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arXiv 2025年

作者： Krueger, Ryan K. Aviran, Sharon Mathews, David H. Zuber, Jeffrey Ward, Max School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Biomedical Engineering Genome Center University of California Davis DavisCA95616 United States Department of Biochemistry and Biophysics Center for RNA Biology University of the Rochester Medical Center RochesterNY14642 United States Department of Computer Science and Software Engineering The University of Western Australia PerthWA Australia

The Nearest Neighbor model is the de facto thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains ≈ 13, 000 underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in differentiable folding, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems. Copyright © 2025, The Authors. All rights reserved.

关键词： RNA

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Leveraging Ai and NLP in Chatbot Development: An Experimental Study

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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Frontiers of information Technology (FiT)

作者： Abdul Wahab Paracha Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Faculty of Electronic Systems Engineering University of Regina Regina Canada

in the current era of chatbots, this research delves into the advancements in Ai chatbots, drawing on artificial intelligence (Ai) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model’s performance indicates a considerable dependence on the dataset’s size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems.

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Mitigating Crop Losses: Ai-enabled Disease Detection in Tomato Plants

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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Frontiers of information Technology (FiT)

作者： Hassan Ashfaq Usama Arshad Raja Hashim Ali Zain ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (Ai) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of Ai in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model’s performance. To address this, we propose using regularization and data augmentation techniques to enhance the model’s generalizability on unseen data. Additionally, we delve into inherent challenges that plague Ai-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. in identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of Ai technologies in agriculture.

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image Reconstruction with B0 inhomogeneity using an interpretable Deep Unrolled Network on an Open-bore MRi-Linac

arXiv

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arXiv 2024年

作者： Shan, Shanshan Gao, Yang Waddington, David E.J. Chen, Hongli Whelan, Brendan Liu, Paul Z.Y. Wang, Yaohui Liu, Chunyi Gan, Hongping Gao, Mingyuan Liu, Feng Soochow University Suzhou215123 China Image X Institute Sydney School of Health Sciences Faculty of Medicine and Health University of Sydney Sydney2015 Australia Department of Medical Physics Ingham Institute of Applied Medical Research Liverpool2170 Australia School of Computer Science and Engineering Central South University Changsha410083 China School of Electrical Engineering and Computer Science University of Queensland Brisbane4072 Australia Institute of Electrical Engineering Chinese Academy of Science Beijing100190 China School of Software Northwestern Polytechnical University Taicang215400 China

MRi-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. in this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free images from B0 inhomogeneity-corrupted k-space for fast MRi-guided radiotherapy applications. RebinNet includes convolutional neural network (CNN) blocks to perform image regularizations and nonuniform fast Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information. The RebinNet was trained on a publicly available MR dataset from eleven healthy volunteers for both fully sampled and subsampled acquisitions. Grid phantom and human brain images acquired from an open-bore 1T MRi-Linac scanner were used to evaluate the performance of the proposed network. The RebinNet was compared with the conventional regularization algorithm and our recently developed UnUNet method in terms of root mean squared error (RMSE), structural similarity (SSiM), residual distortions, and computation time. imaging results demonstrated that the RebinNet reconstructed images with lowest RMSE (0.92) at four-time acceleration for simulated brain images. The RebinNet could better preserve structural details and substantially improve the computational efficiency (ten-fold faster) compared to the conventional regularization methods, and had better generalization ability than the UnUNet method. The proposed RebinNet can achieve rapid image reconstruction and overcome the B0 inhomogeneity distortions simultaneously, which would facilitate accurate and fast image guidance in radiotherapy treatments. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Performance Evaluation of Popular Deep Neural Networks for N...

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Frontiers of information Technology (FiT)

作者： Muhammad Naeem Abu Bakar Siddique Raja Hashim Ali Usama Arshad Zain ul Abideen Talha Ali Khan Muhammad Huzaifa Shah Ali Zeeshan ijaz Nisar Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels.

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

International Journal of Thermofluids

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international Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. in this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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Use of digital platforms for enhancing academic performance. Systematic Review

Use of digital platforms for enhancing academic performance....

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international iEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Cristina Anamaria Costescu Mălina Șogor Sabina Pălimariu Jozsef Katona ioana Tufar Carla Maria Avram Special Education Department Babeș-Bolyai University Cluj-Napoca Romania Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills.

关键词： Performance evaluation Power engineering Databases Personal digital devices Tablet computers Libraries

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incremental Reduction Methods Based on Granular Ball Neighborhood Rough Sets and Attribute Grouping

SSRN

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SSRN 2023年

作者： Li, Yan Wu, Xiaoxue Wang, Xizhao Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal University Zhuhai Campus Zhuhai519087 China College of Mathematics and Information Science Hebei University Baoding071002 China Big Data Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China

in the framework of rough sets, incremental algorithms can effectively reduce repetitive computations for dynamic datasets by discovering the update principles of relevant *** the variations in attribute sets, incremental methods based on neighborhood granulation provide fast updates to attribute reduction in continuous-valued information systems. However, most of these methods assume that the sample distribution and neighborhood radius remain fixed with changes in the attribute set, which may mislead the granulation process and affect the model’s classification ability. in this paper, a new incremental reduction method based on granular balls and attribute grouping is proposed for dynamic information systems with multiple attribute additions. Different neighborhood radii are adaptively determined when the attribute set changes, and the number of neighborhood granules can also be effectively ***, the attributes are grouped based on the k-means algorithm, and only attributes from different groups or with small relevance to those in the current reduction set are considered to be incorporated as a reduction attribute, thus reducing the computation time and simultaneously preserving informative *** and nonlinear correlation coefficients are used to measure attribute relevance based on which three corresponding incremental reduction algorithms are ***, extensive experimental results on 12 benchmark datasets are shown to compare the proposed method with non-incremental and typical incremental methods in terms of time cost, classification accuracy, the size of attribute reduction, and coverage of the reduction. © 2023, The Authors. All rights reserved.

关键词： Granulation

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High-temperature Fatigue and Creep Performance of Additively Manufactured NiCu-based Alloy

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Procedia Structural integrity 2024年 52卷 143-153页

作者： ivo Šulák Alice Chlupová Tomáš Záležák ivo Kuběna Jan-Philipp Roth Katrin Jahns Ulrich Krupp Tomáš Kruml Institute of Physics of Materials Czech Academy of Sciences Žižkova 22 616 00 Brno Czech Republic Faculty of Engineering and Computer Science Osnabrück University of Applied Sciences Albrechtstr. 30 49076 Osnabrück Germany Steel Institute IEHK RWTH Aachen University Intzestraße 1 52072 Aachen Germany

NiCu-based alloys are frequently employed as a key material for heat exchangers and superheated steam systems where the emphasis is put on the resistance against metal dusting and corrosion in harsh environments and, at the same time, excellent mechanical properties at elevated temperatures. The present contribution delivers the experimental results on high-temperature fatigue and creep performance of additively manufactured NiCu-based Alloy 400 and compares it with conventionally produced material. The fatigue tests were performed in symmetrical force control loading in laboratory air at elevated temperatures. Standard constant stress uniaxial creep tests were carried out up to the rupture at applied stresses ranging from 30 to 150 MPa and temperatures between 600–880°C in a protective argon atmosphere. Results indicate inferior fatigue and creep properties of additively manufactured Alloy 400. This detrimental effect of the manufacturing process on mechanical properties can be caused by several factors, including the orientation of the building direction relative to the loading direction, strong texture in 〈011〉 direction, and also by the presence of defects revealed by means of scanning electron microscopy on fracture surfaces of additively manufactured materials.

关键词： Additive manufacturing Alloy 400 fatigue lifetime creep behaviour electron microscopy

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Pareto optimality based PiD controller design for vehicle active suspension system using grasshopper optimization algorithm

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Journal of Electrical Systems and information Technology 2022年第1期9卷 1-18页

作者： Gampa, Srinivasa Rao Mangipudi, Siva Kumar Jasthi, Kiran B., Mahesh Babu Goli, Preetham Das, D. Balas, Valentina E. Electrical and Electronics Engineering Department Seshadri Rao Gudlavalleru Engineering College Gudlavalleru Krishna District India Division of Energy Matter and Systems School of Science and Engineering University of Missouri Kansas City Kansas City USA Electrical Engineering I.I.T Kharagpur Kharagpur India Department of Automatics and Applied Software Aurel Vlaicu University of Arad Arad Romania

in this paper, a Pareto multiobjective and grasshopper optimization algorithm (GOA) based optimum proportional–integral–derivative (P–i–D) controller design is proposed for improving the vehicle active suspension system dynamics under road disturbance conditions. The Pareto objectives considered are minimization of sprung mass suspension deflection, tyre deflection, sprung mass acceleration minimization and eigenvalue-based objective function. State space model for quarter vehicle active suspension system with P–i–D controller is developed for analyzing the stability and dynamic performance of the system. The sinusoidal-based bump road disturbances are used for testing the robustness of the proposed control technique. Simulation results have been presented to show the advantage of the proposed Pareto multiobjective and GOA-based P–i–D controller over the weighted multiobjective and genetic algorithm-based P–i–D controller in terms of stability and dynamics of the active suspension system.

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