id,title,description,date_created,date_modified,date_published,original_publication_date,publication_doi,provider,is_published,reviews_state,version,is_latest_version,preprint_doi,license,tags_list,tags_data,contributors_list,contributors_data,first_author,subjects_list,subjects_data,download_url,has_coi,conflict_of_interest_statement,has_data_links,has_prereg_links,prereg_links,prereg_link_info,last_updated
sjz4e_v1,HistoEnder: a 3D printer-based histological slide autostainer that retains 3D printer functions,"Automated microscope slide stainers are usually very expensive and unless the laboratory performs heavy histological work it is difficult to justify buying a 2000-10000€ machine. As a result, histology and pathology labs around the world lose thousands of working hours for following procedures that could be easily automated. Herein, we propose a simple modification of an open-source 3D printer, the Creality Ender-3, into an automated microscope slide autostainer, the HistoEnder. The HistoEnder is cheap (less than 200€), modular, and easy to set up, with only two 3D-printed parts needed. Additionally, the 3D printer retains its full functionality, and it can be reverted back into 3D printing in less than 1 minute. The g-code associated with the procedure is extremely simple, and can be written by anyone. The HistoEnder can also be used in chemistry and material science laboratories for automating surface modifications and dip coating.",2022-01-19T18:49:05.161854,2022-03-01T18:52:19.502287,2022-01-19T19:07:40.546595,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/sjz4e,CC-By Attribution 4.0 International,dip-coating; haematoxylin eosin; histoender; histology; pathology; slide autostainer,"[""dip-coating"", ""haematoxylin eosin"", ""histoender"", ""histology"", ""pathology"", ""slide autostainer""]",Marco Ponzetti; Ganga Chinna Rao Devarapu; Nadia Rucci; Armando Carlone; Vittorio Saggiomo,"[{""id"": ""qsa28"", ""name"": ""Marco Ponzetti"", ""index"": 0, ""orcid"": ""0000-0002-7554-1325"", ""bibliographic"": true}, {""id"": ""v4u8c"", ""name"": ""Ganga Chinna Rao Devarapu"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""zqfsv"", ""name"": ""Nadia Rucci"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""yz2d8"", ""name"": ""Armando Carlone"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""zebd6"", ""name"": ""Vittorio Saggiomo"", ""index"": 4, ""orcid"": ""0000-0001-7196-602X"", ""bibliographic"": true}]",Marco Ponzetti,Engineering; Biomedical Engineering and Bioengineering; Biomedical Devices and Instrumentation,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7b54be8100732d4401"", ""text"": ""Biomedical Engineering and Bioengineering""}, {""id"": ""5994df7b54be8100732d4405"", ""text"": ""Biomedical Devices and Instrumentation""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T20:03:50.155599
qmpey_v1,A pump-free microfluidic device for fast magnetic labeling of ischemic stroke biomarkers,"This research proposes a low-cost and simple operation microfluidic chip to enhance the magnetic labeling efficiency of two ischemic stroke biomarkers: cellular fibronectin (c-Fn) and matrix metallopeptidase-9 (MMP9). This fully portable and pump-free microfluidic chip is operated based on capillary attractions without any external power source and battery. It uses an integrated cellulose sponge to absorb the samples. At the same time, a magnetic field is aligned to hold the target labeled by the magnetic nanoparticles (MNPs) in the pre-concentrated chamber. By using this approach, the specific targets are labeled from the beginning of the sampling process without preliminary sample purification. The proposed study enhanced the labeling efficiency from 1 hr to 15 min. The dynamic interactions occur in the serpentine channel, while the crescent formation of MNPs in the pre-concentrated chamber, acting as a magnetic filter, improves the biomarker-MNP interaction. The labeling optimization by the proposed device influences the dynamic range by optimizing the MNP ratio to fit the linear range across the clinical cutoff value. The limit of detection (LOD) of 2.8 ng/mL and 54.6 ng/mL of c-Fn measurement were achieved for undiluted and four times dilutions of MNP, respectively. While for MMP9, the LODs were 11.5 ng/mL for undiluted functionalized MNP and 132 ng/mL for four times dilutions of functionalized MNP. The results highlight the potential use of this device for clinical sample preparation and specific magnetic target labeling. When combined with a detection system could also be used as an integrated component of a point-of-care platform.",2022-01-19T14:35:52.401350,2022-03-01T18:52:19.462810,2022-01-19T15:11:29.076069,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/qmpey,CC-BY Attribution-NonCommercial 4.0 International,MMP9; Magnetic nanoparticle; fibronectin; magnetoresistive; microfluidic; sample preparation,"[""MMP9"", ""Magnetic nanoparticle"", ""fibronectin"", ""magnetoresistive"", ""microfluidic"", ""sample preparation""]",Briliant Adhi Prabowo,"[{""id"": ""t6kcu"", ""name"": ""Briliant Adhi Prabowo"", ""index"": 0, ""orcid"": ""0000-0002-7543-5143"", ""bibliographic"": true}]",Briliant Adhi Prabowo,Engineering; Engineering Science and Materials; Nanoscience and Nanotechnology; Materials Science and Engineering; Biomedical Engineering and Bioengineering; Biomedical Devices and Instrumentation; Engineering Physics,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7a54be8100732d43ca"", ""text"": ""Engineering Science and Materials""}, {""id"": ""5994df7a54be8100732d43d0"", ""text"": ""Nanoscience and Nanotechnology""}, {""id"": ""5994df7a54be8100732d43d2"", ""text"": ""Materials Science and Engineering""}, {""id"": ""5994df7b54be8100732d4401"", ""text"": ""Biomedical Engineering and Bioengineering""}, {""id"": ""5994df7b54be8100732d4405"", ""text"": ""Biomedical Devices and Instrumentation""}, {""id"": ""5994df7b54be8100732d441b"", ""text"": ""Engineering Physics""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T20:04:20.028522
fn924_v1,A moving object bearing angle measurement using orthogonal circle polarized radio beacon signals,There is investigated the opportunity to find the moving object bearing angle using radio beacon signals polarized within the left and right circles. Signals are illuminated simultaneously from two space diversed in the horizontal plane points. Amplitude-phase processing of the vector signals received onboard the moving object and treated in the linear and circular polarized  bases is utilized in order to find the bearing angle.,2022-01-18T06:36:12.402649,2022-03-01T18:52:19.429888,2022-01-18T15:11:26.199231,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/fn924,No license,,[],Alexander Mescheryakov; Vladimir Gulko,"[{""id"": ""9dv72"", ""name"": ""Alexander Mescheryakov"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""epv63"", ""name"": ""Vladimir Gulko"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}]",Alexander Mescheryakov,Engineering; Aviation; Aviation Safety and Security,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7b54be8100732d43f2"", ""text"": ""Aviation""}, {""id"": ""5994df7b54be8100732d43f4"", ""text"": ""Aviation Safety and Security""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T20:03:55.672730
3ejnh_v1,Mpemba Effect Demystified,"The Mpemba Effect (ME) is known as the counter intuitive effect of faster freezing of a beaker of warm water than the same but colder sample, under apparently same conditions (e.g. in the freezer, Aristotle will have done it outdoor).
The astonishment about this misled all the investigations and explanations intuitively into looking for water/ice/solidification properties, and the key, the heat transfer, was not adequately taken into account. If we trust classical physics and energy conservation, faster freezing of water must be primarily associated with a greater heat flow, not with mystical water properties or behavior.
It is shown that the trivial conductivity through the bottom of the beaker adequately explains the ME.",2022-01-17T22:40:27.691271,2022-03-01T18:52:19.055141,2022-01-18T15:12:16.250832,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/3ejnh,CC0 1.0 Universal,,[],Ren Tier,"[{""id"": ""2rw6u"", ""name"": ""Ren Tier"", ""index"": 0, ""orcid"": ""0000-0001-7033-1110"", ""bibliographic"": true}]",Ren Tier,Engineering; Engineering Physics,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7b54be8100732d441b"", ""text"": ""Engineering Physics""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T20:04:14.914840
vz496_v1,Logistic Regression Teams Up With Artificial Intelligence To Beat Neural Network and Gradient Boosted Machine,"We systematically explore the universe of all models using AI search methods. We automate much of the data preparation and testing of each model built along the way. The result is a method and system that generate superior production logistic regression models built from the ground up, beating an industry standard consumer credit risk score by five points. In a machine learning modeling project our reference logistic regression model beats out GBM, and has performance comparable to a NN model but much more amenable in explanation and generating reasons for adverse action. We also incorporate into our system a method to eliminate disparate impact used by the FRB and the FTC.",2022-01-17T17:24:36.695470,2023-03-05T14:55:35.957099,2022-01-17T18:21:58.898819,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/vz496,No license,AI; AIC; ARM; BISG; BLAS; CRA; FRB; FTC; Federal Reserve Board; Federal Trade Commission; GBM; GPGPU; GPU; Gini coefficient; IRLS; IV; Intel; KS; LAPACK; Logistic; ML; Modeling; NN; PSI; VIF; Wald chi squared; artificial intelligence; condition index; correlation coefficient; disparate impact; machine learning; neural network; normalization; proportion of variation; regression; reject inference; transformation,"[""AI"", ""AIC"", ""ARM"", ""BISG"", ""BLAS"", ""CRA"", ""FRB"", ""FTC"", ""Federal Reserve Board"", ""Federal Trade Commission"", ""GBM"", ""GPGPU"", ""GPU"", ""Gini coefficient"", ""IRLS"", ""IV"", ""Intel"", ""KS"", ""LAPACK"", ""Logistic"", ""ML"", ""Modeling"", ""NN"", ""PSI"", ""VIF"", ""Wald chi squared"", ""artificial intelligence"", ""condition index"", ""correlation coefficient"", ""disparate impact"", ""machine learning"", ""neural network"", ""normalization"", ""proportion of variation"", ""regression"", ""reject inference"", ""transformation""]","Daniel M. Tom, Ph.D.","[{""id"": ""98gq4"", ""name"": ""Daniel M. Tom, Ph.D."", ""index"": 0, ""orcid"": ""0000-0003-4853-2498"", ""bibliographic"": true}]","Daniel M. Tom, Ph.D.","Engineering; Computational Engineering; Operations Research, Systems Engineering and Industrial Engineering; Risk Analysis","[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7a54be8100732d43c2"", ""text"": ""Computational Engineering""}, {""id"": ""5994df7a54be8100732d43c3"", ""text"": ""Operations Research, Systems Engineering and Industrial Engineering""}, {""id"": ""5994df7b54be8100732d43e8"", ""text"": ""Risk Analysis""}]",,0,,no,no,[],,2025-04-09T20:03:40.641412
7yb4c_v1,Ben-Sarc: A Corpus for Sarcasm Detection from Bengali Social Media Comments and Its Baseline Evaluation,"Sarcasm detection research of the Bengali language so far can be considered to be narrow due to the unavailability of resources. In this paper, we introduce a
large-scale self annotated Bengali corpus for sarcasm detection research problem in the Bengali language named ’Ben-Sarc’ containing 25,636 comments, manually collected from different public Facebook pages and evaluated by external evaluators. Then we present a complete strategy to utilize different models of traditional machine learning, deep learning, and transfer learning to detect sarcasm from text using the Ben-Sarc corpus. Finally, we demonstrate a comparison between the performance of traditional machine learning, deep learning, and transfer learning models on our Ben-Sarc corpus. Transfer learning using Indic-Transformers Bengali BERT as a pre-trained source model has achieved
the highest accuracy of 75.05%. The second highest accuracy is obtained by the LSTM model with 72.48% and Multinomial Naive Bayes is acquired the third highest with 72.36% accuracy for deep learning and machine learning, respectively. The Ben-Sarc corpus is made publicly available in the hope of advancing the Bengali Natural Language Processing community.",2022-01-17T14:30:22.705261,2022-03-01T18:52:19.286313,2022-01-17T15:43:24.658213,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/7yb4c,CC-By Attribution 4.0 International,Bengali sarcasm; Bengali sarcasm detection; sarcasm; sarcasm detection,"[""Bengali sarcasm"", ""Bengali sarcasm detection"", ""sarcasm"", ""sarcasm detection""]",Sanzana Karim Lora; G. M. Shahariar; Tamanna Nazmin; Noor Nafeur Rahman; Rafsan Rahman; Miyad Bhuiyan; Faisal Muhammad shah,"[{""id"": ""738rd"", ""name"": ""Sanzana Karim Lora"", ""index"": 0, ""orcid"": ""0000-0001-6647-1639"", ""bibliographic"": true}, {""id"": ""b2xs5"", ""name"": ""G. M. Shahariar"", ""index"": 1, ""orcid"": ""0000-0001-9757-7663"", ""bibliographic"": true}, {""id"": ""2seyf"", ""name"": ""Tamanna Nazmin"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""dbqpt"", ""name"": ""Noor Nafeur Rahman"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""hrjpv"", ""name"": ""Rafsan Rahman"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}, {""id"": ""mhc9v"", ""name"": ""Miyad Bhuiyan"", ""index"": 5, ""orcid"": null, ""bibliographic"": true}, {""id"": ""dc4qy"", ""name"": ""Faisal Muhammad shah"", ""index"": 6, ""orcid"": ""0000-0002-5118-8571"", ""bibliographic"": true}]",Sanzana Karim Lora,Engineering; Computer Engineering; Other Computer Engineering,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7a54be8100732d43ba"", ""text"": ""Computer Engineering""}, {""id"": ""5994df7a54be8100732d43c0"", ""text"": ""Other Computer Engineering""}]",,0,,no,no,[],,2025-04-09T20:03:59.488268
pu2jx_v1,Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations,"Computational Fluid Dynamics (CFD) simulations are useful to the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high- resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the Super-Resolution Generative Adversarial Network (SRGAN) model to a multiphase turbulent fluid flow problem, specifically to reconstruct fluid phase fraction at a higher resolution. Two models were created in this work, one which incorporates a physics-informed term in the loss function and one which does not, and the results are discussed and compared. We found that both models significantly outperform non-machine learning upsampling methods and can preserve a substantial amount of detail, showing the versatility of the SRGAN model for upsampling multi-phase fluid simulations. However, the difference in accuracy between the two models is minimal indicating that, in the context studied here, the additional complexity of a physics- informed approach may not be justified.",2022-01-17T14:23:00.627706,2022-03-01T18:52:19.396766,2022-01-17T15:42:58.062171,2022-01-17T05:00:00,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/pu2jx,CC-BY Attribution-NonCommercial 4.0 International,CFD; ML; computational fluid dynamics; machine learning; physics-informed machine learning; super resolution,"[""CFD"", ""ML"", ""computational fluid dynamics"", ""machine learning"", ""physics-informed machine learning"", ""super resolution""]",Matthew Li; Christopher McComb,"[{""id"": ""emhbv"", ""name"": ""Matthew Li"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""9kbjc"", ""name"": ""Christopher McComb"", ""index"": 1, ""orcid"": ""0000-0002-5024-7701"", ""bibliographic"": true}]",Matthew Li,Engineering; Mechanical Engineering; Fluid Mechanics,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7a54be8100732d43af"", ""text"": ""Mechanical Engineering""}, {""id"": ""5994df7c54be8100732d441d"", ""text"": ""Fluid Mechanics""}]",,0,,no,no,[],,2025-04-09T20:03:40.615731
mer43_v1,Road Deformation Monitoring and Event Detection using Asphalt-embedded Distributed Acoustic Sensing (DAS),"Distributed acoustic sensing (DAS) is a new technology that is being adopted widely in the geophysics and earth science communities to measure seismic signals propagating over 10’s of kilometers using an optical fiber. DAS uses the technique of phase-coherent optical time domain reflectometry (φ-OTDR) to measure dynamic strain in an optical fiber as small as nε by examining interferences in scattered light. This technology is opening a new field of research of examining very small strains in infrastructure that can be indicative of performance and use level. In this study, a fiber optic strain sensing cable was embedded into an asphalt concrete test road and spatially distributed dynamic road strain was measured during different types of loading. It is demonstrated that φ-OTDR can be used to quantitatively measure strain in roads associated with events as small as a dog walking on the surface. Optical frequency domain reflectometry (OFDR), a widely implemented but less accurate distributed fiber optic strain monitoring technology, is used along with traditional pavement strain gauges and 3D finite element modeling to validate the φ-OTDR pavement strain measurements. After validation, φ-OTDR strain measurements from various events are presented including a vehicle, pedestrian, runner, cyclist and finally a dog moving along the road. This study serves to demonstrate the deployment of φ-OTDR to monitor roadway systems.",2022-01-16T22:30:28.478808,2022-03-01T18:52:19.323073,2022-01-17T15:42:02.365961,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/mer43,No license,Distributed Acoustic Sensing; Distributed Fiber Optic Sensing; Instrumentation; Pavement; Smart Infrastructure,"[""Distributed Acoustic Sensing"", ""Distributed Fiber Optic Sensing"", ""Instrumentation"", ""Pavement"", ""Smart Infrastructure""]",Peter George Hubbard; Ruonan Ou; Tianchen Xu; Linqing Luo; Hayato Nonaka; Martin Karrenbach; Kenichi Soga,"[{""id"": ""zra7c"", ""name"": ""Peter George Hubbard"", ""index"": 0, ""orcid"": ""0000-0003-3537-8225"", ""bibliographic"": true}, {""id"": ""upqxb"", ""name"": ""Ruonan Ou"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""aq475"", ""name"": ""Tianchen Xu"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""zm2g4"", ""name"": ""Linqing Luo"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""yq4ch"", ""name"": ""Hayato Nonaka"", ""index"": 4, ""orcid"": ""0000-0002-2061-2528"", ""bibliographic"": true}, {""id"": ""w4pj5"", ""name"": ""Martin Karrenbach"", ""index"": 5, ""orcid"": null, ""bibliographic"": true}, {""id"": ""dk4up"", ""name"": ""Kenichi Soga"", ""index"": 6, ""orcid"": null, ""bibliographic"": true}]",Peter George Hubbard,"Engineering; Mechanical Engineering; Acoustics, Dynamics, and Controls; Operations Research, Systems Engineering and Industrial Engineering; Engineering Science and Materials; Materials Science and Engineering; Electrical and Computer Engineering; Signal Processing; Civil and Environmental Engineering; Transportation Engineering; Geotechnical Engineering; Civil Engineering","[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7a54be8100732d43af"", ""text"": ""Mechanical Engineering""}, {""id"": ""5994df7a54be8100732d43b1"", ""text"": ""Acoustics, Dynamics, and Controls""}, {""id"": ""5994df7a54be8100732d43c3"", ""text"": ""Operations Research, Systems Engineering and Industrial Engineering""}, {""id"": ""5994df7a54be8100732d43ca"", ""text"": ""Engineering Science and Materials""}, {""id"": ""5994df7a54be8100732d43d2"", ""text"": ""Materials Science and Engineering""}, {""id"": ""5994df7a54be8100732d43db"", ""text"": ""Electrical and Computer Engineering""}, {""id"": ""5994df7b54be8100732d43e5"", ""text"": ""Signal Processing""}, {""id"": ""5994df7b54be8100732d43e9"", ""text"": ""Civil and Environmental Engineering""}, {""id"": ""5994df7b54be8100732d43ea"", ""text"": ""Transportation Engineering""}, {""id"": ""5994df7b54be8100732d43ec"", ""text"": ""Geotechnical Engineering""}, {""id"": ""5994df7b54be8100732d43f1"", ""text"": ""Civil Engineering""}]",,0,,no,no,[],,2025-04-09T20:04:03.068288
dv7s3_v1,Bayesian Updating of Solar Panel Fragility Curves and Implications of Higher Panel Strength for Solar Generation Resilience,"Solar generation can become a major and global source of clean energy by 2050. Nevertheless, few studies have assessed its resilience to extreme events, and none have used empirical data to characterize the fragility of solar panels. This paper develops fragility functions for rooftop and ground-mounted solar panels calibrated with solar panel structural performance data in the Caribbean for Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. After estimating hurricane wind fields, we follow a Bayesian approach to estimate fragility functions for rooftop and ground-mounted panels based on observations supplemented with existing numerical studies on solar panel vulnerability. Next, we apply the developed fragility functions to assess failure rates due to hurricane hazards in Miami-Dade, Florida, highlighting that panels perform below the code requirements, especially rooftop panels. We also illustrate that strength increases can improve the panels' structural performance effectively. However, strength increases by a factor of two still cannot meet the reliability stated in the code. Our results advocate reducing existing panel vulnerabilities to enhance resilience but also acknowledge that other strategies, e.g., using storage or deploying other generation sources, will likely be needed for energy security during storms.",2022-01-16T03:59:07.273018,2022-03-01T18:52:16.504136,2022-01-16T16:24:14.052513,,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/dv7s3,CC-By Attribution 4.0 International,Bayesian update; fragility functions; hurricane hazards; solar panels; structural reliability,"[""Bayesian update"", ""fragility functions"", ""hurricane hazards"", ""solar panels"", ""structural reliability""]",Luis Ceferino; Ning Lin; Dazhi Xi,"[{""id"": ""4phaz"", ""name"": ""Luis Ceferino"", ""index"": 0, ""orcid"": ""0000-0003-0322-7510"", ""bibliographic"": true}, {""id"": ""r8nzt"", ""name"": ""Ning Lin"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""ukqem"", ""name"": ""Dazhi Xi"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}]",Luis Ceferino,Engineering; Civil and Environmental Engineering; Structural Engineering; Civil Engineering,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7b54be8100732d43e9"", ""text"": ""Civil and Environmental Engineering""}, {""id"": ""5994df7b54be8100732d43ed"", ""text"": ""Structural Engineering""}, {""id"": ""5994df7b54be8100732d43f1"", ""text"": ""Civil Engineering""}]",,0,,available,not_applicable,[],,2025-04-09T20:03:40.576017
m7rtg_v1,An integrated system design interface for operating 8-DoF robotic arm,"This study examines the system integration of a game engine with robotics middleware to drive an 8 degree of
freedom (DoF) robotic upper limb to generate human-like motion for telerobotic applications. The developed architecture
encompasses a pipeline execution design using Blender Game Engine (BGE) including the acquisition of real human
movements via the Microsoft Kinect V2, interfaced with a modeled virtual arm, and replication of similar arm movements on the physical robotic arm. In particular, this study emphasizes the integration of a human “pilot” with ways to drive such a robotic arm through simulation and later, into a finished system. Additionally, using motion capture technology, a human upper limb action was recorded and applied onto the robot arm using the proposed architecture flow. Also, we showcase the robotic arm’s actions which include reaching, picking, holding, and dropping an object. This paper presents
a simple and intuitive kinematic modeling and 3D simulation process, which is validated using 8-DoF articulated robot to demonstrate methods for animation, and simulation using the designed interface.",2022-01-15T07:44:45.934622,2022-03-01T18:52:19.088793,2022-01-15T15:45:47.838911,2022-01-14T18:30:00,,engrxiv,0,withdrawn,1,1,https://doi.org/10.31224/osf.io/m7rtg,No license,Game Engine; Motion capture; Robot Arm; Robot Simulation; Visual Servoing,"[""Game Engine"", ""Motion capture"", ""Robot Arm"", ""Robot Simulation"", ""Visual Servoing""]",Madhav Rao,"[{""id"": ""y8kz4"", ""name"": ""Madhav Rao"", ""index"": 0, ""orcid"": ""0000-0003-2278-9148"", ""bibliographic"": true}]",Madhav Rao,Engineering; Computer Engineering; Robotics; Biomedical Engineering and Bioengineering; Vision Science,"[{""id"": ""5994df7a54be8100732d43ae"", ""text"": ""Engineering""}, {""id"": ""5994df7a54be8100732d43ba"", ""text"": ""Computer Engineering""}, {""id"": ""5994df7a54be8100732d43c1"", ""text"": ""Robotics""}, {""id"": ""5994df7b54be8100732d4401"", ""text"": ""Biomedical Engineering and Bioengineering""}, {""id"": ""5994df7b54be8100732d4409"", ""text"": ""Vision Science""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T20:03:41.530357