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Mesoporous silica catalyst using Sapindus rarak extract as template for deoxygenation of waste cooking oil to biofuels

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Jovita, Stella Holilah, Holilah Khairunisa, Nada Nabila Bahruji, Hasliza Nugraha, Reva Edra Sholeha, Novia Amalia Aziz, Abdul Ediati, Ratna Jalil, Aishah Abdul Prasetyoko, Didik Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya 60111 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya 60294 Indonesia College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia UTM Skudai Johor Johor Bahru 81310 Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia UTM Skudai Johor Johor Bahru 81310 Malaysia

Mesoporous silica was synthesized using biosurfactant templates to tailor the mesoporosity and catalytic activity in waste cooking oil conversion to liquid hydrocarbon. Facile maceration of Sapindus rarak fruit produced extract with amphiphilic properties. The effect of saponin biosurfactant in controlling mesoporosity was determined by varying silica to extract at 1:0.5, 1:1, and 1:2 w/w ratios. The silica formed sponge-like morphology with 3D intraparticle disordered mesopores. The mesoporosity reaches optimum with 6.08 nm pore size, 541 m²/g surface area, and 0.57 cc/g pore volume. High mesoporosity in silica enhances waste cooking oil conversion during deoxygenation reaction, achieving 80.60 % hydrocarbon selectivity and reducing carbon coke. © 2024 The Authors

关键词： Biofuels Green template Mesoporous silica Sapindus rarak Waste cooking oil

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Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

arXiv

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arXiv 2023年

作者： Zhang, Linglin Brown-Mulry, Beatrice Nalla, Vineela Hwang, InChan Gichoya, Judy Wawira Gastounioti, Aimilia Banerjee, Imon Seyyed-Kalantari, Laleh Woo, MinJae Trivedi, Hari School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Information Technology Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Computational Imaging Research Center Washington University in St. Louis School of Medicine 4525 Scott Avenue St. LouisMO63110 United States Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd ScottsdaleAZ85259 United States School of Computing and Augmented Intelligence Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging dataset (EMBED) containing mammograms from 115,931 patients imaged at Emory Healthcare between 2013-2020, with Breast Imaging Reporting and data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Multiple deep learning models were trained to distinguish between abnormal tissue patches and randomly selected normal tissue patches from screening mammograms. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics and investigated their associations with false negatives and false positives. We also performed multivariate logistic regression to control for confounding between subgroups. The top-performing model, ResNet152V2, achieved accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operating characteristics curve 0.975 (95%CI=0.972-0.978). Before controlling for confounding, nearly all subgroups showed statistically significant differences in model performance. However, after controlling for confounding, we found lower false negative risk associates with Other race (RR=0.828;p= .050), biopsy-proven benign lesions (RR=0.927;p= .011), and mass (RR=0.921;p= .010) or asymmetry (RR=0.854;p= .040);higher false negative risk associates with architectural distortion (RR=1.037;p Copyright © 2023, The Authors. All rights reserved.

关键词： Mammography

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Revisiting mid-long term ecosystem services projections: Integrating the interaction effects of future climate and land use changes

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Fundamental Research 2024年

作者： Yang, Qing Liu, Gengyuan Gonella, Francesco Wang, Qingrui Chen, Shiju Yang, Zhifeng Advanced Interdisciplinary Institute of Environment and Ecology Beijing Normal University Zhuhai 519087 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 China Department of Molecular Sciences and Nanosystems Ca’ Foscari University of Venice Venezia Mestre 30170 Italy State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Beijing Tengjing Big Data Research Institute Beijing 100020 China Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education School of Ecology Environment and Resources Guangdong University of Technology Guangzhou 510006 China

Two of the most critical issues of sustainability are how future ecosystem services (ES) will change under natural and artificial stressors. To answer the above problems accurately with a quantitative manner and develop an intelligent and sustainable management of ecosystems, the projections of ES considering climate change under different Shared Socioeconomic Pathways (SSPs) are needed. However, current ES projection studies often lack quantitative methods to simulate ES dynamics, especially incapability to integrate the interactions between climate and land use changes on ES dynamics. To overcome these challenges, this study proposes a framework for ES simulation under climate and land use change stressors. The framework developed includes four modules: climatic attributes simulation, land use change simulation, scenario design, and ecosystem services simulation modules. Taking China as a case study, this study simulated the evolution of China's ES under the interactions between climate and land use change during the period of 2020–2100. The simulation results show that China's ES increases by 33%, 34%, 9%, and 60% under scenarios SSP1, SSP2, SSP3, and SSP5, respectively. Additionally, the overall growth rate of ES decreases by a gradually slowing rate from the mean at 1% during 2000–2020 to 0.5% during 2020–2100, respectively. Meanwhile, the ES in China shows significantly spatial variability based on simulation results. Shandong province yields the largest potential to be the key area with ES declining in the future. At ecosystem scale, increases in woodland ES are expected to dominate China's ES growth continuously. In the first four decades (2020–2060), development ways under SSP1 scenario are more helpful to promote increase in subtypes of ES, while in the last four decades (2060–2100), ES are expected to increase under SSP5 scenario. The key contributions of this study include tracking the causation and revealing the influencing mechanism of interactions of

关键词： Climate change Ecosystem conservation and management Ecosystem services Land use change Shared socioeconomic pathways (SSPs)

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Cache Characterization of Workloads in a Microservice Environment

Cache Characterization of Workloads in a Microservice Enviro...

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IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

作者： Sanjith Athlur Nishant Sondhi Sachit Batra Subramaniam Kalambur Dinkar Sitaram Department of Computer Science Center for Cloud Computing and Big Data PES University Bengaluru

ISBN: (数字)9781728163345

ISBN: (纸本)9781728163352

The advent of Microservice based architecture has lead to the obsolescence of the tedious way of building and deploying applications in a monolithic way. The number of organizations taking advantage of cloud computation and microservice architecture is steadily increasing. From the cloud providers perspective, the purpose of cloud computation is to leverage economies of scale by co-locating several workloads dynamically on the same powerful many-core servers. In this paper, we attempt to characterize cache usage and its impact on performance when multiple workloads are collocated on multicore servers.

关键词： Servers Interference Cloud computing Benchmark testing Hardware Multicore processing

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The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave

arXiv

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arXiv 2022年

作者： Silva, Miguel E.P. Fyles, Martyn Pi, Li Panovska-Griffiths, Jasmina Jay, Caroline House, Thomas Fearon, Elizabeth Department of Computer Science University of Manchester United Kingdom Department of Mathematics University of Manchester United Kingdom The Alan Turing Institute London United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford United Kingdom The Queen’s College University of Oxford United Kingdom Pandemic Sciences Institute Nuffield Department of Medicine University of Oxford United Kingdom Department of Global Health and Development Faculty of Public Health and Policy London School of Hygiene and Tropical Medicine United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene and Tropical Medicine United Kingdom

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with ‘lockdown’ interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures. © 2022, CC BY.

关键词： Coronavirus

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A novel hybrid maximum power point tracking method based on improving the effectiveness of different configuration partial shadow

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Sustainable Energy Technologies and Assessments 2022年 50卷

作者： Yongchun Jiang Jianguo Xu Xiujuan Leng Nasrin Eghbalian School of Big Data College Qingdao Huanghai University Qingdao Shandong 266427 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao Shandong 266590 China Electrical Engineering Department Sunlife Company Baku Azerbaijan

The generation power of solar cells depends on the ambient temperature and the intensity of the sun's radiation. Also, a set of unconventional phenomena such as buildings, trees, clouds, etc. causes a slight shadow on solar arrays. Partial shadows will create a disparate effect between the modules such as reduced production capacity and hotspots phenomenon. While in this condition several maximum power points in the power-voltage characteristic are created. In the case of uniform radiation, the conventional MPPT method can be used. However, in partial shade conditions, these methods are not effective due to the presence of several maximum power points in the power-voltage characteristic. One of the ways to improve the condition of the solar array is rearranging the array to distribute the uniform shadow in the array. In this paper, an algorithm based on the different shadow patterns is proposed for a fixed reconfiguration in such a way that maximum efficiency is achieved in different shadow conditions. On the other hand, a new method for the MPPT is provided under partial shadow conditions, so that in the case of uniform radiation, tracking operations are observed and followed by the proposed algorithm, and in the case of partial shadow, it works by constant voltage. The simulation results show that the configuration and distribution of the shadow at the level of the solar array with the proposed algorithm with a novel MPPT has a good improvement in the higher productivity of photovoltaic arrays compared to other methods introduced for reconfiguration.

关键词： Photovoltaic systems Maximum power point tracking Partial shadow Fixed re-configuration

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Tight sensitivity bounds for smaller coresets

arXiv

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arXiv 2019年

作者： Maalouf, Alaa Statman, Adiel Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

An ϵ-coreset for Least-Mean-Squares (LMS) of a matrix A ϵ n×dis a small weighted subset of its rows that approximates the sum of squared distances from its rows to every affine k-dimensional subspace of d, up to a factor of 1±ϵ. Such coresets are useful for hyper-parameter tuning and solving many least-mean-squares problems such as low-rank approximation (k-SVD), k-PCA, Lassso/Ridge/Linear regression and many more. Coresets are also useful for handling streaming, dynamic and distributed big data in parallel. With high probability, non-uniform sampling based on upper bounds on what is known as importance or sensitivity of each row in A yields a coreset. The size of the (sampled) coreset is then near-linear in the total sum of these sensitivity bounds. We provide algorithms that compute provably tight bounds for the sensitivity of each input row. It is based on two ingredients: (i) iterative algorithm that computes the exact sensitivity of each point up to arbitrary small precision for (non-affine) k-subspaces, and (ii) a general reduction of independent interest from computing sensitivity for the family of affine k-subspaces in dto (non-affine) (k + 1)- subspaces in d+1. Experimental results on real-world datasets, including the English Wikipedia documentsterm matrix, show that our bounds provide significantly smaller and data-dependent coresets also in practice. Full open source is also provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix algebra

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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Fast and accurate least-mean-squares solvers

arXiv

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arXiv 2019年

作者： Maalouf, Alaa Jubran, Ibrahim Feldman, Dan The Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

Least-mean squares (LMS) solvers such as Linear / Ridge / Lasso-Regression, SVD and Elastic-Net not only solve fundamental machine learning problems, but are also the building blocks in a variety of other methods, such as decision trees and matrix factorizations. We suggest an algorithm that gets a finite set of n d-dimensional real vectors and returns a weighted subset of d + 1 vectors whose sum is exactly the same. The proof in Caratheodory's Theorem (1907) computes such a subset in O(n2d2) time and thus not used in practice. Our algorithm computes this subset in O(nd) time, using O(log n) calls to Caratheodory's construction on small but "smart" subsets. This is based on a novel paradigm of fusion between different data summarization techniques, known as sketches and coresets. As an example application, we show how it can be used to boost the performance of existing LMS solvers, such as those in scikit-learn library, up to x100. Generalization for streaming and distributed (big) data is trivial. Extensive experimental results and complete open source code are also provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

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Rare Trajectories in a Prototypical Mean-field Disordered Model: Insights into Landscape and Instantons

arXiv

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arXiv 2025年

作者： Charbonneau, Patrick Folena, Giampaolo Malatesta, Enrico M. Rizzo, Tommaso Zamponi, Francesco Department of Chemistry Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States Dipartimento di Fisica Sapienza Università di Roma 00185 Italy Department of Computing Sciences Bocconi University Milano20136 Italy Institute for Data Science and Analytics Bocconi University Milano20136 Italy CNR Rome unit Piazzale Aldo Moro 5 Rome00185 Italy

For disordered systems within the random first-order transition (RFOT) universality class, such as structural glasses and certain spin glasses, the role played by activated relaxation processes is rich to the point of perplexity. Over the last decades, various efforts have attempted to formalize and systematize such processes in terms of instantons similar to the nucleation droplets of first-order phase transitions. In particular, Kirkpatrick, Thirumalai, and Wolynes proposed in the late’80s an influential nucleation theory of relaxation in structural glasses. Already within this picture, however, the resulting structures are far from the compact objects expected from the classical droplet description. In addition, an altogether different type of single-particle hopping-like instantons has recently been isolated in molecular simulations. Landscape studies of mean-field spin glass models have further revealed that simple saddle crossing does not capture relaxation in these systems. We present here a landscape-agnostic study of rare dynamical events, which delineates the richness of instantons in these systems. Our work not only captures the structure of metastable states, but also identifies the point of irreversibility, beyond which activated relaxation processes become a fait accompli. An interpretation of the associated landscape features is articulated, thus charting a path toward a complete understanding of RFOT instantons. © 2025, CC BY.

关键词： Structural relaxation

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