Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases by Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba

“…Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. …”
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Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety by Olly Kravchenko, Julia Bäckman, David Mataix-Cols, James J. Crowley, Matthew Halvorsen, Patrick F. Sullivan, John Wallert, Christian Rück

“…The baseline model explained 27%, while the full model accounted for 34% of the variance. …”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…Characterization of the final desulfurization products, particularly using XRD, showed the emergence of new phases such as hannebachite (CaSO3.0·5H2O), while FTIR analysis identified unreacted portlandite and calcite. …”
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The Impact of Integrated Project-Based Learning and Flipped Classroom on Students’ Computational Thinking Skills: Embedded Mixed Methods by Muh Fitrah, Anastasia Sofroniou, Caly Setiawan, Widihastuti Widihastuti, Novi Yarmanetti, Melinda Puspita Sari Jaya, Jontas Gayuh Panuntun, Arfaton Arfaton, Septrisno Beteno, Ika Susianti

“…Although there was an improvement in algorithmic thinking skills, some students still faced difficulties in developing systematic solutions. …”
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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

“…Notably, the coefficient of determination (R2) on the test set increased by 16.14%, while the root mean square error decreased by 20.44%, respectively. …”
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Trading off Iodine and Radiation Dose in Coronary Computed Tomography by Guillaume Fahrni, Thomas Saliba, Damien Racine, Marianna Gulizia, Georgios Tzimas, Chiara Pozzessere, David C. Rotzinger

“…This review explores the balance between radiation and CM volumes, emphasizing patient-specific protocol optimization to improve diagnostic accuracy while minimizing risks. Radiation dose reduction strategies, such as low tube voltage protocols, prospective ECG-gating, and modern reconstruction algorithms, have significantly decreased radiation exposure, with some studies achieving sub-millisievert doses. …”
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NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care by Tarun Vats, Shrey Mehra, Uday Madan, Amit Chhabra, Akashdeep Sharma, Kunal Chhabra, Sarabjeet Singh, Utkarsh Chauhan

“…In light of the ongoing global hunger crisis, it is imperative to improve food production in accordance with Sustainable Development Goal 2.0, which aims to eliminate hunger while promoting sustainable agricultural practices. …”
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An innovative UAV-based approach for estimating crop stand counts amidst weed infestation by Gurjinder S. Baath, Arun Bawa, Bala Ram Sapkota, K. Colton Flynn, Sayantan Sarkar, Douglas R. Smith

“…However, most existing algorithms assume clean crop rows and do not account for common weed infestation. …”
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A Multi-Head Attention-Based Lightweight Generative Adversarial Network for Thyroid Ultrasound Video Super-Resolution by Yuchen Ning, Xianshuang Meng, Lingxiao Zhou, Jun Liu, Shijie Qiu, Jingfang Wu, Xi Wei, Jun Ying, Siwei Zhu, Yantian Zhang

“…Result: The MHVSR showcased exceptional performance on <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> super-resolution task on Thyroid dataset, achieving a PSNR of 31.43dB and an SSIM of 0.98, surpassing current VSR algorithms. It delivered reconstructions that closely approximated the quality of original high-resolution videos, while meeting the stringent real-time diagnostic criteria with an impressive inference speed of 27.89FPS. …”
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Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings by Vinoth Kanna I, Raja Subramani, Maher Ali Rusho, Shubham Sharma, Ramachandran T, Abinash Mahapatro, Deepak Gupta, Jasmina Lozanovic

“…This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. …”
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Artificial Intelligence in Healthcare: Medical Students' Perspectives on Balancing Innovation, Ethics, and Patient-Centered Care by Eleanor Roy, Sara Malafa, Lina M. Adwer, Houda Tabache, Tanishqa Sheth, Vasudha Mishra, Moaz Elsayed Abouelmagd, Andrea Cushieri, Sajjad Ahmed Khan, Mihnea-Alexandru Gaman, Juan C. Puyana, Francisco Javier Bonilla-Escobar

“…Algorithmic bias further threatens equity in care, as many AI systems are trained on unrepresentative datasets, leading to disparities in diagnosis and treatment. …”
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Modelling water table depth at rewetted peatlands with Sentinel-1 and Sentinel-2 by Eoin Reddin, Jennifer Hanafin, Mingming Tong, Laurence Gill, Mark G. Healy

“…For rewetting to be successful, water table depth should be maintained in the upper 0.2 m of the soil to promote carbon sequestration while minimising net greenhouse gas emissions. There is evidence that satellite remote sensing techniques may be effective tools at monitoring water table depth. …”
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Radiomics for Machine Learning—A Multi-Class System for the Automatic Detection of COVID-19 and Community-Acquired Pneumonia from Computed Tomography Images by Vasileia Paschaloudi, Dimitris Fotopoulos, Ioanna Chouvarda

“…The accuracy for the independent test set was 83.11% in the Normal–COVID case, 88.77% in the COVID–CAP case, 93.97% in the Normal–CAP case and 94.13% in the Normal–Disease case, when referring to two-class cases, while, in the three-class case, the accuracy was 78.55%. …”
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Integrated Multi-Omics Profiling Reveals That Highly Pyroptotic MDMs Contribute to Psoriasis Progression Through CXCL16 by Liping Jin, Xiaowen Xie, Mi Zhang, Wu Zhu, Guanxiong Zhang, Wangqing Chen

“…<b>Methods:</b> We integrated 21 transcriptomic datasets (from 2007 to 2020) obtained from the GEO database and two single-cell RNA sequencing datasets to quantify pyroptotic activity using Gene Set Variation Analysis and AUCell algorithms. Immune cell infiltration profiles were evaluated via CIBERSORT, while cell-cell communication networks were analyzed by CellChat. …”
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Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel by Fariha Ahmed Nishat, M. F. Mridha, Istiak Mahmud, Meshal Alfarhood, Mejdl Safran, Dunren Che

“…Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. …”
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Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine by Toru Aonishi, Tatsuya Nagasawa, Toshiyuki Koizumi, Mastiyage Don Sudeera Hasaranga Gunathilaka, Kazushi Mimura, Masato Okada, Satoshi Kako, Yoshihisa Yamamoto

“…By modifying the sequence control code for the calculation control module, other algorithms such as Simulated Bifurcation (SB) can also be implemented. …”
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Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways by LiLi Ma, Yue Liu, Yajing Ren, Na Mi, Jing Fang, Rui Bao, Xiuzhi Xu, Hongjia Zhang, Ying Tang

“…Conclusions This study identifies LINC00269 as a potential contributor to pyroptosis pathways in PD. While further investigation is required to fully elucidate its role, these findings provide new insights into PD pathogenesis and suggest potential avenues for future research on diagnostic and therapeutic targets. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…The generalization performance of each model was examined by systematically altering input variables (fiber type, fiber content, w/c ratio, and aggregate size) while holding other parameters constant. GPR showed remarkable agreement with empirical trends across all validation cases, accurately identifying strength peaks and non-linear behavioral shifts, such as the parabolic relationship between w/c ratio and CS. …”
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The Effect of Hearing Aid Amplification on Gait Parameters: A Pilot Study Using Ear-Worn Motion Sensors by Ann-Kristin Seifer, Arne Küderle, Kaja Strobel, Ronny Hannemann, Björn M. Eskofier

“…To achieve this, we used a hearing-aid-integrated accelerometer and our open-source EarGait framework comprising gait-related algorithms specifically developed for ear-worn sensors. …”
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Round, just-below, or precise prices? Cultural differences in the prevalence of price endings in E-commerce by Alfio Ventura, Alfio Ventura, Alfio Ventura, Eve Sarah Troll, Meikel Soliman, David D. Loschelder

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