Application of Wireless Sensor Networks for Indoor Temperature Regulation by Biljana Risteska Stojkoska, Andrijana Popovska Avramova, Periklis Chatzimisios

“…In this paper we focus on a system used for temperature regulation for residential, educational, industrial, and commercial premises, and so forth. …”
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Fault Diagnosis and Prognosis With Inferential Sensors: A Hybrid Approach Integrating Symbolic Regression and Information Theory by Efi Safikou, Krishna R. Pattipati, George M. Bollas

“…We propose a fault diagnosis approach that integrates symbolic regression and information theory to optimize inferential sensors, which, along with traditional detection techniques and hard sensors, contribute to a comprehensive fault detection and diagnosis algorithm. Additionally, we introduce an advanced predictive scheme to estimate time-dependent fault evolution functions that enable us to compute the remaining useful life (RUL) of the system. …”
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Frequency-constrained optimal power-gas flow with fast frequency response from power-to-gas units by Yu Liang, Xia Zhao

“…The substantial increase in renewable energy integration has resulted in a reduction in inertia and frequency regulation resources within the power system. …”
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A public benchmark for human performance in the detection of focal cortical dysplasia by Lennart Walger, Matthias H. Schmitz, Tobias Bauer, David Kügler, Fabiane Schuch, Christophe Arendt, Tobias Baumgartner, Johannes Birkenheier, Valeri Borger, Christoph Endler, Franziska Grau, Christian Immanuel, Markus Kölle, Patrick Kupczyk, Asadeh Lakghomi, Sarah Mackert, Elisabeth Neuhaus, Julia Nordsiek, Anna‐Maria Odenthal, Karmele Olaciregui Dague, Laura Ostermann, Jan Pukropski, Attila Racz, Klaus von derRopp, Frederic Carsten Schmeel, Felix Schrader, Aileen Sitter, Alexander Unruh‐Pinheiro, Marilia Voigt, Martin Vychopen, Philip vonWedel, Randi vonWrede, Ulrike Attenberger, Hartmut Vatter, Alexandra Philipsen, Albert Becker, Martin Reuter, Elke Hattingen, Alexander Radbruch, Rainer Surges, Theodor Rüber

“…Performance was measured based on the overlap between predicted regions of interest (ROIs) and ground‐truth lesion masks, using the Dice‐Soerensen coefficient (DSC). …”
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A Study of the Soil–Wall–Indoor Air Thermal Environment in a Solar Greenhouse by Zhi Zhang, Yu Li, Liqiang Wang, Weiwei Cheng, Zhonghua Liu

“…The CFD model and LSTM prediction model have been shown to be superior, and the findings of this study offer a theoretical basis for the optimization of thermal environment control in solar greenhouses.…”
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A Voronoi-Based Location Privacy-Preserving Method for Continuous Query in LBS by Chunguang Ma, Changli Zhou, Songtao Yang

“…Firstly, based on the prediction of a user's moving direction, a cooperative k -anonymity method is proposed without constructing cloaking regions which may lead to efficiency decline in continuous query. …”
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Extracting Pole Characteristics of Complex Radar Targets for the Aircraft in Resonance Region Using RMSPSO_ARMA by Sang Zhou, Huotao Gao, Fangyu Ren

“…The extraction algorithm of target characteristics resonance set at the high-frequency band plays a significant role in defense applications like early warning. …”
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Personalized Product Recommendation Model of Automatic Question Answering Robot Based on Deep Learning by Jie Peng, Jianhui Xu

“…Then, the bidirectional gated recurrent unit (Bi-GRU) is used to construct the score prediction matrix, and through the dynamic collaborative filtering algorithm to integrate the time characteristics of the user’s interest changes. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…Firstly, voxel-based gray-level discretization (binWidth = 25) and Z-Score normalization were applied to preprocess the patient's ROI and normalize the extracted features. Then, the most predictive radiomic features were selected and their corresponding coefficients were evaluated using the correlation coefficient algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Strategies to Reduce Left Anterior Descending Artery and Left Ventricle Organ Doses in Radiotherapy Planning for Left-Sided Breast Cancer by Umut Diremsizoglu, Nezihan Topal, Aykut Oguz Konuk, Ibrahim Halil Suyusal, Dogacan Genc, Onur Ari, Hasan Furkan Cevik, Aysegul Ucuncu Kefeli, Maksut Gorkem Aksu, Emine Binnaz Sarper

“…Conclusions: We achieved significant reductions in all heart, LAD, and LV parameters without making any changes to the planned treatment volume coverage by adding LAD and LV OARs to the optimization algorithms. …”
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DEVELOPING THE METHODICAL THINKING OF PROSPECTIVE RUSSIAN LANGUAGE TEACHERS by T. S. Tabachenko

“…The methodology basis incorporates the procedural and cognitive approach along with the strategic direction on the cognitive process management at all educational levels. The author considers the main characteristics of methodical thinking, its content and structure including the set of cognitive components. …”
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Low latency Montgomery multiplier for cryptographic applications by khalid javeed, Muhammad Huzaifa, Safiullah Khan, Atif Raza Jafri

“…Usually, this operation is performed by integer multiplication (IM) followed by a reduction modulo M. However, the reduction step involves a long division operation that is expensive in terms of area, time and resources. …”
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Using machine learning to simultaneously quantify multiple cognitive components of episodic memory by Soroush Mirjalili, Audrey Duarte

“…Using a machine learning algorithm known as “transfer learning”, we leveraged visual perception, sustained attention, and selective attention brain states to better predict episodic memory performance from trial-to-trial encoding electroencephalography (EEG) activity. …”
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Simulation of the Performance of a Centrifugal Chiller by Luo Yi, Zhang Libiao, Hang Bingbing, Gong Chengcheng

“…The simulation software successfully predicts various parameters, such as chilled water temperatures, cooling water temperatures, and flow rates of the centrifugal chiller under different loads.…”
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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. …”
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Comprehensive assessment on estimating the thermodynamic and mechanical properties of multicomponent Fe–Cr-based alloys using machine learning techniques by Ahed Habib, Bashar Alibrahim, Mahdi Z. Alnunu, Hussein Moussa, Maan Habib

“…This study investigates the performance of 11 different machine learning algorithms to predict the mixing enthalpy, Young’s modulus, and the shear-to-bulk modulus ratio of Fe–Cr alloys with additions of Ni, Mo, Al, W, V, and Nb. …”
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Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models by Hongxia Ma, Jiandong Liu, Jia Zhang, Jiandong Huang

“…The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. …”
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Damage Detection in Railway Truss Bridges Employing Data Sensitivity under Bayesian Framework: A Numerical Investigation by Kanta Prajapat, Samit Ray-Chaudhuri

“…Further, in a Bayesian algorithm, it can be quite beneficial if a damage localization algorithm is first used to localize damage in the structure. …”
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Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination by Ying Liu, Lihua Huang

“…Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. …”
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Numerical Simulation Study on the Strengthening Mechanism of Rock Materials under Impact Loads by Xin Liu, Kai Wang, Chunan Tang, Xikun Qian

“…Inhomogeneities and inertial effects of rock materials are the fundamental reasons for the increase in the dynamic strength of rock materials. A reduction in inertial effects is the main reason for strength reduction in rock samples with hole defects.…”
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