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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Construction Process Quality Control Assisted Mobile Application 8

Construction Process Quality Control Assisted Mobile Applica...

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8th IEEE International Conference on Industrial engineering and Applications, ICIEA 2021

作者： Kruachottikul, Pravee Cooharojananone, Nagul Phanomchoeng, Gridsada Wongsanao, Keetapat Thabsuwan, Sirada Suwannatrai, Kuptipong Pongsuttisatta, Watcharapong Ganyaporngul, Sirapavee Kovitanggoon, Kittikul Vajarodaya, Napatsapa Graduate School Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Mechanical Engineering Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Solitech Innovations and Technologies Co. Ltd. Bangkok Thailand

ISBN: (纸本)9781665428958

Digital transformation plays a big role in many industries, including construction. One of the obstacles to manage large-scale and complex construction projects is inefficient communication between involving personnel. Most problems are such as misunderstanding contexts, incomplete information, ignorance, missing multimedia files, etc. In Thailand, organizations adopt digital transformation by utilizing a variety of applications and software to complete their projects, such as LINE, G-Suite, Asana, etc. Users have to spend a lot of time to learn each application and software to be able to achieve maximum performance. Construction process quality control assisted application is the new tool developed for uniting all needs and solving communication problems. Requirements are gathered by five interviews with three major organizations, the Thailand Department of Highway, Team Built Co., Ltd., and one major construction company. Requirements were analyzed to extract the most useful features that are beneficial to all users. React Native and the Expo framework were used to develop the frontend part of the application. For the backend part, the application used microservice architecture with various cloud services, like Amazon AWS, Expo Server, and Firebase Cloud Messaging. In the end, the application was put through hand-on tests by 13 on-site personnel. The final results showed the average scores of the user interface, hands-on experience, and benefits for the users were 4, 4.16, and 3.92 (out of 5), respectively, in which the total satisfaction score is 4.03(out of 5). © 2021 IEEE.

关键词： Construction

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Potential of fungal thermostable alpha amylase enzyme isolated from Hot springs of Central Anatolia(Turkey)in wheat bread quality

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Food Bioscience 2022年第1期45卷 214-222页

作者： Arzu Unal Asiye Seis Subasi Semra Malkoc Ijlal Ocak SElif Korcan Elif Yetilmezer Seyhun Yurdugül Hülya Yaman Turgay Sanal Alaettin Keceli Igdır University Faculty of AgricultureDepartment of Agricultural BiotechnologyI˘gdırTurkey Ministry of Agriculture and Forestry Field Crops Central Research Institute Department of Quality and TechnologyAnkaraTurkey Eskisehir Technical University Faculty of EngineeringDepartment of Environmental EngineeringEskisehirTurkey Afyon Kocatepe University Faculty of EducationDepartment of Mathematics and Science EducationDivision of Science EducationAfyonTurkey Usak University Vocational School of Health ServicesMedical Laboratory Techniques ProgramUsakTurkey Ministry of Agriculture and Forestry Field Crops Central Research Institute Department of BiotechnologyAnkaraTurkey Bolu Abant Izzet Baysal University Faculty of Arts and ScienceDepartment of BiologyBoluTurkey Bolu Abant Izzet Baysal University Vocational School of the Technical Sciences of BoluFood Technology ProgramBoluTurkey Pamukkale University School of Applied SciencesDepartment of Organic Farming Business ManagementDenizliTurkey

Many industrial processes may quickly proceed with biotechnological and enzymatic *** and production facilitator properties of enzymes have provided variable advantages for ***,enzyme utilization has become inevitable for industry and supplied financial advantages in case of the high energy requirement *** the study,the amylase enzymes were isolated from thermal spring sources,and the effect on bread quality was ***,fungal amylases were isolated from thermal spring sources(29-98◦C)in various places around *** determining the functional properties of amylase enzymes,the most active enzyme was used in bread production to examine their effect on bread *** maximum alpha-amylase activity(38.6 U/mg)has been detected in Aspergillus niger G 2-1 *** compared with the commercially available one,native alpha-amylase increased the bread volume.A significant difference(p<0.01)was found in the color properties and size of the bread between microbial produced in this study and commercially produced *** ppm alpha-amylase addition showed the optimum bread properties for dough processing.

关键词： Alpha-amylase Thermal spring Quality of bread Commercial enzymes

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Review of mayflies (Insecta Ephemeroptera) as a bioindicator of heavy metals and microplastics in freshwater

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science of the Total Environment 2025年 958卷 178057页

作者： Benhadji, Nadhira Kurniawan, Setyo Budi Imron, Muhammad Fauzul Institute of Technology and Life Sciences—National Research Institute Falenty Hrabska Avenue 3 Raszyn05-090 Poland Department of Chemical and Process Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor UKM Bangi43600 Malaysia Study Program of Environmental Engineering Department of Biology Faculty of Science and Technology Universitas Airlangga Campus C UNAIR Jalan Mulyorejo Surabaya60115 Indonesia Department of Water Management Faculty of Civil Engineering and Geosciences Delft University of Technology Stevinweg 1 CN Delft2628 Netherlands

Heavy metal and microplastic pollutions are prevalent in freshwater ecosystems, with many freshwater bodies being contaminated by one or both of these pollutants. Recent studies reported extreme detections of Cd, Pb and Zn, high concentrations of Cr, Pb and Cu and microplastics acting as vectors of pollutants, including heavy metals. Mayflies can serve as bioindicators of heavy metal contamination in freshwater ecosystems because changes in their community structure, physiology, and behaviour can reflect and help predict the concentrations of metals in these environments. This review discusses the ecological alterations induced by tissue metal concentration in mayflies and other macroinvertebrates. As sensitive taxa to heavy metal contamination, mayflies can reflect the impacts of this pollution through their ethology and relationship to the substrate, highlighting issues such as eutrophication, alterations in community structure, inhibitory effects and sediment toxicity. Mayflies are also highly affected by microplastic exposure, which leads to ingestion, bioaccumulation, biomagnification, habitat and community alteration, behavioural changes, physiology alteration and toxicity. Mayflies bioindication metrics for assessing the impact of heavy metals and microplastics include the examination of community alteration, functional feeding behaviour, molecular structure, dietary and toxicity impacts, bioaccumulation and biomagnification and biomarkers. Current challenges for the utilization of mayflies as bioindicators include temporal variations in sensitivity, lack of universally recognised protocols and need for standardised protocols for microplastic analysis. Additionally, the applicability of mayflies as bioindicators may vary across different ecosystems, emphasising the need for selecting suitable indicators that align with the unique characteristics of the ecosystem. © 2024 The Authors

关键词： Macroinvertebrates

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Prediction tools for assessing functional impacts of gene mutations in Clear Cell Renal Cell Carcinoma: A comparative study

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Computers in Biology and Medicine 2025年第PT A期192卷 110187-110187页

作者： Yang, Cheng-Hong Lin, Guan-Cheng Wu, Chih-Hsien Chen, Jin-Bor Chuang, Li-Yeh Department of Information Management Tainan University of Technology Tainan Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Drug Development and Value Creation Research Center Kaohsiung Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taiwan Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering I-Shou University Kaohsiung Taiwan Taiwan

Renal cell carcinoma (RCC) is a well-known malignancy characterized by specific gene mutations that elevate its occurrence. It can be categorized into clear cell RCC (ccRCC), papillary RCC, and chromophobe RCC, which account for approximately 85 % of all primary RCC cases. The mutations typically involve single-nucleotide variants (SNVs) that lead to amino acid substitutions, which influence various biological functions, including gene expression. Therefore, predicting the functional consequences of RCC-related single-nucleotide polymorphisms (SNPs), including substitutions, insertions, deletions, and duplications, is crucial for the effective clinical management of RCC. In recent years, the accessibility and popularity of tools for predicting SNP functional variations have grown, especially in research concerning the potential risks associated with key gene mutations in cancer. Accordingly, this study focused on commonly mutated genes in ccRCC, namely VHL, BAP1, PBRM1, and SETD2. Public data from sources such as The Cancer Genome Atlas and the National Center for Biotechnology Information were used to identify 61 gene mutation positions. Nine nonsynonymous mutation prediction tools were used for analysis, namely SIFT, Polyphen-2, Mutation Assessor, Fathmm, MutPred, CHASM, Revel, Provean, and SNP&GO. The tools were evaluated by their statistical performance, prediction distribution, and receiver operating characteristic curves. The three tools with the highest accuracy for predicting the functional consequences of mutations in the four frequently mutated genes associated with ccRCC were SIFT (accuracy of 0.75), Provean (0.7), and Polyphen-2 (0.69). These findings offer valuable insights into predicting ccRCC-related gene mutation effects, but further research and validation are essential to support their clinical applications. © 2025

关键词： Transcription

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A critical review of the interactions between rhizosphere and biochar during the remediation of metal(loid)contaminated soils

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Biochar 2023年第1期5卷 1481-1513页

作者： Chuanfang Fan Yanshan Cui Qianru Zhang Naiyi Yin Xiaolin Cai Xiangzhou Yuan Sachini Senadheera Yoora Cho Yong Sik Ok College of Resources and Environment University of Chinese Academy of SciencesBeijing 101408China Research Center for Eco-Environment Sciences Chinese Academy of SciencesBeijing 100085China Korea Biochar Research Center APRU Sustainable Waste Management Program&Division of Environmental Science and Ecological EngineeringKorea UniversitySeoul 02841Republic of Korea State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081China Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing 210096China

Biochar has a large specific surface area,well-developed pore structure,abundant surface functional groups,and superior nutrient supply capacity,which is widely available and environmentally friendly with its advantages in waste resource utilization,heavy metal(loid)remediation,and carbon *** review focuses on the interactions between biochar(including raw biochar,functional biochar(modified/engineered/designer biochar),and composite biochar)and rhizosphere during the remediation of soil contaminated with heavy metal(loid)s(Pb,As,Cd,Hg,Co,Cu,Ni,Zn,Cr,etc.)and the effects of these interactions on the microbial communities and root exudates(enzymes and low-molecular-weight organic acids(LMWOAs)).In terms of microorganisms,biochar affects the composition,diversity,and structure of microbial communities through the supply of nutrients,provision of microbial colonization sites,immobilization of heavy metal(loid)s,and introduction of exogenous *** regard to root exudates,biochar provides electron transfer support between the microorganisms and exudates,regulates the secretion of enzymes to resist the oxidative stress stimulated by heavy metal(loid)s,ameliorates rhizosphere acidification caused by LMWOAs,and promotes the activity of soil *** roles and mechanisms of biochar on rhizosphere soils are discussed,as well as the challenges of biochar in the remediation of heavy metal(loid)-contaminated soils,and the issues that need to be addressed in future research are foreseen.

关键词： Green and sustainable remediation Sustainability Biochar Soil Root Heavy metal(loid)s

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Uncertainty Quantification and Propagation for Multiscale Materials Systems with Agglomeration and Structural Anomalies

SSRN

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SSRN 2024年

作者： Comlek, Yigitcan Mojumder, Satyajit van Beek, Anton Prabhune, Prajakta Ciampaglia, Alberto Apley, Daniel W. Brinson, L. Catherine Liu, Wing Kam Chen, Wei Department of Mechanical Engineering Northwestern University Evanston United States Theoretical and Applied Mechanics Program Northwestern University Evanston United States School of Mechanical and Materials Engineering University College Dublin Dublin Ireland Thomas Lord Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Department of Industrial Engineering and Management Sciences Northwestern University Evanston United States

Advancements in manufacturing technologies have enabled material system design optimization at multiple length scales. However, the microstructural anomalies and uncertainties present at each scale have not been considered comprehensively enough for systems to be robust to manufacturing variations. Consequently, considering anomalies at various length scales of the material’s structure and understanding their effects on a part’s response are crucial for designing engineered parts that are robust to various sources of uncertainty. In this work, an efficient bottom-up hierarchical uncertainty quantification and propagation framework bridging multiple scales to establish a design allowable range for the material system at the part-scale is presented. We demonstrate our framework on an additively manufactured polymer nanocomposite material system that is subject to anomalies at different length scales that impact the material properties. Specifically, agglomeration defects formed due to attractive forces between nanoparticles at the microscale and structural variabilities caused by the voids resulting from different processing conditions at the mesoscale are considered. The introduced hierarchical sampling framework combines (i) an innovative microstructure image characterization and reconstruction, (ii) a mechanistic reduced-order model that enables fast property predictions for high-dimensional microstructural design spaces, and (iii) efficient uncertainty quantification and propagation through Copula-based sampling across multiple length scales to reduce the sampling budget by 95%. Through validation of our method, we demonstrate the importance of considering the effects of process-induced microstructure features on the performance of a part. © 2024, The Authors. All rights reserved.

关键词： Microstructure

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Detecting defects of railway tracks by using computer vision methodology

Detecting defects of railway tracks by using computer vision...

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IISE Annual Conference and Expo 2021

作者： Ravishankar, Premkumar Tokgoz, Berna Eren Zhang, Xulong Zhang, Jing Hwang, Seokyon Department of Industrial and Systems Engineering Lamar University BeaumontTX United States Department of Computer Science Lamar University BeaumontTX United States Construction Management Program Lamar University BeaumontTX United States

ISBN: (纸本)9781713838470

Maintaining railway tracks in healthy conditions is critical to ensuring the safe operation of railroad transportation. According to the Federal Railroad Administration (FRA), nearly 23% of train accidents that occurred between 2015 to 2020 were due to the defects of the tracks. In an effort to prevent such accidents, railway companies regularly inspect tracks to detect and fix defects of track systems. This research proposes an innovative method for conducting railroad track inspection to enhance the maintenance operation. The proposed approach aims to overcome the existing inspection practices based on human observation that is costly, time-consuming, and error prone. To this end, the proposed method utilizes computer vision methodologies to achieve high-level automation and accuracy of defect detection through the analysis of digital images. The outcome of this study is envisioned to help railway companies perform predictive maintenance more effectively and, thereby, reduce the risk of train accidents and increase the resiliency of their assets. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Railroad tracks

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Wasserstein robust classification with fairness constraints

arXiv

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arXiv 2021年

作者： Wang, Yijie Nguyen, Viet Anh Hanasusanto, Grani A. Graduate Program in Operations Research and Industrial Engineering University of Texas at Austin Department of Management Science and Engineering Stanford University

We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We use a type-1 Wasserstein ambiguity set centered at the empirical distribution to model distributional uncertainty and derive a conservative reformulation for the worst-case equal opportunity unfairness measure. We establish that the model is equivalent to a mixed binary optimization problem, which can be solved by standard off-the-shelf solvers. To improve scalability, we further propose a convex, hinge-loss-based model for large problem instances whose reformulation does not incur any binary variables. Moreover, we also consider the distributionally robust learning problem with a generic ground transportation cost to hedge against the uncertainties in the label and sensitive attribute. Finally, we numerically demonstrate that our proposed approaches improve fairness with negligible loss of predictive accuracy. © 2021, CC BY.

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