A Novel Approach for Solving Large-Scale Bike Sharing Station Planning Problems

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

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Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽

10.1093/gigascience/giaa133 ◽

2020 ◽

Vol 9 (12) ◽

Author(s):

Ariel Rokem ◽

Kendrick Kay

Keyword(s):

Ridge Regression ◽

Large Scale ◽

Imaging Data ◽

Regularization Technique ◽

Large Scale Data ◽

Novel Approach ◽

Manual Exploration ◽

L2 Norm ◽

Software Implementations ◽

Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

Integrated Environmental Assessment and Management ◽

10.1002/ieam.4493 ◽

2021 ◽

Author(s):

Silvia Huber ◽

Lars B. Hansen ◽

Lisbeth T. Nielsen ◽

Mikkel L. Rasmussen ◽

Jonas Sølvsteen ◽

...

Keyword(s):

Large Scale ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Novel Approach

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Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

Computational Visual Media ◽

10.1007/s41095-021-0207-y ◽

2021 ◽

Author(s):

Jin Zhou ◽

Qing Zhang ◽

Jian-Hao Fan ◽

Wei Sun ◽

Wei-Shi Zheng

Keyword(s):

Large Scale ◽

Assessment Model ◽

Generic Model ◽

Small Subset ◽

Deep Convolutional Neural Networks ◽

Personal Taste ◽

Hinge Loss ◽

Novel Approach ◽

Large Scale Dataset ◽

Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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Reservoir-Induced Land Deformation: Case Study from the Grand Ethiopian Renaissance Dam

Remote Sensing ◽

10.3390/rs13050874 ◽

2021 ◽

Vol 13 (5) ◽

pp. 874

Author(s):

Yu Chen ◽

Mohamed Ahmed ◽

Natthachet Tangdamrongsub ◽

Dorina Murgulet

Keyword(s):

Land Surface ◽

Large Scale ◽

Surface Deformation ◽

Vertical Displacement ◽

Nile River ◽

Reservoir Volume ◽

Novel Approach ◽

Land Deformation ◽

Large Scale Land ◽

The Impact

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.

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A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720006002272 ◽

2006 ◽

Vol 04 (03) ◽

pp. 639-647 ◽

Cited By ~ 6

Author(s):

ELEAZAR ESKIN ◽

RODED SHARAN ◽

ERAN HALPERIN

Keyword(s):

Large Scale ◽

Computational Cost ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Novel Approach ◽

Maximum Likelihood Criterion ◽

The Common ◽

Genomic Regions ◽

High Computational Cost ◽

Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

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O Ensino da Antibioterapia: Estado da arte

História da Ciência e Ensino construindo interfaces ◽

10.23925/2178-2911.2019v20espp632-637 ◽

2019 ◽

Vol 20 ◽

pp. 632-637

Author(s):

Maria José Saavedraa ◽

João Carlos Sousa

Keyword(s):

Infectious Diseases ◽

Large Scale ◽

Action Plan ◽

Antimicrobial Properties ◽

Hospital Setting ◽

Multidrug Resistant ◽

World Health ◽

Novel Approach ◽

Paul Ehrlich ◽

Alexander Fleming

Resumo A elevada mortalidade pelas doenças infecciosas, sobretudo epidémicas, mobilizou os cientistas na pesquisa de compostos naturais e produtos de síntese química dotados de propriedades antimicrobianas. Fazendo um pouco de história, referimos Paul Ehrlich, que utilizou o primeiro agente quimioterapêutico -Salvarsan, mais tarde Gerhard Domagk, que utilizou um pro-fármaco percursor de uma sulfamida. Em 1928, Alexander Fleming, descobriu de forma “casual” a penicilina, o primeiro antibiótico. Posteriormente em 1941 Howard Florey e Ernest Chain isolam e purificam a penicilina o que permitiu a sua utilização em larga escala -Era dos Antibióticos. A utilização dos antibióticos (AB) no tratamento das doenças infecciosas constituiu um dos maiores avanços da Medicina no séc. XX. No entanto a sua utilização em larga escala promoveu o aumento da incidência de estirpes multiresistentes aos AB, sobretudo em ambiente hospitalar. Adicionalmente verifica-se uma ocorrência cada vez mais elevada de estirpes resistentes na comunidade–humanos, animais e ambiente. O conhecimento dos mecanismos de ação e da ineficácia dos diferentes grupos farmacológicos de antibióticos é vital para o desenvolvimento de futuros microbianos, estando a ser estudados microrganismos do solo com a finalidade de encontrara novos fármacos. De realçar que a OMS preconiza que caminhamos rumo a uma "era pós-antibiótico”. Se não houver um plano de ação global para o "uso racional de antibióticos" a OMS prevê que em 2050 a resistência aos antibióticos, poderá matar mais de 10 milhões de pessoas.Palavras-chave: antibioterapia; resistência; antibióticos Abstract The current research on infectious diseases, especially with epidemic potential, has mobilized the scientific community to research on the natural substance and chemical probing products with antimicrobial properties. In a brief history of antibiotics, we refer to Paul Ehrlich, who used the first chemotherapeutic agent - Salvarsan, later Gerhard Domagk, who used a sulfamide precursor prodrug. In 1928 Alexander Fleming "casually" discovered penicillin, the first antibiotic. Later in 1941 Howard Florey and Ernest Chain isolate and purify penicillin that can be used on a large scale - Antibiotics Era. The use of antibiotics (AB) in the treatment of infectious diseases is one of the greatest advances of medicine in the 19th century. However, its large-scale use has increased the incidence of multidrug-resistant processes in AB, especially in a hospital setting. Besides, there is an increasing occurrence of resistant strains in different communities - humans, animals and in the environment. Understand the mechanisms of action and the ineffectiveness of the diverse pharmacological groups of antibiotics is crucial to provide further new antibiotic therapies in the near future. Recent studies have highlighted the soil-derived microorganisms as a novel approach to identify new drug substances. In this context, it is noteworthy that the World Health Organization (WHO) considers that we are moving towards a “post-antibiotic era”. If there is no global action plan for “rational use of antibiotics” WHO predicts that in 2050 the global impacts of antibiotic resistance on human heath will be catastrophic, killing more than 10 million people worldwide. Keywords: antibiotic therapy; resistence; antibiotics

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Participatory Mapping for Strengthening Environmental Governance on Socio-Ecological Impacts of Infrastructure in the Amazon: Lessons to Improve Tools and Strategies

Sustainability ◽

10.3390/su132414048 ◽

2021 ◽

Vol 13 (24) ◽

pp. 14048

Author(s):

Carla Mere-Roncal ◽

Gabriel Cardoso Carrero ◽

Andrea Birgit Chavez ◽

Angelica Maria Almeyda Zambrano ◽

Bette Loiselle ◽

...

Keyword(s):

Large Scale ◽

Environmental Governance ◽

Oil And Gas ◽

Good Governance ◽

Knowledge Exchange ◽

Ecological Impacts ◽

Participatory Mapping ◽

Infrastructure Development ◽

Development Agenda ◽

Novel Approach

The Amazon region has been viewed as a source of economic growth based on extractive industry and large-scale infrastructure development endeavors, such as roads, dams, oil and gas pipelines and mining. International and national policies advocating for the development of the Amazon often conflict with the environmental sector tasked with conserving its unique ecosystems and peoples through a sustainable development agenda. New practices of environmental governance can help mitigate adverse socio-economic and ecological effects. For example, forming a “community of practice and learning” (CoP-L) is an approach for improving governance via collaboration and knowledge exchange. The Governance and Infrastructure in the Amazon (GIA) project, in which this study is embedded, has proposed that fostering a CoP-L on tools and strategies to improve infrastructure governance can serve as a mechanism to promote learning and action on factors related to governance effectiveness. A particular tool used by the GIA project for generating and sharing knowledge has been participatory mapping (Pmap). This study analyzes Pmap exercises conducted through workshops in four different Amazonian regions. The goal of Pmap was to capture different perspectives from stakeholders based on their experiences and interests to visualize and reflect on (1) areas of value, (2) areas of concern and (3) recommended actions related to reducing impacts of infrastructure development and improvement of governance processes. We used a mixed-methods approach to explore textual analysis, regional multi-iteration discussion with stakeholders, participatory mapping and integration with ancillary geospatial datasets. We believe that by sharing local-knowledge-driven data and strengthening multi-actor dialogue and collaboration, this novel approach can improve day to day practices of CoP-L members and, therefore, the transparency of infrastructure planning and good governance.

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Discovering Latent Class Labels for Multi-Label Learning

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/423 ◽

2020 ◽

Author(s):

Jun Huang ◽

Linchuan Xu ◽

Jing Wang ◽

Lei Feng ◽

Kenji Yamanishi

Keyword(s):

Large Scale ◽

Latent Class ◽

Training Data ◽

Data Sets ◽

Robust Learning ◽

Large Scale Data ◽

Novel Approach ◽

Fixed Set ◽

Class Labels ◽

Scale Data

Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets. Discovering and exploring the latent labels hidden in the data may not only find interesting knowledge but also help us to build a more robust learning model. In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously. Extensive experiments show a competitive performance of DLCL against other state-of-the-art MLL approaches.

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The Yale Department of Medicine COVID-19 Data Explorer and Repository (DOM-CovX): An Innovative Approach to Promoting Collaborative Scholarship During a Pandemic

10.1101/2021.08.23.21262241 ◽

2021 ◽

Author(s):

Tanima Arora ◽

Michael Simonov ◽

Jameel Alausa ◽

Labeebah Subair ◽

Brett Gerber ◽

...

Keyword(s):

Clinical Data ◽

Large Scale ◽

Innovative Approach ◽

Academic Productivity ◽

List Type ◽

Digital Revolution ◽

Institutional Climate ◽

Novel Approach ◽

Dissemination Of Research ◽

Academic Faculty

ABSTRACTBackgroundThe COVID-19 pandemic has led to an explosion of research publications spanning epidemiology, basic and clinical science. While a digital revolution has allowed for open access to large datasets enabling real-time tracking of the epidemic, detailed, locally-specific clinical data has been less readily accessible to a broad range of academic faculty and their trainees. This perpetuates the separation of the primary missions of clinically-focused and primary research faculty resulting in lost opportunities for improved understanding of the local epidemic; expansion of the scope of scholarship; limitation of the diversity of the research pool; lack of creation of initiatives for growth and dissemination of research skills needed for the training of the next generation of clinicians and faculty.ObjectivesCreate a common, easily accessible and up-to-date database that would promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. By providing a robust dataset, a broad range of researchers (faculty, trainees) and clinicians are encouraged to explore and collaborate on novel clinically relevant research questions.MethodsWe constructed a research platform called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), to house cleaned, highly granular, de-identified, continually-updated data from over 7,000 patients hospitalized with COVID-19 (1/2020-present) across the Yale New Haven Health System. This included a front-end user interface for simple data visualization of aggregate data and more detailed clinical datasets for researchers after a review board process. The goal is to promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise.Expected OutcomesAccelerate generation of new knowledge and increase scholarly productivity with particular local relevanceImprove the institutional academic climate by:Broadening research scopeExpanding research capability to more diverse group of stakeholders including clinical and research-based faculty and traineesEnhancing interdepartmental collaborationsConclusionsThe DOM-CovX Data Explorer and Repository have great potential to increase academic productivity. By providing an accessible tool for simple data analysis and access to a consistently updated, standardized and large-scale dataset, it overcomes barriers for a wide variety of researchers. Beyond academic productivity, this innovative approach represents an opportunity to improve the institutional climate by fostering collaboration, diversity of scholarly pursuits and expanding medical education. It provides a novel approach that can be expanded to other diseases beyond COVID 19.

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