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Microelectronics Technology and Devices (SBMicro)

作者： Reinaldo Velásquez Diego Tami Gilberto Medeiros-Ribeiro Cássio G. Rego Jhonattan C. Ramirez Graduate Program in Electrical Engineering UFMG Brazil Institute of Technological Sciences UNIFEI Brazil Department of Computer Science UFMG Brazil Department of Electronics Engineering UFMG Brazil

In this paper, we demonstrated a highly sensitive microwave-based resonator sensor capable of detecting variations in an aqueous medium of up to 0.94 mg/dL. In the presented device, the intrinsic resonant response (resonant amplitude, resonant frequency, and quality factor) is capacitively perturbed in reaction to the sample presence. To achieve higher sensitivity, the sample under testing generally is placed on the resonator surface, where the electric field is highly concentrated at the operating frequency. Thus, demonstrating excellent results in terms of sensitivity and reproducibility.

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Towards an Applied Multicriteria Model to the Diagnosis of Alzheimer’s Disease:A Neuroimaging Study Case

Towards an Applied Multicriteria Model to the Diagnosis of A...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Isabelle Tamanini Ana Karoline de Castro Plcido Rogrio Pinheiro Mirian Calíope Dantas Pinheiro University of Fortaleza （UNIFOR）-Graduate Program in Applied Computer Sciences Av.Washington Soares 1321-Bl J Sl 30-60.811-905-Fortaleza-Brazil

The Alzheimer’s disease has become the most fre-quent cause of dementia in the last few years,and it is responsible,alone or in association with other diseases,for 50% of the cases in western *** main focus of this work is to develop a multicriteria model for aiding in decision making on the diagnosis of Alzheimer’s disease by using the Aranau Tool,structured on the Verbal Decision *** this work,the modeling and evaluation processes were conducted with the aid of a medical expert,bibliographic sources and *** questionnaires taken into account were based mainly on patients’ neuroimaging tests,and we analyzed wheter or not there were problems in the patients’ brain that could be relevant to the diagnosis of Alzheimer’s disease.

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Disaster recovery plan implementation evaluation model at the corporation

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Procedia computer Science 2024年 234卷 1658-1663页

作者： Wahyu Sardjono Widhilaga Gia Perdana Gustian Rama Putra Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta 13220 Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

The application of information technology in addition to making internal business processes more effective and efficient, is also to improve customer service. This role is important for companies to maintain the sustainability of information systems as a strategic tool for competitive advantage so system availability is absolutely essential. Research related to the Disaster Recovery Plan (DRP) needs to be carried out to see the readiness of a corporation to build a backup system in different places and locations. By using the method of factor analysis and regression analysis to form an evaluation model, it will obtain an overview of what factors must be considered and what the current conditions are. The results of the study show that there are Core Technical DRP, Organizational Preparation, and Costing factors which are the main variables in supporting DRP readiness. The conclusion from this study, through these three new factors a mathematical model can be built as a reference for improving DRP implementation in corporations.

关键词： disaster recovery planning factor analysis corporation evaluation model

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Parsimonious reconstruction of network evolution

Parsimonious reconstruction of network evolution

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Lecture Notes in computer Science

作者： Patro, Rob Sefer, Emre Malin, Justin Marçais, Guillaume Navlakha, Saket Kingsford, Carl Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 United States Department of Computer Science University of Maryland College Park MD 20742 United States Computational Biology Bioinformatics and Genomics Concentration Biological Sciences Graduate Program University of Maryland College Park MD 20742 United States Program in Applied Mathematics Statistics and Scientific Computation University of Maryland College Park MD 20742 United States School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783642230370

We consider the problem of reconstructing a maximally parsimonious history of network evolution under models that support gene duplication and loss and independent interaction gain and loss. We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. © 2011 Springer-Verlag.

关键词： Genes

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Computational and biological approach to understand the 3′ UTR-mediated posttranscriptional regulation of alfalfa cytosolic glutamine synthetase genes

Computational and biological approach to understand the 3′ ...

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2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010

作者： Simon, Bindu Sengupta-Gopalan, Champa Yelavarthi, Pavan K. Ranjan, Desh Graduate Program in Molecular Biology New Mexico State University Las Cruces NM 88003 United States Dept. of Plant and Environmental Sciences New Mexico State University Las Cruces NM 88003 United States Department of Computer Science New Mexico State University Las Cruces NM 88003 United States Department of Computer Science Old Dominion University Norfolk VA 23529 United States

ISBN: (纸本)9781617381119

Glutamine synthetase (GS) is a key enzyme in nitrogen metabolism in plants. One of the GS isoforms in plants, GS1, is transcriptionally regulated. Our recent studies show that a soybean GS1, Gmglnβ 1, gene is posttranscriptionally regulated by its 3′UTR. It is of interest to know if the 3′UTR-mediated posttranscriptional regulation of GS1 genes is a universal phenomenon or is it limited to Gmglnβ 1 gene only. The 3′UTR-mediated posttranscriptional regulation of alfalfa GS1 genes (MsGS100 and MsGS13) was investigated using both computational and biological approaches. For our first set of study, we used CisFind, a software tool that identifies unextendable common patterns among sequences, to analyze the 3′UTRs of GS1 genes. CisFind analysis of the 3′UTRs showed that the 3′UTR of MsGS100 shared common putative cis-element motifs with the Gmglnβ 1 3′UTR whereas these elements were absent in the 3′UTR of MsGS13 gene. The computational results were verified using transgenic approach, and the results confirmed the computational analysis. The 3′UTR of MsGS 100 plays a similar role as the 3′UTR of Gmglnβ 1 gene in posttranscriptional regulation of a reporter gene while the 3′UTR of the MsGS13 does not. CisFind, was also used in analyzing the region in the 3′UTR of MsGS13 that could be responsible for its nodule enhanced expression (by detecting putative cis-elements that are overrepresented in the 3′UTR of nodule-enhanced GS1 genes).

关键词： Amino acids

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Optimizing Preacher Assignments Using AHC-PCA Algorithm in Kampar Regency

Optimizing Preacher Assignments Using AHC-PCA Algorithm in K...

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International Conference on Electrical Engineering, computer and Information Technology (ICEECIT)

作者： Rahmad Kurniawan Nuraina Suhada Sukamto Tisha Melia Zailani Joko Risanto Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798331504373

ISBN: (纸本)9798331504380

Inefficiencies in assigning preachers for Islamic preaching activities often arise due to distant placements, leading to delays and logistical challenges. In Kampar Regency, geographical diversity intensifies this issue, resulting in suboptimal preacher placements. This study aims to optimize preacher assignments by considering distance and mosque profiles. The dataset, which includes 102 mosques and 148 preachers was obtained from the Indonesian Ulema Council in Kampar. The Agglomerative Hierarchical Clustering (AHC) algorithm was selected for its capacity to cluster data across multiple characteristics. Principal Component Analysis (PCA) was employed to reduce data dimensionality, thereby enhancing clustering outcomes. The optimization of the AHC-PCA algorithm generated an optimal clustering solution with a silhouette score of 0.625524 at three clusters. Furthermore, the elbow method also indicated that three clusters are optimal with a value of 390.234457, while the lowest Davies-Bouldin Index (DBI) score of 0.468573 was achieved with three clusters. Business Intelligence techniques visualized these clusters, providing a spatial distribution of preachers and mosques for detailed analysis. This study improves preacher assignments by reducing travel time and aligning preacher placements with mosque proximity and needs, thus enhancing the efficiency of Islamic preaching in Kampar Regency.

关键词： Measurement Graphical models Clustering algorithms Indexes Business intelligence Elbow Information technology Population density Optimization Principal component analysis

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An Expert System Applied to the Diagnosis of Psychological Disorders

An Expert System Applied to the Diagnosis of Psychological D...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Luciano Comin Nunes Plácido Rogério Pinheiro Tarcísio Cavalcante Pequeno Graduate Program in Applied Computer Sciences University of Fortaleza Av.Washington Soares 1321-Bloco Jsala 30CEP:60811-905FortalezaCearáBrazil

Psychological disorders have kept away and incapacitated professionals in different sectors of *** most serious problems may be associated with various types of pathologies,however,it appears,more often,as psychotic disorders,mood disorders,anxiety disorders,antisocial personality,multiple personality and addiction,causing a micro level damage to the individual and his/her family and in a macro level to the production system and the country *** lack of early diagnosis has provided reactive measures,and sometimes very late,when the professional is already showing psychological signs of incapacity to *** study aims to help the early diagnosis of psychological disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (Multi-Criteria Decision Analysis-MCDA) and knowledge structured representations into production rules and probabilities (Artificial Intelligence-AI).

关键词： Early diagnosis,Expert SINTA,expert systems,psychological disorders,health,multicriteria

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Physics-based learned design: Optimized coded-illumination for quantitative phase imaging

arXiv

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arXiv 2018年

作者： Kellman, Michael R. Bostan, Emrah Repina, Nicole Waller, Laura Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Graduate Program in Bioengineering University of California BerkeleyCA94720 United States

Coded-illumination can enable quantitative phase microscopy of transparent samples with minimal hardware requirements. Intensity images are captured with different source patterns and a non-linear phase retrieval optimization reconstructs the image. The non-linear nature of the processing makes optimizing the illumination pattern designs complicated. Traditional techniques for experimental design (e.g. condition number optimization, spectral analysis) consider only linear measurement formation models and linear reconstructions. Deep neural networks (DNNs) can efficiently represent the non-linear process and can be optimized over via training in an end-to-end framework. However, DNNs typically require a large amount of training examples and parameters to properly learn the phase retrieval process, without making use of the known physical models. Here, we aim to use both our knowledge of the physics and the power of machine learning together. We develop a new data-driven approach to optimizing coded-illumination patterns for a LED array microscope for a given phase reconstruction algorithm. Our method incorporates both the physics of the measurement scheme and the non-linearity of the reconstruction algorithm into the design problem. This enables efficient parameterization, which allows us to use only a small number of training examples to learn designs that generalize well in the experimental setting without retraining. We show experimental results for both a well-characterized phase target and mouse fibroblast cells using coded-illumination patterns optimized for a sparsity-based phase reconstruction algorithm. Our learned design results using 2 measurements demonstrate similar accuracy to Fourier Ptychography with 69 measurements. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

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Clustering Preachers and Mosques for Effective Religious Outreach in Kampar Using DHC-PCA Algorithm

Clustering Preachers and Mosques for Effective Religious Out...

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International Conference on Electrical Engineering, computer and Information Technology (ICEECIT)

作者： Rahmad Kurniawan Natania Adela Surya Fatayat Evfi Mahdiyah Ilyas Husti Arisman Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798331504373

ISBN: (纸本)9798331504380

Preacher assignments to mosques in Kampar Regency have often been inefficient due to geographical distance and compatibility issues, limiting effective religious outreach. This study aims to optimize preacher assignments by applying the Divisive Hierarchical Clustering (DHC) algorithm. Analysis was conducted on a dataset of 250 records, clustering preachers and mosques based on geographical proximity and relevant attributes. Initial clustering modelling produced the highest silhouette score of 0.336. Dimensionality reduction using Principal Component Analysis (PCA) significantly improved the silhouette score to 0.8197, reflecting enhanced clustering quality. The elbow method validated eight clusters as optimal, with a value of 292.33. Research findings demonstrate that preachers can be effectively placed in geographically closer mosques aligned with their profiles, reducing inefficiencies. Business intelligence techniques visualized the clustering results, providing decision-makers with insights into geographical distribution and facilitating improved assignment strategies. These findings hold substantial implications for optimizing religious outreach in Kampar by minimizing logistical challenges and increasing engagement effectiveness.

关键词： Electrical engineering Dimensionality reduction Visualization Limiting Computational modeling Clustering algorithms Business intelligence Elbow Information technology Principal component analysis

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Physics-informed machine learning with smoothed particle hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence

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Physical Review Fluids 2023年第5期8卷 054602-054602页

作者： Michael Woodward Yifeng Tian Criston Hyett Chris Fryer Mikhail Stepanov Daniel Livescu Michael Chertkov Graduate Interdisciplinary Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA Department of Mathematics University of Arizona Tucson Arizona 85721 USA Computer Computational and Statistical Sciences Division LANL Los Alamos New Mexico 87544 USA

Building efficient, accurate, and generalizable reduced-order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrangian models for turbulent flows, and it investigates the effects of enforcing physical structure through smoothed particle hydrodynamics (SPH) versus relying on neural networks (NNs) as universal function approximators. Starting from NN parametrizations of a Lagrangian acceleration operator, this hierarchy of models gradually incorporates a weakly compressible and parameterized SPH framework, which enforces physical symmetries, such as Galilean, rotational, and translational invariances. Within this hierarchy, two new parameterized smoothing kernels are developed to increase the flexibility of the learn-able SPH simulators. For each model we experiment with different loss functions which are minimized using gradient based optimization, where efficient computations of gradients are obtained by using automatic differentiation and sensitivity analysis. Each model within the hierarchy is trained on two data sets associated with weakly compressible homogeneous isotropic turbulence: (1) a validation set using weakly compressible SPH; and (2) a high-fidelity set from direct numerical simulations. Numerical evidence shows that encoding more SPH structure improves generalizability to different turbulent Mach numbers and time shifts, and that including the novel parameterized smoothing kernels improves the accuracy of SPH at the resolved scales.

关键词： Sensitivity analysis

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