Error Performance of DDPSK Receiver in the Presence of Composite Fading by Goran T. Djordjevic, Milica Petkovic, Nenad Milosevic, Bane Vasic

“…We investigate the justification for implementing double differential phase-shift keying (DDPSK) instead of differential phase-shift keying (DPSK) in the presence of carrier frequency offset (CFO) over wireless channels affected by thermal noise, multipath fading, and shadowing. When shadowed multipath fading is modeled using the Fisher-Snedecor <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> distribution, we present the probability density function (PDF) of the received composite signal phase in the form of the Fourier series and derive novel analytical expressions for its Fourier coefficients. …”
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Optimization of thermal conductivity in coir fibre-reinforced PVC composites using advanced computational techniques by Saksham Anand, Venkatachalam Gopalan, Shenbaga Velu Pitchumani

“…Abstract This research focuses on enhancing the thermal conductivity of coir fibre-reinforced polyvinyl chloride (PVC) composites using advanced optimization techniques. …”
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Predicting composition of a functional food product using computer simulation by M. A. Nikitina, I. M. Chernukha, M. P. Artamonova, A. T. Qusay

“…One of the frontiers of science is the development of a digital twin for a food product to predict composition and properties of a future product. Today, however, computer simulation (modeling) is used for predicting the composition of a food product. …”
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A Comparative Study on Various Au Wire Rinse Compositions and Their Effects on the Electronic Flame-Off Errors of Wire-Bonding Semiconductor Package by Jisu Lim, Wonbin Kim, Suk Jekal, Jiwon Kim, Ha-Yeong Kim, Zambaga Otgonbayar, Jeoung Han Kim, Jinsung Rho, Woo-Jin Song, Chang-Min Yoon

“…Hydrocarbon and silicone surfactants are commonly used for Au wire surface treatment, and five different 1.0 wt% rinse solutions with varying compositions are applied to the Au wire surface. To determine the origin of EFO errors, an experiment is conducted to examine the reactivity of the rinse-coated Au wires with silicon (Si)-containing dust, which is inevitably generated in the wire-bonding process. …”
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Optimization of thermal characteristics of buried composite phase-change energy storage walls based on nonlinear engineering methods by Diao Rongdan, Feng Hao, Chi Congwen, Michel Mushagalusa Murhambo, Guan Yajuan, Guerrero Josep M.

“…Nonlinear engineering methods improve the thermal insulation of building walls and save energy. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…Error analysis demonstrated significant differences in prediction accuracy across performance indicators: compressive strength was the easiest to predict, followed by flexural strength, while thermal conductivity was the most challenging. …”
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Optimizing base fluid composition for PEMFC cooling: A machine learning approach to balance thermal and rheological performance by Praveen Kumar Kanti, Prashantha Kumar H. G, Nejla Mahjoub Said, V. Vicki Wanatasanappan, Prabhu Paramasivam, Leliso Hobicho Dabelo

“…The thermal conductivity model showed that XGBoost has the best predictive power, with Test R² = 0.9941, Test mean square error (MSE) = 0.0000, and Test KGE = 0.9613. …”
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Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning by Qiping Deng, Yu Xiong, Zirui Du, Jinping Cui, Cheng Peng, Zhiyong Luo, Jinli Xie, Hailong Qin, Zhimin Sun, Qingfeng Zeng, Kang Guan

“…Our method achieves a prediction error for coating residual stress within 15.70% to 24.11% before and after thermal shock tests. …”
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Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis by Zhi-xiang Zhang, Yi-lin Cao, Chao Chen, Lin-yuan Wen, Yi-ding Ma, Bo-zhou Wang, Ying-zhe Liu

“…The tree-based models established demonstrated acceptable predictive abilities, yielding a low mean absolute error (MAE) of 31°C. By analyzing the dataset through hierarchical classification, this study insightfully identified the factors affecting EMs’ thermal decomposition temperatures, with the overall accuracy improved through targeted modeling. …”
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Design of Computer Numerical Control System for Fiber Placement Machine Based on Siemens 840D sl by Kun Xia, Di Zhao, Qingqing Yuan, Jingxia Wang, Aodong Shen

“…This research provides a technical framework for the design of multi-axis cooperative control systems and thermal error compensation in automated fiber placement equipment, offering critical insights for advancing manufacturing technologies in aerospace composite applications. …”
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Spatiotemporal Distribution of Soil Thermal Conductivity in Chinese Loess Plateau by Yan Xu, Yibo Zhang, Wanghai Tao, Mingjiang Deng

“…The objective of this study is to investigate the spatial distribution characteristics of soil thermal conductivity on the Loess Plateau. The research primarily employed soil heat transfer models and the Google Earth Engine (GEE) platform for remote sensing cloud computing, compares and analyzed the suitability of six models (Cambell model, Lu Yili model, Nikoosokhan model, LT model, LP1 model, and LP2 model), and utilized the selected improved model (LT model) to analyze the spatiotemporal characteristics of thermal conductivity on the CLP, examining the impacts of soil particle composition, bulk density, elevation, moisture content, and land use on thermal conductivity. …”
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Quantitative Identification of Detrital Minerals by Mineral Characteristic Automatic Analysis System and Error Analysis with Traditional Microscopic Identification by Ze NING, Lei XU, Xuehui LIN, Yuanyuan ZHANG, Yong ZHANG

“…The two methods identified similar types of detrital minerals, and the absolute error of each mineral content was less than 5%. The AMICS system can be used to identify oxides (limonite, chromite, etc.), phosphates (apatite, etc.), sulfates (barite, etc.), sulfides (pyrite, etc.), carbonates (calcite, dolomite, etc.), and some silicates (zircon, titanite, olivine, quartz, potassium feldspar, sodium feldspar, garnet group, etc.) accurately but it is difficult to accurately identify polymorphic and isomorphic detrital minerals based solely on mineral chemical composition. …”
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Modeling of Moisture Diffusion in Carbon Braided Composites by S. Laurenzi, T. Albrizio, M. Marchetti

“…In this study, we develop a methodology based on finite element analysis to predict the weight gain of carbon braided composite materials exposed to moisture. The analysis was based on the analogy between thermal conduction and diffusion processes, which allowed for a commercial code for finite element analysis to be used. …”
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Temperature effects in composite structures of resonant frequency pressure sensor by Vtorushin Sergey, Talovskaya Alena, Barbin Evgenii, Kulinich Ivan

“…An analysis of their frequency characteristics was conducted, and options for reducing thermal stress effects in the composite layers were proposed.…”
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Air-side Thermal-hydraulic Analysis and Parametric Optimization for Vertical-fin Microchannel Heat Exchanger by Zhao Rijing, Wei Xinghua, Huang Dong, Li Feng, Zhao Yongfeng

“…The model was verified using experimental results with an error of less than 20%. Subsequently, the sequence and connection of waves and louvers on composite fins were compared, and the effects of fin pitch, fin length, tube pitch, and fin thickness were investigated. …”
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Thermo-elastic topology optimization and experimental characterization for multiphase structure with high thermal stiffness invariability by Yiming Fan, Guangkai Wei, Yuan Chen

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Super Resolution Reconstruction of Mars Thermal Infrared Remote Sensing Images Integrating Multi-Source Data by Chenyan Lu, Cheng Su

“…This technology enables the recording of Martian thermal radiation properties. However, the current spatial resolution of Mars thermal infrared remote sensing images remains relatively low, limiting the detection of fine-scale thermal anomalies and the generation of higher-precision surface compositional maps. …”
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Improving the prediction of bitumen’s density and thermal expansion by optimizing artificial neural networks with Optuna and TensorFlow by Eli I. Assaf, Xueyan Liu, Sandra Erkens

“…Beyond simply replacing RFRs, we develop a fully automated framework for constructing Machine Learning Models (MLMs) to predict density and thermal expansion coefficients of bitumen. Using Optuna for hyperparameter optimization, we ensure that the information extracted from MD simulations is utilized effectively.The resulting ANN models accurately reproduce MD-predicted densities, achieving R2>0.99, MSEs below 0.1 %, and maximum absolute errors below 5 % on test data. …”
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Experimental Research and Numerical Analysis on Fire Resistance of Prefabricated Concrete Composite Beams by LIU Yanchun, SUN Shuqi, LIU Caiwei, WANG Pengfei, LU Xiuliang, MAO Jiayuan, MIAO Jijun

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Dust impact on photovoltaic modules: Global data, predictive models, emphasis on chemical composition by Hussam Almukhtar, Tek Tjing Lie, Wisam Al-Shohani

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