Building Up a Hexacopper(II)-Pyrazolate/Oxamate Magnetic Complex with Rare Ethane-1,2-Dioxide (–OCH₂CH₂O–) as a Bridge Between Copper(II) Units by Willian X. C. Oliveira, Victor G. Araújo, Carlos B. Pinheiro, Miguel Julve, Cynthia L. M. Pereira

“…The crystal structure of <b>1</b>, obtained by the single-crystal X-ray diffraction technique, revealed that the hexacopper(II) complex is built from two linear tricopper(II) complex subunits. …”
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A study of the correlation between residents’ humanistic care skills and their level of emotional intelligence—A cross-sectional survey by Mingwei Luo, Jie Pang, Shiwei Xie, Huamin Xu, Jing Yan

“…Abstract Background There is variability in the structure of junior doctors’ knowledge of humanistic medicine. …”
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Cryptanalysis of hyperchaotic S-box generation and image encryption by Mohammad Mazyad Hazzazi, Gulraiz, Rashad Ali, Muhammad Kamran Jamil, Sameer Abdullah Nooh, Fahad Alblehai

“…Because they feature substitution boxes, substitution-permutation networks (SPNs) are crucial for cryptographic algorithms such as the popular Advanced Encryption Standard (AES). The structure and properties of S-boxes have a significant impact on the overall security of cryptographic systems. …”
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High-Performance Cu_1.8Se Nanosheets for Dual-Sensing: H₂O₂ Electrochemical Detection and SERS Substrate by Ying-Chu Chen, Michael Chen, Yu-Kuei Hsu

“…The flower-like structure of the Cu_1.8Se NSs exhibited linear dependence between analyte concentration and detection signals, along with satisfactory reproducibility in dual-sensing applications. …”
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RMIS-Net: a fast medical image segmentation network based on multilayer perceptron by Binbin Zhang, Guoliang Xu, Yiying Xing, Nanjie Li, Deguang Li

“…The network incorporates layer normalization and dropout regularization to ensure training stability, complemented by Gaussian error linear unit (GELU) activation functions for improved non-linear modeling. …”
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Predicting the permeability and compressive strength of pervious concrete using a stacking ensemble machine learning approach by Fan Yu, Wei Chu, Rui Zhang, Zhang Gao, Yunan Yang

“…However, the current performance prediction models mainly relied on porosity, ignoring the influence of other pore structure parameters, resulting in insufficient prediction accuracy. …”
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Robust weighted ridge regression based on S – estimator by Taiwo Stephen Fayose, Kayode Ayinde, Olatayo Olusegun Alabi, Abimbola Hamidu Bello

“…Monte – Carlo simulation experiments were conducted on a linear regression model with three and six exogenous variables exhibiting different degrees of Multicollinearity, with heteroscedasticity structure of powers, size of outlier in endogenous variable and error variances and five levels of sample sizes. …”
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Exotic Megaherbivores as Ecosystem Engineers in Australian Savannas: Do They Facilitate Predator Movement? by Georgina Neave, Brett P. Murphy, Tiwi Rangers, Hugh F. Davies

“…Mammalian predators often use anthropogenic linear features—such as roads, fencelines, and infrastructure corridors—to increase movement efficiency and prey encounter rates. …”
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Genetic Algorithm for Optimal Control Design to Gust Response for Elastic Aircraft by Mauro Iavarone, Umberto Papa, Alberto Chiesa, Luca de Pasquale, Angelo Lerro

“…Developing control systems for high aspect ratio aircraft can be challenging due to the flexibility of the structure involved in the control loop design. A model-based approach can be straightforward to tune the control system parameters and, to this aim, a reliable aircraft flexible model is mandatory. …”
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Green synthesis of self-oriented flower-like Ag@Ag2O nanostructures functionalized with L-Tryptophan for colorimetric simultaneous determination of ultra-trace level of thiamin and... by Maryam Abbasi Tarighat, Zahra Khosravani, Gholamreza Abdi

“…The nanostructures are characterized using techniques like XRD, FESEM, FTIR, TEM, AFM, and DLS to understand their morphology, structure, and interactions with target molecules. …”
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A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition by Guohua Li, An Liu, Huajie Shen

“…For the complexity of association relationships between attributes of incomplete data, a single-output subnetwork modelling method for incomplete data is proposed to build a neural network model with each missing attribute as output alone and other attributes as input in turn, and the network structure can deeply portray the association relationships between each attribute and other attributes. …”
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Sap flow simulation of Cunninghamia lanceolata in degraded red soil region based on back propagation neural network by Tu Jie, Liu Qijing, Wei Jun, Hu Liang

“…The optimum network model of C. lanceolata sap flow velocity was built with the topological structure of 4-10-1.Based on Bayesian regularization algorithm and Levenberg-Marquardt algorithm, good fitting results were obtained from the linear regression between predictive and measured values, with correlation coefficients both higher than 0.93. …”
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Explaining drivers of housing prices with nonlinear hedonic regressions by Heng Wan, Pranab K. Roy Chowdhury, Jim Yoon, Parin Bhaduri, Vivek Srikrishnan, David Judi, Brent Daniel

“…We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). …”
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Validation of the Chinese version of the financial toxicity scale in patients with wet age-related macular degeneration by Xiuli Chen, Hong Bian, Zhifeng Wu

“…Critical ratio analysis and correlation analysis were used to examine the items on the scale. The structural validity of the scale was evaluated using factor analysis, the reliability of the scale was evaluated using Cronbach′s α coefficient and retest reliability, and the content validity of the scale was evaluated using the Content Validity Index (CVI). …”
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Factors affecting the migration intention in medical students in Shiraz; south of Iran: a cross sectional study by Fatemeh Parvizi, Alireza Salehi, Atefeh Seghatoleslam, Mohammad Kia, Mohammadmehdi Pope

“…Data analysis included bivariate and multivariate methods, with linear regression applied to identify significant predictors. …”
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Association between cumulative TNF inhibitor dose and spinal radiographic progression in radiographic axial spondyloarthritis in patients with modified stoke ankylosing spondylitis... by Tae-Hwan Kim, Seo Young Park, Ji Hui Shin, Seunghun Lee, Kyung Bin Joo, Bon San Koo

“…Maintaining a standard TNFi dose may slow the progression of spinal structural changes in early stage SpA.…”
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“Old” and “New” Trust in Higher Education by P. A. Ambarova, G. E. Zborovsky, N. V. Shabrova

“…The need to preserve trust as a fundamental basis and source of development of Russian higher education requires the study of its structural characteristics and understanding of the resource potential of trust by representatives of educational communities.The aim of the research was the sociological substantiation of actualisation in higher education of the old resource properties of trust and the emergence of new ones associated with the prospects of transition to a non-linear model of higher education.Methodology and research methods. …”
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Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing by Guolan Xian, Jiangang Liu, Yongxin Lin, Shuang Li, Chunsong Bian

“…The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. …”
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Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions by Hari Singh, Dola Sundeep, C. Chandrasekhara Sastry, Eswaramoorthy K Varadharaj

“…Conversely, Test-07 exceeded 355–372 ksc with burn rates surpassing 41 mm/s, exhibiting erosive burning and accelerated steel erosion.Material characterization techniques, including X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive Spectroscopy (EDS), revealed significant oxidation, scale formation, and localized surface degradation, in high-temperature regions around 2500 Kelvinat the nozzle throat inducing structural changes. To enhance predictive accuracy, machine learning models Linear Regression, Random Forest Regression, Support Vector Machines (SVM), K-Means Clustering, and Artificial Neural Networks (ANN) were employed to analyze combustion-induced degradation trends, confirming Test-06 as the optimal balance of stability and high performance. …”
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The intelligent fault identification method based on multi-source information fusion and deep learning by Dashu Guo, Xiaoshuang Yang, Peng Peng, Lei Zhu, Handong He

“…Abstract Faults represent significant geological structures. Conventional fault identification methods pri-marily rely on the linear features of faults, achieved through the interpretation of remote sensing imagery (RSI). …”
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