Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms by Alejandro Kevin Méndez Castillo, Elizabeth Contreras López, Jesús Guadalupe Pérez Flores, Laura García Curiel, Emmanuel Pérez Escalante, Karla Soto Vega, Carlos Ángel-Jijón, Alicia Cervantes Elizarrarás

“…The integration of GAs enhanced the robustness of kinetic fitting by effectively navigating parameter space and avoiding local minima, outperforming traditional optimization methods, demonstrating their ability to refine parameter estimation and improve model generalization. …”
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Effect of Chemical Processed Barley Grain (Hordeum vulgare) Through Steam Infrared Flaking on Productive and Parameters of Ruminal Fermentation in Holstein Lactating Cows by Amir Honarmand, Seyed Alireza Vakili, Mohsen Danesh Mesgarani, Abdolmansour Tahmasebi

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Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum by Maryam Mazaheri-Tirani, Soleyman Dayani, Majid Iranpour Mobarakeh

“…Abstract Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. …”
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Optimizing CNC turning of AISI D3 tool steel using Al₂O₃/graphene nanofluid and machine learning algorithms by Leta Daba Gemechu, Dame Alemayehu Efa, Robsan Abebe

“…Machine learning helps in predicting the optimal parameters, whereas nanofluids enhance cooling efficiency while preserving both the tool and the workpiece. …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…However, optimizing the preparation parameters for PSCs is crucial. This study establishes a machine learning model incorporating a crude estimation of property (CEP) strategy to enhance prediction accuracy and precisely control process parameters. …”
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Multi-source data based refined transient modelling and analysis method for thermal performance of precision machine tool feed system by Yingjie Zheng, Lingtao Weng, Yuhong Dai, Yutao Fu, Kai Shi, Zheng Wang, Weiguo Gao, Dawei Zhang, Tian Huang

“…Variation in feed speed, acceleration, preload and other parameters significantly influence the inertia loss torque and friction torque within the feed system of machine tool. …”
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Prediction of physicochemical characteristics of Lemon (Citrus limon cv. Montaji Agrihorti) using Vis-NIR spectroscopy and machine learning model by Jihan Nada Salsabila Erha, Dina Wahyu Indriani, Zaqlul Iqbal, Bambang Susilo, Dimas Firmanda Al Riza

“…In this study, standardizing measurement on maturity level was conducted through Vis-NIR spectroscopy and machine learning models. In this case, non-destructive data from Vis-NIR spectroscopy were correlated with parameters related to fruit maturity and quality, such as soluble solid content (SSC), acidity, firmness, essential oil yield, and essential oil content. …”
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Effect of Cutting Speed, Depth of Cut, and Feed Rate on Metal Removal Rate in the Machining of a Cylindrical Mild Steel Bar by P. H. H Ucheonwu, S. O. Sada, L. C. Enyi, J. E. Sinebe, M. Ekpu

“…To ensure sustainable growth in the quality of produced designed and manufactured, certain area of interest such as the cutting parameters, must be fully developed. Hence, the objective of this paper is to evaluate the effect of cutting speed, depth of cut, and feed rate on the metal removal rate in the machining of a cylindrical mild steel bar using appropriate standard method. …”
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Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication by Jayant Kumar Rai, Swati Yadav, Ajay Kumar Dwivedi, Vivek Singh, Pinku Ranjan, Anand Sharma, Somesh Kumar, Stuti Pandey

“…The findings showed that there was a good correlation between measurement and simulation data for several parameters, including S-parameters, radiation patterns, and MIMO parameters like diversity gain (DG), channel capacity loss (CCL), mean effective gain (MEG), envelope correlation coefficients (ECC), and total active reflection coefficients (TARC). …”
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Modeling of working processes in the frequency-adjustable hydraulic drive of manipulation systems with separate movement of links during operation of mobile machines by Lagerev A.V., Lagerev I.A.

“…The adequacy of simulation results and real physical phenomena observed during the operation of mobile machines is shown. Oscillatory instability of kinematic and hydraulic parameters was revealed under certain forms of the laws of frequency regulation.…”
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Machine learning-based spirometry reference values for the Iranian population: a cross-sectional study from the Shahedieh PERSIAN cohort by Mohammad Sadegh Loeloe, Reyhane Sefidkar, Seyyed Mohammad Tabatabaei, Amir Houshang Mehrparvar, Sara Jambarsang

“…ObjectiveThis study aimed to determine spirometric norm values for the healthy Iranian adult population and compare them with established norm equations, specifically the GLI-Caucasian and Iranian equations.MethodsDuring the recruitment phase of the Shahedieh Prospective Epidemiological Research Studies in Iran (PERSIAN) in 2016, spirometric parameters of 998 participants were obtained. KNN regression was used to extract reference values for spirometric parameters FEV1, FVC, FEV1/FVC, and FEF25–75%, considering height and age as features. …”
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Development of a 24-h Preservation Protocol of Forearm Vascularized Composite Allotransplants in Nonhuman Primates Using Subnormothermic Machine Perfusion by Haïzam Oubari, MD, Loïc Van Dieren, MS, Yanis Berkane, MD, Lucile Cabanel, MD, Mark A. Randolph, MAS, Curtis L. Cetrulo, Jr, MD, Alexandre G. Lellouch, MD, Korkut Uygun, PhD

“…Background. Subnormothermic machine perfusion shows promise as a viable alternative to static cold storage for prolonged preservation of vascularized composite allografts. …”
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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…Reflectance and spectral indices were processed for the geo-tagged fields across temporal observations to enable real-time, spatially explicit monitoring. Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. …”
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Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study by Guo-kang Sun, Yun-hui Xiang, Lu Wang, Pin-pin Xiang, Zi-xin Wang, Jing Zhang, Ling Wu

“…Based on machine learning, eight silicosis biomarkers were screened and identified from routine blood cell, biochemical and coagulation parameters. …”
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An Investigation of the Effects of Cutting Edge Geometry and Cooling/Lubrication on Surface Integrity in Machining of Ti-6Al-4V Alloy by J. Caudill, R. Sarvesha, G. Chen, I. S. Jawahir

“…This investigation sought to characterize the combined influence of cutting-edge microgeometry and cooling/lubricating strategies on process thermo-mechanics and the resultant surface integrity in orthogonal machining of the Ti-6Al-4V alloy. Reverse waterfall cutting inserts were prepared with varying cutting-edge geometries, and machining experiments were conducted under cryogenic cooling with liquid nitrogen (LN₂), minimum quantity lubrication (MQL), and dry machining conditions, using constant machining parameters. …”
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Study on cutting force in laser-assisted ultrasonic elliptical vibration machining (LUEVM) of high volume SiCp/Al composites by Peicheng Peng, Tian Tian, Heshuai Yu, Daohui Xiang, Ke Niu, Wei Gao, Yanqin Li, Zhaojie Yuan, Guofu Gao

“…Laser assisted machining (LAM) and ultrasonic elliptical vibration assisted machining (UEVAM) are effective in reducing cutting force and boost the cutting capability of SiCp/Al composite materials, compared to traditional machining (TM). …”
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Experimental Investigation of Heat-Treated Tool on Wire Electric Discharge Machining of Titanium Alloy (Ti-6Al-4V) by R. Manoj Samson, Ashvin Sridhar, R. Ranjith, Solomon Jenoris Muthiya, Joshuva Arockia Dhanraj, Murgayya S. Basavankattimath, Agegnehu Shara Shata

“…In this study, Ti-6Al-4V has been machined using wire EDM machine with two different wire materials. …”
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ASPECTS REGARDING THE METHOD OF REALIZING THE TECHNICAL EXPERTISE FOR REPAIRING THE TRANSLATION MECHANISM OF A M4A COAL-MINING MACHINE by Marius Liviu CÎRȚÎNĂ, Constanţa RĂDULESCU, Emil MILITARU

“…This paper presents the technical state of the mechanism of translation of the coalmining machine after the technical expertise. The rehabilitation to which the translation mechanism will be subjected will be carried out by performing the intervention works that will bring back into the normal operating parameters both the structural part and the functional part. …”
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Machine learning based prediction modeling of micro-EDM of Ti–29Nb–13Ta–4.6Zr (TNTZ) by Shahid Ali, Didier Talamona, Asma Perveen

“…The present research evaluates the micromachining performance of the TNTZ (Ti–29Nb–13Ta–4.6Zr) alloy employing a tungsten carbide electrode using the µ-EDM (micro-electro-discharge-machining). The primary input parameters examined are voltage (80–130 V) and capacitance (10–400nF), with a feed rate of 0.09 mm/s during the experiments. …”
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An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning by Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Subhajit Saha, T. Subhash Chandra Bose

“…The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. …”
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