smooth interpolation over time is simpler. We tested our approach on mouse limb buds and embryonic hearts. A key advantage of this method is that the resulting 4D trajectory can take advantage of all the available data while also being able to interpolate well through time intervals for which there are little or no data. This allows for a quantitative, data-driven 4D description of mouse limb morphogenesis.

Modelling geographical and built-environment attributes as predictors of human vulnerability during tsunami evacuations: a multi-case-study and paths to improvement

Abstract. Evacuation is the most important and effective method to save human lives during a tsunami. In this respect, challenges exist in developing quantitative analyses of the relationships between the evacuation potential and the built-environment and geographical attributes of coastal locations. This paper proposes a computer-based modelling approach (including inundation, evacuation, and built-environment metrics), followed by multivariate regressive analysis, to estimate how those attributes might influence the expected tsunami death ratios of seven Chilean coastal cities. We obtained, for the examined variables, their average values to different thresholds of the death ratio. Also, our statistical analysis allowed us to compare the relative importance of each metric, showing that the maximum flood, the straightness of the street network, the total route length, and the travel time can have a significant impact on the expected death ratios. Moreover, we suggest that these results could lead to spatial planning guidelines for developing new urban areas into exposed territories (if this expansion cannot be restricted or discouraged) or retrofitting existing ones, with the final aim of enhancing evacuation and therefore increasing resilience.

Simulation-Based Training for Radiography Students as a Solution in Covid-19 Pandemic: Student's Perspective from a Developing Country

Background and Objective: Under the impact of the global Covid-19 pandemic, in-hospital training for radiologic technologist students meets several difficulties. The specificity of radiography sciences requires numerous practical exercises. We conducted a tutorial lesson for radiologic technologist students using X-ray simulation software to learn how to operate X-ray machines. The study aimed to evaluate students' satisfaction with the teaching method based on simulation software in radiological sciences. Method: 57 third-year radiologic technologist students were enrolled. All the students joined practical classes onsite from May to August 2021. The main training topic was routine X-ray radiography with in-house X-ray simulation software that was built by Hue University in collaboration with Duy Tan University. The satisfaction of students was evaluated following Likert 5 scale with 14 specific items. Descriptive statistics and a one-sample t-test were performed with SPSS Version 20 (IBM, USA). Result: The findings show that 68% of students were satisfied with the hypothetical clinical situation given in the simulation-based training class. One-sample t-test suggests that the average score of all the criteria is greater than 3, from 3.49 to 4.07. Most of the students wanted to participate further in a similar simulation-based training class in the future (91.23%). Simulation-based training software improves students' experience, radiograph creation ability, and image quality evaluation. Conclusion: Simulation-based training improves clinical skills, enhances visual thinking ability, and clinical practice ability of radiologic technologist students. This teaching method seems to be the appropriate solution for medical imaging technology training in the Covid-19 pandemic. However, further studies with a larger population are needed to comprehensively evaluate the effectiveness of the computer-based simulation teaching approach compared to conventional methods.

From LiDAR to deep learning: A case study of computer-assisted approaches to the archaeology of Guadalupe and northeast Honduras

Abstract Archaeologists are interested in better understanding matters of our human past based on material culture. The tools we use to approach archaeological research questions range from the trowel and brush to, more recently, even those of artificial intelligence. As access to computing technology has increased over time, the breadth of computer-assisted methods in archaeology has also increased. This proliferation has provided us a considerable toolset towards engaging both new and long-standing questions, especially as interdisciplinary collaboration between archaeologists, computer scientists, and engineers continues to grow. As an example of an archaeological project engaging in computer-based approaches, the Guadalupe/Colón Archaeological Project is presented as a case study. Project applications and methodologies range from the regional-scale identification of sites using a geographic information system (GIS) or light detection and ranging (LiDAR) down to the microscopic scale of classifying ceramic materials with convolutional neural networks. Methods relating to the 3D modeling of sites, features, and artifacts and the benefits therein are also explored. In this paper, an overview of the methods used by the project is covered, which includes 1) predictive modeling using a GIS slope analysis for the identification of possible site locations, 2) structure from motion (SfM) drone imagery for site mapping and characterization, 3) airborne LiDAR for site identification, mapping, and characterization, 4) 3D modeling of stone features for improved visualization, 5) 3D modeling of ceramic artifacts for more efficient documentation, and 6) the application of deep learning for automated classification of ceramic materials in thin section. These approaches are discussed and critically considered with the understanding that interdisciplinary cooperation between domain experts in engineering, computer science, and archaeology is an important means of improving and expanding upon digital methodologies in archaeology as a whole.

A Computer-Based Approach to Solving the Diophantine Equation $7^x-3^y=100$

Assisted by Octave and by a website where one can find the order of a modulo m (here a and m are relatively prime natural numbers), I found a proof for showing that the diophantine equation $7^x - 3^y = 100$ has only the solution (x,y) = (3,5) in positive integers.

Prediction of viral-host interactions of COVID-19 by computational methods

Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.

A complete computer-based approach for data generation patterning to a pdf in \(\mathbb{R}\) and application to gamma and gig data

Here, we present an automatic data generation method which is fully computer-based for a variate $X$ with an absolutely continuous probability density function (<b>pdf</b>) $f$ exactly computable. The method uses computer-based on calculations of integrals (trapezoidal and/or the Monte-Carlo method) for approximating the cumulative distribution function and next, the dichotomy algorithm to get the quantile function from which we obtain data from \(f\). We apply the method to generate <i>gig(a,b,c)</i> data. The comparison with analogues, as in \textbf{R} Software is very successful. The method may work where the rejection method fails because of a lack of \textit{pdf} bound which can be generated. The method might be slower but the area of more and more powerful computer is favorable to it. The implementation for <i>gamma<\i> and/or <i>gig<\i> laws in <b>R</b> codes are presented.