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19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020

作者： Varghese, Christina Varde, Aparna S. Du, Xu Department of Computer Science Montclair State University MontclairNJ United States Department of Earth and Environmental Science Montclair State University MontclairNJ United States Environmental Science and Management PhD Program Montclair State University MontclairNJ United States

ISBN: (纸本)9783030450014

This paper focuses on how populations by the use of technology, more specifically an app, can comprehend the enactment of ordinances (local laws) in an urban area along with their public reactions expressed as tweets. Furthermore, they can understand how well the area is developing and enhancing as a Smart City. The main goal of this research is to develop an Ordinance-Tweet Mining App that disseminates the results of analyzing ordinances and tweets about them, especially related to Smart City Characteristics such as Smart Environment, Smart Mobility etc. This app would be beneficial to various users such as environmental scientists, policy makers, city committees as well as the common public in becoming more aware of legislative bodies, and possibly contributing in different aspects to make the urban area improve as a Smart City. This work fits the realm of Smart Governance due to transparency via public involvement. © 2020, IFIP International Federation for Information Processing.

关键词： Sentiment analysis

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Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm

arXiv

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arXiv 2022年

作者： Gagolewski, Marek Bartoszuk, Maciej Cena, Anna Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland International PhD Studies Program Institute of Computer Science Polish Academy of Sciences Poland

The time needed to apply a hierarchical clustering algorithm is most often dominated by the number of computations of a pairwise dissimilarity measure. Such a constraint, for larger data sets, puts at a disadvantage the use of all the classical linkage criteria but the single linkage one. However, it is known that the single linkage clustering algorithm is very sensitive to outliers, produces highly skewed dendrograms, and therefore usually does not reflect the true underlying data structure - unless the clusters are well-separated. To overcome its limitations, we propose a new hierarchical clustering linkage criterion called Genie. Namely, our algorithm links two clusters in such a way that a chosen economic inequity measure (e.g., the Gini- or Bonferroni-index) of the cluster sizes does not increase drastically above a given threshold. The presented benchmarks indicate a high practical usefulness of the introduced method: it most often outperforms the Ward or average linkage in terms of the clustering quality while retaining the single linkage speed. The Genie algorithm is easily parallelizable and thus may be run on multiple threads to speed up its execution further on. Its memory overhead is small: there is no need to precompute the complete distance matrix to perform the computations in order to obtain a desired clustering. It can be applied on arbitrary spaces equipped with a dissimilarity measure, e.g., on real vectors, DNA or protein sequences, images, rankings, informetric data, etc. A reference implementation of the algorithm has been included in the open source genie package for R. [See https://genieclust. gagolewski. com for a new implementation - available for both R and Python.]. © 2022, CC BY.

关键词： Clustering algorithms

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Functional Connectome-Based Predictive Modeling in Autism

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BIOLOGICAL PSYCHIATRY 2022年第8期92卷 626-642页

作者： Horien, Corey Floris, Dorothea L. Greene, Abigail S. Noble, Stephanie Rolison, Max Tejavibulya, Link O'Connor, David McPartland, James C. Scheinost, Dustin Chawarska, Katarzyna Lake, Evelyn M. R. Constable, R. Todd [a]Interdepartmental Neuroscience Program Yale School of Medicine New Haven Connecticut [b]MD-PhD Program Yale School of Medicine New Haven Connecticut [c]Department of Pediatrics Yale School of Medicine New Haven Connecticut [d]Department of Neurosurgery Yale School of Medicine New Haven Connecticut [e]Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven Connecticut [f]Department of Biomedical Engineering Yale University New Haven Connecticut [g]Department of Psychology Yale University New Haven Connecticut [h]Department of Statistics and Data Science Yale University New Haven Connecticut [i]Yale Child Study Center New Haven Connecticut [j]Methods of Plasticity Research Department of Psychology University of Zürich Zurich Switzerland [k]Donders Center for Brain Cognition and Behavior Radboud University Nijmegen Nijmegen The Netherlands

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

关键词： Clinical translation Development Fingerprinting Individual differences Machine learning Resting-state fMRI

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The performance of biomarkers for the diagnosis of Parkinson’s disease: A systematic review

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The American Journal of Medicine 2025年

作者： Jaden Lim Yeonglong Ay Bachelor of Medical Science and Doctor of Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia PhD student in Integrative Neuroscience Program Dartmouth College Hanover USA

Background Early diagnosis of Parkinson’s disease remains challenging due to the current clinical diagnostic approach. With machine learning emerging as a powerful tool for biomarker discovery, we aim to determine whether biomarkers processed by machine learning models can enable earlier detection of Parkinson’s disease. Methods We conducted a literature search with a 10-year limit that yielded 161 biomarkers derived from serum, cerebrospinal fluid and genes. Initially, biomarkers were classified into four groups according to the biological timeline of Parkinson’s disease pathogenesis. Subsequently, we further organised the biomarkers into two categories: pre-motor phase and motor phase biomarkers. Two analyses were then conducted according to the aforementioned classifications, with the performance of biomarkers evaluated via their AUC values as derived from machine learning models. Results No significant differences were found in either analysis, suggesting that all biomarkers, regardless of their role in the biological sequences underpinning Parkinson’s disease pathogenesis, nor their association with the pre-motor or motor phases of Parkinson’s disease, have the potential to serve as equally valid diagnostic predictors. Additionally, we identified 26 top-performing biomarkers with high AUC values (>0.8). Conclusion The main finding in our analyses was that pre-motor phase biomarkers, which offer the advantage of enabling an earlier diagnosis compared to clinical methods, can achieve a comparably high level of diagnostic accuracy as motor phase biomarkers. Therefore, our foremost suggestion is further research into the clinical viability of pre-motor phase biomarkers that compose part of the aforementioned 26 top-performing biomarkers.

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Pairwise Learning for Imbalanced Data Classification

Pairwise Learning for Imbalanced Data Classification

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Shu Liu Qiang Wu Computational and Data Science PhD Program Middle Tennessee State University Murfreesboro TN USA Department of Mathematical Sciences Middle Tennessee State University Murfreesboro TN USA

ISBN: (纸本)9781665458429

Imbalanced data classification problems appear quite commonly in real-world applications and impose great challenges to traditional classification approaches which work well only on balanced data but usually perform poorly on the minority class when the data is imbalanced. Resampling preprocessing by oversampling the minority class or downsampling the majority class helps improve the performance but may suffer from overfitting or loss of information. In this paper we propose a novel method called pairwise robust support vector machine (PRSVM) to overcome the difficulty of imbalanced data classification. It adapts the non-convex robust support vector classification loss to the pairwise learning setting. In the training process, samples from the minority class and the majority class always appear as pairs. This automatically balances the impact of two classes. Simulations and real-world applications show that PRSVM is highly effective.

关键词： Training Adaptation models Scientific computing Computational modeling Support vector machine classification Computational intelligence

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Unveiling the potential of bacterial keratinase with antiacne drugs for cosmeceutical applications: an in silico evaluation

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Journal of Umm Al-Qura University for Applied sciences 2025年 1-24页

作者： Gaur, Peehoo Polipalli, Yuvaraj Bandhu, Xena Chakravarty, Suhani Banerjee, Srijan Meza, Cynthia Suravajhala, Renuka Sarkar, Shrabana Banerjee, Aparna India International School (IIS) Rajasthan Jaipur India Dr. Durgabhai Deshmukh Senior Secondary School DDU Marg New Delhi India KC Public School Akhnoor Road J&K Jammu India Ankur Senior Secondary School Chandigarh Panjab University Campus Sector 14 Chandigarh India PhD Program in Science R & D Bioactive Products Department Chemistry Institute of Natural Resources University of Talca Talca Chile Doctorado en Biotecnología Traslacional (DBT) Facultad de Ciencias Agrarias y Forestales Universidad Católica del Maule Talca Chile *** Hyderabad India Amrita School of Biotechnology Amrita Vishwa Vidyapeetham Kerala Amritapuri India Functional Polysaccharides Research Group Instituto de Ciencias Aplicadas Facultad de Ingeniería Universidad Autónoma de Chile Talca Chile

The keratinase serves as a catalyst with potential application in various industries, including textiles, leather, pharmaceuticals, cosmetics, and biotechnological applications. Previous studies have predominantly focused on fungal keratinase;however, our present work focuses on bacterial keratinase only. The novelty of using bacterial keratinase over fungal keratinase is because of its faster enzymatic activity, broader substrate specificity, increased stability and cost-effective production, making it efficient and adaptable for industrial applications. However, our present research investigates bacterial keratinase through in silico structural (physicochemical properties, primary, secondary, and tertiary structures) as well as functional analyses (protein–protein interaction networks, subcellular localization, and domain/motif presence), coupled with molecular docking against standard anti-acne drugs in use (adapalene, erythromycin, tretinoin, isotretinoin, and benzoyl peroxide). In this study, two bacterial keratinases, one from mesophilic Pseudomonas aeruginosa (6FZX) and another from thermophilic Meiothermus taiwanensis WR-220 (5WSL) have been studied to demonstrate comparative structural stability and functional activity. These enzymes are intracellular, facilitating proteolysis with metalloendopeptidase activity. Notably, keratinase exhibited a strong binding affinity for adapalene (binding energies of − 8.5 kcal/mol and − 9.6 kcal/mol for 5WSL and 6FZX, respectively) and lowest affinity to erythromycin (binding energy − 6.4 kcal/mol and − 5.9 kcal/mol for 5WSL and 6FZX respectively) among other studied anti-acne drugs like, tretinoin, isotretinoin, and benzoyl peroxide. In the case of de novo designing of anti-acne compounds, enhanced binding affinities are the key factor for selection of potential components. Our in silico analyses indicate that thermophilic keratinase displays better structural stability and functionality, particularly under high pH and h

关键词： Bacteria Cosmeceuticals Functional analyses Keratinase Molecular docking Structural analyses

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Multispectral Drone Data Analysis on Coastal Dunes

Multispectral Drone Data Analysis on Coastal Dunes

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IEEE International Conference on Big Data

作者： Britnie Gonzalez-Moodie Shane Daiek Jorge Lorenzo-Trueba Aparna S. Varde Montclair State University NJ USA Environmental Science and Management PhD Program Montclair State University NJ USA Visiting Researcher Max Planck Institute for Informatics Saarbrucken Germany

ISBN: (纸本)9781665445993

We devise a method to study coastal dune vegetation based on drone data orthomosaic mapping and machine learning algorithms to distinguish objects with spatial and color accuracy from numerous high-quality drone multispectral images. It allows accurate surveying on the density of coastal dune vegetation and potentially individual species. We thus analyze big data to develop tools for coastal resiliency and sustainability.

关键词： Machine learning algorithms Data analysis Image color analysis Sea measurements Vegetation mapping Forestry Big Data

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Systematic designs of single metal compounds synthesized using ammonia fluoride-based complex as structure directing agents for energy storage

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Journal of Colloid and Interface science 2023年第PartA期652卷 294-304页

作者： Kuo, Tsung-Rong Zher Yu, You Wu, Chung-Hsien Lee, Pin-Yan Kongvarhodom, Chutima Chen, Hung-Ming Husain, Sadang Yougbaré, Sibidou Lin, Lu-Yin International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Gingen technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin70124 Indonesia /Nanoro 03 B.P 7192 Ouagadougou 03 Burkina Faso

Tailoring morphology and composition of metal organic frameworks (MOF) can improve energy storage by establishing high surface area, large porosity and multiple redox states. Structure directing agents (SDA) is functional of designing surface properties of electroactive materials. Ammonium fluoride has functional abilities for designing MOF derivatives with excellent energy storage abilities. Systematic design of MOF derivatives using ammonia fluoride-based complex as SDA can essentially create efficient electroactive materials. Metal species can also play significant roles on redox reactions, which are the main energy storage mechanism for battery-type electrodes. In this work, 2-methylimidazole, two novel SDAs of NH4BF4 and NH4HF2, and six metal species of Al, Mn, Co, Ni, Cu and Zn are coupled to synthesize MOF derivatives for energy storage. Metal species-dependent compositions including hydroxides, oxides, and hydroxide nitrates are observed. The nickel-based derivative (Ni-HBF) shows the highest specific capacitance (CF) of 698.0F/g at 20 mV/s, due to multiple redox states and advanced flower-like surface properties. The diffusion and capacitive-control contributions of MOF derivatives are also analyzed. The battery supercapacitor hybrid with Ni-HBF electrode shows a maximum energy density of 27.9 Wh/kg at 325 W/kg. The CF retention of 170.9% and Coulombic efficiency of 93.2% are achieved after 10,000 cycles. © 2023 Elsevier Inc.

关键词： Supercapacitor

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Cancer miRNA detection by highly sensitive SERS via self-assembly nano particle based micro periodic domain (SANP-MPD) 21

Cancer miRNA detection by highly sensitive SERS via self-ass...

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21st International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2017

作者： Koduri, Manohar Prasad Kumar, Ashish Goudar, Venkanagouda Tseng, Fan Gang International Intercollegiate PhD Program National Tsing Hua University Taiwan Department of Engineering and System Science National Tsing Hua University Taiwan Research Center for Applied Sciences Academia Sinica Taipei Taiwan

ISBN: (纸本)9780692941836

In this study, we demonstrated a novel micro periodic domain (MPD), results in rough surface on a silicon wafer, formed by self-assembled nano particles (SANP) and observed a 100 fold improvement in SERS signal for detection of Colon cancer miRNA21 on Au coated nano mushroom periodic domains compared to uniform nano mushroom domain on Si wafer. The single layer deposition on Silicon wafer is achieved by using the in house developed self-assembly deposition technique . The mono layer retains the shape of hexagonal arrangement in between nano particles and forms a periodic micro domains. © 17CBMS-0001.

关键词： Self assembly

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Public participation in museums and cultural heritage sites: iCommunity mobile application

Public participation in museums and cultural heritage sites:...

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2020 AVI2CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI2CH 2020

作者： Nasrolahi, Ahmad Messina, Vito Gena, Cristina Tech4Culture Phd program University of Torino Turin Italy Historical Studies Dept. University of Torino Turin Italy Computer Science Dept. University of Torino Turin Italy

This paper presents iCommunity, an application that will be aimed at finding a universal method for inclusive societies in decision-making processes for cultural heritage management. iCommunity is being designed as a mobile application facilitating public participation in cultural heritage management. The main idea is to give all people, who are affected by a decision, the opportunity to be involved in the decision-making process. Any cultural heritage institution will be able to publish its activities on the application so as to evaluate users' feedback before the implementation of the activity itself. Based on the user's location, the application displays ongoing activities to the user himself. As a part of a participatory approach, users share each other's experience or data related to the posted activity. They also can show their agreement or disagreement to the activities by voting. The comments, votes and user's activities will be analysed real time for helping authorities to consider the consequence of their activities for more transparent and effective decision-making. © Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Decision making

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