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Journal articles on the topic 'Runway ML'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kanjanasurat, I., W. Jungsuwadee, A. Lasakul, and C. Benjangkaprasert. "Comparison of logistic regression and random forest algorithms for airport’s runway assignment." Journal of Physics: Conference Series 2497, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2497/1/012016.

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Abstract Various automation systems are currently developed using machine learning techniques. It is used to predict and decide on numerous complex tasks in order to reduce the likelihood of human error. Logistic regression is one of the most widely employed machine learning (ML) algorithm. In this study, the accuracy of logistic regression was compared to that of random forest for the assignment of Suvarnabhumi Airport runways to arriving aircraft. The accuracy of the logistic regression model was determined to be 82%, while the accuracy of the random forest model was 77%. Logistic regression

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Suryanto, Suryanto, and Nurokhman Nurokhman. "Evaluasi Properti Marshall Terhadap Mutu Aspal Beton Lapangan Pada Runway Bandara Yogyakarta International Airport." CivETech 4, no. 1 (2022): 59–72. http://dx.doi.org/10.47200/civetech.v4i1.1106.

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Pada pekerjaan runway Bandara YIA dengan lapis perkerasan aspal beton ACBC dan lapisan aus ACWC merupakan konstruksi penting dalam mendukung beban pesawat untuk landing dan take off yang harus sesuai spesifikasi yang telah ditentukan. Dalam penentuan kadar aspal yang akan berpengaruh pada kepadatan lapisan ACBC dan lapisan ACWC telah dilakukan pengujian Marshall di laboratorium untuk mendapatkan formula yang tepat agar hubungan kadar aspal dan kepadatan lapisan laston sesuai yang diharapkan. Studi bertujuan mengetahui hasil parameter pepada ngujian Marshall lapisan ACBC dan lapisan ACWC di lab

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Cornejo-Bueno, Sara, David Casillas-Pérez, Laura Cornejo-Bueno, et al. "Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain." Symmetry 12, no. 6 (2020): 1045. http://dx.doi.org/10.3390/sym12061045.

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This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated

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Kao, Dina, Eoin Lalor, Gurpal Sandha, et al. "A Randomized Controlled Trial of Four Precolonoscopy Bowel Cleansing Regimens." Canadian Journal of Gastroenterology 25, no. 12 (2011): 657–62. http://dx.doi.org/10.1155/2011/486084.

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BACKGROUND: The ideal bowel cleansing regimen for colonoscopy has yet to be determined.OBJECTIVE: To compare the cleansing efficacy, and patient tolerability and safety of four bowel preparation regimens.METHODS: A total of 834 patients undergoing outpatient colonoscopy were randomly assigned to one of four regimens: 4 L polyethylene glycol (PEG); 2 L PEG + 20 mg bisacodyl; 90 mL of sodium phosphate (NaP); or two sachets of a commercially available bowel cleansing solution (PSMC) + 300 mL of magnesium citrate (M). The primary outcome measure was cleansing efficacy, which was scored by blinded

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Duong, Nguyen Hong. "Application of Artificial Intelligence and 3D Printing in Visual Arts - Innovation and Sustainable Development." Journal of Lifestyle and SDGs Review 5, no. 6 (2025): e06471. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n06.pe06471.

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Objective: This study explores the integration of Artificial Intelligence (AI) and 3D printing in visual arts to promote creative innovation and sustainable development. It investigates how these technologies optimize design, reduce material waste, and improve education, contributing to SDG 4 and SDG 9. Theoretical Framework: The research is grounded in theories of digital creativity, sustainable design, and interdisciplinary art pedagogy. It situates AI and 3D printing within the context of Industry 4.0 and the creative economy, emphasizing their transformative potential in art, education, an

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Boulis, Nicholas M., Vikas Bhatia, Theodore I. Brindle та ін. "Adenoviral nerve growth factor and β-galactosidase transfer to spinal cord: a behavioral and histological analysis". Journal of Neurosurgery: Spine 90, № 1 (1999): 99–108. http://dx.doi.org/10.3171/spi.1999.90.1.0099.

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Object. The present study characterizes the time course and loci of gene expression induced by the administration of adenoviral vectors into spinal cord. Although a marked inflammatory response to these vectors occurred, no effect on spinal cord function was seen in the 1st postoperative week. The expression of transgenic genes delivered by viral vectors is being exploited throughout the nervous system. The present study utilized adenoviral vectors containing the Rous sarcoma virus (RSV) promoter and a nuclear localization signal to achieve transgenic expression in mammalian spinal cord. Metho

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Al-kahtani, M. S. M., Han Zhu, Yasser E. Ibrahim, S. I. Haruna, and S. S. M. Al-qahtani. "Study on the Mechanical Properties of Polyurethane-Cement Mortar Containing Nanosilica: RSM and Machine Learning Approach." Applied Sciences 13, no. 24 (2023): 13348. http://dx.doi.org/10.3390/app132413348.

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Polymer-modified cement mortar has been increasingly used as a runway/road pavement repair material due to its improved bending strength, bonding strength, and wear resistance. The flexural strength of polyurethane–cement mortar (PUCM) is critical in achieving a desirable maintenance effect. This study aims to evaluate and optimize the flexural strength of PUCM involving nano silica (NS) using a central composite design/response surface methodology (CCD/RSM) to design and establish statistical models. The PU binder and NS were utilized as input parameters to evaluate the responses, such as com

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Khattak, Afaq, Jianping Zhang, Pak-Wai Chan, Feng Chen, and Hamad Almujibah. "Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data." Atmosphere 15, no. 1 (2023): 20. http://dx.doi.org/10.3390/atmos15010020.

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Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the sev

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Khudazarov, Ravshan Saparovich. "USING ARTIFICIAL INTELLIGENCE IN MODELING MATHEMATICAL ACTIVITIES IN THE HIGHER EDUCATION SYSTEM AS A TOOL FOR CREATIVELY ORGANIZING THE EDUCATIONAL PROCESS." Multidisciplinary Journal of Science and Technology 5, no. 5 (2025): 1337–41. https://doi.org/10.5281/zenodo.15528134.

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 This scientific article explores the methods of using artificial intelligence (AI), a new computer system widely applied across various fields due to technological advancements, for modeling mathematical activities in the higher education system. Additionally, it provides insights into the potential of leveraging AI to transform the educational process from traditional (primitive) learning to a creative learning approach.<strong></strong>

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Coman, Paul T., Andrew Weng, Jason Ostanek, Eric C. Darcy, Donal P. Finegan, and Ralph E. White. "Modeling of Li-ion Battery Thermal Runaway: Insights into Modeling and Prediction." Electrochemical Society Interface 33, no. 3 (2024): 63–68. http://dx.doi.org/10.1149/2.f09243if.

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This article discusses the dual challenges of modeling and predicting thermal runaway (TR) in li-ion batteries. It explores the current challenges of TR modeling, the methods for progressing from single cells to battery packs, and future directions involving the use of probabilistic methods and ML/AI to tackle this multifaceted issue. Accurate prediction of TR events remains a major challenge, especially because of the cell-to-cell variability, but also due to parametrization, which requires complex experimentation or exhaustive experimental data for training data-driven ML models. Continued r

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Mukhnizar, Bio Oktonius Manurung, and Afdal. "ANALISIS PERAWATAN INJECTION PUMP PADA MOTOR DIESEL." Journal of Scientech Research and Development 5, no. 2 (2024): 915–23. http://dx.doi.org/10.56670/jsrd.v5i2.265.

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Mesin diesel merupakan bentuk pembakaran dalam (internal combustion engine) dengan prinsip kerja penyalaan bahan bakar yang dilakukan oleh suhu secara kompresi di ruang bakar. Perawatan motor diesel pada kendaraan bermotor terutama pompa ijeksi sangat diperlukan. Tujun penelitian untuk mengetahui prinsip kerja injection pump, penyebab tersumbatnya injection pump dan mengetahui prosedur perawatan injection pump. Penelitian dilakukan dengan mengambil data tekanan penginjeksian pada Injection pump dan Injektor motor Diesel 6 silinder menggunakan tool set, nozzle pump tester, ragum dan test banch

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Osuntokun, O. T., V. O. Azuh, O. A. Thonda, and S. D. Olorundare. "Random Amplified Polymorphic DNA (RAPD) Markers Protocol of Bacterial Isolates from Two selected General Hospitals Wastewater (HWW)." Journal of Plant Biota 3, no. 1 (2024): 28–33. http://dx.doi.org/10.51470/jpb.2024.3.1.28.

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In this research work, Random amplified polymorphic DNA (RAPD) markers Protocol was used to characterize bacterial isolates from two selected General Hospitals Waste Water (HWW). The hospital environment and its wastes accumulate diseases from both inward and outward patients. It is pertinent to investigate the wastewater and study its microbial community. Wastewater samples were collected from two major General Hospitals in Akoko area, namely, Ikare and Akungba-Akoko General Hospitals. Samples were microbiologically examined for the presence of bacterial colonies. Isolated bacterial species w

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Alawi, Ali, Ahmed Saeed, Mostafa H. Sharqawy, and Mohammad Al Janaideh. "A Comprehensive Review of Thermal Management Challenges and Safety Considerations in Lithium-Ion Batteries for Electric Vehicles." Batteries 11, no. 7 (2025): 275. https://doi.org/10.3390/batteries11070275.

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The transition to electric vehicles (EVs) is accelerating due to global efforts to reduce greenhouse gas emissions and reliance on fossil fuels. Lithium-ion batteries (LIBs) are the predominant energy storage solution in EVs, offering high energy density, efficiency, and long lifespan. However, their adoption is overly involved with critical safety concerns, including thermal runaway and overheating. This review systematically focuses on the critical role of battery thermal management systems (BTMSs), such as active, passive, and hybrid cooling systems, in maintaining LIBs within their optimal

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Jin, Guangyao, Wanwei Zhao, Jianing Zhang, Wenyu Liang, Mingyang Chen, and Rui Xu. "High-Temperature Stability of LiFePO4/Carbon Lithium-Ion Batteries: Challenges and Strategies." Sustainable Chemistry 6, no. 1 (2025): 7. https://doi.org/10.3390/suschem6010007.

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Lithium-ion batteries that use lithium iron phosphate (LiFePO4) as the cathode material and carbon (graphite or MCMB) as the anode have gained significant attention due to their cost-effectiveness, low environmental impact, and strong safety profile. These advantages make them suitable for a wide range of applications including electric vehicles, stationary energy storage, and backup power systems. However, their adoption is hindered by a critical challenge: capacity degradation at elevated temperatures. This review systematically summarizes the corresponding modification strategies including

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Yurkiv, Vitaliy, Yasaman AbdiSobbouhi, Qusai Alahmad, and Todd A. Kingston. "(Invited) Thermal Runaway Prediction in Li-Ion Batteries: Combining Experimental Insights with Multiphysics and Transformer-Based Models." ECS Meeting Abstracts MA2025-01, no. 8 (2025): 822. https://doi.org/10.1149/ma2025-018822mtgabs.

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Thermal runaway (TR) in commercial lithium-ion batteries (LIBs), is a critical challenge for electric vehicles (EVs) safety. This research presents a comprehensive approach to predicting and mitigating TR by integrating experimental testing, multiphysics modeling, and machine learning (ML). Experimental tests were conducted on high capacity commercial cylindrical batteries (e.g., 24 Ah) under thoroughly controlled temperature and humidity conditions, with surface temperatures monitored via FLIR infrared cameras and thermal sensors. Battery cycling was performed across a range of C-rates, inclu

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Kiasari, Mahmoud M., and Hamed H. Aly. "Enhancing Fire Protection in Electric Vehicle Batteries Based on Thermal Energy Storage Systems Using Machine Learning and Feature Engineering." Fire 7, no. 9 (2024): 296. http://dx.doi.org/10.3390/fire7090296.

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Thermal Energy Storage (TES) plays a pivotal role in the fire protection of Li-ion batteries, especially for the high-voltage (HV) battery systems in Electrical Vehicles (EVs). This study covers the application of TES in mitigating thermal runaway risks during different battery charging/discharging conditions known as Vehicle-to-grid (V2G) and Grid-to-vehicle (G2V). Through controlled simulations in Simulink, this research models real-world scenarios to analyze the effectiveness of TES in controlling battery conditions under various environmental conditions. This study also integrates Machine

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Wang, Huiming, Jie Lin, and Shengpin Guo. "Study on the Compressive Strength Predicting of Steel Fiber Reinforced Concrete Based on an Interpretable Deep Learning Method." Applied Sciences 15, no. 12 (2025): 6848. https://doi.org/10.3390/app15126848.

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Steel fiber reinforced concrete (SFRC) exhibits excellent material enhancement and toughening properties. It is widely used in applications such as airport runways, highway pavements, and bridge deck overlays. In order to predict the compressive strength of SFRC efficiently and accurately, this study proposes a deep learning-based prediction model, trained and tested on a large set of experimental data. Additionally, the SHapley Additive exPlanations (SHAP) interpretability method is employed to analyze and interpret the prediction outcomes. SHAP facilitates the identification and visualizatio

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Zaidi, Hijab Zehra, Ijaz Ali Shoukat, Amina Nawaz, Tahmina Asghar, Muhammad Amjad, and Anjum Ali. "Next-Gen Facial Recognition for Criminal Detection: Leveraging Advanced Machine Learning Techniques." Pakistan Journal of Scientific Research 4, no. 1 (2024): 83–92. https://doi.org/10.57041/pjosr.v4i1.1141.

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The process of identifying and spotting a criminal is slow and difficult creating a criminal detection framework that could help policemen to recognize the face of a criminal or a suspect is proposed. The framework is a client-server video-based face recognition surveillance in real-time. The framework applies face detection and tracking the video footage from the camera can be used to identify suspects, criminals, runaways, missing persons, etc. This project focuses on the development of the client side of the proposed framework, face detection, and tracking using Android mobile devices. For

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Kokkonen, Tuomo, Siru Salin, Kari Elo, Rashid Safari, Juhani Taponen, and Aila Vanhatalo. "Ummessaolokauden ruokinnan koostumuksen vaikutus lypsylehmien insuliiniresistenssiin." Suomen Maataloustieteellisen Seuran Tiedote, no. 28 (January 31, 2012): 1–6. http://dx.doi.org/10.33354/smst.75512.

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Liian runsas energian saanti poikimista edeltävänä ummessaoloaikana nostaa veren insuliinipitoisuutta huomattavasti, mikä saattaa voimistaa kudosten insuliiniresistenssiä. Rasvakudoksen insuliiniherkkyyden väheneminen voi lisätä rasvahappojen mobilisaatiota poikimisen läheisyydessä. Tutkimuksessa selvitettiin, miten ummessaolokauden ruokinnan energiasisältö vaikuttaa lehmien kuntoluokan kehittymiseen sekä insuliiniresistenssiin ja rasvahappojen mobilisaatioon tiineyden loppuvaiheessa ja tuotoskauden alussa. Kokeessa oli mukana 16 vähintään toista kertaa poikivaa ay-lehmää. Lehmiä ruokittiin 8

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Mahmood, Ahmed, Timothy Cockerill, Greg de Boer, Jochen Voss, and Harvey Thompson. "Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems." Energies 17, no. 18 (2024): 4575. http://dx.doi.org/10.3390/en17184575.

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Lithium ion (Li-ion) battery packs have become the most popular option for powering electric vehicles (EVs). However, they have certain drawbacks, such as high temperatures and potential safety concerns as a result of chemical reactions that occur during their charging and discharging processes. These can cause thermal runaway and sudden deterioration, and therefore, efficient thermal management systems are essential to boost battery life span and overall performance. An electrochemical-thermal (ECT) model for Li-ion batteries and a conjugate heat transfer model for three-dimensional (3D) flui

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Dini, Pierpaolo, and Davide Paolini. "Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review." Batteries 11, no. 3 (2025): 107. https://doi.org/10.3390/batteries11030107.

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Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements in the State of Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, in extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result in overheating, fires, and even explosions. Addressing these risks requires advanced diagnostic and management strategies, and machine learning presents a pow

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Das Goswami, Basab Ranjan, Massimiliano Mastrogiorgio, Marco Ragone, Farzad Mashayek, and Vitaliy Yurkiv. "Predicting Thermal Failures Using an Advanced Data-Driven Modeling Framework in a Cylindrical Li-Ion Battery Pack." ECS Meeting Abstracts MA2022-02, no. 3 (2022): 230. http://dx.doi.org/10.1149/ma2022-023230mtgabs.

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Li-ion cells have been widely used in battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and stationary energy storage units due to their high specific energy and excellent cycle life. However, the safety of Li-ion battery (LIB) packs has become a vital issue, especially under extreme or abusive conditions. Fire and explosion due to the onset of thermal runaway often lead to catastrophic damages. Thermal failures are caused by drastic and successive processes and reactions that feed into one another, eventually leading to fire and explosio

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Dewi, Lisa Sukma, Masrullita Masrullita, Azhari Azhari, Rozanna Dewi, and Lukman Hakim. "Karakteristik Minyak Dari Biji Alpukat (Persea Americana Mill) Menggunakan Metode Ekstraksi Dengan Pelarut N-Heksana." Chemical Engineering Journal Storage (CEJS) 2, no. 4 (2022): 37. http://dx.doi.org/10.29103/cejs.v2i4.7469.

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Minyak biji alpukat adalah minyak nabati yang diperoleh dari biji buah alpukat (persea Americana mill). Penelitian ini bertujuan Mengkajih Pengaruh waktu dan massa terhadap % rendemen minyak biji alpukat dan mengkaji mutu minyak biji alpukat dengan menganalisa % rendemen, ALB, kadar air, densitas dan komposisi lemak hidrokarbon dengan GCMS. Pembuatan minyak biji alpukat dilakukan dengan metode ekstraksi menggunakan pelarut n-heksana sebanyak 250 ml, massa 90, 120, dan 150 gram, dengan waktu 120, 150, dan 180 menit pada suhu 65oC. Hasil minyak biji alpukat tersebut diuji sifat fisika-kimianya b

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Li, Ming, Boyang Zhou, Lihong Zhou, and Linfeng Li. "Efficacy and Safety of Runzao Zhiyang Capsule as an Add-On Therapy for Chronic Eczema: A Systematic Review and Meta-Analysis." Evidence-Based Complementary and Alternative Medicine 2021 (March 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/6693268.

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Background. Runzao Zhiyang capsule (RZC), an oral Chinese herbal medicine, has been widely used for chronic eczema in China for many years. This study aims to evaluate the efficacy and safety of RZC as an add-on therapy to conventional treatment for chronic eczema. Methods. Randomized controlled trials (RCTs) assessing the efficacy and safety of RZC as an add-on therapy for chronic eczema were retrieved from eight literature databases from their inception to 31 August, 2020, including CNKI, WanFang, VIP, Sinomed, PubMed, Cochrane Library, Web of Science, and Embase. The Cochrane risk of bias t

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AbdiSobbouhi, Yasaman, Basab Ranjan Das Goswami, Farzad Mashayek, Todd A. Kingston, and Vitaliy Yurkiv. "From Electrochemical Analysis to Machine Learning Prediction: A Comprehensive Approach to LIB Safety." ECS Meeting Abstracts MA2024-02, no. 3 (2024): 380. https://doi.org/10.1149/ma2024-023380mtgabs.

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The advancement of lithium-ion battery (LIB) technology has been pivotal in powering a wide range of applications, from portable electronics to electric vehicles (EVs). However, the safety of LIBs remains a significant concern, primarily due to the risk of thermal runaway (TR), a process where an increase in temperature leads to a self-sustaining chain reaction resulting in catastrophic failure. Our study introduces a groundbreaking framework that integrates multiphysics modeling with machine learning (ML) to predict the onset of TR in LIB modules, thereby advancing battery safety and reliabil

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Borshon, Ishraque Zaman, Vahid Jabbari, Todd A. Kingston, Farzad Mashayek, Reza Shahbazian-Yassar, and Vitaliy Yurkiv. "Deep Learning Analysis of Solid Electrolyte Interface Microstructures in Lithium-Ion Batteries." ECS Meeting Abstracts MA2024-02, no. 7 (2024): 811. https://doi.org/10.1149/ma2024-027811mtgabs.

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Understanding the structure of the solid-electrolyte interface (SEI) in lithium-ion batteries (LIBs) is crucial for improving battery performance and safety. This study introduces a novel deep learning (DL) framework to analyze transmission electron microscopy (TEM) images of SEI, specifically focusing on identifying different types of grains and grain boundaries. Utilizing advanced machine learning (ML) algorithms, including deep convolutional neural networks, our approach enables the accurate segmentation and classification of complex microstructural features in SEI layers based on the exper

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Das Goswami, Basab Ranjan, Massimiliano Mastrogiorgio, Marco Ragone, Farzad Mashayek, and Vitaliy Yurkiv. "A Combined Multi-Physics Modelling and Machine Learning to Predict Electro-Thermal Failures of Cylindrical Li-Ion Batteries." ECS Meeting Abstracts MA2022-01, no. 2 (2022): 190. http://dx.doi.org/10.1149/ma2022-012190mtgabs.

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Safety aspects of Li-ion batteries (LIBs) operation become increasingly important due to their integration into large-scale systems such as electric vehicles (EVs). Recent events involving many EVs explosions endangering consumers’ lives have shown that electro-thermal aspects of LIBs are far from being adequately managed. Thus, one of the most crucial goals of current research is to steer LIBs’ technology toward long-lasting and safe operation. In this work, we employ data-driven methods to predict and potentially prevent the thermal runaway (TRA) in LIBs. Specifically, we use various machine

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Mastrogiorgio, Massimiliano, Basab Ranjan Das Goswami, Marco Ragone, Farzad Mashayek, and Vitaliy Yurkiv. "Advanced Data-Driven Modeling Framework for Predicting Thermal Failures in Li-Ion Pouch Batteries." ECS Meeting Abstracts MA2022-01, no. 2 (2022): 434. http://dx.doi.org/10.1149/ma2022-012434mtgabs.

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With the rapid development and widespread applications of lithium-ion batteries (LIBs), there is an ongoing need to extend and apply theoretical models that assist LIB’s safety aspects. It is particularly important for electric vehicles (EVs) due to numerous recent fire accidents. Thermal runaway (TRA) is one of the principal causes of LIB’s failures in EVs occurring due to thermal or mechanical breakdown, internal/external short-circuiting, or electrochemical abuse. During EV’s operation, it is impossible to directly monitor the TRA; however, the change in thermo-electrical characteristics (p

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Atış, Kader, and Duygu İrem Can. "Yapay Zekâ Programlarının Tekstil Baskı Tasarımında Kullanılması: Runway ML / Paul Klee Örneği." Sanat ve Tasarım Dergisi, September 6, 2024. http://dx.doi.org/10.18603/sanatvetasarim.1484885.

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Teknoloji, tasarım alanlarında hızla gelişmekte tasarımcılara çalışabilecekleri yeni iş alanları oluşturmaktadır. Alışılagelmiş tasarım süreçleri ile ilgili tüm bilinenleri bilgisayar ortamında tasarımcılara sunmaktadır. Tasarımcılar ürünlerini 3 boyutlu olarak oluşturulabildiği programlar sayesinde üretime hazır ürünlerini ve kalıplarını oluşturabilmektedir. Tekstil ve moda alanında bu programa örnek olarak CLO 3D giysi tasarım programı gösterilebilir. Hazırlanan tasarım ile ilgili kalıplar, kumaşlar, aksesuarlar, renk vb hizmeti sunmaktadır. Teknoloji ile iç içe olduğumuz bu dönemde son yıll

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Getman, Rachel Rose, Denise Nicole Green, Kavita Bala, et al. "Machine Learning (ML) for Tracking Fashion Trends: Documenting the Frequency of the Baseball Cap on Social Media and the Runway." Clothing and Textiles Research Journal, June 18, 2020, 0887302X2093119. http://dx.doi.org/10.1177/0887302x20931195.

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With the proliferation of digital photographs and the increasing digitization of historical imagery, fashion studies scholars must consider new methods for interpreting large data sets. Computational methods to analyze visual forms of big data have been underway in the field of computer science through computer vision, where computers are trained to “read” images through a process called machine learning. In this study, fashion historians and computer scientists collaborated to explore the practical potential of this emergent method by examining a trend related to one particular fashion item—t

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P., Selvi, Madhumitha M., Shrimathi V.A., and Suresh R. "AI-DRIVEN FASHION DESIGN: HOW MACHINE LEARNING IS TRANSFORMING THE CREATIVE PROCESS." ShodhKosh: Journal of Visual and Performing Arts 5, no. 1 (2024). https://doi.org/10.29121/shodhkosh.v5.i1.2024.4387.

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The fashion industry is undergoing a transformative paradigm shift with the integration of Artificial Intelligence (AI) and Machine Learning (ML). These technologies are revolutionizing the creative process, enabling designers to innovate, streamline production, and meet evolving consumer demands. This paper explores how AI-driven tools are reshaping fashion design, focusing on three key areas: trend forecasting, design generation, and personalized styling, while also addressing sustainability and ethical considerations. AI-powered trend forecasting leverages vast datasets from social media, r

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Shaw, Prateek, and Puja Devgun. "Artificial Intelligence in Content Creation." International Journal For Multidisciplinary Research 7, no. 3 (2025). https://doi.org/10.36948/ijfmr.2025.v07i03.45053.

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Artificial Intelligence has revolutionized the way one makes content. It brought in faster, more efficient, and scalable production of quality digital content. AI tools are shaking up the creative sphere from text-writing to image-making video editing and music composing. Advanced AI models like OpenAI's ChatGPT, Google's Bard, and of DeepMind's AlphaCode are generating human-like text, brainstorming ideas, and automating content workflows, drastically cutting down the hours and labor associated with making a content. Similarly, AI-powered image and video-generation platforms such as DALL-E, M

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Wang, Shiyao, Zhouying Wu, Shiyang Fan, et al. "Cooling Performance of Hybrid Thermal Management System for the Large‐Capacity LiFePO₄ Battery." Energy Technology, July 13, 2025. https://doi.org/10.1002/ente.202500670.

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This study develops a composite sandwich‐type thermal management system integrating aerogel, liquid cooling, and heat pipes to optimize lithium‐ion battery (LIB) module performance while balancing heat dissipation and thermal runaway prevention. The hybrid system couples aerogel insulation with a bottom‐mounted liquid cooling plate, significantly reducing peak temperatures and improving thermal uniformity. Key parameters investigated include coolant flow rates (25 and 200 mL/min) and aerogel thickness (0–3 mm). By incorporating heat pipes (high‐efficiency passive heat transfer devices) and opt

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Salvage, Rebecca O., and David W. Eaton. "The Influence of a Transitional Stress Regime on the Source Characteristics of Induced Seismicity and Fault Activation: Evidence from the 30 November 2018 Fort St. John ML 4.5 Induced Earthquake Sequence." Bulletin of the Seismological Society of America, April 4, 2022. http://dx.doi.org/10.1785/0120210210.

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ABSTRACT On 30 November 2018, a sequence of seismicity including a felt (ML∼4.5) induced earthquake occurred ∼16 km southwest of Fort St. John, British Columbia. Using a local seismograph network around the epicentral region, we identified &gt; 560 seismic events over a two-week period, incorporating two mainshock events within a 45 min time interval, both with ML&gt;4.3. This seismicity occurred close in location and depth to ongoing hydraulic fracturing operations. Using previously unpublished data, our analysis suggests that events, including the largest mainshock, occurred at the i

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Beyer, Sue. "Fantasyland Autofiction." M/C Journal 27, no. 5 (2024). http://dx.doi.org/10.5204/mcj.3104.

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This essay explores a return to hope and romanticism by contemporary artists looking at themes of fantasy worlds and mapping imaginary lands as a type of autofiction. These fantasylands are created in collaboration with hallucinating machine learning platforms, as a tool for contemporary art-making. Seen through the framework of Metamodernism, how does AI hallucination contribute to Metamodern structure of feeling? AI, as part of the metacrisis, places society and culture in a type of no man’s land or in-between, where rapid and unchecked advancements in machine learning and generative technol

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Binns, Daniel. "The Allure of Artificial Worlds." M/C Journal 27, no. 6 (2024). http://dx.doi.org/10.5204/mcj.3105.

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Fig. 1: ‘Vapourwave Hall’, generated by the author using Leonardo.Ai, 2024. Introduction With generative AI (genAI) and its outputs, visual and aural cultures are grappling with new practices in storytelling, artistic expression, and meme-farming. Some artists and commentators sit firmly on the critical side of the discourse, citing valid concerns around utility, longevity, and ethics. But more spurious judgements abound, particularly when it comes to quality and artistic value. This article presents and explores AI-generated audiovisual media and AI-driven simulative systems as worlds: virtua

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