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arXiv 2025年

作者： Logan, Jack A. Sumner, Jacob Grigas, Alex T. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Applied Physics Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States

Proteins are composed of linear chains of amino acids that fold into complex three-dimensional structures. A general feature of folded proteins is that they are compact with a radius of gyration Rg(N) ∼ Nν that obeys power-law scaling with the number of amino acids N in each protein and ν ∼ 1/3. In this study, we investigate the internal scaling of the radius of gyration Rg(n) versus the chemical separation n between amino acids for all subchains of length n. We show that for globular proteins Rg(n) ∼ nν1,2 with a larger exponent ν1 > 1/3 for small n and a smaller exponent ν2 g(N) ∝ Nν. To describe this scaling behavior for Rg(n), we carry out folding simulations for a series of coarse-grained models for proteins beginning with the freely-jointed and freely-rotating chain models composed of spherical monomers and varying degrees of stereochemical constraints. We show that a minimal model, which coarse-grains amino acids into a single spherical backbone atom and one variable-sized spherical side-chain atom, and enforces bend- and dihedral-angle constraints, can recapitulate Rg(n) for x-ray crystal structures of globular proteins. In addition, this model predicts the correct average packing fraction and size of the hydrophobic core, which are two key physical features that can be used to distinguish between computational 'decoys' and correctly folded proteins in protein design applications. © 2025, CC BY.

关键词： Spheres

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Implementation of MCL algorithm in clustering digital news with graph representation 4

Implementation of MCL algorithm in clustering digital news w...

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4th International Conference on Information and Communication Technology, ICoICT 2016

作者： Al-Fath, Alwan M Ubaidillah Saleh, W. Kemas Rahmat Sa'Adah, Siti Informatics Engineering Program Informatics Faculty Telkom University Bandung Indonesia

ISBN: (纸本)9781467398794

Digital news will continue to grow up and evolve, it bring up the new issue for modeling digital news data that are stored in the database so that it is easier to understand and be able to take some important information thoroughly. To simplify the information processing in the database it is require a model and a specific method for clustering the news based on proximity and characteristics of the digital news. Using a graph database models and methods, MCL graph clustering algorithm (Markov Cluster Algorithm) can simplify information processing by identifying the characteristics of each vertex in the graph so that it will establish cluster vertices with a specific label. In the process of identifying a clusters of graph, the digital news documents will be stored into one vertex to be connected with other vertex in common category of news. The process of expanding and inflating matrix will be the main process in the clustering of digital news that has been transformed into a graph database models that expand aims to show a new edge and remove the old edge in common not needed in the graph. Meanwhile the process of inflating aims to strengthen the strong edge and weaken the weak edge. So that with the clustering of digital news, process of inflating matrix is very influential for execution time in MCL algorithm and process of inflating matrix influence the number of cluster will be formed. © 2016 IEEE.

关键词： Graph Databases

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Convolutional Neural Network for Cataract Clasification Using Primary Dataset 9

Convolutional Neural Network for Cataract Clasification Usin...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Totok Suryawan, I Gede Putra, I Wayan Murmayasa Adi Informatics Engineering Study Program Faculty of Technology and Informatics Denpasar Indonesia

ISBN: (纸本)9798350383010

Cataracts are the main factor causing blindness in Indonesia. Traditional diagnostic methods involving clinical examination by an ophthalmologist using a slit-lamp and visual tests have many challenges such as limited resources, time, and medical expertise may not be available in all locations, especially in less developed areas. Utilizing medical image technology is a wise choice because it allows early detection and monitoring of the development of this cataract disease Technology is needed to identify medical images that have valuable information so that early detection or monitoring of cataract development can be carried out. CNN as a part of deep learning has the ability to perform automatic feature extraction so that it can identify eye images. A CNN model for recognizing cataracts in eye images taken directly from Bhayangkara Hospital in Denpasar Bali has been developed in this research. The model developed is equipped with a dropout layer technique to prevent overfitting. The test results show that the CNN developed in this research produces very good performance with an average accuracy level of 92% on public dataset images, and 94% on primary dataset images. Tested using the primary dataset and public dataset, this CNN model shows very good performance because it can correctly identify an average of 94% of eye images in the primary dataset, and 92% of eye images in the public dataset.. © 2024 IEEE.

关键词： Convolutional neural networks

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A ghost mechanism: An analytical model of abrupt learning

arXiv

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arXiv 2025年

作者： Dinc, Fatih Cirakman, Ege Jiang, Yiqi Yuksekgonul, Mert Schnitzer, Mark Tanaka, Hidenori CNC Program Stanford University StanfordCA94305 United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States Stanford University StanfordCA94305 United States James H. Clark Center for Biomedical Engineering & Sciences Stanford University Stanford United States Howard Hughes Medical Institute Stanford University StanfordCA94305 United States CBS-NTT Program in Physics of Intelligence Harvard University CambridgeMA94305 United States

Abrupt learning is commonly observed in neural networks, where long plateaus in network performance are followed by rapid convergence to a desirable solution. Yet, despite its common occurrence, the complex interplay of task, network architecture, and learning rule has made it difficult to understand the underlying mechanisms. Here, we introduce a minimal dynamical system trained on a delayed-activation task and demonstrate analytically how even a one-dimensional system can exhibit abrupt learning through ghost points rather than bifurcations. Through our toy model, we show that the emergence of a ghost point destabilizes learning dynamics. We identify a critical learning rate that prevents learning through two distinct loss landscape features: a no-learning zone and an oscillatory minimum. Testing these predictions in recurrent neural networks (RNNs), we confirm that ghost points precede abrupt learning and accompany the destabilization of learning. We demonstrate two complementary remedies: lowering the model output confidence prevents the network from getting stuck in no-learning zones, while increasing trainable ranks beyond task requirements (i.e., adding sloppy parameters) provides more stable learning trajectories. Our model reveals a bifurcation-free mechanism for abrupt learning and illustrates the importance of both deliberate uncertainty and redundancy in stabilizing learning dynamics. © 2025, CC BY.

关键词： Recurrent neural networks

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Secure E-Voting System for Student Associations in Private University Using Paillier Algorithm 21

Secure E-Voting System for Student Associations in Private U...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Tenusa, Ivan Surbakti, Eunike Endariahna Tobing, Fenina Adline Twince Kusnadi, Adhi Universitas Multimedia Nusantara Informatics Study Program Informatics and Engineering Faculty Indonesia

ISBN: (纸本)9798350318821

The representatives of student associations at Private University are entrusted with the responsibility of empowering, facilitating, and nurturing the growth of the students, lecturers, and alumni within their respective programs. If the elected representatives do not align with the general consensus, problems are bound to arise. Therefore, student participation during the annual voting period is of utmost importance. When voting participation is low, the risk of the majority voice not being heard increases. Data from the General Election Commission in the private university reveals that among the four student associations in the Tech and IT Faculty, three of them have a participation rate lower than 50%.This research aims to develop an E-Voting system for Student Associations within the university, with the intention of replacing the currently utilized Google Form system and enhancing student participation in future voting periods. While constructing an electronic voting system, the security and confidentiality of participants are vital. The Paillier homomorphic encryption algorithm is employed in the system to encrypt and decrypt voting messages sent by voters, ensuring their confidentiality. This algorithm was chosen due to its relative recency, speed, and relevance to this research in comparison to other algorithms. A user acceptance test conducted using the Technology Acceptance Model (TAM) has demonstrated that the application has been successfully developed, with user assessments of 87.50% for perceived ease of use, 72.27% for perceived usefulness, 74.74% for behavioral intention, and 86.46% for actual usage. © 2023 IEEE.

关键词： Students

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Sensing the world:: Challenges on WSNs

Sensing the world:: Challenges on WSNs

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IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2008)

作者： Rocha, Valter Goncalves, Gil Agilus I+D PRODEI - Doctoral Program in Informatics Engineering Faculty of Engineering of the University of Porto Informatics Engineering Department Faculty of Engineering of the University of Porto

ISBN: (纸本)9781424425761

Wireless sensor network (WSNs) is a demanding multidisciplinary field that has been target of research in the last decade. The increasing demand for security and automated monitoring of things and places makes WSNs a promising technology although there are still many open research contributions needed to make this the standard of environmental sensing. The goal of this article is to identify the research challenges on WSNs by dividing them into functional groups, building on previous work. We followed a structured approach based on a simplified yet complete vision of a design space for WSNs. Moreover, this work aims to identify research gaps and investigation fields yet unexplored or hardly explored by researchers in order to plot paths for future research. Several challenges and research areas were identified, like Models for Sensor Networks, Benchmarking Methodologies, Distributed Processing, Interface WSNs and Network Reprogramming. Some of these challenges will become even clearer as increasingly WSNs become Wireless Sensor and Actuator Networks (WSANs).

关键词： wireless sensor networks, WSN pervasive computing mesh networking

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Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

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Sentiment Analysis using Recurrent Neural Network 1

Sentiment Analysis using Recurrent Neural Network

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Kurniasari, Lilis Setyanto, Arif Program of Informatics Engineering Universitas AMIKOM Yogyakarta Indonesia

This study aims to measure the accuracy of the sentiment analysis classification model using deep learning and neural networks. This study used the algorithm Recurrent Neural Network (RNN) and Word2vec. No previous research has used this model to analyze sentiments written using Indonesian language so that the level of accuracy is unknown. The research began by making a classification model of sentiment analysis. Then, the model was tested through experiments. In this study, They used two classifications (positive and negative). Experiments are carried out using training data sets and the test used data sets sourced from Traveloka theybsite. The result shows that the model presents outstanding results and reaches about 91.9%. © Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

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Design of Waste Management System Using QR Code for Effective Management in Wastebank 1

Design of Waste Management System Using QR Code for Effectiv...

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1st Paris Van Java International Seminar on Computer, Science, engineering, and Technology, PVJ ISComSET 2020

作者： Widaningsih, S. Suheri, A. Informatics Engineering Program Suryakancana University Cianjur Indonesia

Waste management is one of the problems faced in almost every city in Indonesia. One way to manage waste is through the waste bank. The business process of a waste bank is similar to a bank, but it is processed in the form of inorganic waste that has been sorted. In general, data management in waste banks is still conventional but the others have used of web-based or mobile technology. But in this way there are still some weaknesses such as the the time needed to input the collected waste data because the data cannot be directly entered into the system. In addition it can occur inaccurately entered data. The proposed method is using the QR code for the waste management system. In this method, admin only need to scan the QR code label attached on the waste bin stored above the weighing device using a smartphone camera. With this system QR scanner application based on mobile can be synchronized automatically with anroid smartphone. Admin then input the weight of the waste that is weighed and will be stored in the database. In this way the data input process can be faster and with the use of QR code, more data can be stored. © Published under licence by IOP Publishing Ltd.

关键词： Information management

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Design Technology of Geographical Information System of Rice Pandanwangi Cianjur Prediction Factors Products 1

Design Technology of Geographical Information System of Rice...

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1st Paris Van Java International Seminar on Computer, Science, engineering, and Technology, PVJ ISComSET 2020

作者： Musrifah, Ai Adikusuma, Andri Sutono Informatics Engineering Study Program Faculty of Engineering University of Suryakancana Indonesia

Cianjur is one of the renowned rice producers, particularly with the presence of Pandanwangi rice that can be grown in the Cianjur area. Pandanwangi has become one of Cianjur City's icons, the rice supremacy created by Pandanwangi Cianjur's tenacity and distinctive scent (pandanus fragrance). The Interest of pandanwangi was very high, both within the town of Cianjur and outside the town of Cianjur, but this was inversely proportional to the desires of the groups of farmers who appeared to prefer to plant rice other than Pandanwangi, on the grounds that the planting of Pandanwangi had a long harvest and that the likelihood of crop failure was also very high. Problems with information flows that are not well integrated between farmers' groups, farmers' group organizations, and the Agriculture Department in adjusting land details, planting time, planting location, estimating total harvest and harvesting time. To solve this problem, a framework must be built that can integrate the information needed for Pandanwangi Cianjur Harvest Prediction with Pandanwangi farmers' regional analysis based on geographic information system technology, using Google Maps Service. © Published under licence by IOP Publishing Ltd.

关键词： Harvesting

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