RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors by Al-Hakim Rosyid R., Setiawan Retno Agus, Hidayat Rachman, Arkananta Edgina R., Samodra Galih, Jayusman Hadi, Suryani Riska, Famuji Tri Styo

“…Future work includes expanding the dataset and integrating machine learning for improved adaptability.…”
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Effects of Scheduled Exercise on Cancer-Related Fatigue in Women with Early Breast Cancer by Anne Marie Lunde Husebø, Sindre Mikal Dyrstad, Ingvil Mjaaland, Jon Arne Søreide, Edvin Bru

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Determination of the deformation modulus of binary composite using artificial neural network by E. S. Klimanova

“…With a confidence probability of P = 95 % the absolute value of the relative error is equal to 11,8 % the proposing learning artificial neural network in 11 times less than the absolute value of the relative error of classical regression equation. …”
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Application, opportunities, and challenges of digital technologies in the decarbonizing shipping industry: a bibliometric analysis by Guangnian Xiao, Lei Pan, Fengbo Lai

“…Ultimately, it examines research gaps in speed optimization, emission prediction, and autonomous ships by integrating keyword co-occurrence analysis with the content of recent publications, and then proposes prospective research options.DiscussionsFuture studies on ship speed optimization could benefit from adopting multi-objective optimization methods, combining more machine-learning techniques with the FCP model, etc. Concerning emission prediction, future research efforts could focus on integrating more diverse external data sources into emission prediction models, adopting emerging technology applications, such as ship-based carbon capture (SBCC), introducing blockchain into smart emission monitoring systems, etc. …”
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A Watershed Approach to Co-Creating Just Sustainabilities: Reflections from the Lake Superior Living Labs Network by Charles Z. Levkoe, Nairne Cameron, Lindsay P. Galway, Rachel L. W. Portinga, Randy Hanson, Kathryn Milun, Erika Vye

“…Living labs aim to co-create innovative solutions to complex challenges through interdisciplinary, placed-based experiential learning and community-engaged action in the built and natural environments. …”
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The Influence of Personality Traits and Domain Knowledge on the Quality of Decision-Making in Engineering Design by Muhammad Ahmad, Guoxin Wang

“…The analysis of personality traits was carried out utilizing the complete Big Five model, while the estimate of the structural equation model was executed by employing partial least squares structural equation modeling (PLS-SEM) and a machine learning model for quality estimation. The available empirical research indicates that individuals who have a lower degree of extraversion and agreeableness, and higher levels of conscientiousness and openness, are more likely to make decisions of higher quality. …”
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Digital Devices Use and Chinese-Canadian First Graders’ Early English Literacy Development: A Mixed-Methods Study by Guofang Li, Fubiao Zhen, Ziwen Mei

“…Parents of 66 children participated in interviews, providing context-specific insights into devices use purposes and language learning practices. Data analysis included descriptive statistics and independent samples <i>t</i>-tests to examine group differences. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. …”
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Characterizing Sparse Spectral Diversity Within a Homogenous Background: Hydrocarbon Production Infrastructure in Arctic Tundra near Prudhoe Bay, Alaska by Daniel Sousa, Latha Baskaran, Kimberley Miner, Elizabeth Josephine Bushnell

“…Analysis involves two stages: first, computing the mixture residual of a generalized linear spectral mixture model; and second, nonlinear dimensionality reduction via manifold learning. Anthropogenic targets and lakeshore sediments are successfully isolated from the Arctic tundra background. …”
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College Students’ Mental Health Education Consulting Management System Design Based on Big Data Algorithms by Xuezhen Song

“…Students’ psychology is not yet fully mature, so students are often unable to withstand these pressures, resulting in many psychological discomfort or problems. The normal life, learning, and growth of students are affected. Considering the increasing number of psychological problems among students, education on students’ mental health has become particularly important, which has become an important part of students’ work. …”
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Towards better and unlinkable protected biometric templates using label‐assisted discrete hashing by Kiran Raja, R. Raghavendra, Christoph Busch

“…The proposed approach can easily be adopted for a closed‐enrolment set in which enrolment images are known a priori whereas the challenge of learning templates for a single subject remains open. …”
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Perfectionistic Self-Presentation and Suicide in a Young Woman with Major Depression and Psychotic Features by Sabrina Hassan, Gordon L. Flett, Rohan Ganguli, Paul L. Hewitt

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Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering by Sahar Qaadan, Aiman Alshare, Alexander Popp, Myrel Tiemann, Utz Spaeth, Benedikt Schmuelling

“…This dataset is particularly valuable for advanced machine learning applications, enabling accurate battery state-of-health estimation and predictive maintenance. …”
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Computer-Based Clinical Examination (CCE) in Surgery: Would It Complement or Replace the OSCE in the Post-COVID-19 Era? by Gamal E H A El Shallaly, Mudather M Bafadni, Hozifa M A Abdelmaged, Maysa H A Hamza

“…We used the modular objectoriented dynamic learning environment (MOODLE) program as a platform to upload and deliver the exam. …”
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Semantic Image Synthesis via Class-Adaptive Cross-Attention by Tomaso Fontanini, Claudio Ferrari, Giuseppe Lisanti, Massimo Bertozzi, Andrea Prati

“…By design, such layers learn pixel-wise modulation parameters to de-normalize the generator activations based on the semantic class each pixel belongs to. …”
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Voice assimilation of morphemic -s in the L2 English of L1 French, L1 Italian and L1 Spanish learners by Leonardo Contreras Roa, Paolo Mairano, Marc Capliez, Caroline Bouzon

“…Given the different distributions and status of [s] and [z] in the participants’ L1s and based on SLM (Speech Learning Model) and MDH (Markedness Differential Hypothesis), we hypothesized that L1 French learners and L1 Italian learners would find it easier than L1 Spanish learners to reproduce the outcome of the voice assimilation rule, but our predictions are only partially confirmed by the results: L1 French learners (who have /s/ and /z/ in their L1 as phonemes occurring in word-final position) are indeed the most successful in producing the expected patterns of periodicity. …”
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cLegal-QA: a Chinese legal question answering with natural language generation methods by Yizhen Wang, Xueying Shen, Zixian Huang, Lihui Niu, Shiyan Ou

“…Furthermore, we evaluated the real-world performance of these models with expert validation and applied transfer learning to new civil disputes. While the QA models demonstrate commendable performance on the dataset, there is still potential for further improvement.…”
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Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL by WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui

“…Based on this，a comprehensive energy system short-term load forecasting model is proposed，which combines complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN），cross optimization algorithm （CSO），long short term memory （LSTM） network，and multi task learning （MTL）. Firstly，preprocess the collected raw load data and calculate the actual load value considering system energy loss； Secondly，the maximum information coefficient （MIC） is used to analyze the correlation between multiple loads and between multiple loads and weather factors，and to extract strongly correlated variables of multiple loads； Once again，the strongly correlated variables of multiple loads are substituted into CEEMDAN，and the load data is decomposed into stationary subsequences； Then，the feature sequence is substituted into the LSTM-MTL shared layer and the CSO algorithm is used to optimize the prediction model，achieving collaborative prediction of multiple loads； Finally，the performance of the constructed model was validated using a multivariate load dataset from a chemical park in Jilin City，Jilin Province，China. …”
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A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits. by Dennis Khodasevich, Nina Holland, Lars van der Laan, Andres Cardenas

“…<h4>Conclusions</h4>We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.…”
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