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International Conference on Logistics and Operations Management (GOL)

作者： Chafik Razouk Youssef Benadada Jaouad Boukachour PhD from University Mohamed V Rabat Cedoc ST2I-ROL Morocco Professor at ENSIAS Unversity Mohamed V Rabat Team Leader of Operations Resear Morocco Associate Professor of Computer Science University le Havre France

ISBN: (数字)9781728164250

ISBN: (纸本)9781728164267

International maritime transport has becoming an important and interesting way comparing to the land and the rail transport. This improvement have mainly reduced the travel time and the expected delivery time, except sometimes in difficult weather conditions where actual times may be slightly longer than expected. Containers terminals are considered as an interface between different maritime actors: ship owner, freight forwarder, consignee, containers terminal and port authority. In this paper, we focus our work on a part of the marine transportation, which is the transfer of containers by internal trucks inside a containers terminal. We solve this problem by proposing a new mathematical model and new resolution methods, the obtained results prove the effectiveness of the proposed approach, which help to improve the port efficiency.

关键词： Containers Resource management Optimization Mathematical model Loading Cranes Productivity

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Object Identification Model using Deep Reinforcement Machine Learning Concept for Image

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IOP Conference Series: Materials science and Engineering 2021年第1期1085卷

作者： Saurabh Tiwari S Veenadhari Sanjeev K Gupta Phd Research Scholar Department of Computer Science and Engineering RNTU Bhopal 462003 Madhya Pradesh India Associate Professor RNTU Department of Computer Science and Engineering RNTU Bhopal 462003 Madhya Pradesh India Professor RNTU Department of Electronics and Communication Engineering RNTU Bhopal 462003 Madhya Pradesh India

This paper presents a model which gives the detailed process of object identification. We need to identify class and location of object in image for completing process of objet identification. Proposed model works on the principal of reinforcement learning which takes action on the basis of rewards and experiences. Normally methods in literature uses sliding window which moves in same direction but proposed algorithm provides a variable mask which moves 360 degree for identifying object using action history vector proposed with RL also not only this work focuses on localization like other work but also used class information with Softmax classification able to classify multiple object in single image with efficient time which is novel. Proposed mask acts as agent and focuses on proposed candidate reason this saves time and works in efficient manner for identification. Agent depends on transformation action and by applying top down reasoning it gives location of object. Classification is done using Softmax classification as we are having features of image by CNN. Reinforcement learning concept used for training of agent and Pascal voc dataset used for testing. Analysis of only 10 to 25 regions is sufficient with proposed work to identify first instance of object. Experiment and performance evaluation shows the efficiency of proposed work.

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MiR34 contributes to spinal muscular atrophy and AAV9-mediated delivery of MiR34a ameliorates the motor deficits in SMA mice

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Molecular therapy. Nucleic acids 2023年 32卷 144-160页

作者： Tai-Heng Chen Shih-Hsin Chang Yu-Fu Wu Ya-Ping Yen Fang-Yu Hsu Yen-Chung Chen Yang Ming Ho-Chiang Hsu Yi-Ching Su Sheng-Tang Wong Jui-Hung Hung Shih-Hwa Chiou Yuh-Jyh Jong Jun-An Chen PhD Program in Translational Medicine Kaohsiung Medical University Kaohsiung 80708 and Academia Sinica Taipei 11529 Taiwan. Institute of Molecular Biology Academia Sinica Taipei 11529 Taiwan. Department of Pediatrics Kaohsiung Medical University Kaohsiung 80708 Taiwan. School of Post-Baccalaureate Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Neuroscience Program of Academia Sinica Academia Sinica Taipei 11529 Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu 30093 Taiwan. Institute of Pharmacology School of Medicine National Yang Ming Chiao Tung University Taipei 11221 Taiwan. Innovative Cellular Therapy Center Department of Medical Research Taipei Veterans General Hospital Taipei 11217 Taiwan. Department of Laboratory Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Translational Research Center of Neuromuscular Diseases Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung 80708 Taiwan. Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 80708 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 30093 Taiwan.

Spinal muscular atrophy (SMA) is a neurodegenerative disease characterized by the selective loss of spinal motor neurons (MNs) and concomitant muscle weakness. Mutation of is known to cause SMA, and restoring SMN protein levels via antisense oligonucleotide treatment is effective for ameliorating symptoms. However, this approach is hindered by exorbitant costs, invasive procedures, and poor treatment responses of some patients. Here, we seek to circumvent these hurdles by identifying reliable biomarkers that could predict treatment efficacy. We uncovered that MiR34 exhibits consistent downregulation during SMA progression in both human and rodent contexts. Importantly, family-knockout mice display axon swelling and reduced neuromuscular junction (NMJ) endplates, recapitulating SMA pathology. Introducing MiR34a via scAAV9 improved the motor ability of SMNΔ7 mice, possibly by restoring NMJ endplate size. Finally, we observed a consistent decreasing trend in MiR34 family expression in the cerebrospinal fluid (CSF) of type I SMA patients during the loading phase of nusinersen treatment. Baseline CSF MiR34 levels before nusinersen injection proved predictive of patient motor skills 1 year later. Thus, we propose that MiR34 may serve as a biomarker of SMA since it is associated with the pathology and can help evaluate the therapeutic effects of nusinersen.

关键词： ASO MT: Non-coding RNAs MiR34 SMA biomarker microRNA nusinersen spinal muscular atrophy

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Breed as One of Significant Factors that Determine the Level of Yield and Its Quality

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IOP Conference Series: Earth and Environmental science 2022年第4期988卷

作者： E V Matvienko A L Zolkin D K Suchkov L A Pankratova PhD in Biological Science Junior Researcher of the Laboratory of Breeding and Seed Farming of Cereal and Sorghum Crops Volga NIISS - branch of the Samara Scientific Center of the Russian Academy of Sciences Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Junior Researcher Federal Research Center for Agroecology Integrated Land Reclamation and Protective Afforestation of the Russian Academy of Sciences (Laboratory for Predicting the Bio-productivity of Agroforestscapes) PhD in Geography Senior Lecturer of the Physical Geography and Landscape Planning Department Institute of Earth Sciences St.Petersburg State University

Breed is studied in the article as of significant factors that determine the level of yield and its quality. World experience shows that the consistent increase in the yield of cultivated crops is based on the improvement of the cultivation technology and the achievements of breeding. The production of agricultural products is based on the variety of breeds. According to A.A. Zhuchenko, breed determines the basic requirements for cultivation technologies: productivity, energy efficiency, environmentally friendly quality and environmental protection. It is believed that 25% of the yield is determined by the genetic characteristics of the cultivated breed. The role of the genotype in increasing and stabilizing the yield is constantly increasing, and the contribution of the breed during zoning is estimated at 30 ... 50%.

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Prognosis for patients with cognitive motor dissociation identified by brain-computer interface (vol 143, pg 1177, 2020)

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BRAIN 2020年第8期143卷 1177-1189页

作者： Pan, Jiahui Xie, Qiuyou Qin, Pengmin Chen, Yan He, Yanbin Huang, Haiyun Wang, Fei Ni, Xiaoxiao Cichocki, Andrzej Yu, Ronghao Li, Yuanqing Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Studies of Psychological Applications Guangdong Key Laboratory of Mental Health and Cognitive Science School of Psychology South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangdong Work Injury Rehabilitation Hospital Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Skolkovo Institute of Science and Technology (Skoltech) Moscow 143026 Russia Nicolaus Copernicus University (UMK) Torun 87-100 Poland Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Correspondence to: Yuanqing Li PhD Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou 510640 China E-mail: auyqli@scut.edu.c

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients’ behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cogn

关键词： cognitive motor dissociation brain-computer interface disorders of consciousness prognosis unresponsive wakefulness syndrome

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A Thematic Portfolio and Recommended Study on the Usefulness of Online Medical Reviews Based on QCA Methods

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Yonghong Wang Lei Huang Hunan Provincial Engineering Research Center Intelligent Monitoring of Ecological No.508 Yingbing Road Yiyang China Hunan City University No.508 Yingbing Road Yiyang China City University of Hong Kong PhD of Computer Science.

The online comment for doctors plays an important role in assisting patients to master the real medical situation and choose medical treatment, which greatly reduces the adverse effects of online medical market information asymmetry. but because ordinary patients lack professional medical knowledge and can not accurately and efficiently write comments that are useful to similar patients, we identify useful topics from online medical reviews and make combination recommendations based on patients with different diseases. guide patients to write comments from the most useful dimensions to alleviate the problem of information overload, thus maximizing the limited human resource utility. This study aimed at online medical platforms such as good doctors and micro-doctors collected online doctor reviews for major diseases and used LDA topic mining and fsQCA fuzzy set qualitative comparative analysis to analyze key topics that affect the usefulness of reviews and optimal topic combinations under different disease types.

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Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Automated Breast Tumor Segmentation in DCE-MRI Using Deep Le...

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International Conference on Cloud Technologies and Applications (CloudTech)

作者： Mohammed Benjelloun Mohammed El Adoui Mohamed Amine Larhmam Sidi Ahmed Mahmoudi PhD Computer Science Unit Faculty of Engineering UMONS Belgium

ISBN: (纸本)9781728116389;9781728116372

Segmentation of breast tumor is an important step for breast cancer follow-up and treatment. Automating this challenging task can help radiologists to reduce the high workload of breast cancer analysis. In this paper, we propose a deep learning approach to automate the segmentation of breast tumors in DCE-MRI data. We build an architecture based on U-net fully convolutional neural network. The trained model can handle both detection and segmentation on each single breast slice. In this study, we used a dataset of 86 DCE-MRI, acquired before and after chemotherapy, of 43 patients with local breast cancer, a total of 5452 slices. The data have been annotated manually by an experienced radiologist. The model was trained and validated on 85% and 15% of the data and achieved a mean IoU of 76,14%.

关键词： Image segmentation Tumors Magnetic resonance imaging Breast cancer Biomedical imaging Training

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The Influence of Using Responsive Façade as a Tool for Improving the Built Environment: Case study: Attaba – Opera square

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IOP Conference Series: Earth and Environmental science 2022年第1期992卷

作者： M F Eltanboly M M Afify Architecture Department Teaching Assistant Cairo Higher Institute for Engineering Computer Science and Management No.2 Banafseg Center2 first Settlement Cairo Postcode 11477 Egypt and PhD. Student Environmental Design and Energy Efficiency Department Cairo University Faculty of Engineering Giza Architecture department Professor in Environmental Design and Energy Efficiency Department Cairo University Faculty of Engineering Giza

The path we are currently following to improve the physics of construction due to the destruction of our environment through the negative impact of the built environment around us. Current efforts to decrease carbon and energy are noted by modifying and improving modern 'layered' envelopes and improving a misunderstanding of thermal comfort that has become much less effective than expected. Understanding construction physics is an important factor in improving the quality of life indoor and outdoor, relying primarily on theoretical and computational aspects, then applying the practical part to reduce excessive use of carbon and energy and hinder the elaboration of a sustainable and built environment. This paper shows how the problem stems from neglecting to think about the first principles and basic construction physics. Similarly, we show how combining good construction physics with reassessing old methodologies to construction and structure use provides some powerful and effective tools to address the climate emergency. The use of construction physics methods supports the search for new solutions to express the most important factors affecting thermal conduction and minimum surface temperatures. However, buildings have integrated concepts in which advanced systems work together to optimize energy, comfort, and health performance. Convergence between various engineering fields and the overlapping field of construction techniques and services has great potential in the future to achieve the next step in energy saving. As a result, the research highlights new methods that make our buildings interact with the external environment using climate data, responsive envelope design, simulation software, thereby achieving architectural response to surrounding variables, improving building physics to reduce environmental pollution, thermal comfort, and achieving the highest energy efficiency and performance in buildings. This is illustrated by the presentation of a case study

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Mixture-of-experts variational autoencoder for clustering and generating from similarity-based representations

arXiv

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arXiv 2019年

作者： Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred Institute of Molecular Systems Biology Department of Biology ETH Zürich Switzerland Biomedical Informatics Group Department of Computer Science ETH Zürich Switzerland Switzerland Life Science Graduate School Zurich PhD Program Systems Biology Switzerland

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Model of process and model of natural language processing system

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IOP Conference Series: Materials science and Engineering 2020年第1期878卷

作者： M Zhekova G Totkov PHD student Department of Computer Science University of Plovdiv 'Paisii Hilendarski' Tzar Asen str.24 Plovdiv Bulgaria Prof. Department of Computer Science University of Plovdiv 'Paisii Hilendarski' Tzar Asen str.24 Plovdiv Bulgaria

Natural language processing is the set of methods for making human language accessibility from computers. An approach that facilitates search in database even for casual non-technical literate users is to use a natural language interface (IEE) for databases. It is important for these IEEs to be intelligent and reliable enough to interpret the most complex sentences most intelligently and effectively enough to deliver results in the shortest possible time. The advantage of natural language interfaces is that they are accessible to a wide range of end users in order to 'speak' to the database. Introduction. The solution to the problem of natural-language understanding can be accomplished in two steps: linguistic and database processing. In the first stage of language processing, the natural language query is linked and translated to the corresponding SQL query by appropriate production rules, methods and functions. In the second processing stage, the database is accessed and manipulated and the SQL query is executed by the system. The study presents a research in the field of computational linguistics on a theoretical process model and a natural language processing system model. The process model itself represents knowledge and reflects the conceptual organization of human memory, as well as its flexibility and clarity.

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