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Determining the robust counterpart of uncertain spatial optimization model for water supply allocation problem

AIP Conference Proceedings 2016年第1期1716卷

作者： D. Chaerani B. N. Ruchjana S. P. Dewanto A. S. Abdullah J. Rejito Y. Rosandi I. A. Dharmawan 1Study Program in Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia 2Study Program in Informatics Engineering Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia 3Study Program in Geophysics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia

In this paper we discuss how to get the robust counterpart for the spatial optimization model for water supply allocation (SOMWSA) problem that is proposed by [6]. We employ the robust counterpart methodology that is proposed by Ben-Tal and Nemirovskii [2] also use the new paradigm of robust counterpart methodology of den Hertog et al. [4]. To this end, first we define the uncertain family class of SOMWSA and derive the robust version of the uncertain problem. We assume that there are two uncertain data in SOMWSA problem, ie., the total population and the water demand in a region i at time t. Since the tractability of the problem is very important in Robust Optimization, we discuss three types of uncertainty set, i.e., box, ellipsoidal and polyhedral uncertainty set.

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Heart sound feature extraction and classification using autoregressive power spectral density (AR-PSD) and statistics features

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AIP Conference Proceedings 2016年第1期1755卷

作者： Domy Kristomo Risanuri Hidayat Indah Soesanti Adi Kusjani 1Department of Electrical Engineering and Information Technology Faculty of Engineering Gadjah Mada University Jalan Grafika No. 2 Yogyakarta 55281 Indonesia 2Study Program of Computer Engineering STMIK AKAKOM of Yogyakarta Jalan Raya Janti 143 Karang Jambe Yogyakarta 55198 Indonesia

Heart auscultation is a screening method done by listening using a stethoscope for early diagnosis of heart disease, it is low cost and non-invasive, but it has a limitation of human hearing. This paper presents a noise-robust feature extraction method by combining and selecting a heart sound (HS) feature in time and frequency domain. Wavelet decomposition (WD) is used for noise removal. Features are extracted by AR-PSD and used as inputs for classification. The nine types of abnormal HS that were taken from Michigan Heart Sound Database were classified into nine categories. In this research, statistics features in time and frequency domain are used as additional features. Correlation-based Feature Selection (CFS) is used to select the best feature among 13 features extracted. The performance of a new feature set is called Feature Set 3 compared to CFS. The result shows that the proposed feature set achieves the highest level of accuracy.

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The simulation of organic rankine cycle power plant with n-pentane working fluid

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AIP Conference Proceedings 2016年第1期1712卷

作者： Otong Nurhilal Cukup Mulyana Nendi Suhendi Didi Sapdiana 1Study Program of Physics Padjadjaran University Jalan Raya Bandung-Sumedang Km. 21 Jatinangor West Java 45363 Indonesia 2Study Program of Electrical Engineering Padjadjaran University Jalan Raya Bandung-Sumedang Km. 21 Jatinangor West Java 45363 Indonesia

In the steam power plant in Indonesia the dry steam from separator directly used to drive the turbin. Meanwhile, brine from the separator with low grade temperature reinjected to the earth. The brine with low grade temperature can be converted indirectly to electrical power by organic Rankine cycle (ORC) methods. In ORC power plant the steam are released from vaporization of organic working fluid by brine. The steam released are used to drive an turbine which in connected to generator to convert the mechanical energy into electric energy. The objective of this research is the simulation ORC power plant with n-pentane as organic working fluid. The result of the simulation for brine temperature around 165°C and the pressure 8.001 bar optained the net electric power around 1173 kW with the cycle thermal efficiency 14.61% and the flow rate of n-pentane around 15.51 kg/s. This result enable to applied in any geothermal source in Indonesia.

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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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Speciation and bioavailability of some heavy metals in agricultural soils used for cultivating various vegetables in Bedugul, Bali

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AIP Conference Proceedings 2016年第1期1718卷

作者： I. Made Siaka I. Made Supartha Utama I. B. Putra Manuaba I. Made Adnyana Emmy Sahara 1Chemistry Department of Maths and Sciences Faculty Udayana University Bukit Campus Jimbaran-Bali Indonesia 2Study Program of Agriculture Engineering Faculty of Agricultural Technology Udayana University Bukit Campus Jimbaran-Bali Indonesia 3Agricultural Soil Department of Agricultural Faculty Udayana University Jl. P.B. Sudirman Denpasar-Bali Indonesia

This paper discusses the speciation and bioavailability of some heavy metals in agricultural soils used to cultivate various vegetables in Bedugul, Bali. Vegetables grown on contaminated soils where agrochemicals were applied uncontrolled could contain a number of heavy metals. This could occur in the vegetables produced from agricultural soils of Bedugul as the farmers applied agrochemicals excessively. In considering the metals transport to the vegetables, a speciation and bioavailability methods were necessary to be studied. Wet digestion and sequential extraction techniques were employed to the sample prior to the metals measurement by AAS. The results showed that the average concentrations of Pb, Cu, Cd, Cr, and Zn in the soils were 38.531, 132.126, 7.689, 15.952, and 147.275 mg/kg, respectively. The highest concentrations of Pb and Zn were found in the soil for cultivating lettuce, Cd and Cr in the soil for tomato, and Cu in the soil for potatoes. It was found that the speciation of Pb, Cu, Cd, and Cr were predominantly bound to Fe-Mn oxides fraction, while Zn was mostly associated with the EFLE (easily, freely, leachable, and exchangeable) fractions. The highest bioavailability among the metals in the studied soils was Cr, while the lowest was Cu.

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GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data

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AIP Conference Proceedings 2016年第1期1711卷

作者： Harman Setyadi Lilik Eko Widodo Sudarto Notosiswoyo Putri Saptawati Arief Ismanto Iip Hardjana 1Doctorate Student Mine Engineering Study Program – Faculty of Mine and Petroleum Engineering Institute Technology Bandung Jl. Ganesa 10 Bandung 40132 - INDONESIA 2Earth Resources Exploration Group - Faculty of Mine and Petroleum Engineering Institute Technology Bandung Jl. Ganesa 10 Bandung 40132 – INDONESIA 3Informatics– STIE Institute Technology Bandung Jl. Ganesa 10 Bandung 40132 - INDONESIA 4J. Resources Nusantara Equity Tower 48th floor SCBD. Jl. Jendral Sudirman Kav 52-53 Jakarta 12190 - INDONESIA

The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Contrast of physical properties of every different rock type should be able to be distinguished by the geophysical data. Fuzzy logic approach and weight of evidence were used for geophysical data modeling. Posterior probability was used to calculate the weight of evidence (WofE) of every fuzzy map memberships. By combining each rock type model, the model provides better result compared from the model from mixed rock type on the data training. This method is able to eliminate the potential interference of different geophysical signature. So that, the understanding the geological feature of the area is key success for the mineral prosperity modeling. We verified the model by site visiting and drilling and it is estimated about 90% confident.

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Graph Artificial Intelligence in Medicine

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Biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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