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International Conference on Information science and Control engineering (ICISCE)

作者： Obed Tettey Nartey Guowu Yang Sarpong Kwadwo Asare Jinzhao Wu Dorothy Araba Yakoba Agyapong Lady Nadia Frempong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mathematics and Physics Guangxi University for Nationalities Nanning China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method; novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation.

关键词： Training Deep learning Image segmentation Information science Three-dimensional displays Computed tomography Liver

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A Neural Network Based Modeling of Closed Room Thermal Comfort Environmental Prediction for Sensor Hub

A Neural Network Based Modeling of Closed Room Thermal Comfo...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON

作者： Pornpawee Thongkhome Natasha Dejdumrong Science-Mathematics Program Triamudomsuksa Pattanakarn School Bangkok Thailand Dept. of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164878

Currently, environmental protection and intelligence system are topics of interest in smart homes and intelligent buildings. This work represents an environmental prediction study of the closed room which installed a split-type air-conditioning system and a newly designed sensor hub using a feed-forward back-propagation artificial neural network-based prediction for human thermal comfort and indirectly concerned to the growth or existence of diseases, contagious diseases and allergies. The RPROP algorithm was utilized to train the closed room environmental parameters that get from sensor hub especial relative humidity in the ANN model and validate the model by using cohen's kappa and ROC curve to represents the degree of accuracy and reliability. The result obtained from the ANN trained data agreed closely with those obtained from a sensor hub with the high perfect agreement. The benefit of the ANN-based strategy in this study could be utilized for the design of a sensor hub that can realize the adaptive adjustment of environmental parameters through the air conditioning system.

关键词： Humidity Artificial neural networks Predictive models Buildings Temperature sensors Computational modeling Prediction algorithms

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Liquid-liquid transition in water from first principles

arXiv

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arXiv 2022年

作者： Gartner, Thomas E. Piaggi, Pablo M. Car, Roberto Panagiotopoulos, Athanassios Z. Debenedetti, Pablo G. Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States

A longstanding question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Supplier Selection of Companies Providing Micro Mobility Service Under Type-2 Neutrosophic Number Based Decision Making Model

SSRN

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SSRN 2023年

作者： Önden, İsmail Deveci, Muhammet Kara, Karahan Yalçın, Galip Cihan Önden, Abdullah Eker, Mert Hasseb, Mouad Smart System Research Group of TUBITAK TUSSIDE Turkey Department of Industrial Engineering Turkish Naval Academy National Defence University Turkey The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Artvin Çoruh Universty Hopa Vocational School Logistics Program Artvin Turkey Kırıkkale University Faculty of Mathematics Institute of Science and Technology Kırıkkale Turkey Department of International Trade and Finance Yalova University Turkey Fabrikod Yalova University Turkey

Environmental approaches play an active role in the production platform as well as in the supply chain. Studies that deal with supplier selection problems in the supply chain with a green focus have recently intensified. In this research, the supplier selection problem of companies providing "last mile" service, which is seen as the last step of transportation, is handled with a green focus. The supplier selection problem of these companies is differentiated according to strategic and non-strategic product groups. In this study, a green-oriented supplier selection proposal is presented for both the strategic product group and the non-strategic product group. The supplier selection problem is accepted as a multi-criteria decision making problem (MCDM). Thus, the type-2 neutrosophic numbers (T2NNs) - based CRiteria Importance Through Intercriteria Correlation (CRITIC) – based Multi-Attributive Border Approximation Area Comparison (-MABAC) hybrid model is applied to evaluate the green-oriented supplier. The motivation of the study is to determine the best alternative supplier type for strategic and non-strategic products groups. In this context, two cases are created, ten criteria are selected, and three supplier types are determined. Supplier types are as follows: (i) Classical suppliers, (ii) Classical to green centric suppliers, and (iii) Start-Ups & green centric suppliers. In addition, the findings are strengthened by creating twenty-two sensitivity analysis scenarios. As a result of the research, solution proposals for both product groups of the last mile service company are developed. © 2023, The Authors. All rights reserved.

关键词： Supply chains

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Occurrence of Covid-19 Personal Protective Equipment (Ppe) Litters Along the Eastern Coast of Palawan Island, Philippines

SSRN

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SSRN 2022年

作者： Sajorne, Recca E. Cayabo, Genese Divine B. Madarcos, John Roderick V. Madarcos, Karen G. Omar, Dawin M. Ardines, Lucio B. Sabtal, Serdon A. Mabuhay-Omar, Jhonamie A. Cheung, Victoria Creencia, Lota A. Bacosa, Hernando P. College of Fisheries and Aquatic Sciences Western Philippines University Puerto Princesa Campus Palawan Puerto Princesa5300 Philippines College of Engineering Architecture and Technology Palawan State University Palawan 5300 Philippines Mindanao State University Main Campus Bataraza Extension Bataraza Palawan 5306 Philippines University of Plymouth Drake Circus South West England PlymouthPL4 8AA United Kingdom Environmental Science Program Department of Biological Sciences College of Science and Mathematics Mindanao State University Iligan Institute of Technology Lanao del Norte Iligan9200 Philippines

The emergence of the COVID-19 pandemic has caused worldwide health constraints. To prevent virus transmission, the use of personal protective equipment (PPE) has become one of the necessities of mankind throughout the pandemic. Because of its widespread use and improper disposal, there is an occurrence of PPE litters in various coastal sites around the world. However, the prevalence of these wastes has not been systematically documented in many countries including the Philippines. This study aimed to determine the density, types and accumulation of of PPE litters along the coast of Palawan Island, Philippines. Sampling was done by visual identification and collection of COVID-19 PPE items by walking along the established transects in each sampling site. PPE items collected were classified and sorted into categories. A total of 386 COVID-19 related PPE items were collected over a cumulative area of 48,200 m 2 , with a density of 0.008 items per m 2 . The face mask (98%;n=377) was the predominant type of PPE and out of the total items of face masks, 98% (n=370) were surgical masks while only 2% (n=7) were cloth. On the other hand, the daily density of PPE litters in San Manuel, Puerto Princesa ranged from 0 to 0.099 items per m 2 , with a mean density of 0.008 items per m 2 . The accumulation rates of PPE items ranged from 0.327 items to 1.143 items per day, with an average rate of 0.729 items per day. It was also observed that the prevalence of PPE items decreases over time, however, there was a remarkable increase in PPE density (0.011 items per m 2 ) on day 5 (Monday). Overall, this study revealed that 83% of the coastal sampling sites showed the prevalence of COVID-19 PPE litters. © 2022, The Authors. All rights reserved.

关键词： Coronavirus

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Correction to: Mitigation strategies against cascading failures within a project activity network

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Journal of Computational Social science 2021年第1期5卷 1097-1098页

作者： Ellinas, Christos Nicolaides, Christos Masuda, Naoki Nodes and Links Ltd Cambridge UK Department of Business and Public Administration University of Cyprus Nicosia Cyprus Initiative on the Digital Economy Massachusetts Institute of Technology Cambridge USA Department of Mathematics State University of New York at Buffalo Buffalo USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo USA Faculty of Management and Economics Dalian University of Technology Dalian China

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Study of area change and rehabilitation of mangrove ecosystems in Karimunjawa National Park

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AIP Conference Proceedings 2023年第1期2683卷

作者： M. Arief Rahman Halim Tri Retnaningsih Soeprobowati Hadiyanto Hadiyanto Luri Nurlaila Syahid Master Program of Environmental Science School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Biology Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Geodesy and Geomatics Remote Sensing and Geographic Information Science Research Group Faculty of Earth Science and Technology Institut Teknologi Bandung Bandung Indonesia

The mangrove ecosystem is one of the unique and distinctive ecosystems because it is a compelling natural resource. However, in the current era, many people are greedy, resulting in the conversion of mangrove land, which causes a decrease in the area of the mangrove ecosystem. This study aims to determine changes in the extent of the mangrove ecosystem in Karimunjawa National Park and the appropriate rehabilitation efforts there. The method used in this research is a case study with descriptive analysis. Data collection techniques used literature studies from several related research sources whose research locations were in the Karimunjawa National Park. The collected data were then analyzed using the SWOT method. The main priority to be chosen as the primary strategic plan to repair damage and rehabilitate mangroves in Karimunjawa National Park is to establish synergistic cooperation between the implementation of government programs and the wishes of the local community through revitalizing coastal areas due to abrasion by replanting mangrove trees. This alternative strategy is a Strength-Threats (ST) strategy, where strength is maximized to overcome threats.

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Monitoring Urban Expansion and Loss of Agriculture on the North Coast of West Java Province, Indonesia, Using Google Earth Engine and Intensity Analysis

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Scientific World Journal 2022年第1期2022卷 3123788页

作者： Gandharum, Laju Hartono, Djoko Mulyo Karsidi, Asep Ahmad, Mubariq School of Environmental Science Universitas Indonesia (UI) Jl. Salemba Raya No. 4 Jakarta 10430 Indonesia Center of Regional Resources Development Technology (PTPSW) National Research and Innovation Agency (BRIN) Gedung Geostech 820 Banten 15314 Indonesia Environmental Engineering Study Program Faculty of Engineering Universitas Indonesia (UI) Depok 16424 Indonesia Department of Geography Faculty Mathematics and Natural Sciences Universitas Indonesia (UI) Depok 16424 Indonesia

Uncontrolled urban expansion resulting from urbanization has a disastrous impact on agricultural land. This situation is being experienced by the densely populated and fertile island Java in Indonesia. Remote sensing technologies have developed rapidly in recent years, including the creation of Google Earth Engine (GEE). Intensity analysis (IA) is increasingly being used to systematically and substantially analyze land-use/land-cover (LULC) change. As yet, however, no study of land conversion from agriculture to urban areas in Indonesia has adopted GEE and IA approaches simultaneously. Therefore, this study aims to monitor urban penetration to agricultural land in the north coastal region of West Java Province by applying both methods to two time intervals: 2003-2013 and 2013-2020. Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. Monitoring LULC change using GEE and IA has demonstrated reliable findings. The overall accuracy of Landsat image classification results for 2003, 2013, and 2020 were 88%, 87%, and 88%, respectively. IA outputs at interval levels for all categories showed that the annual change-of-area rate was higher during 2013-2020 than during 2003-2013. At the category level, IA results showed that the area of agricultural land experienced net losses in both periods, with net loss in 2013-2020 being 2.3 times greater than that in 2003-2013 (∼1,850 ha per year). In contrast, the built-up area made net gains in both periods, reaching almost twice as much in the second period as in the first (∼2,030 ha per year). The transition-level IA performed proved that agricultural land had been the primary target for the expansion of built-up areas. The most extensive spatial distribution of land conversion from agriculture to built-up area was concentrated in the regencies of Bekasi, Karawang, and Cirebon. These findings are intended to provide stakeholders with enrichment in terms of available literature

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Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence

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Physical Review Fluids 2020年第5期5卷 054606-054606页

作者： Chenyue Xie Jianchun Wang Weinan E Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Department of Mathematics Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces in the large eddy simulation (LES) of turbulence. The input features are based on the first-order derivatives of the filtered velocity field at different spatial locations. The correlation coefficients of SGS forces predicted by the spatial artifical neural network (SANN) models with reasonable spatial stencil geometry can be made larger than 0.99 in an a priori analysis, and the relative error of SGS forces can be made smaller than 15%, much smaller than that of the traditional gradient model. In a posteriori analysis, a detailed comparison is made on the results of LES using the SANN model, implicit large eddy simulation (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) at grid resolution of 643. It is shown that the SANN model performs better than the ILES, DSM, and DMM models in the prediction of the spectrum and other statistical properties of the velocity field, as well as the instantaneous flow structures. These results suggest that artificial neural network with consideration of spatial characteristics is a very effective tool for developing advanced SGS models in LES of turbulence.

关键词： Compressible flows Turbulence Artificial neural networks

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Synthesis of a biocomposite based on Kluwih (Artocarpus camansi) seed starch incorporated with carrageenan as a functional additive and cellulose nanofiber filler

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Hidayat, Fadlan Indarti, Eti Rahmi Arahman, Nasrul Doctoral Program of Agricultural Science Postgraduate School Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Agricultural Product Technology Agriculture Faculty Universitas Syiah Kuala Banda Aceh 23111 Indonesia Atsiri Research Center PUI Universitas Syiah Kuala Jl. Syeh A Rauf No. 5 Darussalam Banda Aceh 23111 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Chemical Engineering Engineering Faculty Universitas Syiah Kuala Banda Aceh 23111 Indonesia Research Center for Environmental and Natural Resources Universitas Syiah Kuala Jl. Hamzah Fansuri No. 4 Darussalam Banda Aceh 23111 Indonesia

The widespread use of synthetic plastics causes environmental issues because of their nondegradability. In this study, biocomposite from Kluwih seed starch were incorporated with carrageenan and cellulose nanofiber (CNF) to improve their mechanical properties. CNFs were prepared using the TEMPO method with ultrasonication. The variables included starch (1, 1.5, 2 g), carrageenan (3, 4.5, 6 g), and CNF (0.006, 0.12, 0.18 g). The mechanical properties of the biocomposite were optimized using RSM. Zeta potential analysis confirmed the stability of CNFs, and XRD and FTIR analyses revealed improved crystallinity and functional groups. The tensile strength of the biocomposites was 12.08 kgf/mm², which is suitable for sustainable packaging applications. © 2025

关键词： Biocomposite Carrageenan Cellulose nanofiber Kluwih seeds starch Tensile strength

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