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arXiv 2023年

作者： Tripathi, Prasun C. Suvon, Mohammod N.I. Schobs, Lawrence Zhou, Shuo Alabed, Samer Swift, Andrew J. Lu, Haiping Department of Computer Science University of Sheffield Sheffield United Kingdom Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield Sheffield United Kingdom Department of Clinical Radiology Sheffield Teaching Hospitals Sheffield United Kingdom INSIGNEO Institute for in Silico Medicine University of Sheffield Sheffield United Kingdom

Heart failure is a severe and life-threatening condition that can lead to elevated pressure in the left ventricle. Pulmonary Arterial Wedge Pressure (PAWP) is an important surrogate marker indicating high pressure in the left ventricle. PAWP is determined by Right Heart Catheterization (RHC) but it is an invasive procedure. A noninvasive method is useful in quickly identifying high-risk patients from a large population. In this work, we develop a tensor learning-based pipeline for identifying PAWP from multimodal cardiac Magnetic Resonance Imaging (MRI). This pipeline extracts spatial and temporal features from high-dimensional scans. For quality control, we incorporate an uncertainty-based binning strategy to identify poor-quality training samples. We leverage complementary information by integrating features from multimodal data: cardiac MRI with short-axis and four-chamber views, and cardiac measurements. The experimental analysis on a large cohort of 1346 subjects who underwent the RHC procedure for PAWP estimation indicates that the proposed pipeline has a diagnostic value and can produce promising performance with significant improvement over the baseline in clinical practice (i.e., ∆AUC = 0.10, ∆Accuracy = 0.06, and ∆MCC = 0.39). The decision curve analysis further confirms the clinical utility of our method. The source code can be found at: https://***/prasunc/PAWP. © 2023, CC BY.

关键词： Pipelines

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Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

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Gondwana Research 2024年

作者： Kumar Lilhore, Umesh Simaiya, Sarita Dalal, Surjeet Radulescu, Magdalena Balsalobre-Lorente, Daniel Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Amity University Gurugram Haryana India Institute of Doctoral and Post-Doctoral Studies University Lucian Blaga of Sibiu Sibiu Romania UNEC Research Methods Application Center Azerbaijan State University of Economics (UNEC) Baku 1001 Istiqlaliyyat Str. 6 Azerbaijan Department of Applied Economics I University Castilla-La Mancha Spain Department of Management Faculty of Economics and Management Czech University of Life Sciences Prague Prague 16500 Czech Republic Economic Research Center (WCERC) Western Caspian University Baku Azerbaijan

The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the ’TrashNet Waste’ database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models’ trash classification performance and provided specific viewpoints. The results explain each technique's pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method's excellent performance suggests its seamless integration into practical waste management solutions. © 2024 International Association for Gondwana Research

关键词： Convolutional neural network (CNN) Deep learning Smart Waste Management Transfer Learning Waste classification

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Image Dehazing Algorithm Based on Particle Swarm Optimization for Sky Region Segmentation

Image Dehazing Algorithm Based on Particle Swarm Optimizatio...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Hao Zhou Changjiu Yuan Hongbo Pan Yue Yang Ziyan Wang Xiangyang Chen College of Computer and Information Science Southwest University College of Big Data and Artificial Intelligence Chizhou University College of Big Data and Intelligent Engineering Chongqing College of International Business and Economics Basic Teaching Department Anqing Medical College

The well-known Dark Channel Prior(DCP)dehazing algorithm does not work well in the sky area,thus,we propose an image dehazing method for segmented sky regions based on Particle Swarm Optimization(PSO).First,we apply PSO to the Otsu segmentation method to accurately segment sky and non-sky regions in the hazy ***,the transmission of non-sky regions is derived by DCP.A transmission compensation method is proposed to obtain the transmission for the sky *** transmission is then refined by gradient-domain guided ***,the restored haze-free image is obtained through the Atmospheric Scattering Model(ASM).Quantitative and qualitative experiments show that the proposed method has a better dehazing effect,especially for hazy images with the sky areas.

关键词： image dehazing Ostu sky area segmentation DCP

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Multilevel model-based clustering: A new proposal of maximum-a-posteriori assignment

Multilevel model-based clustering: A new proposal of maximum...

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Biennial Conference of the International Federation of Classification Societies, IFCS 2017

作者： Bacci, Silvia Bartolucci, Francesco Pennoni, Fulvia Department of Statistics Computer Science Applications "Giuseppe Parenti" University of Florence Florence Italy Department of Economics University of Perugia Perugia Italy Department of Statistics and Quantitative Methods University of Milano-Bicocca Milano Italy

ISBN: (纸本)9789811533105

We deal with the problem of latent variable prediction in the context of multilevel latent class models for categorical responses provided by individuals nested in groups. In particular, we propose a posterior assignment rule that jointly predicts the individual- and group-level latent variables. This proposal is alternative to the common maximum-a-posteriori rule, which is based on first predicting the latent variables at cluster level and, then, those at individual level. To illustrate the proposal, we show the results of two simulation studies and two applications on data related to the national and the international assessment of student skills. © Springer Nature Singapore Pte Ltd 2020.

关键词： Regression analysis

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Prediction of Flight Arrival Delay Time Using U.S. Bureau of Transportation Statistics

Prediction of Flight Arrival Delay Time Using U.S. Bureau of...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jiarui Li Ran Ji Cheng'ao Li Xiaoying Yang Jiayi Li Yiran Li Xihan Xiong Yutong Fang Shusheng Ding Tianxiang Cui School of Computer Science University of Nottingham Ningbo China Ningbo China School of Aerospace Engineering University of Nottingham Ningbo China Ningbo China Department of Computing Imperial College London London UK Teaching Research Center Ningbo Open University Ningbo China Business School Ningbo University Ningbo China

According to the data from the Bureau of Trans-portation Statistics (BTS), the number of passengers and flights has been increasing year by year. However, flight delay has become a pervasive problem in the United States in recent years due to various factors, including human factors such as security regulations, as well as natural factors such as bad weather. Flight delay not only affects the profits of airlines but also affects the satisfaction of passengers. Therefore, a model that can predict the arrival time of airplanes needs to be developed. Machine learning methods have been widely applied to prediction problems. In this paper, a variety of machine learning and computational intelligence methods, including linear regression, decision tree (DT), random forest (RF), gradient boosting (GB), gaussian regression models and genetic programming were trained on the U.S. department of Transportation's (DOT) BTS dataset. The results show that genetic programming performs best and can be used to predict the arrival time of the U.S. flights in advance, which is beneficial for airlines and passengers to make timely decisions.

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Uncertainties About Waste Using an Online Survey and Review Approach: Environmentalist Perceptions, Waste Compositions and Views from Media and science

SSRN

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SSRN 2023年

作者： Markley, Laura A.T. Grünzner, Maja Walker, Tony R. Syracuse University College of Engineering and Computer Science Civil and Environmental Engineering 223 Link Hall SyracuseNY13244 United States University of Vienna Faculty of Psychology Department of Cognition Emotion and Methods Environmental Psychology Group Wächtergasse 1 Vienna1010 Austria School for Resource and Environmental Studies Dalhousie University Halifax Canada

Waste generation and subsequent plastic pollution pose a major threat to both human and environmental health. Furthering our understanding of waste at individual levels can inform future waste reduction strategies, education, and policies. This study explores components and perceptions among individuals using survey data combined with a mini-review. An online Qualtrics survey was distributed pre-COVID19 following a global social media challenge, Futuristic February, which directed participants to collect their non-perishable waste during February 2020. Participants were asked about their waste generation, perceptions toward waste and plastic pollution issues, and environmental worldview using the New Ecological Paradigm (NEP) scale (n=50). We also conducted a mini-review of 8 waste and plastic pollution statements from our survey in both popular media and scientific journal articles. Survey results indicated participants had an overall pro-ecological worldview (M=4.32, SD=0.88) and reported cardboard and paper (66%) as the most commonly occurring non-perishable waste category. Across categories, food packaging was the most common waste type. Participants were most uncertain about statements focusing on bioplastic or biodegradable plastic, respectively (44% and 30%), while the statement on microplastic toxicity obtained 100% mild or strong agreement among participants. Uncertainty for reviewed statements varied depending on topic and group. Popular media and scholarly articles did not always agree, possibly due to differences in communication of uncertainty or terminology definitions. These results can inform future policy and educational campaigns around topics of misinformation. © 2023, The Authors. All rights reserved.

关键词： Health risks

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Mexican violence displaces people, discourages international migration, and shrinks highway network connections

arXiv

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arXiv 2023年

作者： Coscia, Michele Gutiérrez-Romero, Roxana IT University of Copenhagen Computer Science Department Rued Langgaards Vej 7 Copenhagen2300 Denmark Center for International Development Harvard University CambridgeMA United States Policy and Quantitative Methods School of Business and Management Queen Mary University of London. Mile End campus LondonSE14 NS United Kingdom

This paper estimates the impact of violence on domestic and international migration in Mexico during 2005-2020. Unlike earlier studies, we use network analysis to quantify changes in the degree of interconnectedness among all municipalities in Mexico and with the US, breaking it down by origin-destination pairs. To identify the impact of violence on the changes of the migration network, we use instrumental variables. We exploit the exogenous variation in heroin street prices in the US, local prices of corn in Mexico, and differences in which regions received more Chinese immigrant communities during the 1930s, members of which introduced opium cultivation and drug smuggling networks in Mexico. These instruments help us identify the areas with a current presence of drug trafficking organizations and facing increases in violence. We show that municipalities that experience rises in homicide rates continue to attract immigrants within the country, but they face a larger rise in domestic emigration. The violence also discourages the return migration of Mexicans from the US and non-transit international emigration. Consistent with our findings, areas affected by violence also experience a long-term reduction in vehicle trips made in the highway network connecting them with the rest of the country. © 2023, CC BY-SA.

关键词： Costs

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PARITY OF 3-REGULAR PARTITION NUMBERS AND DIOPHANTINE EQUATIONS

arXiv

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arXiv 2022年

作者： Ballantine, Cristina Merca, Mircea Radu, Cristian-Silviu Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center University Politehnica of Bucharest BucharestRO-060042 Romania Johannes Kepler University Linz4040 Austria

Let b3(n) be the number of 3-regular partitions of n. Recently, W. J. Keith and F. Zanello discovered infinite families of Ramanujan type congruences modulo 2 for b3(2n) involving every prime p with p ≡ 13, 17, 19, 23 (mod 24), and O. X. M. Yao provided new infinite families of Ramanujan type congruences modulo 2 for b3(2n) involving every prime p > 5. In this paper, we introduce new infinite Ramanujan type congruences modulo 2 for b3(2n). They complement naturally the results of Keith-Zanello and Yao and involve primes in P = {p prime: ∃ j ∈ {1, 4, 8}, x, y ∈ Z, gcd(x, y) = 1 with x2 + 216y2 = jp} whose Dirichlet density is 1/6. As a key ingredient in our proof we show that of the number of primitive solutions for x2 + 216y2 = pm, p ∈ P, p ∤ m and pm ≡ 1 (mod 24), is divisible by 8. Here, the difficulty arises from the fact that 216 is not idoneal. We also give a conjectural exact formula for the number of solutions for this Diophantine equation. In the second part of the article, we study reversals of Euler-type identities. These are motivated by recent work of the second author on a reversal of Schur’s identity which involves 3-regular partitions weighted by the parity of their *** Codes 11D09, 11D45, 11P83, 05A17, 11F33 © 2022, CC BY.

关键词： Algebra

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Improving the Encapsulation Efficiency of Lipase in Molecular Cages and Its Application

SSRN

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SSRN 2022年

作者： Xiao, Yun-Yan Zhang, Jiang-Tao Dong, Yong-Hua Li, Shu-Li Zhi, Gao-Ying Zhang, Ya-Fang Zhang, Dong-Hao College of Pharmaceutical Science Hebei University Baoding071002 China Key Laboratory of Pharmaceutical Quality Control of Hebei Province College of Pharmaceutical Science Hebei University Baoding071002 China Institute of Life Science and Green Development Hebei University Baoding071002 China Department of Computer Teaching Hebei University Baoding071002 China Pharmacy Department Baoding Children’s Hospital China

Here, lipase encapsulation is constructed by locking enzyme molecules in nanomolecular cages on the surface of SH-PEI@PVAC magnetic microspheres. The encapsulation efficiency is improved obviously by efficiently modifying thiol groups using 3-mercaptopropionic acid. N2 adsorption-desorption isotherms reveal the existence of mesoporous molecular cages on microsphere surface. The robust immobilizing strength of carrier to lipase demonstrates the successful encapsulation of enzyme in nanomolecular cages. The encapsulated lipase shows high enzyme loading (52.9 mg/g) and high activity (51.4 U/mg). Different sizes of molecular cages are established and cage size showed important effects on lipase encapsulation. It shows that enzyme loading is low at small size of molecular cages, which is attributed to that the nanomolecular cage is too small to house lipase. The investigation in lipase conformation suggests that the encapsulated lipase retains its active conformation. Compared with the adsorbed lipase, the encapsulated lipase shows higher thermal stability (4.9 times) and higher resistance to denaturant (5.0 times). Encouragingly, the encapsulated lipase shows high activity and reusability in lipase-catalyzed synthesis of propyl laurate, suggesting the potential application value of encapsulated lipase. © 2022, The Authors. All rights reserved.

关键词： Enzyme immobilization

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LLaVA-docent: Instruction tuning with multimodal large language model to support art appreciation education

arXiv

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arXiv 2024年

作者： Lee, Unggi Jeon, Minji Lee, Yunseo Byun, Gyuri Son, Yoorim Shin, Jaeyoon Ko, Hongkyu Kim, Hyeoncheol Department of Computer Science and Engineering Korea University Korea Republic of Teaching Learning and Teacher Education University of Nebraska-Lincoln United States Poongnap Elementary School Seoul Metropolitan Office of Education Korea Republic of Department of Education Seoul National University Korea Republic of Interdisciplinary Program in Art Education Art Education Major Seoul National University Korea Republic of Department of Elementary Art Education Seoul National University of Education Korea Republic of

Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and challenging endeavor for most students, can be more accessible with a generative AI enabled conversation partner that provides tailored questions and encourages the audience to deeply appreciate artwork. This study explores the application of multimodal large language models (MLLMs) in art appreciation education, with a focus on developing LLaVA-Docent, a model designed to serve as a personal tutor for art appreciation. Our approach involved design and development research, focusing on iterative enhancement to design and develop the application to produce a functional MLLM-enabled chatbot along with a data design framework for art appreciation education. To that end, we established a virtual dialogue dataset that was generated by GPT-4, which was instrumental in training our MLLM, LLaVA-Docent. The performance of LLaVA-Docent was evaluated by benchmarking it against alternative settings and revealed its distinct strengths and weaknesses. Our findings highlight the efficacy of the MMLM-based personalized art appreciation chatbot and demonstrate its applicability for a novel approach in which art appreciation is taught and experienced. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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