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Preprint REVIEW | doi:10.20944/preprints201908.0203.v1

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Sina Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; deep learning; ensemble models

Online: 20 August 2019 (08:41:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201610.0055.v1

Numerical Simulation of Hypervelocity Impact FEM-SPH Algorithm Based on Large Deformation of Material

Aimin Yang, Jinze Li, Hengheng Qu, Yuhang Pan, Yanhong Kang, Yuzhu Zhang

Subject: Computer Science And Mathematics, Analysis Keywords: finite element method; smoothed particle method; fem-sph algorithm; large deformation; super high speed collision

Online: 14 October 2016 (11:05:45 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints201808.0240.v1

Monitoring Moisture Content for Various Kind of Tea Leaves in Drying Processes Using RF Reflectometer-Sensor System

Kok Yeow You, Chia Yew Lee, Kok San Chan, Kim Yee Lee, Ee Meng Cheng, Yeng Seng Lee

Subject: Engineering, Electrical And Electronic Engineering Keywords: tea leaves; microcontroller-based RF reflectometer; large open-ended coaxial probe; reflected voltage; moisture content

Online: 14 August 2018 (04:13:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201608.0206.v3

Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Xikun Hu, Tian Jin

Subject: Engineering, Electrical And Electronic Engineering Keywords: impulse radio ultra-wideband (IR-UWB) radar; noncontact; short-range; vital signs; ensemble empirical mode decomposition (EEMD); continuous-wavelet transform (CWT)

Online: 10 November 2016 (07:13:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints201609.0031.v1

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Bijay Neupane, Wei Lee Woon, Zeyar Aung

Subject: Computer Science And Mathematics, Computer Science Keywords: electricity price forecasting; ensemble model; expert selection

Online: 8 September 2016 (11:52:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201607.0069.v1

Testing Ststistical Hypothesis in Light of Mathematical Aspects in Analysis of Probability

Michael Fundator

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Principal of prediction; random sequences; recurrence; Law of Large Numbers; exponential; normal; bivariate; distribution; Entropy; Information

Online: 23 July 2016 (09:25:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.1723.v1

Truth-O-Meter: Collaborating with LLM in Fighting its Hallucinations

Boris A. Galitsky

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large Language Model; hallucination; fact-checking; multiple inconsistent sources

Online: 25 July 2023 (13:09:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202303.0473.v1

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

Md. Mostafizer Rahman, Yutaka Watanobe

Subject: Social Sciences, Education Keywords: ChatGPT; educational technology; research; programming education; large language model; GPT-3; artificial intelligence

Online: 28 March 2023 (04:14:47 CEST)

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Preprint REVIEW | doi:10.20944/preprints201702.0002.v1

Tests and Problems of the Standard Model in Cosmology

Martin Lopez-Corredoira

Subject: Physical Sciences, Astronomy And Astrophysics Keywords: Cosmology; Observational cosmology; Origin, formation, and abundances of the elements; dark matter; dark energy; superclusters; large-scale structure of the Universe

Online: 1 February 2017 (16:06:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202304.0827.v3

ROSGPT: Next-Generation Human-Robot Interaction with ChatGPT and ROS

Anis Koubaa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Human-Robot Interaction; ROS; ROS2; ChatGPT; Large Language Model; Robot Operating System

Online: 26 April 2023 (03:54:36 CEST)

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Preprint REVIEW | doi:10.20944/preprints201803.0160.v1

A Bibliographical Survey on Software Architectures for Smart Grid System

A. Ramesh, Prabhakar Karthikeyan, Sanjeevikumar Padmanaban, Saravanan Balasubramanian, Josep M. Guerrero

Subject: Engineering, Electrical And Electronic Engineering Keywords: Advanced Metering Infrastructure (AMI); Distributed Energy Resources (DER); Distribution Management System (DMS); Graph Reduction In Parallel (GRIP); Intelligent Electronic Device (IED); Intelligent Platform Management Interface (IPMI); Service Oriented Architecture (SOA); Ultra Large Scale System (ULSS)

Online: 19 March 2018 (11:42:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints201812.0100.v1

On the Application of Massive MIMO Systems to Machine Type Communications

Felipe A. P. de Figueiredo, Fabbryccio A. C. M. Cardoso, Ingrid Moerman, Gustavo Fraidenraichz

Subject: Engineering, Electrical And Electronic Engineering Keywords: Large-scale antenna systems, 5G networks, machine type communications, channel estimation, linear detection

Online: 10 December 2018 (12:38:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints201710.0199.v1

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case study of Uganda

Isaac Mugume, Michel d. S. Mesquita, Yazidhi Bamutaze, Didier Ntwali, Charles Basalirwa, Daniel Waiswa, Joachim Reuder, Revocatus Twinomuhangi, Fredrick Tumwine, Triphonia Jakob Ngailo, Bob Alex Ogwang

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Ensemble mean; Analogue ensemble mean; Multi–member analogue ensemble mean; Quantitative rainfall prediction

Online: 31 October 2017 (16:28:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints201705.0035.v1

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides Using GIS

Quang-Khanh Nguyen, Dieu Tien Bui, Nhat-Duc Hoang, Phan Trong Trinh, Viet-Ha Nguyen, Isık Yilmaz

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: landslide; classifier ensemble; instance based learning; Rotation Forest; GIS; Vietnam

Online: 4 May 2017 (08:25:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201708.0095.v1

Relative Lagrangian Formulation of Finite Thermoelasticity

I-Shih Liu, Marcello G. Teixeira

Subject: Physical Sciences, Mathematical Physics Keywords: relative Lagrangian formulation; thermoelastic solid; small on large; successive linear approximation; boundary value problem

Online: 27 August 2017 (11:36:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201804.0194.v1

The 2017 North Bay and Southern California Fires: A Case Study

Nicholas J. Nauslar, John T. Abatzoglou, Patrick T. Marsh

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: fire weather; fire climate; large wildfires; downslope windstorm; wildland urban interface; drought; foehn winds

Online: 16 April 2018 (08:00:29 CEST)

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Preprint REVIEW | doi:10.20944/preprints202304.0158.v2

ChatGPT: A Brief Narrative Review

Bulbul Gupta, Tabish Mufti, Shahab Saquib Sohail, Dag Øivind Madsen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; OpenAI; Artificial Intelligence (AI); Machine Learning (ML); Large Language Models (LLM)

Online: 29 June 2023 (08:51:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201710.0147.v1

Study on the Best Water Cushion Depth of Stilling Basin with Shallow-Water Cushion at Different Froude Numbers

Qiulin Li, Lianxia Li, Huasheng Liao, Jingjing Wei, Shengyin Jiang, Faxing Zhang

Subject: Physical Sciences, Fluids And Plasmas Physics Keywords: energy dissipation; hydraulic jump; Froude number; stilling basin with shallow-water cushion; large eddy simulation; hydraulic characteristics

Online: 23 October 2017 (08:30:57 CEST)

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The water cushion depth of stilling basin with shallow-water cushion is a key factor that affects the flow regime of hydraulic jump in the basin. However, the specific depth at which the water cushion is considered as “shallow” has not be stated clearly for now, and only conceptual description is provided. This paper attempts to specify the best water cushion depth based on the flow regime of hydraulic jump and underflow speed; namely, in case of critical hydraulic jump in the basin, the best water cushion depth is located where the minimum distance to the bottom plate of the stilling basin is 1/5~1/4 of the water cushion depth. The theoretical analysis indicates, at different inclinations of discharge chute (θ) and depth ratios of inlet (m), instead of monotonic change, the Froude number (Fr) at inlet of the stilling basin with shallow-water cushion firstly reduces and then increases as the flow velocity at discharge chute inlet (V) increases; the parameters of inflection point (critical flow velocity and critical Fr) increase as the inclinations of discharge chute (θ) and depth ratios of inlet (m) increase. Such regularity is the theoretical basis for selecting representative study cases. The reliability of the large eddy simulation calculation results are verified by a model test; in the paper, 30 cases including five different Froude numbers and six shallow-water cushion depths are selected, for calculating the hydraulic factors such as flow profile, flow regime and flow velocity in the stilling basin with shallow-water cushion; and the varying pattern between the best depth of stilling basin with shallow-water cushion (depth-to-length ratio) and the inflow Froude number is obtained which indicates that the best depth of stilling basin with shallow-water cushion varies little as the change of the Froude number before reaching the critical Froude number; however, the best depth-to-length ratio of stilling basin with shallow-water cushion increases as the Froude number increases after the critical Froude number is reached. The study results in this paper are of reference significance to design and calculation of the stilling basin with shallow-water cushion.

Preprint ARTICLE | doi:10.20944/preprints201905.0090.v1

A new Deductive Method with Intelligence Using Pseudorandom Number for Solving Coin-Weighing Puzzles: Review of Inductive and Deductive Methods

Yoshiyasu Takefuji, Ren Yamada

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: intelligence; inductive methods; deductive methods; pseudorandom number; artificial intelligence; Prolog; Otter; Z3; deep learning; ensemble methods; automated reasoning; coin-weighing puzzles

Online: 8 May 2019 (10:03:46 CEST)

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Working Paper REVIEW

Ten Golden Rules for Reforestation to Optimise Carbon Sequestration, Biodiversity Recovery and Livelihood Benefits

Alice Di Sacco, Kate Hardwick, David Blakesley, Pedro H. S. Brancalion, Elinor Breman, Loic Cecilio Rebola, Susan Chomba, Kingsley Dixon, Stephen Elliott, Godfrey Ruyonga, Kirsty Shaw, Paul Smith, Rhian J. Smith, Alexandre Antonelli

Subject: Biology And Life Sciences, Forestry Keywords: Afforestation; Climate change mitigation; Ecological Restoration; Forest landscape restoration; Large-scale tree planting; Natural regeneration; Nature-based solutions; Stakeholders participation

Online: 5 September 2020 (08:01:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0195.v1

LLMs and AI: Understanding Its Reach and Impact

Anand Gokul

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: llm; impact; society; ai; large-language-model; transformer; natural language processing; nlp

Online: 4 May 2023 (05:15:50 CEST)

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Preprint REVIEW | doi:10.20944/preprints202203.0285.v1

Large Power Transformers: Time Now for Addressing Their Monitoring and Failure Investigation Techniques

Jonathan Velasco Costa, Diogo F.F. Silva, P.J. Costa Branco

Subject: Engineering, Electrical And Electronic Engineering Keywords: Large power transformer; condition monitoring; transformer fault diagnosis; diagnostic techniques; mechanical or electrical integrity of the core and windings

Online: 21 March 2022 (10:47:56 CET)

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Preprint ARTICLE | doi:10.20944/preprints201802.0008.v1

Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors

Gia-Thuy Pham, Rémy Boyer, Frank Nielsen

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Optimal Bayesian detection, information geometry, minimal error probability, Chernoff/Bhattacharyya upper bound, large random tensor, Fisher information, large random sensing matrix

Online: 1 February 2018 (16:32:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints201709.0092.v1

Principal Mismatch Patterns across a Simplified Highly Renewable European Electricity Network

Mads Raunbak, Timo Zeyer, Kun Zhu, Martin Greiner

Subject: Engineering, Energy And Fuel Technology Keywords: renewable energy networks; principal component analysis; large-scale integration of renewables; wind power; solar power; super grid; energy system design

Online: 20 September 2017 (04:44:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201805.0248.v1

Rough Noise-Filtered Easy Ensemble for Software Fault Prediction

Saman Riaz, Ali Arshad, Licheng Jiao

Subject: Computer Science And Mathematics, Computer Science Keywords: software fault prediction; data preprocessing; feature selection; rough set theory; class imbalance; noise filter; easy ensemble

Online: 17 May 2018 (13:01:51 CEST)

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Preprint REVIEW | doi:10.20944/preprints201806.0433.v1

Electroplating of Electronic Materials for Applications in Large Area Electronics: A Review

A.A. Ojo, I.M. Dharmadasa

Subject: Chemistry And Materials Science, Surfaces, Coatings And Films Keywords: Electroplating; Semiconductors; Large area electronics; Characterisation; Solar cells

Online: 26 June 2018 (16:18:18 CEST)

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Working Paper ARTICLE

Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets

Hai Van Pham, Dat Hoang Thanh, Philip Moore

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Knowledge graphs; hierarchical pooling; graph classification; graph neural networks; FPool; large graph sensor datasets

Online: 15 June 2021 (14:32:53 CEST)

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Working Paper ARTICLE

A New 3D Geological Modeling Method Based On Remote Sensing and Its Application in Ya 'an Area Sichuan Province

Zhaoyang Ma, Lihai Zhang, Yan Wang, Guifan Zhu, Yinping Liang

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Multi-source heterogeneity; remote sensing; large-scale high-precision modeling; grid division; geostatistics; spatial interpolation

Online: 20 January 2021 (16:19:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202012.0092.v1

Machine Learning-based Anomaly Detection with Magnetic Data

Peetak Mitra, Denis Akhiyarov, Mauricio Araya-Polo, Daniel Byrd

Subject: Engineering, Mechanical Engineering Keywords: anomaly detection; machine Learning; large stand off magnetometry; multimodal data; RAPIDS-AI

Online: 4 December 2020 (07:13:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints201712.0131.v1

Is Equilibrium Modelling Outdated for Recent Challenges in River Management?

Arianna Varrani, Michael Nones

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: 1D modelling; large rivers; morphodynamic equilibrium; river concavity; bottom fining

Online: 19 December 2017 (07:15:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202012.0106.v1

A Comparative Assessment of the GWAVA, SWAT and VIC Models in the Hydrological Modelling of the Upper Cauvery Catchment, India

Robyn Horan, R Gowri, Pawan Wable, Helen Baron, Virginie Keller, Kaushal Garg, Pradeep Mujumdar, Helen Houghton-Carr, Gwyn Rees

Subject: Engineering, Automotive Engineering Keywords: Cauvery; hydrological modelling; VIC, SWAT; GWAVA; ensemble modelling; water resources

Online: 4 December 2020 (11:59:41 CET)

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Preprint ARTICLE | doi:10.20944/preprints202107.0520.v1

Bafilomycin A1 Induces Caspase-Dependent Apoptosis and Inhibits Autophagy Flux in Diffuse Large B Cell Lymphoma

Fang Li, Ying Hu, Yizhou Hu, Ren Zhou, Zhengrong Mao

Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: apoptosis; autophagy; bafilomycin A1; diffuse large B cell lymphoma

Online: 22 July 2021 (13:43:07 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202108.0251.v1

Secrecy Analysis and Error Probability of LIS-aided Communication Systems under Nakagami-m Fading

Ricardo Coelho Ferreira, Michelle S. P. Facina, Felipe A. P. de Figueiredo, Gustavo Fraidenraich, Eduardo Rodrigues de Lima

Subject: Engineering, Electrical And Electronic Engineering Keywords: Large intelligent surfaces; 6G; bit error probability; Nakagami fading; Von Mises distribution

Online: 11 August 2021 (10:52:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0331.v1

On the Role of Electric Vehicles towards Low-Carbon Energy Systems: Italy and Germany in Comparison

Sara Bellocchi, Kai Klöckner, Michele Manno, Michel Noussan, Michela Vellini

Subject: Engineering, Energy And Fuel Technology Keywords: large-scale RES; curtailments; electric vehicles; EnergyPLAN; integrated energy systems analysis; CO2 emissions

Online: 28 May 2019 (10:33:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201709.0091.v1

Equilibration in the Nosé-Hoover Isokinetic Ensemble: Effect of Inter-Particle Interactions

Shamik Gupta, Stefano Ruffo

Subject: Physical Sciences, Condensed Matter Physics Keywords: Hamiltonian systems; classical statistical mechanics; ensemble equivalence; long-range interacting systems

Online: 20 September 2017 (04:08:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0213.v1

Extending the Concept of City for the Delineation of Large Urban Regions (LUR) for the Cities of the World

Celine Rozenblat

Subject: Social Sciences, Geography, Planning And Development Keywords: cities; definition; megalopolis; large urban regions; functional urban regions; urban agglomerations; world; statistical delineation; comparative urban research

Online: 12 March 2020 (13:55:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints201804.0296.v1

Study on the Construction of Landscape Ecological Classification System for Large-Scale Coal-Power Base in Semi-Arid Steppe Region

Zhenhua Wu, Shaogang Lei, Zhengfu Bian

Subject: Environmental And Earth Sciences, Environmental Science Keywords: semi-arid steppe region; large-scale coal-power base; landscape ecological classification

Online: 23 April 2018 (12:48:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202005.0451.v1

Bilateral Line Local Binary Patterns for Face Recognition

Hung Phuoc Truong, Minh Bao Nguyen-Khoa, Yong-Guk Kim

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Bilateral Line Local Binary Patterns; Facial matrix; Statistical subspace; Face recognition; Calibrated SVM model; Ensemble learning

Online: 27 May 2020 (12:07:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0641.v2

Brain Tumor Detection based on Ensemble Learning

Donghyun Kim

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: brain tumor; machine learning; ensemble methods; convolutional neural networks

Online: 8 June 2021 (13:53:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202005.0354.v1

DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

Wei Hao Khoong

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ensemble learning; machine learning; Python; spatial distance; statistical distance; weighted ensemble

Online: 23 May 2020 (04:54:39 CEST)

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Preprint REVIEW | doi:10.20944/preprints201905.0175.v1

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Amir Mosavi, Sina Faizollahzadeh Ardabili, Shahabodin Shamshirband

Subject: Engineering, Electrical And Electronic Engineering Keywords: demand prediction, energy systems; machine learning; artificial neural network (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble learning; hybrid models; data science; deep learning; renewable energies; energy informatics; prediction; forecasting; energy demand

Online: 14 May 2019 (14:00:40 CEST)

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Preprint REVIEW | doi:10.20944/preprints201810.0329.v1

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

Sebastian Kmiecik, Maksim Kouza, Aleksandra Elzbieta Badaczewska-Dawid, Andrzej Kloczkowski, Andrzej Kolinski

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: protein dynamics; coarse-grained simulation; Monte-Carlo dynamics; structural flexibility; large-scale dynamics; elastic network model;

Online: 15 October 2018 (17:10:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0029.v1

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Nader Karballaeezadeh, Danial Mohammadzadeh S., Dariush Moazami, Narjes Nabipour, Amir Mosavi, Uwe Reuter

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Mobility; infrastructure; flexible pavement; pavement condition index (PCI); international roughness index (IRI); artificial intelligence (AI); predictive models; ensemble learning; structural health monitoring; machine learning

Online: 3 April 2020 (09:35:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0250.v1

High-throughput of measure-preserving integrators for constant temperature molecular dynamics simulations on GPUs

Luis Guarneros-Nolasco, Ketzasmin A. Terrón-Mejía, Jorge Mulia-Rodríguez, Daniel Osorio-Gonzalez, Roberto López-Rendón, Felipe Rodriguez-Romero

Subject: Physical Sciences, Mathematical Physics Keywords: High-Throughput Molecular Dynamics (HTMD) simulations; Nosé-Hoover Chain (NHC); Canonical ensemble; Graphics Processing Units (GPUs).

Online: 9 November 2018 (15:26:49 CET)

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Preprint REVIEW | doi:10.20944/preprints202009.0718.v1

Novel Therapies for Relapsed or Refractory Diffuse Large B-Cell Lymphoma

Leonard Harris, Kruti Patel, Michael Martin

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: Relapsed or Refractory Diffuse Large B Cell Lymphoma, DLBLC, immunotherapy, chemotherapy-free regimen

Online: 29 September 2020 (15:09:47 CEST)

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Preprint REVIEW | doi:10.20944/preprints202105.0026.v2

Ethical Issues Concerning the Use of Data from Commercially Available Wearable Sensors in Children

Andrie G. Panayiotou, Evangelos D. Protopapadakis

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: wearable devices; ethics; children; privacy; large data; right to an open future; living in the spot-light

Online: 25 June 2021 (11:00:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0429.v1

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

Chayan Mondal, Md. Kamrul Hasan, Md. Tasnim Jawad, Aishwariya Dutta, Md. Rabiul Islam, Md. Abdul Awal, Mohiuddin Ahmad, Salem A. Alyami, Mohammad Ali Moni

Subject: Medicine And Pharmacology, Other Keywords: Acute lymphoblastic leukemia; Deep convolutional neural networks; Ensemble image classifiers; C-NMC-2019 dataset.

Online: 19 May 2021 (07:42:23 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202002.0231.v1

Stochastic Activation Function Layers for Convolutional Neural Networks

Loris Nanni, Alessandra Lumini, Stefano Ghidoni, Gianluca Maguolo

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Convolutional Neural Networks; ensemble of classifiers; activation functions; image classification; skin detection

Online: 17 February 2020 (01:50:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints201806.0175.v1

Antineutrino Star Model of the Big Bang and Dark Energy

Tom F. Neiser

Subject: Physical Sciences, Particle And Field Physics Keywords: cosmology: theory; gravitation; early universe; dark energy; large-scale structure of universe; cosmic background radiation; neutrinos

Online: 12 June 2018 (08:43:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.0766.v1

ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown

Mark Scanlon, Frank Breitinger, Christopher Hargreaves, Jan-Niclas Hilgert, John Sheppard

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; Digital Forensics; Artificial Intelligence; Generative Pre-trained Transformers (GPT); Large Language Models (LLM)

Online: 12 July 2023 (09:09:09 CEST)

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Preprint REVIEW | doi:10.20944/preprints201809.0236.v1

Exploring Configuration Space and Path Space of Biomolecules Using Enhanced Sampling Techniques—Searching for Mechanism and Kinetics of Biomolecular Functions

Hiroshi Fujisaki, Kei Moritsugu, Yasuhiro Matsunaga

Subject: Biology And Life Sciences, Biophysics Keywords: molecular dynamics simulation; rare event; string method; multiscale enhanced sampling; weighted ensemble; multidrug transporter; Onsager-Machlup action

Online: 13 September 2018 (12:01:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201806.0266.v1

Improved Collecting Line Current Protection Scheme against Large-scale Wind Generators Tripping off

Wei Jin, Yuping Lu, Tao Huang

Subject: Engineering, Electrical And Electronic Engineering Keywords: Collecting line current protection (CLPC); coordination protection; large-scale WGs trip-off; risk indicator

Online: 18 June 2018 (13:48:38 CEST)

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There have been several cases of large-scale wind generators (WGs) tripping off caused by untimely fault removing in recent years. Currently, the discoordination between the box-type transformer fuse protection (BTFP) and two-section collecting line current protection (CLCP) brings a security risk to wind farm. In order to ensure the selectivity, the first section (Sec-I) CLCP should be set a enough interval that is longer than the fuse melting time, and another interval is set for the Sec-II CLCP, which weakens the speed of the CLCP. When a fault occurs on the collecting line, there is no doubt that WGs cannot work too long in abnormal operation, which may cause WGs to be placed off the grid. For a power system with high penetration of wind power, large-scale WGs tripping off will cause a great power shortage, and affect the stability of the power system. The selectivity and sensitivity of the CLCP is analyzed in detail to make the CLCP speed better. Considering the fault ride-through ability of WGs, the fault clear time is an important factor to lead to large-scale WGs tripping off. Two main works are done in this paper. The first is to accelerate the speed of the Sec-I CLCP though reducing the protection zone. Another one is introduce the risk assessment module into the CLCP, which not only improve the speed of the CLCP but also ensure the safety of the wind farm during faults. According to the deference in trip-off causes of WGs, the matching functions are created to assess the trip-off risk of WGs on the spot. In the case of fault, the trip-off risk indicators of WGs are timely updated to data sharing center and open to the CLCPs. The set of risk indicators is divided into several subsets according to the risk range. The dynamic changes of the subsets during fault help to improved CLCP scheme. This scheme can accelerate protection speed based on the increasing risk of large-scale WGs tripping off in wind farms. Compared with traditional CLCP, this approach can make the CLCP combines selectivity and speed better based on the analysis of the ride- through ability of WGs.

Preprint ARTICLE | doi:10.20944/preprints201804.0227.v1

Converting Ensemble Clustering Problem to a Mathematical Optimization Problem and Providing an Approach to Solve Based on Optimization Toolbox

Simintaj Salehpour, Hamid Parvin

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Ensemble clustering; cluster stability; F-measure; co-association matrix; genetic algorithm

Online: 17 April 2018 (15:58:22 CEST)

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Preprint REVIEW | doi:10.20944/preprints202306.0243.v1

New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making

Nicolae Iftimia, Raj Pandya, Forest Mahoney

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AI; artificial intelligence; automation; neural networks; machine learning; deep learning; large language models; biomedical research; imaging; healthcare

Online: 5 June 2023 (05:15:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201806.0300.v1

Coupling of Charge Regulation and Conformational Equilibria in Linear Weak Polyelectrolytes: Treatment of Long Range Interactions via Effective Short-Ranged and pH-Dependent Interaction Parameters

Pablo M. Blanco, Sergio Madurga, Francesc Mas, Josep L. Garcés

Subject: Chemistry And Materials Science, Physical Chemistry Keywords: polyelectrolytes; charge regulation; long-range interactions; Debye-Hückel interactions; transfer matrix; Ising models; semi-grand canonical ensemble; Monte Carlo simulations; conformational equilibria; variational methods

Online: 19 June 2018 (12:33:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201809.0245.v1

On the Effect of Filling Rate on Positive Macrosegregation Patterns in Large Size Cast Steel Ingots

Chunping Zhang, Abdelhalim Loucif, Mohammad Jahazi, Rami Tremblay, Louis-Philippe Lapierre Boire

Subject: Engineering, Mechanical Engineering Keywords: medium-carbon low-alloy steel; large size ingot; hot-top; filling rate; positive macrosegregation

Online: 13 September 2018 (15:19:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202109.0181.v1

A New Stacking Based Ensemble Predictive Model to Predict the Mortality after Heart Surgery on a Highly Imbalanced Dataset

Arman Ghavidel, Rouzbeh Ghousi, Alireza Atashi

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Classification; stacking ensemble method; heart surgery; unbalanced data problem; hybrid predictive model; machine learning in healthcare; resampling method; Edited-Nearest-Neighbor; nonparametric test.

Online: 10 September 2021 (10:53:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0534.v1

Saint Venant and Navier-Stokes Models for Tsunami Simulations

Camille Journeau, Kévin Pons, Richard Marcer

Subject: Engineering, Marine Engineering Keywords: tsunami; CFD; Saint Venant (SV) model; Adaptive Mesh Refinement (AMR); large scale propagation; 3D Navier-Stokes (NS) model; coastal impact

Online: 22 November 2018 (04:43:54 CET)

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Preprint REVIEW | doi:10.20944/preprints202010.0263.v1

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi, Filip Ferdinand, Shahab S. Band, Uwe Reuter, Joao Gama, Amir H. Gandomi

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: data science; deep learning; ensemble machine learning models; economics; hybrid models

Online: 13 October 2020 (09:24:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.1729.v1

Improving Existing Segmentators Performance with Zero-Shot Segmentators

Loris Nanni, Carlo Fantozzi, Alberto Pretto, Daniel Fusaro

Subject: Computer Science And Mathematics, Computer Science Keywords: segmentation; deep learning; ensemble; SAM zero-shot segmentator

Online: 26 July 2023 (08:25:16 CEST)

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Working Paper ARTICLE

Effect of Interlayer Cooling Time, Constraint and Tool Path Strategy on Deformation of Large Components made by Laser Metal Deposition with Wire

Yousub Lee, Yashwanth Bandari, Peeyush Nandwana, Brian Gibson, Brad Richardson, Srdjan Simunovic

Subject: Engineering, Mechanical Engineering Keywords: large-scale additive manufacturing; interlayer cooling time; tool path optimization; part deformation; Ti-6Al-4V

Online: 10 November 2019 (09:04:24 CET)

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Preprint ARTICLE | doi:10.20944/preprints201809.0377.v1

Plasmonic Sensing Characteristics of Gold Nanorods with Large Aspect Ratios

Chao Zhuang, Yifan Xu, Ningsheng Xu, Jinxiu Wen, Huanjun Chen, Shaozhi Deng

Subject: Chemistry And Materials Science, Nanotechnology Keywords: large aspect ratios; gold nanorods; refractive index sensitivities; SERS; plasmonic sensing

Online: 19 September 2018 (09:40:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0509.v1

GATA 3 Gene Overexpression Is A Biomarker of Adverse Prognosis for Peripheral T-Cell Lymphoma, Not Otherwise Specified and for ALK-Negative Anaplastic Large Cell Lymphoma: An Exploratory Analysis of 80 Latin American Cases of nodal PTCLs

Luís Alberto de Pádua Covas Lage, Cláudio Vinícius Brito, Débora Levy, Hebert Fabrício Culler, Samuel Campanelli Freitas Couto, Lucas Bassolli, Maria Claudia Nogueira Zerbini, Vanderson Rocha, Juliana Pereira

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: nodal peripheral T-cell lymphomas; peripheral T-cell lymphoma, not otherwise specified (PTCL/NOS); ALK-negative anaplastic large-cell lymphoma (ALCL/ALK-); Angioimmunoblastic T-cell lymphoma (AITL); diagnosis; prognosis; GATA3 gene expression

Online: 29 April 2020 (12:48:00 CEST)

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Background: Nodal peripheral T-cell lymphomas (nPTCLs) encompass a heterogeneous group of mature and aggressive lymphoid malignancies, including peripheral T-cell lymphoma, not otherwise specified (PTCL/NOS), angioimmunoblastic T-cell lymphoma (AITL) and anaplastic large cell lymphoma (ALCL) ALK-positive and ALK-negative. Their differential diagnosis and prognosis are an issue in the clinical practice. Accurate biomarkers to refine the different subtypes of nPTCLs and to stratify their prognosis are essential to improve their treatment approach. The aim of this study was to test the prognostic impact of GATA3 gene expression, and its capability to discriminate the different subtypes of nPTCLs. Patients and Methods: From 2000 to 2017, 80 patients with nPTCLs were eligible for GATA3 gene expression analysis that was assessed retrospectively by quantitative real time PCR (qRT-PCR) of neoplastic biopsies in Formalin-Fixed Paraffin-Embedded samples (FFPE). Results: Median age was 49 years old (IqR 34-59), 43/80 (53.7%) were male. Median follow-up was 1.72 years. Of them, 36.3% were classified as PTCL/NOS, 31.2% ALK-negative ALCL, 21.2% ALK-positive ALCL and 11.3% AITL. The majority of cases had advanced stage (III/IV). Two-year estimated overall survival (OS) and progression-free survival (PFS) were 52.2% and 39.5%, respectively. The median GATA3 gene expression level was 0.49% (range 0 – 7.07) in all cohort, it was 0.11% for ALK-positive ALCL, 0.46% for ALK-negative ALCL, 0.86% for PTCL/NOS and 0.67% for AITL. The difference of GATA3 gene expression among distinct variants of nPTCLs was statistically significant (p < 0.001). GATA3 gene expression levels ≥ 0.71% discriminate PTCL/NOS from ALK-negative ALCL and AITL with sensitivity of 62% and specificity of 80.3%. GATA3 gene expression levels ≥ median was associated with poor 2-year OS for PTCL/NOS (46.7% x 21.4%, p=0.04) and for ALK-negative ALCL (85.7% x 54.5%, p=0.04). Conclusion: Despite the relative small and heterogeneous group of patients with nPTCLs, GATA3 gene overexpression may be an important biomarker associated with poor prognosis in PTCL/NOS and ALK-negative ALCL. Moreover it may also discriminate different subtypes of nPTCLs. Further studies with larger series of patients should confirm our findings.

Preprint ARTICLE | doi:10.20944/preprints201806.0114.v1

Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains

Rodrigo Cofré, Cesar Maldonado, Fernando Rosas

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: computational neuroscience; spike train statistics; maximum entropy principle; large deviation theory; out-of-equilibrium statistical mechanics; thermodynamic formalism; entropy production

Online: 7 June 2018 (11:06:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0263.v1

Precision Permittivity Measurement for Low-Loss Thin Planar Materials Using Large Coaxial Probe from 1 to 400 MHz

Kok Yeow You, Man Seng Sim

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: large coaxial probe; thin planar materials; low-loss materials; relative permittivity; reflection coefficient; calibration

Online: 12 October 2018 (08:19:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0070.v1

Globalization of Russian Cities: Towards a Construction of Large Urban Regions

Mikhail Rogov, Céline Rozenblat

Subject: Social Sciences, Geography, Planning And Development Keywords: city; large urban regions; Russia; globalization; open database

Online: 6 August 2019 (08:33:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1709.v1

LLM- Based Personalized Recommendations in Health

Boris A. Galitsky

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large Language Models; personalization; recommendation; meta-prompts; abductive reasoning

Online: 29 February 2024 (12:51:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202010.0318.v1

Large Intelligent Surfaces Communicating Through Massive MIMO Rayleigh Fading Channels

Ricardo Coelho Ferreira, Michelle S. P. Facina, Felipe A. P. de Figueiredo, Gustavo Fraidenraich, Eduardo Rodrigues de Lima

Subject: Engineering, Automotive Engineering Keywords: Large intelligent surfaces; massive MIMO systems; maximum ratio transmission; Von Mises distribution; Rayleigh fading

Online: 15 October 2020 (11:29:14 CEST)

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Working Paper ARTICLE

Cosmological Constant Decaying with CMB Temperature

Tomohide Sonoda

Subject: Physical Sciences, Mathematical Physics Keywords: dark energy; dark matter; cosmic microwave background; large numbers hypothesis; varying fundamental constants; symmetry

Online: 14 June 2019 (09:53:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202201.0454.v1

Ransomware Family Classification With Ensemble Model Based On Behavior Analysis

Nowshin Tasnim, Iqbal H. Sarker

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Ransomware; Behavior analysis; Cyber Security; Machine Learning; Ensemble model; Supervised classification

Online: 31 January 2022 (11:49:48 CET)

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Preprint REVIEW | doi:10.20944/preprints201912.0128.v1

Comparative Analysis of Risk Management Techniques for Large-Scale Systems

Mehreen Sirshar, Aliya Shahid, Zoya Alam

Subject: Engineering, Control And Systems Engineering Keywords: large scale systems; risk assessment; risk management techniques; risk mitigation

Online: 10 December 2019 (07:14:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202010.0298.v2

Cosmic Ray Extremely Distributed Observatory

Piotr Homola, Dmitriy Beznosko, Gopal Bhatta, Łukasz Bibrzycki, Łukasz Bratek, Michalina Borczyńska, Nikolay M. Budnev, Dariusz Burakowski, David Edwin Alvarez-Castillo, Kevin Almeida Cheminant, Aleksander Ćwikła, Punsiri Dam-o, Niraj Dhital, Alan Duffy, Piotr Głownia, Krzysztof Gorzkiewicz, Dariusz Góra, Alok C. Gupta, Zuzana Hlávková, Martin Homola, Joanna Jałocha, Robert Kamiński, Michał Karbowiak, Marcin Kasztelan, Renata Kierepko, Marek Knap, Péter Kovács, Szymon Kuliński, Bartosz Łozowski, Marek Magryś, Mikhail V. Medvedev, Justyna Mędrala, Jerzy W. Mietelski, Justyna Miszczyk, Alona Mozgova, Antonio Napolitano, Vahab Nazari, Y. Jack Ng, Michał Niedźwiecki, Cristina Oancea, Bogusław Ogan, Gabriela Opiła, Krzysztof Oziomek, Maciej Pawlik, Marcin Piekarczyk, Bożena Poncyljusz, Jerzy Pryga, Matías Rosas, Krzysztof Rzecki, Jilberto Zamora-Saa, Katarzyna Smelcerz, Karel Smolek, Weronika Stanek, Jarosław Stasielak, Slawomir Stuglik, Jolanta Sulma, Oleksandr Sushchov, Manana Svanidze, Kyle Tam, Arman Tursunov, José-M. Vaquero, Tadeusz Wibig, Krzysztof W. Woźniak

Subject: Physical Sciences, Nuclear And High Energy Physics Keywords: cosmic rays; cosmic ray ensembles; extensive air showers; large scale cosmic ray correlations; CREDO Collaboration

Online: 15 October 2020 (16:13:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202312.1987.v1

Enhancing Accuracy in Large Language Models Through Dynamic Real-Time Information Injection

Qian Ouyang, Shiyu Wang, Bing Wang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large Language Models; Real-Time Data Integration; Content Hallucination; Alpaca Model; Accuracy Improvement; Dynamic Data Processing

Online: 26 December 2023 (11:01:25 CET)

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Preprint REVIEW | doi:10.20944/preprints202305.0900.v1

Unraveling the Mysteries of AI Chatbots

Raj Bridgelall

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Generative Artificial Intelligence; Large Language Models; ChatGPT; Bard; Transformer Architecture; Prompt Engineering

Online: 12 May 2023 (07:47:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1487.v1

An Efficient Plug-and-Play Post-Training Pruning Strategy in Large Language Models

Yingtao Zhang, Haoli Bai, Haokun Lin, Jialin Zhao, Lu Hou, Carlo Vittorio Cannistraci

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: post-training pruning; combinatorial optimization; large language models; inference acceleration

Online: 24 October 2023 (07:51:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202010.0526.v1

Animal Sound Classification Using Dissimilarity Spaces

Loris Nanni, Sheryl Brahnam, Alessandra Lumini, Gianluca Maguolo

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio

Online: 26 October 2020 (13:57:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202006.0351.v1

Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection

Shawni Dutta, Samir Bandyopadhyay

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: Brain Tumor; Machine Learning; Ensemble techniques; AdaBoost; Cross-Validation; Stratified technique

Online: 29 June 2020 (07:27:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0302.v1

From Local to Global: A Transfer Learning-Based Approach for Mapping Poplar Plantations at Large Scale

Yousra Hamrouni, Éric Paillassa, Véronique Chéret, Claude Monteil, David Sheeren

Subject: Environmental And Earth Sciences, Environmental Science Keywords: active learning; poplar plantations; spatial transfer; sentinel-2; large scale; image classification; random forest

Online: 17 April 2020 (15:05:54 CEST)

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Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

Preprint ARTICLE | doi:10.20944/preprints201911.0033.v4

Circuit-Based Approaches for a Superconducting-Like Behavior with Relatively High Critical Current Density

Shinichi Ishiguri

Subject: Physical Sciences, Condensed Matter Physics Keywords: temperature-independent superconductivity; circuit-approached superconductivity; electron pair; Bose– Einstein condensation; large superconducting energy gap; London equation; Meissner effect; macroscopic wave function; critical current density; negative voltages

Online: 20 February 2024 (22:14:28 CET)

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In general, a superconductor has zero resistance, although it requires significant refrigeration or high pressures with high manufacturing costs, which prevents it into practical applications. In other words, solving these issues implies main superconducting researches. To solve these problems, this paper describes a new type of superconductivity, which is independent for temperatures and which operates without pressures. The principles of the presented system are as follows:First a voltage source, a current source and a load are connected in series. Then, the voltage of the voltage source is adjusted to balance the voltage of the load. Under this condition, the balance of the two voltages provides a zero voltage between the taps of the current source and the generated current from the voltage source becomes zero because of the internal infinite resistance of the current source. As a result, the electric powers generated by the two sources are zero, and therefore, the load cannot generate Joule heating because of energy conservation. However, the current from the current source (not the voltage source) is not zero; therefore, we can predict that the resistance of the load must be zero. As a theory, we derived a new electric field and transient attractive force, which result in a very short coherence of an electron pair because there is not the Coulomb repulsive force due to the existence of the above transient attractive force. Note that both the forces are derived by the Poisson equation, which implies that they cannot be compatible. Therefore, the pair combination energy from spins becomes extremely strong, which is not destroyed by the normal heat energy. Moreover, every center-of-mass motion of electron pair results in the Bose-Einstein condensation and the macroscopic wave function, which produces the London equation (i.e., the Meissner effect). Moreover, by introducing the equivalent circuit, this paper conducted numerical calculations. As a result, we could derive numerically zero resistance and responses for additional static magnetic fields as a discharged current, which implies the Meissner effect. Note that this paper has prepared Appendix section, which provides a guide to reproduce actual experiments and preliminary experimental results. These results in the Appendix indicate the zero resistances and Meissner effects.

Preprint ARTICLE | doi:10.20944/preprints202105.0670.v1

Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression using Transfer Learning

Jose Marie Antonio Miñoza, Jonathan Adam Rico, Pia Regina Fatima Zamora, Manny Bacolod, Reinhard Laubenbacher, Romulo de Castro

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: melanoma; biomarker; transfer learning; ensemble model; bias; machine learning

Online: 27 May 2021 (13:20:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202106.0533.v1

Pharmacy Impact on Vaccination Progress Using Machine Learning Approach

Samir Bandyopadhyay, Shawni Dutta, Upasana Mukherjee

Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: COVID-19; Vaccine; Prediction; Regression; Ensemble learning; AdaBoost

Online: 22 June 2021 (08:30:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0396.v1

Ensemble Mapping and Change Analysis of the Seafloor Sediment Distribution in the Sylt Outer Reef, German North Sea from 2016-2018

Daphnie S. Galvez, Svenja Papenmeier, Lasse Sanders, H. Christian Hass, Vera Fofonova, A. Bartholomae, Karen H. Wiltshire

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: ensemble modelling; seafloor mapping; sediment change analysis; seafloor sediment distribution; North Sea; seafloor classification; acoustic mapping; small sample size; ensemble map

Online: 17 May 2021 (16:58:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202203.0337.v2

Stacking Ensemble of Machine Learning Methods for Landslide Susceptibility Mapping in Zhangjiajie City, Hunan Province, China

Yuke Huan, Lei Song, Umair Khan, Baoyi Zhang

Subject: Environmental And Earth Sciences, Other Keywords: landslide susceptibility; stacking ensemble; machine learning; random forest; gradient boosting decision tree; extreme gradient boosting

Online: 5 October 2022 (10:29:51 CEST)

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The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

Preprint ARTICLE | doi:10.20944/preprints202007.0303.v1

Detecting Lower Back Pain Using Stacked Ensemble Approach

Samir Bandyopadhyay, Shawni Dutta

Subject: Medicine And Pharmacology, Anesthesiology And Pain Medicine Keywords: Lower back pain; automated tool; ML; ensemble technique; stacked generalization

Online: 14 July 2020 (11:31:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201809.0404.v1

Medium-Range Probabilistic Forecasts of Wind Power Generation and Ramps in Japan Based on a Hybrid Ensemble

Masamichi Ohba, Shinji Kadokura, Daisuke Nohara

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: self-organizing maps; weather patterns; synoptic circulation; multi-model ensemble; wind power

Online: 20 September 2018 (08:17:46 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202305.1540.v3

Revisiting the Political Biases of ChatGPT

Sasuke Fujimoto, Kazuhiro Takemoto

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; algorithm bias; political bias; large language model

Online: 26 May 2023 (07:37:33 CEST)

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Working Paper ARTICLE

Weapon Configuration, Allocation and Route Planning for a Fleet of Unmanned Combat Air Vehicles

Jianmai Shi, Jiaming Zhang, Hongtao Lei, Zhong Liu

Subject: Engineering, Control And Systems Engineering Keywords: unmanned air vehicles; mission planning; routing; weapon configuration; adaptive large neighborhood search

Online: 16 April 2019 (10:40:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0593.v1

Intra-Hour Forecasts of Solar Power and Ramp Events ^†

Mohamed Abuella, Badrul Chowdhury

Subject: Engineering, Electrical And Electronic Engineering Keywords: adjusting; classification; ensemble; post-processing; ramp events; solar power forecast

Online: 25 October 2018 (05:43:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0735.v1

Annotation of Human Exome Gene Variants with Consensus Pathogenicity

Victor Zharavin, James Balmford, Patrick Metzger, Melanie Boerries, Harald Binder, Martin Boeker

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Variant of Unknown Significance (VUS); Single-Nucleotide Variant (SNV); Variant Effect Prediction (VEP); Stacked Ensemble of Supervised Deep Learners (SESDL); Next Generation Sequencing (NGS); Alternative Allele Frequency (AAF).

Online: 31 July 2020 (06:13:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0629.v1

Multiscale Hydro-Meteorological Factors Variations and Its Links to Large-Scale Atmospheric Circulation Systems in the Source Region of Yangtze River

Jian Tang, Hui-Qun Cao

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: hydro-meteorological factors; large-scale atmospheric circulation systems; South Asia monsoon; streamflows; source region of the Yangtze River

Online: 26 October 2018 (11:52:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0282.v2

Design Optimization of Centrifugal Microfluidic "Lab-on-a-Disc" Systems towards Fluidic Larger-Scale Integration

Jens Ducrée

Subject: Computer Science And Mathematics, Software Keywords: centrifugal microfluidics, Lab-on-a-Disc, large-scale integration, reliability, tolerances, band width, packing density

Online: 8 June 2021 (12:07:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202112.0376.v1

Large-Scale Printed Chinese Character Recognition for ID Cards Using Deep Learning and Few Samples Transfer Learning

Yi-Quan Li, Hao-Sen Chang, Daw-Tung Lin

Subject: Engineering, Electrical And Electronic Engineering Keywords: Large-Scale Image Classification; Printed Chinese Character Recognition; Data Synthesis; GoogLeNet-GAP; Transfer Learning

Online: 22 December 2021 (16:31:53 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.0366.v1

Learning Analytics in the Era of Large Language Models

Elisabetta Mazzullo, Okan Bulut, Tarid Wongvorachan, Bin Tan

Subject: Social Sciences, Education Keywords: learning analytics; large language model; artificial intelligence; process data

Online: 4 August 2023 (07:23:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0378.v1

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Dejian DAI, Tao JIANG, Wei LU, Xuan SHEN, Rui XIU, Jingwei ZHANG

Subject: Computer Science And Mathematics, Mathematics Keywords: egg freshness; hyperspectral detection; hyperspectral scattering imaging; ensemble learning

Online: 18 August 2020 (08:02:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202012.0054.v1

Gait Pattern Recognition in Patients with Hereditary Ataxia’s Using Supervised Learning Algorithms Based on Small Subsets Smartphone Sensor Data

Francisco Diego Acosta-Escalante, Elías Beltrán-Naturi, Marie Catherine Boll, José-Adán Hernández-Nolasco

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Patterns recognition; Machine learning; Hereditary Ataxia diseases; K-Nearest Neighbors; Multi Layer Perceptron; Ensemble Classification Trees; SVM.

Online: 2 December 2020 (09:33:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints202102.0338.v1

Ensemble Learning of Multi-Source Satellite Sensors Dataset for Estimating Forest Biomass in New England Region

Ram C. Sharma

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Forests; biomass; ALOS-2 PALSAR-2; Sentinel-1 CSAR; Sentinel-2 MSI; Landsat 8 OLI; ensemble learning.

Online: 16 February 2021 (14:15:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202303.0389.v1

Improved Prediction of Antisense Oligonucleotide Efficacy for Exon Skipping Using Ensemble Learning and Feature Selection

Alex Zhu, Shuntaro Chiba, Yuki Shimizu, Katsuhiko Kunitake, Yasushi Okuno, Yoshitsugu Aoki, Toshifumi Yokota

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: antisense oligonucleotides; exon skipping; machine learning; ensemble learning; personalized medicine; n-of-1 therapy, splice switching; genetic disease; splicing; RNA

Online: 22 March 2023 (03:24:48 CET)

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Preprint ARTICLE | doi:10.3390/sci2020039

A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Subject: Medicine And Pharmacology, Other Keywords: ensemble empirical mode decomposition (EEMD); denoising; mode mixing; electromyographic (EMG) signals; filtering; wavelet method

Online: 3 June 2020 (00:00:00 CEST)

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One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

Preprint REVIEW | doi:10.20944/preprints202010.0250.v1

Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics

Rodrigo Cofré, Cesar Maldonado, Bruno Cessac

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Thermodynamic formalism; neuronal networks dynamics; maximum entropy principle; free energy and pressure; linear response; large deviations, ergodic theory

Online: 12 October 2020 (15:28:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202108.0094.v1

Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

Loris Nanni, Giovanni Minchio, Sheryl Brahnam, Davide Sarraggiotto, Alessandra Lumini

Subject: Computer Science And Mathematics, Discrete Mathematics And Combinatorics Keywords: Siamese networks; Ensemble of classifiers; Loss function; Discrete cosine transform

Online: 3 August 2021 (15:49:22 CEST)

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