Investigation of a chest radiograph-based deep learning model to identify an imaging biomarker for malnutrition in older adults by Ryo Sasaki, Yasuhiko Nakao, Fumihiro Mawatari, Takahito Nishihara, Masafumi Haraguchi, Masanori Fukushima, Ryu Sasaki, Satoshi Miuma, Hisamitsu Miyaaki, Kazuhiko Nakao

“…The predicted data were evaluated by computing the correlation coefficients and area under the curve (AUC). Results: As a numerical variables analysis, albumin and hemoglobin predictions were relatively accurate (R=0.71, 0.74). …”
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Optimization by RSM of reinforced concrete domes with meridian ribs, under static loading. by Logzit N., Raouache E., Bentoumi M., Bentoumi A., Khababa I.

“…Ultimately, a cost-oriented objective function is derived, incorporating a load-bearing capacity coefficient. …”
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COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX) by Yuniar Farida, Ida Purwanti, Nurissaidah Ulinnuha

“…The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. …”
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Assessing the effect of dynamics of unpredictable locust invasive behavior and its effect on food security and community livelihood by Mfano Charles Petro

“…Additionally, it was observed that the deterrent coefficient in the invaded and source zones (ηi and ηs) has a significant impact on controlling the dynamic behavior of locusts.…”
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Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) by Nhat-Duc Hoang, Xuan-Linh Tran, Thanh-Canh Huynh

“…Based on such datasets, LSSVR is capable of generalizing a multivariate function that estimates values of pile bearing capacity based on a set of variables describing pile characteristics and ground conditions. …”
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Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features by Aiguo Lu, Zican Li, Yanwei Liu, Pandi Liu, Ke Wang

“…The algorithm designs a feature extraction method (AugODNet_BRA) rooted in image augmentation, which efficiently captures high-level semantic features of images with few samples, small targets, and weak features through unsupervised learning. …”
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Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks by Aryaman Tamotia, Dhruv Shantu Karmokar, Rethi Komal, K. Khadar Nawas, A. Shahina, A. Nayeemulla Khan

“…One of the critical innovations within this design is a class of trainable activation functions that eliminate the need for traditional weight parameters, thereby providing scalable and computationally efficient solutions. …”
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Rehabilitation impact indices and their independent predictors: a systematic review by Amardeep Thind, Gerald Choon-Huat Koh, Robert Petrella, Cynthia Huijun Chen

“…Then, various names of the same formula were used to identify studies, limited to articles in English and up to 31 December 2011, including case–control and cohort studies, and controlled interventional trials where RIIs were outcome variable and matching or multivariate analysis was performed.Results The five RIIs identified were (1) absolute functional gain (AFG)/absolute efficacy/total gain, (2) rehabilitation effectiveness (REs)/Montebello Rehabilitation Factor Score (MRFS)/relative functional gain (RFG), (3) rehabilitation efficiency (REy)/length of stay-efficiency (LOS-EFF)/efficiency, (4) relative functional efficiency (RFE)/MRFS efficiency and (5) revised MRFS (MRFS-R). …”
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Optimally controlled heating of solid particles in a fluidised bed with a dispersive flow of the solid by Poświata Artur, Szwast Zbigniew

“…The mixing rate was described by the axial dispersion coefficient. As any economic values of variables describing analysing process are subject to local and time fluctuations, the accepted objective function describes the total cost of the process expressed in exergy units. …”
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Dynamic Surrogate Optimization of Vertically Stacked Nanosheet FET Based on Gaussian Process Regression by Christofer N. Yalung, Doldet Tantraviwat

“…Source extension (<inline-formula> <tex-math notation="LaTeX">$S_{ext}$ </tex-math></inline-formula>), drain extension (<inline-formula> <tex-math notation="LaTeX">$D_{ex\mathrm {t}}$ </tex-math></inline-formula>), gate length (<inline-formula> <tex-math notation="LaTeX">$L_{g}$ </tex-math></inline-formula>), nanosheet height (NSh), nanosheet width (NSw), and number of fin (nfin) make up the input variables. In addition to applying normalization to make the weight assignment to the goal functions easier, a penalty component was incorporated to manage the <inline-formula> <tex-math notation="LaTeX">$V_{T}$ </tex-math></inline-formula>. …”
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Meristic and morphometric comparison of an undescribed sucker of the Río Culiacán (Catostomus sp.) and Yaqui sucker (Catostomus bernardini) (Catostomidae, Teleostei) from the Sierr... by Gorgonio Ruiz-Campos, Sergio Sánchez-Gonzáles, Richard L. Mayden, Alejandro Varela-Romero

“…Likewise, the discrimination was associated with the lowest values for predorsal distance, soft posterior ocular margin to occiput, and number of anal rays. The standardized coefficients for canonical variables 1 and 2 accounted 82.6% of the total variation. …”
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A Data-Driven Approach Based on Deep Neural Network Regression for Predicting the Compressive Strength of Steel Fiber Reinforced Concrete by Nhat-Duc Hoang, Van-Duc Tran

“…Experimental results show that the L1 regularization helps achieve the most desired performance, with a coefficient of determination (R2) of roughly 0.96. Notably, an asymmetric loss function is used along with Nadam to decrease the percentage of overestimated cases from 50.83% to 27.08%. …”
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A Model-Driven Approach for Estimating the Energy Performance of an Electric Vehicle Used as a Taxi in an Intermediate Andean City by Jairo Castillo-Calderón, Daniel Cordero-Moreno, Emilio Larrodé Pellicer

“…The results demonstrated a Pearson correlation coefficient of 0.93, indicating a strong positive linear dependence between the variables. …”
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A Computational Approach to Increasing the Antenna System’s Sensitivity in a Doppler Radar Designed to Detect Human Vital Signs in the UHF-SHF Frequency Ranges by David Vatamanu, Simona Miclaus

“…In the context of Doppler radar, studies have examined the changes in the phase shift of the S₂₁ transmission coefficient related to minute movements of the human chest as a response to breathing or heartbeat. …”
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Low-frequency rTMS modulates small-world network properties in an AVH-related brain network in schizophrenia by Lin Zhang, Li Guo, Xiaohui Liu, Jing Han, Yuanqiang Zhu, Chaozong Ma, Ye Li, Weiliang Ye

“…Resting-state fMRI data were collected before and after treatment to assess functional connectivity within the predefined 35-region AVH-related network. small-worldness (σ), normalized clustering coefficient (γ), and normalized characteristic path length (λ), as well as functional segregation (clustering coefficient [Cp], local efficiency [El]) and functional integration (global efficiency [Eg], characteristic path length [Lp])—were analyzed before and after rTMS. …”
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Feasibility and psychometric quality of smartphone administered cognitive ecological momentary assessments in women with metastatic breast cancer by Ashley M Henneghan, Emily W Paolillo, Kathleen M Van Dyk, Oscar Y Franco-Rocha, Soyeong Bang, Rebecca Tasker, Tara Kaufmann, Darren Haywood, Nicolas H Hart, Raeanne C Moore

“…Test-retest reliability was examined using intraclass correlation coefficients (ICCs) for each EMA, and Pearson's correlation were used to evaluate convergent validity between cognitive EMAs and baseline clinical cognitive and psychosocial variables. …”
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Stability analysis of the inner dump with inclined base considering anti-slip pillar by Li MA, Xinyu JIN, Qifeng ZHANG, Kaisheng JIANG, Mengbo LI, Yan HU

“…Taking the angle of inclination of the basement stratum and the discharge height of the soil disposal site as the variables for research, it was obtained that the angle of inclination of the basement stratum α or the height of the soil disposal H and the slope stability coefficient Fs were negatively correlated with the inverse proportionality function, the angle of inclination of the basement stratum had a greater influence on the slope stability, and the height of the discharge height of the soil disposal site had a second greater influence on the stability of the slope. …”
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Analysis of factors influencing the increase of extracellular water ratio in tumor patients without edema signs by Heling Zhu, Panpan Gan, Hao Jiang, Liangliang Bao, Chengjiang Liu, Jiawen Yu, Jiawen Yu

“…PA was the most influential factor among all independent variables affecting ECW/TBW (B = −1.006, p &lt; 0.001). …”
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Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

“…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid activation function exhibited superior predictive accuracy compared to other algorithms. …”
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Correlation of the FIB-4 Liver Biomarker Score with the Severity of Heart Failure by Roxana Buzas, Paul Ciubotaru, Alexandra Corina Faur, Marius Preda, Melania Ardelean, Doina Georgescu, Patrick Dumitrescu, Daniel Florin Lighezan, Mihaela-Diana Popa

“…Statistical analysis was based on ANOVA one-way tests for continuous variables and Chi-square tests for categorical variables. …”
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