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Developing sonication-based method as a preliminary qualitative study to analyze methamphetamine in the hair of the user at Datuk Etham rehabilitation center

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AIP Conference Proceedings 2020年第1期2267卷

作者： Nur Asyiah Dalimunthe Zul Alfian Basuki Wirjosentono Eddiyanto 1Doctoral Program Departement of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia 2Departement of Chemical Engineering Akademi Teknik Indonesia Cut Meutia Medan Indonesia 3Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia 4Department of Chemistry Universitas Negeri Medan Medan 20371 Indonesia

Determination of methamphetamine in the user can be done by analyzing their urine, blood, and hair. In this study, the analysis has been performed using the user's hair based on the advantage that the analysis still can be performed, even after 27 h. The effect of solvent to perform the qualitative study has been conducted at certain day, i.e. 0-12th, 30th, 60th, and 90th day. The reference methamphetamine was obtained from Laboratory of the Technical Service Unit Technical Services Testing and Field Identification Agency (BPIB). The analysis was begun with decontamination, destruction, sonication and Marquis test for qualitative data. Solvent that used in the sonication treatment showed a significant role, especially by using a mixture of methanol: acetone: ammonia.

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g-circulant matrix and its matrix-vector multiplication algorithm on transformer neural network

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AIP Conference Proceedings 2024年第1期3201卷

作者： Euis Asriani Intan Muchtadi-Alamsyah Ayu Purwarianti Doctoral Program of Mathematics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Algebra Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing and Big Data Analytics (U-CoE AI-VLB) Institut Teknologi Bandung Indonesia Informatics Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

A g-circulant matrix is an n-dimensional matrix Cn,g that can be expressed as Cn,g=[c(gr−s)modn]r,s=0n−1 . This study will investigate the use of g-circulant matrices as weight matrices on transformer neural networks for machine translation. On transformer model tested, the g-circulant weight matrix undergoes multiplication with any vector and involves the DCT-DST algorithm. This algorithm is proposed as a substitute for the commonly used FFT algorithm. For this reason, an orthogonal matrix Un,g was defined by multiplying the Un matrix and appropriate permutation matrix, where Un matrix is a matrix that is spanned by the combination of the real and imaginary parts of eigenvectors of the real 1-circulant matrix. Then we form a Schur decomposition of the g-circulant matrix, composes the Qn,g matrix, and using the DCT-DST to formulate the multiplication of Qn,gUn,g. We discovered that 2-circulant matrices are outperform compared to the other g-circulant matrices and the original model of transformer in terms of accuracy and loss training.

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Identification of Groundwater Potential Zones in Samendeni Watershed in Sedimentary and Semi-Arid Contexts of Burkina Faso, Using Analytic Hierarchy Process (AHP) Method and GIS

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American Journal of Climate Change 2023年第1期12卷 172-203页

作者： Sadraki Yabre Youssouf Koussoubé Sauret Élie Serge Gaëtan Nicaise Yalo Stephen Silliman West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL) Doctoral Research Program in Climate Change and Water Resources Institut National de l’eau Université d’Abomey Calavi Cotonou Bénin Laboratoire Géosciences et Environnement (LaGE) Département des Sciences de la Terre Université Joseph KI-ZERBO/Unité de Formation et de Recherche en Sciences de la Vie et de la Terre Ouagadougou Burkina Faso Centre National de Recherche Scientifique et Technologique Institut de l’Environnement et de Recherches Agricoles-Département Gestion des Ressources Naturelles/Systèmes de Productions DRREA-Ouest Station de Farako-ba Bobo-Dioulasso Burkina Faso Laboratoire d’Hydrologie Appliquée (LHA) Institut National de l’eau Université d’Abomey Calavi Cotonou Bénin School of Science Technology Engineering and Math Trevecca Nazarene University Nashville Tennessee USA

Demand for water increases in Samendeni regarding the undertaken agricultural projects while pressure on surface water from global warming/evapotranspiration also increases. Thus, the need to evaluate the groundwater potential in the catchment is crucial as alternative supplier of water and resilience to climate hazards. The AHP was performed integrating ten influencing factors such as geomorphology, geology, soil, land use/land cover (lulc), slope, rainfall, drainage density, borehole rate & depth and piezometric level to generate groundwater potential zones (GWPZs) in Samendeni watershed (4420 km²). All the factors were processed and ranged into five (5) classes. Weight was assigned to each class of thematic layer. These thematic layers were then reclassified based on the normalized weight to be used in the calculation of groundwater potential zones (GWPZ). The final output, groundwater potential map, revealed a significant groundwater potential with very good (11%), good (31%), moderate (30%), poor (20%), and very poor (8%) of proportion. The interesting (very good, good) GWPZs in the study area are mostly in the central towards the east. The poor zones in term of groundwater potential are concentrated in the upper west region of the watershed. Besides the cross-validation with the relationship between different groundwater potential zones and the wells available in the study area, the overall accuracy was estimated to 88% provided from the result of the similarity analysis where 22 out of the 25 validation wells match with the expected yield classes of GWPZs. The statistics from that validation revealed the performance of AHP method to delineate groundwater potential zones at catchment level.

关键词： Climate Change Resilience Groundwater Potential Water Management Conjunctive Use AHP GIS Samendeni Watershed

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From Dissemination to Propagation: A New Paradigm for Education Developers

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Change 2017年第4期49卷 35-42页

作者： Froyd, Jeffrey E. Henderson, Charles Cole, Renée S. Friedrichsen, Debra Khatri, Raina Stanford, Courtney Jeffrey E. Froyd is a Research Professor at Texas A&M University. His research interests include sustained adoption of improvements in learning and teaching systematic reviews in engineering education evaluation of faculty campus climates and assessment of complex learning outcomes. Charles Henderson is a Professor at Western Michigan University with a joint appointment between the Department of Physics and the Mallinson Institute for Science Education. His research focuses on promoting productive changes in higher education at the individual department institution and national levels. Renée S. Cole is an Associate Professor of Chemistry at the University of Iowa. She teaches introductory and advanced courses in chemistry and runs a research program focused on the design and assessment of instructional materials and teaching strategies and faculty professional development. Debra Friedrichsen founded and operates a small consulting company. She received her Ph.D. from Oregon State University in Chemical Engineering with a dissertation focused on engineering education. Debra also has a MBA a MS and a patent for sensor development. Raina Khatri is a doctoral student in the Mallinson Institute for Science Education at Western Michigan University. Her research focus is understanding how education developers in STEM can promote sustained adoption of their innovations. Courtney Stanford is a postdoctoral researcher in the Department of Chemistry at Virginia Commonwealth University. Her current research focuses on the identification development and assessment of process skills (also known as professional skills) in active learning undergraduate STEM classrooms.

Scholarly studies and national reports document failure of current efforts to achieve broad, sustained adoption of research-based instructional practices, despite compelling bodies of evidence supporting efficacy of many of these practices. The authors of this paper argue that many change agents who are working to promote systemic adoption of these teaching practices use approaches that are characterized by what the authors call the "dissemination paradigm." A dissemination paradigm characterizes patterns of these current, failing efforts. Change agents, working within the dissemination paradigm, try to convince adopters that their innovations can help their students. Efforts within the dissemination paradigm have not led, and are unlikely to lead, to systemic adoption. The authors offer the "propagation paradigm" as an alternative with the potential to produce improved results. Change agents, working within the propagation paradigm, engage with adopters early and often to understand their instructional systems and interactively develop a strong product adaptable to specific contexts.

关键词： Models Change Agents Teaching Methods Information Dissemination Educational Innovation Instructional Systems STEM Education Comparative Analysis

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Knowledge, Awareness, and Resilience of Earthquake and Tsunami Disaster Risk Reduction in Coastal Area

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Journal of Physics: Conference Series 2021年第1期1811卷

作者： Rr M I Retno Susilorini Rina Febrina Helmia Adita Fitra Juniastel Rajagukguk Dhiyan Krishna Wardhani B Y Arya Wastanimpuna Laurel Lia Nola Prameswari Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang 50234 Central Java Indonesia Department of Civil Engineering Faculty of Engineering Malahayati University Bandar Lampung 35153 South Sumatera Indonesia Department of Regional and Urban Planning Sumatera Institute of Technology South Lampung 35365 South Sumatera Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Negeri Medan Medan 20221 North Sumatera Indonesia Doctoral Program of Digital Architecture Faculty of Architecture and Design Soegijapranata Catholic University Semarang 50234 Central Java Indonesia

Indonesia is earthquake and tsunami prone areas, included Sumatera Island. Bandar Lampung is also vulnerable to earthquake and tsunami, especially Kangkung fishing village in Lampung Bay. Disaster risk reduction is very important to be implemented in the cases of earthquake and tsunami, which can save more life and build resilience in facing the hazard. This research aimed to study how the knowledge, awareness, and resilience of people in Kangkung fishing village can strengthen the earthquake and tsunami disaster risk reduction. This research conducted by quantitative methods. Survey and interview of 53 respondents were conducted as well as data collecting approached by purposive sampling. The results found that there is little knowledge and awareness of people in Kangkung fishing village of the hazard of earthquake and tsunami. Therefore, this research proposed a model of 'Community-Based Resilience Strategy' Model of earthquake and tsunami disaster risk reduction. It is a comprehensive and sustainable model of how people take a part in disaster risk reduction by intensive participatory. Conclusion of this research emphasized that disaster risk reduction is a big matter in marginalized society such as people in Kangkung fishing village. The 'Community-Based Resilience Strategy' Model of earthquake and tsunami disaster risk reduction that may increase the knowledge and awareness to become more resilience.

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Implementation of Various Artificial Intelligence Approach for Prediction and Recommendation of Personality Disorder Patient

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Journal of Physics: Conference Series 2021年第1期1751卷

作者： H Sulistiani K Muludi A Syarif Doctoral Program of Mathematics and Natural Sciences Faculty University of Lampung Jl. Sumantri Brojonegoro No. 1 Bandar Lampung Indonesia. Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagaralam No. 9-11 Bandar Lampung Indonesia. Department of Computer Science Faculty of Mathematics and Science University of Lampung Jl. Sumantri Brojonegoro No. 1 Bandar Lampung Indonesia.

Personality disorder is known as chronic psychological disorders. It is a serious problem of the individual character and behavior that affect work, family and social life of a person. In many cases, the possibility of people do not realize that they have a personality disorder as a way of thinking and behaving seems natural to the patient, and the patient may blame others. To diagnose a personality disorder early, it is necessary to develop a system that is able to predict personality disorders and recommendation type of therapy patients should undergo. Artificial Intelligence is a technique that is very popular today. Various kinds of approaches and algorithms can be applied in the world of health, such as expert systems, neural networks and fuzzy logic. The aim of this study is to assess the artificial intelligence approach in predicting and recommending the type of therapy for patients with personality disorders.

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Prediction of traffic congestion based on time series dataset number of vehicles using neural network algorithm

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AIP Conference Proceedings 2024年第1期2982卷

作者： Prasetyo Wibowo Yunanto Rahmat Gernowo Oky Dwi Nurhayati Doctoral Program of Information System Diponegoro University Jl. Imam Bardjo SH No.5 Semarang Indonesia Information Systems and Technology Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta Indonesia Department of Physics Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia Computer Engineering Department Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia

The increasing number of vehicles that was not accompanied by an adequate road infrastructure readiness causes traffic jams. Traffic jams often occur repeatedly every day, especially at certain times, such as when people are going to and from work. The same traffic jams can also occur at the beginning or the end of the week; this usually repeats every week. As a result, if the congestion dataset is known, the repeating traffic congestion for daily and weekly congestion can be anticipated. In this study, traffic congestion predictions are modeled using the Neural Network Algorithm based on traffic congestion data collected within 24 hours for one to two weeks. The parameters used in optimizing Neural Network performance are learning rate, momentum, and epochs (training cycles). Based on these experiments, the Neural Network Algorithm successfully modeled traffic density patterns which are recurring congestion patterns, with quite good results.

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Preface

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Journal of Hydrology 2007年第1期333卷 1-2页

作者： Sugita, Michiaki Azzaya, Dolgorsuren Adyasuren, Tsokhio Brutsaert, Wilfried University of Tsukuba Japan Doctoral Program in Geoenvironmental Sciences Graduate School of Life and Environmental Sciences University of Tsukuba Tsukuba Ibaraki 305-8572 Japan Institute of Meteorology and Hydrology National Agency for Meteorology Hydrology and Environment Monitoring of Mongolia Khudaldaany Gudamji-5 Ulaanbaatar-46 Mongolia Environmental Education and Research Institute ECO Asia Ulaanbaatar Mongolia School of Civil and Environmental Engineering Cornell University Ithaca NY 14853-3501 United States

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The Role of MgO as Swelling Cements Admixtures under CO2 Attack on API Class-G Cement

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Journal of Physics: Conference Series 2019年第1期1245卷

作者： Juniarto M Palilu Bambang Soegijono Bonar T H Marbun Doctoral Program Department of Physics Universitas Indonesia Depok Jawa Barat- 16424 Indonesia Oil and Gas General Directory Ministry of Energy and Mineral Resources Gedung Migas Jl. HR Rasuna Said Kav. B-5 Jakarta 12910 Indonesia Department of Physics Universitas Indonesia Depok Jawa Barat 16424 Indonesia Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Jl. Ganesha 10 Bandung Indonesia

API Class-G Portland cement commonly put in as zonal isolation material in oil and/or gas well. Corrosion against the well zonal isolation material could lead to potential leakage of formation fluid such oil, gas, or water. It is widely known that ordinary Portland cement is thermodinamically unstable in CO2 rich environment, which can be found in the CO2 geological sequestration. CO2 attack on API Class-G cement had been observed through experiments that simulate the down hole condition of Geological Sequestration of CO2. Some of the experiment reveal the detrimental effect of CO2 such as increase of porosity and permeability of cement sheath corroded by CO2 attack. The degradation of API Class-G cement by CO2 involves emergence and crystallization of CaCO3 from Ca(OH)2 which is formed during hydration of cement. In this experiment, we examine the role of addition of MgO swelling cement by weight fraction to mitigate the corrosion caused by CO2 against downhole hardened API Class-G cement thus inhibits the occurrence of leakage path of CO2. Observation for characterization and analysis by XRD, optical microscope, and cement paste uniaxial volumetric expansion presented in this paper. The conclusion will provide insights into design of carbonation resistant cement slurries using swelling cements admixtures.

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Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study

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Orthopaedic Journal of Sports Medicine 2024年第3期12卷 23259671241231609页

作者： Chou, Wen-Yi Cheng, Jai-Hong Lien, Yu-Jui Huang, Tian-Hsiang Ho, Wen-Hsien Chou, Paul Pei-Hsi Doctoral Degree Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopedic Surgery Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Leisure and Sport Management Cheng Shiu University Kaohsiung Taiwan Center for Shockwave Medicine and Tissue Engineering Department of Medical Research Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Sports Medicine Department of Orthopaedic Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Orthopaedics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopaedic Surgery Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Taiwan

Background: Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment. Purpose: To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting. Study Design: Case-control study. Methods: Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier. Results: A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm. Conclusion: The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT. © The Author(s) 2024.

关键词： calcific tendinitis data mining extracorporeal shockwave therapy J48 decision tree shoulder

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