MEAT TRADE DYNAMICS: MONTHLY PATTERNS IN ROMANIA’S IMPORTS (CMA AND HOLT-WINTERS METHODS) by Aurelia Vasilica BĂLAN, Elena TOMA

“…The analysis aims to project the values of meat imports until May 2025 by using various forecasting algorithms to understand import trends and seasonality. …”
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Measurement of top-quark pair production in association with charm quarks in proton–proton collisions at s=13 TeV with the ATLAS detector by G. Aad, E. Aakvaag, B. Abbott, S. Abdelhameed, K. Abeling, N.J. Abicht, S.H. Abidi, M. Aboelela, A. Aboulhorma, H. Abramowicz, H. Abreu, Y. Abulaiti, B.S. Acharya, A. Ackermann, C. Adam Bourdarios, L. Adamczyk, S.V. Addepalli, M.J. Addison, J. Adelman, A. Adiguzel, T. Adye, A.A. Affolder, Y. Afik, M.N. Agaras, A. Aggarwal, C. Agheorghiesei, F. Ahmadov, S. Ahuja, X. Ai, G. Aielli, A. Aikot, M. Ait Tamlihat, B. Aitbenchikh, M. Akbiyik, T.P.A. Åkesson, A.V. Akimov, D. Akiyama, N.N. Akolkar, S. Aktas, K. Al Khoury, G.L. Alberghi, J. Albert, P. Albicocco, G.L. Albouy, S. Alderweireldt, Z.L. Alegria, M. Aleksa, I.N. Aleksandrov, C. Alexa, T. Alexopoulos, F. Alfonsi, M. Algren, M. Alhroob, B. Ali, H.M.J. Ali, S. Ali, S.W. Alibocus, M. Aliev, G. Alimonti, W. Alkakhi, C. Allaire, B.M.M. Allbrooke, J.S. Allen, J.F. Allen, C.A. Allendes Flores, P.P. 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Shaw, Q. Shen, D.J. Sheppard, P. Sherwood, L. Shi, X. Shi, S. Shimizu, C.O. Shimmin, I.P.J. Shipsey, S. Shirabe, M. Shiyakova, M.J. Shochet, D.R. Shope, B. Shrestha, S. Shrestha, I. Shreyber, M.J. Shroff, P. Sicho, A.M. Sickles, E. Sideras Haddad, A.C. Sidley, A. Sidoti, F. Siegert, Dj. Sijacki, F. Sili, J.M. Silva, I. Silva Ferreira, M.V. Silva Oliveira, S.B. Silverstein, S. Simion, R. Simoniello, E.L. Simpson, H. Simpson, L.R. Simpson, S. Simsek, S. Sindhu, P. Sinervo, S. Singh, S. Sinha, S. Sinha, M. Sioli, I. Siral, E. Sitnikova, J. Sjölin, A. Skaf, E. Skorda, P. Skubic, M. Slawinska, I. Slazyk, V. Smakhtin, B.H. Smart, S.Yu. Smirnov, Y. Smirnov, L.N. Smirnova, O. Smirnova, A.C. Smith, D.R. Smith, E.A. Smith, J.L. Smith, R. Smith, H. Smitmanns, M. Smizanska, K. Smolek, A.A. Snesarev, H.L. Snoek, S. Snyder, R. Sobie, A. Soffer, C.A. Solans Sanchez, E.Yu. Soldatov, U. Soldevila, A.A. Solodkov, S. Solomon, A. Soloshenko, K. Solovieva, O.V. Solovyanov, P. Sommer, A. Sonay, W.Y. Song, A. Sopczak, A.L. Sopio, F. Sopkova, J.D. Sorenson, I.R. Sotarriva Alvarez, V. Sothilingam, O.J. Soto Sandoval, S. Sottocornola, R. Soualah, Z. Soumaimi, D. South, N. Soybelman, S. Spagnolo, M. Spalla, D. Sperlich, G. Spigo, B. Spisso, D.P. Spiteri, M. Spousta, E.J. Staats, R. Stamen, A. Stampekis, E. Stanecka, W. Stanek-Maslouska, M.V. Stange, B. Stanislaus, M.M. Stanitzki, B. Stapf, E.A. Starchenko, G.H. Stark, J. Stark, P. Staroba, P. Starovoitov, S. Stärz, R. Staszewski, G. Stavropoulos, A. Stefl, P. Steinberg, B. Stelzer, H.J. Stelzer, O. Stelzer-Chilton, H. Stenzel, T.J. Stevenson, G.A. Stewart, J.R. Stewart, M.C. Stockton, G. Stoicea, M. Stolarski, S. Stonjek, A. Straessner, J. Strandberg, S. Strandberg, M. Stratmann, M. Strauss, T. Strebler, P. Strizenec, R. Ströhmer, D.M. Strom, R. Stroynowski, A. Strubig, S.A. Stucci, B. Stugu, J. Stupak, N.A. Styles, D. Su, S. Su, W. Su, X. Su, D. Suchy, K. Sugizaki, V.V. Sulin, M.J. Sullivan, D.M.S. Sultan, L. Sultanaliyeva, S. Sultansoy, T. Sumida, S. Sun, W. Sun, O. Sunneborn Gudnadottir, N. Sur, M.R. Sutton, H. Suzuki, M. Svatos, M. Swiatlowski, T. Swirski, I. Sykora, M. Sykora, T. Sykora, D. Ta, K. Tackmann, A. Taffard, R. Tafirout, J.S. Tafoya Vargas, Y. Takubo, M. Talby, A.A. Talyshev, K.C. Tam, N.M. Tamir, A. Tanaka, J. Tanaka, R. Tanaka, M. Tanasini, Z. Tao, S. Tapia Araya, S. Tapprogge, A. Tarek Abouelfadl Mohamed, S. Tarem, K. Tariq, G. Tarna, G.F. Tartarelli, M.J. Tartarin, P. Tas, M. Tasevsky, E. Tassi, A.C. Tate, G. Tateno, Y. Tayalati, G.N. Taylor, W. Taylor, P. Teixeira-Dias, J.J. Teoh, K. Terashi, J. Terron, S. Terzo, M. Testa, R.J. Teuscher, A. Thaler, O. Theiner, T. Theveneaux-Pelzer, O. Thielmann, D.W. Thomas, J.P. Thomas, E.A. Thompson, P.D. Thompson, E. Thomson, R.E. Thornberry, C. Tian, Y. Tian, V. Tikhomirov, Yu.A. Tikhonov, S. Timoshenko, D. Timoshyn, E.X.L. Ting, P. Tipton, A. Tishelman-Charny, S.H. Tlou, K. Todome, S. Todorova-Nova, S. Todt, L. Toffolin, M. Togawa, J. Tojo, S. Tokár, K. Tokushuku, O. Toldaiev, G. Tolkachev, M. Tomoto, L. Tompkins, E. Torrence, H. Torres, E. Torró Pastor, M. Toscani, C. Tosciri, M. Tost, D.R. Tovey, I.S. Trandafir, T. Trefzger, A. Tricoli, I.M. Trigger, S. Trincaz-Duvoid, D.A. Trischuk, B. Trocmé, A. Tropina, L. Truong, M. Trzebinski, A. Trzupek, F. Tsai, M. Tsai, A. Tsiamis, P.V. Tsiareshka, S. Tsigaridas, A. Tsirigotis, V. Tsiskaridze, E.G. Tskhadadze, M. Tsopoulou, Y. Tsujikawa, I.I. Tsukerman, V. Tsulaia, S. Tsuno, K. Tsuri, D. Tsybychev, Y. Tu, A. Tudorache, V. Tudorache, A.N. Tuna, S. Turchikhin, I. Turk Cakir, R. Turra, T. Turtuvshin, P.M. Tuts, S. Tzamarias, E. Tzovara, F. Ukegawa, P.A. Ulloa Poblete, E.N. Umaka, G. Unal, A. Undrus, G. Unel, J. Urban, P. Urrejola, G. Usai, R. Ushioda, M. Usman, F. Ustuner, Z. Uysal, V. Vacek, B. Vachon, T. Vafeiadis, A. Vaitkus, C. Valderanis, E. Valdes Santurio, M. Valente, S. Valentinetti, A. Valero, E. Valiente Moreno, A. Vallier, J.A. Valls Ferrer, D.R. Van Arneman, T.R. 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Zanzottera, O. Zaplatilek, C. Zeitnitz, H. Zeng, J.C. Zeng, D.T. Zenger, Jr, O. Zenin, T. Ženiš, S. Zenz, S. Zerradi, D. Zerwas, M. Zhai, D.F. Zhang, J. Zhang, J. Zhang, K. Zhang, L. Zhang, L. Zhang, P. Zhang, R. Zhang, S. Zhang, S. Zhang, T. Zhang, X. Zhang, Y. Zhang, Y. Zhang, Y. Zhang, Z. Zhang, Z. Zhang, Z. Zhang, H. Zhao, T. Zhao, Y. Zhao, Z. Zhao, Z. Zhao, A. Zhemchugov, J. Zheng, K. Zheng, X. Zheng, Z. Zheng, D. Zhong, B. Zhou, H. Zhou, N. Zhou, Y. Zhou, Y. Zhou, Y. Zhou, C.G. Zhu, J. Zhu, X. Zhu, Y. Zhu, Y. Zhu, X. Zhuang, K. Zhukov, N.I. Zimine, J. Zinsser, M. Ziolkowski, L. Živković, A. Zoccoli, K. Zoch, T.G. Zorbas, O. Zormpa, W. Zou, L. Zwalinski

“…The measurements are primarily limited by uncertainties in the modeling of inclusive tt¯ and tt¯+bb¯ production, in the calibration of the flavor-tagging algorithm, and by data statistics. Cross-section predictions from various tt¯ simulations are largely consistent with the measured cross-section values, though all underpredict the observed values by 0.5 to 2.0 standard deviations. …”
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Modeling flood zones on the basis of time series forecasting and GIS-technologies by the example of the Republic of Bashkortostan by E.V. Palchevsky, V.V. Antonov, L.E. Rodionova, L.A. Kromina, A.R. Fakhrullina

“…Abstract: A specialized GIS Web is proposed, implemented by integrating an artificial neural network and geotechnologies and providing early forecasting and modeling of flood zones up to five days in advance. …”
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Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism by Yu HE, Chengxiang WANG, Wei YU

“…When the quality of projection data is poor, classical denoising and reconstruction algorithms are ineffective in removing the noise. To improve the quality of low signal-to-noise CT reconstructed images, this study proposes a deep learning-based denoising method. …”
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Geotechnical evaluation of embankment stability in seismic zones using Monte Carlo and subset simulations within an LRFD framework aided by machine learning by Furquan Ahmad, Pramod Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, Pijush Samui

“…This study uniquely integrates probabilistic techniques with hybrid machine learning optimization algorithms (RF-YYPO, RF-BWOA, and RF-SMA) under an LRFD framework, which has not been previously implemented for seismic slope stability of railway embankments.…”
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Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. by Yingke Xu, Qing Wu

“…Performance metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive and negative predictive values (PPV and NPV), and F1-score. The fourth model, integrating SNPs with XGBoost, outperformed all other models. …”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…Key predictive variables included radiomics features like wavelet-HHH_firstorder_Mean and mean normalized TIC values. Our combined approach integrating radiomics and DSC MRI parameters shows strong potential for distinguishing radionecrosis from glioma progression. …”
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Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction by Yalong Li, Ye Jin, Yangqing Dan, Wenting Zha

“…Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. …”
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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

“…This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. …”
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Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach by Mehdi Fasihi, Mirko Sodini, Alex Falcon, Francesco Degano, Paolo Sivilotti, Giuseppe Serra

“…To ensure the robustness of the proposed Pseudo-labelling strategy, we integrated it into eight machine-learning algorithms. …”
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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…This research not only advances the methodological landscape of fake news detection but also contributes to the broader discourse on safeguarding democratic values, media integrity, and responsible AI deployment in digital environments.…”
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Options for Strengthening the Ensemble of Hypotheses under Uncertainty of the Objective Learning Function Formation by A. F. Chernyavsky, A. I. Kazlova

“…These parts are integral elements in their work on the formation of knowledge bases, learning strategies, assessment procedures, as well as in organizing interaction between the system and users. …”
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Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel by Hojik Kim, Hyung-Joo Choi, Woojin Kim, Seungmin Lee, Chul Hee Min, Sung-Woo Kwak

“…Unlike traditional SNF defect detection algorithms that estimate specific values, the proposed method estimates distributions, thus providing belief in the estimates. …”
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Health system costs of providing outpatient care for diabetes in people with TB in the Philippines by T. Yamanaka, M.C. Castro, J.P. Ferrer, J.A. Solon, S.E. Cox, Y.V. Laurence, A. Vassall

“…A key activity in the Philippines is to integrate TB services with other disease programmes, with a target of DM screening in 90% of TB cases. …”
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Complex Neutrosophic Approach for Uncertainty Management in Employment Competence for Higher Vocational College Students by Shuo Du, Lijuan Zhao

“…It also presents a novel ICNS de-neutrosophication method to provide representative and expressive scoring of ICNS decisions. Then, algorithmic analytical weighting is applied to infer the significance of ICNS-valued criteria. …”
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Yield Response of Different Rice Ecotypes to Meteorological, Agro-Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting a... by Md. Sabbir Ahmed, Md. Tasin Tazwar, Haseen Khan, Swadhin Roy, Junaed Iqbal, Md. Golam Rabiul Alam, Md. Rafiul Hassan, Mohammad Mehedi Hassan

“…The result shows that the root mean squared error (RMSE) of three different ecotypes are in between 9.38% and 24.37% and that of R-squared values are between 89.74% and 99.13% on two different machine learning algorithms. …”
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Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…However, challenges persist in linking algorithmic findings to documented violations, emphasizing the need for integrated data frameworks to enhance environmental oversight. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. In contrast, linear regression and KNN demonstrated the lowest performance, with MSE values up to 188% higher than CatBoost. …”
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Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework by Rui Guo, Xiaodong Huang, Yangjing Xiu, Minglu Che, Jinlong Gao, Shuai Fu, Qisheng Feng, Tiangang Liang

“…In this study, a high-accuracy R/S model was constructed using the AutoGluon framework and traditional machine learning (ML) algorithms with 1,367 R/S samples of grassland in China, integrating climate, soil, terrain and spectral features. …”
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An explainable AI-based approach for predicting undergraduate students academic performance by Fatema Tuz Johora, Md Nahid Hasan, Aditya Rajbongshi, Md Ashrafuzzaman, Farzana Akter

“…Initially the dataset was produced utilizing data-preprocessing techniques such as one-hot encoding, column remaining, and managing missing values. SMOTE (Synthetic Minority Oversampling Technique) and normalizing algorithms were employed to attain data balance and feature scaling, respectively. …”
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