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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarch...

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IEEE International Conference on Big data

作者： Hongryul Ahn Inuk Jung Heejoon Chae Minsik Oh Inyoung Kim Sun Kim Division of Data Science DS&ML Center The University of Suwon Hwaseong Republic of Korea School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea BK21 Four Intelligence Computing Seoul National University Seoul Republic of Korea Artificial Intelligence Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics and Bioinformatics Institute Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665445993

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

关键词： Costs Shape Clustering methods Conferences Big data Cost function Minimization

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Consciousness in Artificial Intelligence: Insights from the science of Consciousness

arXiv

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arXiv 2023年

作者： Butlin, Patrick Long, Robert Elmoznino, Eric Bengio, Yoshua Birch, Jonathan Constant, Axel Deane, George Fleming, Stephen M. Frith, Chris Ji, Xu Kanai, Ryota Klein, Colin Lindsay, Grace Michel, Matthias Mudrik, Liad Peters, Megan A.K. Schwitzgebel, Eric Simon, Jonathan VanRullen, Rufin Future of Humanity Institute University of Oxford United Kingdom Center for AI Safety United States University of Montreal MILA - Quebec AI Institute Canada Centre for Philosophy of Natural and Social Science London School of Economics and Political Science United Kingdom School of Engineering and Informatics The University of Sussex Centre de Recherche en Éthique University of Montreal United Kingdom Department of Philosophy University of Montreal Canada Department of Experimental Psychology Wellcome Centre for Human Neuroimaging University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London Institute of Philosophy University of London United Kingdom Araya Inc. School of Philosophy The Australian National University Australia Department of Psychology Center for Data Science New York University United States Center for Mind Brain and Consciousness New York University United States School of Psychological Sciences and Sagol School of Neuroscience Tel-Aviv University CIFAR Program in Brain Mind and Consciousness Israel Department of Cognitive Sciences University of California Irvine and CIFAR Program in Brain Mind and Consciousness United States Department of Philosophy University of California Riverside United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.1 © 2023, CC BY-NC-SA.

关键词： Neurophysiology

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Computational modeling of potential milciclib derivatives inhibitor-CDK2 binding through global docking and accelerated molecular dynamics simulations

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Informatics in Medicine Unlocked 2022年 33卷

作者： Khanam, Mushira Moin, Abu Tayab Ahmed, Kazi Ahsan Patil, Rajesh B. Ripon Khalipha, Abul Bashar Ahmed, Nafisa Bagchi, Rajat Ullah, Md Asad Ferdoush, Jannatul Islam, Saiful Rudra, Bashudev Department of Genetic Engineering and Biotechnology Faculty of Biological Sciences University of Chittagong Chattogram Bangladesh Department of Biochemistry and Molecular Biology Life Science Faculty Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Department of Biochemistry and Molecular Biology Faculty of Agriculture Bangladesh Agricultural University Mymensingh Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Pharmaceutical Chemistry Sinhgad Technical Education Society's Sinhgad College of Pharmacy Vadgaon (Bk) Maharashtra India Department of Pharmacy Life Science Faculty Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Department of Biotechnology and Genetic Engineering Faculty of Biological Sciences Jahangirnagar University Savar Dhaka Bangladesh Department of Pharmacology BGC Trust Medical College Chattogram Bangladesh Bangladesh Council of Scientific and Industrial Research (BCSIR) Chattogram Laboratories Chattogram Bangladesh

Hepatocellular carcinoma (HCC) is the most common malignant condition of the liver that occurs as a result of uncontrolled cellular proliferation after a series of disruptions at cell cycle regulatory checkpoints in the normal cell. Due to the lack of appropriate therapeutics or remedial treatment methods, new treatment strategies against HCC need to be developed. Cyclin dependent kinases (CDKs) are required to control the cell cycle and apoptosis, but their overexpressionis critical in the progression of cancer and is often expressed in HCC. Thus, CDKs are considered a promising class of target-defined therapy for HCC. Milciclib is a potential candidate for HCC which exhibits inhibitory activity against CDK2 leading to cell cycle arrest and apoptosis of tumor cells. Herein, we have halogenated the parent drug milciclib to improve its efficacy against CDK2. The primary structure of milciclib (D) was modified with F, Cl and,CF₃ groups. The frontier molecular orbital features, binding affinity, non-bonded interaction and the pharmacokinetic parameters were analyzed for milciclib and its derivatives. We also performed molecular docking and extended molecular dynamics simulation studies to study the binding interactions and binding affinity more closely. Our computational investigation showed the derivatives D-F and D-CF₃ have significant chemical reactivity, the best binding affinity, nonbonding interactions, and improved pharmacokinetic properties compared to the parent drug milciclib. Molecular dynamics analysis and MM-PBSA calculations indicated that D-Cl had a slightly more stable conformation and higher binding affinity compared to D-CF₃. © 2022 The Authors

关键词： Anticancer drug Halogenated derivatives Hepatocellular carcinoma Milciclib Molecular docking Molecular dynamics simulation Non-bond interactions

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The effect of organic acid hydrolysis on the properties of nanocellulose from edamame husk (Glycine max (L.) merill)

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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The evolution of lithium-ion battery recycling

Nature Reviews Clean Technology

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Nature Reviews Clean Technology 2025年第1期1卷

作者： Xiaotu Ma Zifei Meng Yan Wang Marilena Velonia Bellonia Jeffrey Spangenberger Gavin Harper Eric Gratz Elsa Olivetti Renata Arsenault Mechanical and Materials Engineering Department Worcester Polytechnic Institute Worcester MA USA Technology and Policy Program Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA ReCell Center Argonne National Laboratory Lemont IL USA School of Metallurgy and Materials University of Birmingham Birmingham UK The Faraday Institution Harwell Science and Innovation Campus Didcot UK Ascend Elements Inc. Westborough MA USA Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA USA Ford Ion Park Battery Center of Excellence Romulus MI USA

Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which will require recovering materials from spent LIBs and reintegrating them into new batteries. In this Review, we outline the current state of LIB recycling, evaluating industrial and developing technologies. Among industrial technologies, pyrometallurgy can be broadly applied to diverse electrode materials but requires operating temperatures of over 1,000 °C and therefore has high energy consumption. Hydrometallurgy can be performed at temperatures below 200 °C and has material recovery rates of up to 93% for lithium, nickel and cobalt, but it produces large amounts of wastewater. Developing technologies such as direct recycling and upcycling aim to increase the efficiency of LIB recycling and rely on improved pretreatment processes with automated disassembly and cleaner mechanical separation. Additionally, the range of materials recovered from spent LIBs is expanding from the cathode materials recycled with established methods to include anode materials, electrolytes, binders, separators and current collectors. Achieving an efficient recycling ecosystem will require collaboration between recyclers, battery manufacturers and electric vehicle manufacturers to aid the design and automation of battery disassembly lines.

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Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

arXiv

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arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

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Design and energy assessment of a new hybrid solar drying dome - Enabling Low-Cost, Independent and Smart Solar Dryer for Indonesia Agriculture 4.0

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： F E Gunawan A S Budiman B Pardamean E Djuana S Romeli N Hananda C Harito D P B Aji D N N Putri Stevanus Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT. Impack Pratama Industri Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia. Mechanical Engineering Department. Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia

Solar dryer is typically used for agricultural purposes in Indonesia. There are many economically important crops requiring storage or drying under particular environmental conditions such as temperature and humidity. High temperatures inside solar dryer prevents the growth of microorganism, and quickly reduce moisture content from the substance. A hybrid solar dryer is generally considered to provide the most optimum solution, however solar panels may be expensive and they still only provide heat or energy in the daytime. Hence, we propose here a new kind of hybrid solar dryer for 24/7 optimum conditions for crops - enabled by recent advances in energy technologies as well as Industry 4.0. This study aims to create an efficient, affordable and a self-sufficient intelligent energy system that will be applied to agriculture for storage or drying purposes by measuring the energy needs for the optimal drying system. Therefore, it is crucial to estimate and assess the critical energy needs for such new systems in order to optimize and design such smart solar dryer (SSD) system especially for Indonesia's agricultural needs. We use design experience of our industry partner (PT Impack Pratama Industri, Indonesia) who has been working extensively on such solar dryer dome (SDD) based on polycarbonate material (only solar irradiation, no other technologies) and theoretical framework based on first principles in thermodynamics to estimate and assess critical energy needs for such dome with all the smart technologies. The calculation was performed based on Mollier diagram and the result still a rough estimation of energy required.

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Clustering in Networks Via Kernel-ARMA Modeling and the Grassmannian: The Brain-Network Case

arXiv

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arXiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Patil, Pratik V. Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States Neuroscience Program University at Buffalo BuffaloNY14260 United States Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program University at Buffalo SUNYNY14260 United States Department of Psychology Drexel University Perelman School of Medicine University of Pennsylvania PA19104 United States

This paper introduces a clustering framework for networks with nodes are annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or community detection), and even subnetwork-state-sequence identification/tracking. Via a bottom-up approach, features are first extracted from the raw nodal time-series data by kernel autoregressive-moving-average modeling to reveal non-linear dependencies and low-rank representations, and then mapped onto the Grassmann manifold (Grassmannian). All clustering tasks are performed by leveraging the underlying Riemannian geometry of the Grassmannian in a novel way. To validate the proposed framework, brain-network clustering is considered, where extensive numerical tests on synthetic and real functional Magnetic Resonance Imaging (fMRI) data demonstrate that the advocated learning framework compares favorably versus several state-of-the-art clustering schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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Anomaly Detection in Climate data Using Stacked and Densely Connected Long Short-Term Memory Model

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Journal of Computers (Taiwan) 2020年第4期31卷 42-53页

作者： Saleh Abbas, Bahtiar Wahyono, Teguh Heryadi, Yaya Soeparno, Haryono Doctor of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projected that the rising global temperature will increase the draught, especially in the mid-latitude areas. Taking those problems into account, studies on anomaly detection in climate are crucial. While climate prediction aims to analyze and model regular pattern of climate, climate anomaly studies aim to model climate deviation from its previous general patterns. Long Short Term Memory (LSTM) is a method employed in this research because it has been proven to work effectively in several anomaly detection studies, especially for data with similar characteristics. This paper presents empirical results of using Basic LSTM, Densely Connected (DC) LSTM, and Stacked-DC LSTM models to detect temperature anomaly as a manifestation of climate change in Semarang City. The results show that Stacked DC LSTM produced higher accuracy in detecting anomaly than the other two methods. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Long short-term memory

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