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Application for sugarcane plantation: Manpower and transportation planning

AIP Conference Proceedings 2023年第1期2594卷

作者： H. G. Mawandha D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Sugarcane plantations in Indonesia are still managed in a labor-intensive manner, especially plantation-owned by farmers. Labor intensive means that every stage of work in sugarcane plantation management still uses manpower. One of the jobs in sugarcane plantations that requires a lot of labor is sugar cane harvesting. Trend changing in the workforce gender would affect the quantity and quality of jobs, which is determined by the workforce's capacity and the workload of the work performed. The result of this research was a sugarcane agricultural calculator that made it simple for farmers to measure manpower and transportation requirements in estimating harvest costs. Manpower and transportation of harvested sugarcane commodities were estimated through the workload approach, namely, crop production and manpower's ability to carry a load. These two components helped sugarcane farmers to determine manpower and transportation during the harvest. The final yield produced by the projected harvest labor cost would be known immediately.

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia Computer science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study

arXiv

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arXiv 2022年

作者： Ning, Yilin Li, Siqi Ong, Marcus Eng Hock Xie, Feng Chakraborty, Bibhas Ting, Daniel Shu Wei Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Risk scores are widely used for clinical decision making and commonly generated from logistic regression models. Machine-learning-based methods may work well for identifying important predictors, but such ‘black box’ variable selection limits interpretability, and variable importance evaluated from a single model can be biased. We propose a robust and interpretable variable selection approach using the recently developed Shapley variable importance cloud (ShapleyVIC) that accounts for variability across models. Our approach evaluates and visualizes overall variable contributions for in-depth inference and transparent variable selection, and filters out non-significant contributors to simplify model building steps. We derive an ensemble variable ranking from variable contributions, which is easily integrated with an automated and modularized risk score generator, AutoScore, for convenient implementation. In a study of early death or unplanned readmission, ShapleyVIC selected 6 of 41 candidate variables to create a well-performing model, which had similar performance to a 16-variable model from machine-learning-based ranking. © 2022, CC BY.

关键词： Machine learning

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Single-cell analysis of angiotensin-converting enzyme II expression in human kidneys and bladders reveals a potential route of 2019 novel coronavirus infection

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Chinese Medical Journal 2021年第8期134卷 935-943页

作者： Wei Lin Jue Fan Long-Fei Hu Yan Zhang Joshua DOoi Ting Meng Peng Jin Xiang Ding Long-Kai Peng Lei Song Rong Tang Zhou Xiao Xiang Ao Xiang-Cheng Xiao Qiao-Ling Zhou Ping Xiao Yong Zhong Department of Pathology Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Bioinformatics and Data Science Singleron BiotechnologiesNanjingJiangsu 210032China Centre for Inflammatory Diseases Monash University Department of MedicineMonash Medical CentreClaytonVIC 3168Australia Department of Nephrology Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Organ Transplantation Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Kidney Transplantation The Second Xiangya Hospital of Central South UniversityChangshaHunan 410011China

Background::Since 2019,a novel coronavirus named 2019 novel coronavirus(2019-nCoV)has emerged *** from fever and respiratory complications,acute kidney injury has been observed in a few patients with coronavirus disease ***,according to recent findings,the virus has been detected in ***-converting enzyme II(ACE2)has been proposed to serve as the receptor for the entry of 2019-nCoV,which is the same as that for the severe acute respiratory *** study aimed to investigate the possible cause of kidney damage and the potential route of 2019-nCoV infection in the urinary ***::We used both published kidney and bladder cell atlas data and new independent kidney single-cell RNA sequencing data generated in-house to evaluate ACE2 gene expression in all cell types in healthy kidneys and *** Pearson correlation coefficients between ACE2 and all other genes were first ***,genes with r values larger than 0.1 and P values smaller than 0.01 were deemed significant co-expression genes with ***::Our results showed the enriched expression of ACE2 in all subtypes of proximal tubule(PT)cells of the ***2 expression was found in 5.12%,5.80%,and 14.38%of the proximal convoluted tubule cells,PT cells,and proximal straight tubule cells,respectively,in three published kidney cell atlas *** addition,ACE2 expression was also confirmed in 12.05%,6.80%,and 10.20%of cells of the proximal convoluted tubule,PT,and proximal straight tubule,respectively,in our own two healthy kidney *** the analysis of public data from three bladder samples,ACE2 expression was low but detectable in bladder epithelial *** 0.25%and 1.28%of intermediate cells and umbrella cells,respectively,had ACE2 ***::This study has provided bioinformatics evidence of the potential route of 2019-nCoV infection in the urinary system.

关键词： 2019-nCoV Acute kidney injury Angiotensin-converting enzyme 2 Bladder COVID-19 Kidney RNA sequence analysis Single-cell analysis

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

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Nature Communications 2023年第1期14卷 6870页

作者： Cai, Mingxuan Wang, Zhiwei Xiao, Jiashun Hu, Xianghong Chen, Gang Yang, Can Department of Biostatistics City University of Hong Kong Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou 511458 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data Shenzhen 518172 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China WeGene Shenzhen Zaozhidao Technology Co. Ltd Shenzhen 518040 China Graduate Affairs Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution. © 2023, The Author(s).

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Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference

arXiv

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arXiv 2021年

作者： Lundén, Daniel Öhman, Joey Kudlicka, Jan Senderov, Viktor Ronquist, Fredrik Broman, David EECS and Digital Futures KTH Royal Institute of Technology Stockholm Sweden AI Sweden Stockholm Sweden Department of Data Science and Analytics BI Norwegian Business School Oslo Norway Department of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden Department of Zoology Stockholm University Stockholm Sweden

Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling probabilistic checkpoints in PPLs through continuation-passing style transformations or non-preemptive multitasking—as is done in many popular PPLs—often disallows compilation to low-level languages required for high-performance platforms such as GPUs. To solve the checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs)—a simple and efficient approach to checkpoints in low-level languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs and illustrate its application when compiling Miking CorePPL—a high-level universal PPL—to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6× speedups over state-of-the-art PPLs implementing SMC inference. Copyright © 2021, The Authors. All rights reserved.

关键词： C++ (programming language)

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Unveiling roles of non-coding RNAs in cancer through advanced technologies

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Quantitative Biology 2025年第4期13卷 e70005-e70005页

作者： Wang, Runhao Han, Leng Brown Center for Immunotherapy Indiana University School of Medicine Indianapolis IN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN United States Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN United States

Non-coding RNAs (ncRNAs) have emerged as key regulators in tumorigenesis. In this perspective, we briefly review the significance of ncRNA in cancer biology and highlight recent technological advancements in characterization of ncRNA in cancer research. Specifically, we discuss how these advanced approaches, such as Patho-DBiT, CRISPR screens, and snoKARR-seq, hold the potential to revolutionize ncRNA research by offering comprehensive insights into their spatial expression patterns and functional roles. © 2025 The Author(s). Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.

关键词： cancer CRISPR screens ncRNAs patho-DBiT snoKARR-seq spatial transcriptomics

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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