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作者： Lang, Jan J. Baylacher, Veronika Micheler, Carina M. Wilhelm, Nikolas J. Hinterwimmer, Florian Schwaiger, Benedikt Barnewitz, Dirk von Eisenhart-Rothe, Rüdiger Grosse, Christian U. Burgkart, Rainer Munich Germany Department of Orthopedics and Sports Orthopedics School of Medicine TUM Munich Germany Institute for Machine Tools and Industrial Management School of Engineering and Design TUM Munich Germany Munich Institute of Robotics and Machine Intelligence Department of Electrical and Computer Engineering TUM Munich Germany School of Medicine Institute for AI and Informatics in Medicine TUM Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine TUM Munich Germany Research Centre of Medical Technology and Biotechnology Bad Langensalza Germany Non-destructive Testing Department of Mechanical Engineering TUM Munich Germany

Introduction: Osteosynthesis of the equine femur is still a challenge for veterinary medicine. Even though intramedullary fracture fixation is possible nowadays, the varying geometry of the medullary cavity along the bone axis is a critical factor. Limited contact area between implant and bone can cause insufficient primary stability. In this study, it was investigated whether the osteosynthesis stability can be improved with a form-adaptive reinforcement for the diaphyseal part of the proximal fragment. Material and Methods: Eight equine femora were fitted with intramedullary nail osteosynthesis and analyzed by 4-point bending. Virtual position planning of the ex-vivo implantation using CT-data increased comparability. For five femora the proximal fragment was reinforced with a flexible polymer mixture. Longterm stability was tested via cyclic loading. Bending stiffness and its development due to cyclic loading was evaluated before and after reinforcement procedure. Finally, load-to-failure was tested in the same setup. Results and Discussion: The application of the polymer reinforcement increased the maximum torque in the load-to-failure measurement by 26%. Bending stiffness was not affected in the measured loading range by the reinforcement. Cyclic loading increased bending stiffness for a conditioned state but showed to be reversible for the most part. Conclusion: The fracture adjacent reinforcement showed to be beneficial to the osteosynthesis stability, but further investigation is necessary for surgical application. © 2022 The Author(s), published by De Gruyter.

关键词： Reinforcement

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An Optimized Approach to Hourly Temperature and Humidity Setpoint Generation for Reducing Tomato Disease and Saving Power Cost in Greenhouses

SSRN

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SSRN 2024年

作者： Sánchez-Molina, Jorge Antonio Wang, Hui Laktionov, Ivan Díaz, Francisco Rodríguez Li, Ming BIPP Institute of Plant Protection Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Department of Computer Systems Software Dnipro University of Technology DniproUA49005 Ukraine Department of Informatics University of Almería ceiA3 CIESOL Almería04120 Spain Beijing Research Center for Information Technology in Agriculture National Engineering Research Centre for Information Technology in Agriculture National Engineering Laboratory for Agri-product Quality Traceability Key Laboratory of Agri-informatics Ministry of Agriculture Beijing100097 China

Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops. The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop. Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems. The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day-1 in warm weather condition, as well as 3.74 €*day-1 in cold weather condition, the uninfected LAI (m2[Leaves](m-2[soils]*day-1)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose. The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth. © 2024, The Authors. All rights reserved.

关键词： Crops

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Quantum advantage for noisy channel discrimination

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Physical Review A 2022年第3期105卷 032401-032401页

作者： Zane M. Rossi Jeffery Yu Isaac L. Chuang Sho Sugiura Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Department of Electrical Engineering and Computer Science and Co-Design Center for Quantum Advantage Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. Sunnyvale California 94085 USA

Many quantum mechanical experiments can be viewed as multiround interactive protocols between known quantum circuits and an unknown quantum process. Fully quantum “coherent” access to the unknown process is known to provide an advantage in many discrimination tasks compared to when only incoherent access is permitted, but it is unclear if this advantage persists when the process is noisy. Here, we show that a quantum advantage can be maintained when distinguishing between two noisy single-qubit rotation channels. Numerical and analytical calculations reveal a distinct transition between the performance of fully coherent and fully incoherent protocols as a function of noise strength. Moreover, the size of the region of coherent quantum advantage shrinks inverse polynomially in the number of channel uses, and in an intermediate regime an improved strategy is a hybrid of fully coherent and fully incoherent subroutines. The fully coherent protocol is based on quantum signal processing, suggesting a generalizable algorithmic framework for the study of quantum advantage in the presence of realistic noise.

关键词： Quantum algorithms Quantum channels Quantum computation Quantum information processing Quantum sensing Quantum simulation

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A Comparative Study of Machine Learning Approaches for Autism Detection in Children from Imaging Data

A Comparative Study of Machine Learning Approaches for Autis...

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2022 International Conference of Yearly Reports on informatics, Mathematics, and engineering, ICYRIME 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects language, communication, cognitive, and social skills. Early detection of ASD in children is crucial for effective intervention, and machine learning techniques have emerged as promising tools to improve the accuracy and efficiency of detection. This paper presents a range of Machine Learning approaches that have been applied to identify individuals with ASD, with a particular focus on children, using images as input data. The results of these studies demonstrate the potential for Machine Learning to aid in the early detection and diagnosis of ASD in children, which can lead to better outcomes for individuals with this condition. © 2022 Copyright for this paper by its authors.

关键词： Machine learning

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Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

arXiv

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arXiv 2023年

作者： Lin, Junhao Dai, Qian Zhu, Lei Fu, Huazhu Wang, Qiong Li, Weibin Rao, Wenhao Huang, Xiaoyang Wang, Liansheng School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Medicine Xiamen University Xiamen China

Breast lesion segmentation in ultrasound (US) videos is essential for diagnosing and treating axillary lymph node metastasis. However, the lack of a well-established and large-scale ultrasound video dataset with high-quality annotations has posed a persistent challenge for the research community. To overcome this issue, we meticulously curated a US video breast lesion segmentation dataset comprising 572 videos and 34,300 annotated frames, covering a wide range of realistic clinical scenarios. Furthermore, we propose a novel frequency and localization feature aggregation network (FLA-Net) that learns temporal features from the frequency domain and predicts additional lesion location positions to assist with breast lesion segmentation. We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos. Our experiments on our annotated dataset and two public video polyp segmentation datasets demonstrate that our proposed FLA-Net achieves state-of-the-art performance in breast lesion segmentation in US videos and video polyp segmentation while significantly reducing time and space complexity. Our model and dataset are available at https://***/jhlDet/FLA-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Location

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Multidetector CT in the Analysis of Pulmonary Thromboembolism

Multidetector CT in the Analysis of Pulmonary Thromboembolis...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Rohini M. S Balaji V Vijai R Rajesh V D. ChandraMohan Ajay Singh Yadav Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Mechanical Engineering Sri Sairam College of Engineering Karnataka India Department of Mechanical Engineering St. Peter’s Institute of Higher Education and Research Chennai India Department of Mathematics Delhi-NCR Campus SRM Institute of Science and Technology Uttar Pradesh India

This study aims to investigate the utility of Multidetector computed tomography (MDCT) with a focus on the application of reformats in maximum intensity projection (MIP) in enhancing the diagnostic accuracy of PTE. A retrospective analysis was conducted on a cohort of [insert number] patients who underwent MDCT for suspected PTE. The MDCT images were reconstructed using MIP, allowing for comprehensive visualization of vascular structures. The diagnostic performance of MDCT with MIP reformats was compared to standard MDCT images. The inclusion of MIP reformats significantly improved the detection of PTE, providing enhanced visualization of thrombi within the pulmonary vasculature. The sensitivity and specificity of MDCT with MIP were [insert percentage] and [insert percentage], respectively, compared to [insert percentage] and [insert percentage] for standard MDCT. The findings highlight the added diagnostic value of MDCT with reformats in MIP for the evaluation of PTE. The improved visualization of thromboemboli within the pulmonary arteries (PA) enhances diagnostic accuracy, aiding clinicians in making timely and informed decisions. MDCT with MIP reformats stands as a valuable technique in the comprehensive assessment of pulmonary thromboembolism, contributing to better patient outcomes.

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UAV image acquisition and processing for high-throughput phenotyping in agricultural research and breeding programs

Plant Phenome Journal

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Plant Phenome Journal 2024年第1期7卷

作者： Bongomin, Ocident Lamo, Jimmy Guina, Joshua Mugeziaubwa Okello, Collins Ocen, Gilbert Gilibrays Obura, Morish Alibu, Simon Owino, Cynthia Awuor Akwero, Agnes Ojok, Samson National Crops Resources Research Institute (NaCRRI) Kampala Uganda Department of Manufacturing Industrial and Textile Engineering School of Engineering Moi University Eldoret Kenya Department of Information and Communication Technology National Agricultural Research Organisation (NARO) Secretariat Entebbe Uganda Department of Biosystems Engineering Faculty of Agriculture and Environment Gulu University Gulu Uganda Department of Computer Engineering & Informatics Faculty of Engineering Busitema University Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University Kampala Uganda School of Agricultural Sciences Makerere University Kampala Uganda

We are in a race against time to combat climate change and increase food production by 70% to feed the ever-growing world population, which is expected to double by 2050. Agricultural research plays a vital role in improving crops and livestock through breeding programs and good agricultural practices, enabling sustainable agriculture and food systems. While advanced molecular breeding technologies have been widely adopted, phenotyping as an essential aspect of agricultural research and breeding programs has seen little development in most African institutions and remains a traditional method. However, the concept of high-throughput phenotyping (HTP) has been gaining momentum, particularly in the context of unmanned aerial vehicle (UAV)-based phenotyping. Although research into UAV-based phenotyping is still limited, this paper aimed to provide a comprehensive overview and understanding of the use of UAV platforms and image analytics for HTP in agricultural research and to identify the key challenges and opportunities in this area. The paper discusses field phenotyping concepts, UAV classification and specifications, use cases of UAV-based phenotyping, UAV imaging systems for phenotyping, and image processing and analytics methods. However, more research is required to optimize UAVs’ performance for image data acquisition, as limited studies have focused on the effect of UAVs’ operational parameters on data acquisition. © 2024 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.

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Enhancing IoT Security Through AI-Based Anomaly Detection and Intrusion Prevention

Enhancing IoT Security Through AI-Based Anomaly Detection an...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Narayana Swamy Ramaiah S. Kevin Andrews A. Shenbagharaman M. S. Gowtham Bandi Bhaskar Mohit Tiwari Dept of Computer Engineering SPCE Sankalchand Patel University Visnagar Gujarat India Department of Computer Applications Dr. Mgr Educational and Research Institute Chennai Tamil Nadu Department of CSE National Engineering College Kovilpatti Tamilnadu India ECE Department Karpagam Institute of Technology Coimbatore Tamilnadu India School of CS & AI SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India

Enhancing the Internet of Things (IoT) security is among the most pressing concerns confronting the information technology sector. With significant numbers of loT systems being created and established, it is difficult for these systems to interact securely without affecting performance. The difficulties arise since the majority of loT systems are resource restricted and so possess restricted processing capability. In, this study, we intend to study the enhancement of IoT security of anomaly detection and intrusion prevention. To enhance anomaly detection and intrusion prevention performance, a binary categorization of typical and unusual IoT traffic is created. In this paper, we carefully evaluate the specificity and complication of IoT security protection, and then discover that Artificial Intelligence (AI) approaches like Machine Learning (ML) and ensemble classifiers may offer new strong abilities to satisfy IoT security demands. This enhancement can be associated with ensemble learning methods that make use of a variety of learning processes with variable capacities. We were capable of improving the predictability of our predictions while decreasing the likelihood of classification errors through the combination of these methods. The outcomes of the experiments suggest that the architecture could enhance the effectiveness of the anomaly detection and intrusion prevention System, with an accuracy level of 0.9863.

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GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

arXiv

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arXiv 2024年

作者： Kavouras, Loukas Psaroudaki, Eleni Tsopelas, Konstantinos Rontogiannis, Dimitrios Theologitis, Nikolaos Sacharidis, Dimitris Giannopoulos, Giorgos Tomaras, Dimitrios Markou, Kleopatra Gunopulos, Dimitrios Fotakis, Dimitris Emiris, Ioannis Institute for the Management of Information Systems Athena Research Center Athens Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece Université Libre de Bruxelles Bruxelles Belgium Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Department of Informatics Athens University of Economics and Business Athens Greece Archimedes Research Unit Athena Research Center Athens Greece

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. Effectiveness is measured by the fraction of the population that is provided recourse, ensuring that the actions benefit as many individuals as possible. Keeping the cost of actions low ensures the proposed recourse actions remain practical and actionable. Limiting the number of actions that provide global counterfactuals is essential to maximize interpretability. The primary challenge, therefore, is balancing these trade-offs—maximizing effectiveness, minimizing cost, while maintaining a small number of actions. We introduce GLANCE, a versatile and adaptive framework, comprising two algorithms, that allows the careful balancing of the trade-offs among the three key objectives, with the size objective functioning as a tunable parameter to keep the actions few and easy to interpret. C-GLANCE employs a clustering approach that considers both the feature space and the space of counterfactual actions, thereby accounting for the distribution of points in a way that aligns with the structure of the model. T-GLANCE provides additional features to enhance flexibility. It employs a tree-based approach, that allows users to specify split features, to build a decision tree with a single counterfactual action at each node that can be used as a subgroup policy. Our extensive experimental evaluation demonstrates that our method consistently shows greater robustness and performance compared to existing methods across various datasets and models. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

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A Systematic Overview on Simulation of Connected Vehicles Infrastructure in Smart Cities

A Systematic Overview on Simulation of Connected Vehicles In...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Manik Gupta Imed Ben Dhaou Kamal Deep Garg Bhisham Sharma Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The development and use of connected vehicles are becoming increasingly important areas of study in the wireless networking and transportation research communities. These networks can provide various services such as collision warning. Unlike other types of devices, such as hand-held devices, the vehicles’ nodes are not limited by their energy consumption and storage capacity. Therefore, the development of connected networks can be carried out with minimal resources. It is necessary to run a simulation in order to determine whether they are feasible. The availability of multiple devices and protocols has led to the creation of new utilization scenarios. Due to the complexity of the task, many simulation tools were developed during the past few years. This paper systematically reviews the current state of these tools and discusses their capabilities to evaluate the various scenarios related to the development of connected vehicles network.

关键词： Wireless communication Technological innovation Connected vehicles Systematics Smart cities Vehicular ad hoc networks Transportation

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