Interpretation of the piezoelectric photothermal spectra of p-type silicon samples by M. Maliński, A. Memon, T. Ikari

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Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning by Teemu Koivisto, Marcin Mińkowski, Lasse Laurson

“…Here, we study the relationship between the atomic structure and the elastic and plastic properties of polycrystalline tantalum nanopillars under uniaxial compression by means of interpretable machine learning. …”
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Discourse in curriculum: A focus on film, television, and media studies by Firdaus Noor, Nuril Ashivah Misbah, Dede Suprayitno, Putrawan Yuliandri

“…The result is that the curriculum discourse produces four themes: scientific vision and mission, graduate profile, learning outcomes, and curriculum structure. In the experience model, participants express discourse themes with actual social reality. …”
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Cooperation and Competition in the European Parliament: A Game Theoretical Interpretation by Alexandru Volacu

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Interpretation of the Psalm Genre in the Works of Jaroslav Zatloukal and Eugen Suchon by Tetiana ROSUL

“…Their works demonstrate a secular model of the genre, which is characterized by filling the poetic structure of psalmody with a specific historical meaning. …”
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Lightweight LDA-UNet for accurate and interpretable visualization of retinopathy of prematurity by Honghao Lu, Madhavi Devaraj

“…The Dice coefficient is 0.927, which exceeds both the current typical lightweight UNet models and the Transformer-based hybrid structure TransUNet. …”
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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. …”
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The case of professors in 1821 at St. Petersburg University: New interpretations by T.N. Zhukovskaya

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Empirical interpretation and measurement of the productivity and efficiency of regions: the case of Latvia by Evgeniy Korshenkov, Sergey Ignatyev

“…But by the earnings index, calculated taking into account the sectoral structure of employment in a region, exactly the Latgale region as usually occupies the last place in Latvia, and the Riga region – the first one. …”
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Interpretable correlator Transformer for image-like quantum matter data by Abhinav Suresh, Henning Schlömer, Baran Hashemi, Annabelle Bohrdt

“…While local correlation structures in image-like data of physical systems can reliably be detected, identifying phases of matter characterized by global, non-local structures with interpretable ML methods remains a challenge. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…The shapley additive explanation (SHAP) method was employed to interpret the XBoost and RF models.<h4>Results</h4>The logistic regression (LR) of the Lasso regression model showed that IVS-AO Angle, Left Ventricular Mass Index (LVMI), Diastolic Left Ventricular Internal Diameter Index (LVIDdI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Distance from mitral valve closure point to basal segment of interventricular septum (MVCP-Sd), GLU, and Mitral Valve peak A (MV-A) were associated with BSH, with odds ratios (OR) of 0.86 (0.831-0.888), 1.034 (1.018-1.052), 0.104 (0.023-0.403), 1.041 (1.021-1.064), 0.964 (0.93-0.998), 0.852 (0.764-0.949), 1.146 (1.023-1.281), and 0.967 (0.947-0.987), respectively. …”
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Improving Trust via XAI and Pre-Processing for Machine Learning of Complex Biomedical Datasets by Brandon Hines, Douglas Talbert, Steven Anton

“…Ma-chine learning often results in models that perform the as-signed task quite well, but the issue lies in the often black-box nature of machine learning models. …”
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Mediating effect of pubertal stages on the family environment and neurodevelopment: An open-data replication and multiverse analysis of an ABCD Study® by Michael I. Demidenko, Dominic P. Kelly, Felicia A. Hardi, Ka I. Ip, Sujin Lee, Hannah Becker, Sunghyun Hong, Sandra Thijssen, Monica Luciana, Daniel P. Keating

“…When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (Steegen et al., 2016) are an efficacious technique for investigating the effect of different operationalizations of the same construct on underlying interpretations. …”
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Research on the Closure Polarity of the Paleo‐Asian Ocean: Evidence From the Three‐Dimensional Lithospheric Resistivity Structure of Central Asian Orogenic Belt by Kejie Yang, Gaofeng Ye, Xiangcheng Yi, Zhiguo An, Sheng Jin, Bin Liang, Hao Dong

“…The resistivity model shows alternating high and low resistivities along the southern margin of the CAOB in the north, with the low resistivities in the middle and lower crust interpreted as remnants of the subducted crust of the Paleo‐Asian Ocean (PAO). …”
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A Methodological Approach for Assessing the Interaction Between Rural Landscapes and Built Structures: A Case Study of Winery Architecture in Tuscany, Italy by Stefano Bigiotti, Carlo Costantino, Mariangela Ludovica Santarsiero, Alvaro Marucci

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Associations between forest vertical structure and habitat preferences of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in high-elevation environments by Zhipang Huang, Yuling Chen, Haitao Yang, Yihao Fang, Kai Cheng, Hongcan Guan, Cyril C. Grueter, Wen Xiao, Qinghua Guo

“…Using machine learning models, we identified structural and disturbance-related attributes that were consistently associated with R. bieti behavior at the home-range scale. …”
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Mapping the Λ_sCDM Scenario to <i>f</i>(<i>T</i>) Modified Gravity: Effects on Structure Growth Rate by Mateus S. Souza, Ana M. Barcelos, Rafael C. Nunes, Özgür Akarsu, Suresh Kumar

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exoALMA. XV. Interpreting the Height of CO Emission Layer by Giovanni P. Rosotti, Cristiano Longarini, Teresa Paneque-Carreño, Gianni Cataldi, Maria Galloway-Sprietsma, Sean M. Andrews, Jaehan Bae, Marcelo Barraza-Alfaro, Myriam Benisty, Pietro Curone, Ian Czekala, Stefano Facchini, Daniele Fasano, Mario Flock, Misato Fukagawa, Himanshi Garg, Cassandra Hall, Jane Huang, John D. Ilee, Andrés F. Izquierdo, Kazuhiro Kanagawa, Geoffroy Lesur, Giuseppe Lodato, Ryan A. Loomis, Ryuta Orihara, Christophe Pinte, Daniel J. Price, Jochen Stadler, Richard Teague, Gaylor Wafflard- Fernandez, Andrew J. Winter, Lisa Wölfer, Hsi-Wei Yen, Tomohiro C. Yoshida, Brianna Zawadzki

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Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data. by Michael H Schwartz, Andrew G Georgiadis

“…The EB-GAIT approach addresses the limitations of the conventional CGA interpretation method, offering a more structured and data-driven decision-making process. …”
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Multidisciplinary 3D geological-petrophysical reservoir characterization of Abu Sennan Field, Abu Gharadig Basin, Egypt by Mohamed Mamdouh, Adel Ali Ali Othman, Taher Mostafa

“…This study applies an integrated approach combining well logs from four wells, core analysis, and 2D seismic data to assess reservoir quality and structural framework. The workflow includes: (1) petrophysical evaluation to quantify shale volume (Vsh), effective porosity (ϕe), and hydrocarbon saturation (Sh) across five reservoir intervals—Abu Roash C (net pay 1–32.5 m, Vsh 29–35%, ϕe 19–29%, Sh 52–67%), Abu Roash D (7–10.5 m, Vsh 2–13%, ϕe 17–23%, Sh 60–90%), Abu Roash E (3.4–48.6 m, Vsh 20–30%, ϕe 18–24%, Sh 62–76%), Abu Roash G (3–12.5 m, Vsh 11–18%, ϕe 22–24%, Sh 58–73%), and Bahariya (2.5–52.5 m, Vsh 17–27%, ϕe 15–26%, Sh 46–77%); (2) seismic interpretation identifying a NW–SE-trending horst structure bounded by E–W and NNW–SSE fault systems, which compartmentalize the reservoirs; and (3) 3D static modeling to visualize the distribution of facies, porosity, and saturation. …”
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