Results 191 to 200 of about 24,646 (253)

Exploring the Application of Waxes and Their Mixtures Oleogels in Ice Cream

Journal of the American Oil Chemists' Society, EarlyView.
ABSTRACT The objective of this work was to produce ice cream using waxes oleogels, aiming to improve its nutritional value while maintaining its physical‐chemistry characteristics. Five ice cream formulations were manufactured using (1) milk fat, (2) canola oil, and (3–5) canola oil‐oleogels. The oleogels were produced with beeswax (BEW), carnauba wax (
Ana Paula Lima Ribeiro, Sérgio Augusto Sousa de Campos, Natália Leite Oliveira, Cleiton Antônio Nunes, Sandra Maria Pinto, Thais Lomonaco Teodoro da Silva, João de Deus Souza Carneiro   +6 more
wiley   +1 more source

ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact. [PDF]

Sensors (Basel)
Liu X, Goh HH, Xie H, He T, Yew WK, Zhang D, Dai W, Kurniawan TA.   +7 more
europepmc   +1 more source

Characterization of Bio‐Based Phenolic Plasticizer Extracted From Rambutan Fruit Shell Waste: A Sustainable Alternative to Synthetic Plasticizers for Green Polymer Applications

Journal of the American Oil Chemists' Society, EarlyView.
Comprehensive Characterization of Bio‐Based Phenolic Plasticizer Extracted From Rambutan Fruit Shell Waste. ABSTRACT The development of eco‐friendly plasticizer is driving the interest of many scientists due to its environmentally benign nature.
Divya Divakaran, Indran Suyambulingam, Sunesh Narayanaperumal, Nadir Ayrilmis, Murugesan Palaniappan, Sivasubramanian Palanisamy, P. Senthamaraikannan, Siva Avudaiappan   +7 more
wiley   +1 more source

Real-world outcomes with elranatamab in multiple myeloma: a multicenter analysis from the U.S. Multiple Myeloma Immunotherapy Consortium. [PDF]

Blood Cancer J
Portuguese AJ, Davis JA, Raza S, Castaneda Puglianini O, Freeman C, Alsina M, Wright C, Blue BJ, Baz RC, Shain KH, Goel U, Khouri J, Anwer F, Ali HM, Pasvolsky O, Gaballa M, Patel K, Garcia-Pleitez H, Mikulski D, Fogel L, Zolotov E, Sannareddy A, Afrough A, Anderson LD, Julian K, Dima D, Banerjee R, Liang EC, Cassano Cassano R, Herr MM, Hassan H, Shune L, Kort J, Midha S, Nadeem O, Sidana S, Lin Y, Locke FL, Hansen DK, Sborov D, Biran N, Grajales-Cruz A.   +41 more
europepmc   +1 more source

Short‐Term Scheduling Optimization of a Single‐Pipeline Refining System With High Melting Point Crude Oil

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT In order to reflect the actual production situation more comprehensively and optimize the production cost, this paper solves the short‐term scheduling optimization problem for a single pipeline containing high melting point crude oil. Based on the refining plan given by the upper layer, a multi‐objective optimization model with high melting ...
Jing Yao, Xianxin Liu, Zhen Pan, Chengming Hao, Xiangwei Xu, Wei Jia   +5 more
wiley   +1 more source

Deep reinforcement learning using deep-Q-network for Global Maximum Power Point tracking: Design and experiments in real photovoltaic systems. [PDF]

Heliyon
Giraldo LF, Gaviria JF, Torres MI, Alonso C, Bressan M.   +4 more
europepmc   +1 more source

Thermal Calculations for Co‐Firing of Hydrogen/Ammonia and Coal in 660‐MW Coal‐Fired Boilers

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT To achieve a clean energy transition under the dual carbon goals, blending hydrogen/ammonia into pulverized coal boilers represents a key technological pathway. Different hydrogen/ammonia co‐firing ratios (5%–50%) are investigated for their impact on the thermodynamic characteristics of 660‐MW supercritical boiler at 100% and 20% boiler ...
Yaodong Da, Nana Hou, Weipeng Wang, Yongqiang Chang, Xin Liu, Feng Pan, Defu Che, Lei Deng   +7 more
wiley   +1 more source

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction. [PDF]

Nat Commun
Schwade M, Zhang S, Vonhoff F, Delgado FP, Egger DA.   +4 more
europepmc   +1 more source

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation

Advanced Physics Research, EarlyView.
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab, Houssam Eddine Hailouf, Kingsley Onyebuchi Obodo, Mireille Megnidio‐Tchoukouegno, Mohammed Benali Kanoun, Souraya Goumri‐Said   +5 more
wiley   +1 more source

Correlation between the color stability and contact profilometry results of different CAD/CAM ceramic materials after staining with black shammah and DZRT smokeless tobacco. [PDF]

Tob Induc Dis
AlWadei MH.
europepmc   +1 more source