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2. Challenges and strategies toward oncolytic virotherapy for leptomeningeal metastasis

Author

Jia-Li Zhao, Bi-Lin Lin, Chen Luo, Yan-ling Yi, Peng Huang, Yu Chen, Sha Zhao, Zhen-Jie Huang, Xin-Yi Ma, and Long Huang

Subjects
Leptomeningeal metastasis, Oncolytic virotherapy, Intra-CSF, Oncolytic virus, Medicine
Abstract

Abstract Meningeal metastasis (LM) is commonly seen in the advanced stages of cancer patients, often leading to a rapid decline in survival time and quality of life. Currently, there is still a lack of standardized treatments. Oncolytic viruses (OVs) are a class of emerging cancer therapeutics with the advantages of selectively replicating in cancer cells, delivering various eukaryotic transgenes, inducing immunogenic cell death, and promoting anti-tumor immunity. Some studies applying OVs intrathoracically or intraperitoneally for the treatment of malignant pleural and peritoneal effusions have shown promising therapeutic effects. If OVs could be applied to treat LM, it would bring significant survival benefits to patients with LM. In this review, we analyzed past research on the use of viruses to treat meningeal metastasis, summarized the efficacy and safety demonstrated by the research results, and analyzed the feasibility of oncolytic virus therapy for meningeal metastasis. We also summarized the existing data to provide guidance for the development of OVs that can be injected into the cerebrospinal fluid (CSF).

Published
2024
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4. Corrected Navier-Stokes equations for compressible flows

Author

Xu, Jinglei, Ma, Dong, Liu, Pengxin, Bi, Lin, Yuan, Xianxu, and Chen, Longfei

Subjects
Physics - Fluid Dynamics, Physics - Computational Physics
Abstract

For gas flows, the Navier-Stokes (NS) equations are established by mathematically expressing conservations of mass, momentum and energy. The advantage of the NS equations over the Euler equations is that the NS equations have taken into account the viscous stress caused by the thermal motion of molecules. The viscous stress arises from applying Isaac Newton's second law to fluid motion, together with the assumption that the stress is proportional to the gradient of velocity1. Thus, the assumption is the only empirical element in the NS equations, and this is actually the reason why the NS equations perform poorly under special circumstances. For example, the NS equations cannot describe rarefied gas flows and shock structure. This work proposed a correction to the NS equations with an argument that the viscous stress is proportional to the gradient of momentum when the flow is under compression, with zero additional empirical parameters. For the first time, the NS equations have been capable of accurately solving shock structure and rarefied gas flows. In addition, even for perfect gas, the accuracy of the prediction of heat flux rate is greatly improved. The corrected NS equations can readily be used to improve the accuracy in the computation of flows with density variations which is common in nature., Comment: 13 pages, 7 figures

Published
2022

5. Lattice eddy simulation of turbulent flows

Author

Xu, Jinglei, Li, Qi, Yuan, Xianxu, Bi, Lin, Liu, Pengxin, and Chen, Jianqiang

Subjects
Physics - Fluid Dynamics
Abstract

Kolmogorov's (1941) theory of self-similarity implies the universality of small-scale eddies, and holds promise for a universal sub-grid scale model for large eddy simulation. The fact is the empirical coefficient of a typical sub-grid scale model varies from 0.1 to 0.2 in free turbulence and damps gradually to zero approaching the walls. This work has developed a Lattice Eddy Simulation method (LAES), in which the sole empirical coefficient is constant (Cs=0.08). LAES assumes the fluid properties are stored in the nodes of a typical CFD mesh, treats the nodes as lattices and makes analysis on one specific lattice, i. To be specific, LAES express the domain derivative on that lattice with the influence of nearby lattices. The lattices right next to i, which is named as i+, "collide" with i, imposing convective effects on i. The lattices right next to i+, which is named as i++, impose convective effects on i+ and indirectly influence i. The influence is actually turbulent diffusion. The derived governing equations of LAES look like the Navier-Stokes equations and reduce to filtered Naiver-Stokes equations with the Smagorinsky sub-grid scale model (Smagorinsky 1963) on meshes with isotropic cells. LAES yields accurate predictions of turbulent channel flows at Re=180, 395, and 590 on very coarse meshes and LAES with a constant Cs perform as well as the dynamic LES model (Germano et al. 1991) does. Thus, this work has provided strong evidence for Kolmogorov's theory of self-similarity., Comment: 12 pages,11 figures

Published
2022

6. A splice mutation in RASGRP2 gene in the patient with recurrent epistaxis and nasal vascular malformation

Author

Zhong-Yu Shi, Qing-Ling Lei, Shao-Qin Duan, Yan Zhou, Ting-Ting Cui, Yun-Bi Lin, Chun-Hui Yang, Chun-Yan Song, Chun-Lian Fang, Xin Tian, Xian-Wen Zhang, and Ti-Long Huang

Subjects
BDPLT18, epistaxis, RASGRP2 gene, vascular malformation, Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Platelet type bleeding disorder-18 (BDPLT18) caused by mutations of Ras guanyl releasing protein 2 (RASGRP2) is a relatively rare, new autosomal recessive disorder. Here, we reported a splice mutation in RASGRP2 gene in the patient with recurrent epistaxis and nasal vascular malformation. The patient, an 8-year-old girl, suffered from anemia due to frequently severe recurrent epistaxis, requiring regular blood transfusions every 2–3 months. Hematological investigations showed moderate anemia (Hb: 89 g/L), normal platelet count, morphology, and platelet glycoproteins. Arachidonic acid and adenosine diphosphate induced platelet aggregation was markedly reduced in the patient. A homozygous splice variant (C.74–1 G>C) in RASGRP2 gene, located within the exon 3, was detected by next-generation sequencing. Interestingly, we identified nasal vascular malformation by percutaneous super-selective angiography during the treatment of an intractable epistaxis. Our case further support that genetic testing should be performed for some unexplained bleeding diseases.

Published
2024
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7. Asymptomatic multicentric Castleman disease: a potential early stage of idiopathic MCD

Author

Zhang, Lu, Liu, Qin-hua, Zhou, Hui, Zhang, Hui-lai, Dong, Yu-jun, Wang, Xiao-bo, Wang, Lu-qun, Su, Li-ping, Yan, Xiao-jing, Li, Yan, Zhang, Ming-zhi, Ding, Kai-yang, Wang, Hui-han, Peng, Hong-ling, Zhong, Li-ye, Yang, Lin, Bi, Lin-tao, Gao, Da, Gao, Guang-xun, Huang, Liang, Sun, Chun-yan, Song, Jia, Qian, Wen-bin, Huang, Wen-rong, Li, Zhen-ling, Liu, Yao, and Li, Jian

Published
2024
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17. Morphologies and composition changes in nonculprit subclinical atherosclerosis in diabetic versus nondiabetic patients with acute coronary syndrome who underwent long-term statin therapy

Author

Pei-na Meng, Jia-cong Nong, Yi Xu, Wei You, Tian Xu, Xiang-qi Wu, Zhi-ming Wu, Bi-lin Tao, Ya-jie Guo, De-lu Yin, Hai-bo Jia, Song Yang, and Fei Ye

Subjects
Medicine, Science
Abstract

Abstract Although patients are undergoing similar lipid-lowering therapy (LLT) with statins, the outcomes of coronary plaque in diabetic mellitus (DM) and non-DM patients are different. Clinical data of 239 patients in this observational study with acute coronary syndrome was from our previous randomized trial were analyzed at 3 years, and 114 of them underwent OCT detection at baseline and the 1-year follow-up were re-anlayzed by a novel artificial intelligence imaging software for nonculprit subclinical atherosclerosis (nCSA). Normalized total atheroma volume changes (ΔTAVn) of nCSA were the primary endpoint. Plaque progression (PP) was defined as any increase in ΔTAVn. DM patients showed more PP in nCSA (ΔTAVn; 7.41 (− 2.82, 11.85) mm3 vs. − 1.12 (− 10.67, 9.15) mm3, p = 0.009) with similar reduction of low-density lipoprotein cholesterol (LDL-C) from baseline to 1-year. The main reason is that the lipid component in nCSA increases in DM patients and non-significantly decreases in non-DM patients, which leads to a significantly higher lipid TAVn (24.26 (15.05, 40.12) mm3 vs. 16.03 (6.98, 26.54) mm3, p = 0.004) in the DM group than in the non-DM group at the 1-year follow-up. DM was an independent predictor of PP in multivariate logistic regression analysis (OR = 2.731, 95% CI 1.160–6.428, p = 0.021). Major adverse cardiac events (MACEs) related to nCSA at 3 years were higher in the DM group than in the non-DM group (9.5% vs. 1.7%, p = 0.027). Despite a comparable reduction in LDL-C levels after LLT, more PP with an increase in the lipid component of nCSA and a higher incidence of MACEs at the 3-year follow-up was observed in DM patients. Trial registration: ClinicalTrials.gov. identifier: NCT02140801.

Published
2023
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20. A national, multicenter, retrospective study of Castleman disease in China implementing CDCN criteria

Author

Zhang, Lu, Dong, Yu-jun, Peng, Hong-ling, Li, Hao, Zhang, Ming-zhi, Wang, Hui-han, Liu, Qin-hua, Su, Li-ping, Zhong, Li-ye, Wu, Wen-jun, Huang, Liang, Yan, Xiao-jing, Fan, Lei, Tang, Wen-jiao, Li, Zhen-ling, Bi, Lin-tao, Li, Yan, Gao, Guang-xun, Gao, Li, Liu, Ting-bo, Wei, Yong-qiang, Liu, Yao, Yu, Li, Zhou, Hui, Sun, Chun-yan, Qian, Wen-bin, Zou, De-hui, Zhang, Hui-lai, Ding, Kai-yang, Wang, Xiao-bo, Bai, Ou, Huang, Wen-rong, Chen, Bing, Yang, Lin, Song, Jia, Gao, Da, Chen, Tong, Luo, Jun, Wang, Shu-ye, Ma, Liang-ming, Fajgenbaum, David C., and Li, Jian

Published
2023
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21. Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study

Author

Abrough, Fekri, Acharya, Subhash P, Amin, Pravin, Arabi, Yaseen, Aragao, Irene, Bauer, Philippe, Beduneau, Gaëtan, Beitler, Jeremy, Berkius, Johan, Bugedo, Guillermo, Camporota, Luigi, Cerny, Vladimir, Cho, Young-Jae, Clarkson, Kevin, Estenssoro, Elisa, Goligher, Ewan, Grasselli, Giacomo, Gritsan, Alexey, Hashemian, Seyed Mohammadreza, Hermans, Greet, Heunks, Leo M, Jovanovic, Bojan, Kurahashi, Kiyoyasu, Laake, Jon Henrik, Matamis, Dimitrios, Moerer, Onnen, Molnar, Zsolt, Ozyilmaz, Ezgi, Panka, Bernardo, Papali, Alfred, Peñuelas, Óscar, Perbet, Sébastien, Piquilloud, Lise, Qiu, Haibo, Razek, Assem Abdel, Rittayamai, Nuttapol, Roldan, Rollin, Serpa Neto, Ary, Szuldrzynski, Konstanty, Talmor, Daniel, Tomescu, Dana, Van Haren, Frank, Villagomez, Asisclo, Zeggwagh, Amine Ali, Abe, Toshikazu, Aboshady, Abdelrhman, Acampo-de Jong, Melanie, Acharya, Subhash, Adderley, Jane, Adiguzel, Nalan, Agrawal, Vijay Kumar, Aguilar, Gerardo, Aguirre, Gaston, Aguirre-Bermeo, Hernan, Ahlström, Björn, Akbas, Türkay, Akker, Mustafa, Al Sadeh, Ghamdan, Alamri, Sultan, Algaba, Angela, Ali, Muneeb, Aliberti, Anna, Allegue, Jose Manuel, Alvarez, Diana, Amador, Joaquin, Andersen, Finn H, Ansari, Sharique, Apichatbutr, Yutthana, Apostolopoulou, Olympia, Arellano, Daniel, Arica, Mestanza, Arikan, Huseyin, Arinaga, Koichi, Arnal, Jean-Michel, Asano, Kengo, Asín-Corrochano, Marta, Avalos Cabrera, Jesus Milagrito, Avila Fuentes, Silvia, Aydemir, Semih, Aygencel, Gulbin, Azevedo, Luciano, Bacakoglu, Feza, Badie, Julio, Baedorf Kassis, Elias, Bai, Gabriela, Balaraj, Govindan, Ballico, Bruno, Banner-Goodspeed, Valerie, Banwarie, Preveen, Barbieri, Rosella, Baronia, Arvind, Barrett, Jonathan, Barrot, Loïc, Barrueco-Francioni, Jesus Emilio, Barry, Jeffrey, Bawangade, Harshal, Beavis, Sarah, Beck, Eduardo, Beehre, Nina, Belenguer Muncharaz, Alberto, Bellani, Giacomo, Belliato, Mirko, Bellissima, Agrippino, Beltramelli, Rodrigo, Ben Souissi, Asma, Benitez-Cano, Adela, Benlamin, Mohamed, Benslama, Abdellatif, Bento, Luis, Benvenuti, Daniela, Bernabe, Laura, Bersten, Andrew, Berta, Giacomo, Bertini, Pietro, Bertram-Ralph, Elliot, Besbes, Mohamed, Bettini, Lisandro Roberto, Beuret, Pascal, Bewley, Jeremy, Bezzi, Marco, Bhakhtiani, Lakshay, Bhandary, Rakesh, Bhowmick, Kaushik, Bihari, Shailesh, Bissett, Bernie, Blythe, David, Bocher, Simon, Boedjawan, Narain, Bojanowski, Christine M, Boni, Elisa, Boraso, Sabrina, Borelli, Massimo, Borello, Silvina, Borislavova, Margarita, Bosma, Karen J, Bottiroli, Maurizio, Boyd, Owen, Bozbay, Suha, Briva, Arturo, Brochard, Laurent, Bruel, Cédric, Bruni, Andrea, Buehner, Ulrike, Bulpa, Pierre, Burt, Karen, Buscot, Mathieu, Buttera, Stefania, Cabrera, Jorge, Caccese, Roberta, Caironi, Pietro, Canchos Gutierrez, Ivan, Canedo, Nancy, Cani, Alma, Cappellini, Iacopo, Carazo, Jesus, Cardonnet, Luis Pablo, Carpio, David, Carriedo, Demetrio, Carrillo, Ramón, Carvalho, João, Caser, Eliana, Castelli, Antonio, Castillo Quintero, Manuel, Castro, Heloisa, Catorze, Nuno, Cengiz, Melike, Cereijo, Enrique, Ceunen, Helga, Chaintoutis, Christos, Chang, Youjin, Chaparro, Gustavogcha, Chapman, Carmel, Chau, Simon, Chavez, Cecilia Eugenia, Chelazzi, Cosimo, Chelly, Jonathan, Chemouni, Frank, Chen, Kai, Chena, Ariel, Chiarandini, Paolo, Chilton, Phil, Chiumello, Davide, Chou-Lie, Yvette, Chudeau, Nicolas, Cinel, Ismail, Cinnella, Gilda, Clark, Michele, Clark, Thomas, Clementi, Stefano, Coaguila, Luis, Codecido, Alexis Jaspe, Collins, Amy, Colombo, Riccardo, Conde, Juan, Consales, Guglielmo, Cook, Tim, Coppadoro, Andrea, Cornejo, Rodrigo, Cortegiani, Andrea, Coxo, Cristina, Cracchiolo, Andrea Neville, Crespo Ramirez, Mónica, Crova, Philippe, Cruz, José, Cubattoli, Lucia, Çukurova, Zafer, Curto, Francesco, Czempik, Piotr, D'Andrea, Rocco, da Silva Ramos, Fernando, Dangers, Laurence, Danguy des Déserts, Marc, Danin, Pierre-Eric, Dantas, Fabianne, Daubin, Cédric, Dawei, Wu, de Haro, Candelaria, de Jesus Montelongo, Felipe, De Mendoza, Diego, de Pablo, Raúl, De Pascale, Gennaro, De Rosa, Silvia, Decavèle, Maxens, Declercq, Pierre-Louis, Deicas, Alberto, del Carmen Campos Moreno, María, Dellamonica, Jean, Delmas, Benjamin, Demirkiran, Oktay, Demirkiran, Hilmi, Dendane, Tarek, di Mussi, Rossella, Diakaki, Chrysi, Diaz, Anatilde, Diaz, Willy, Dikmen, Yalim, Dimoula, Aikaterini, Doble, Patricia, Doha, Nagwa, Domingos, Guilherme, Dres, Martin, Dries, David, Duggal, Abhijit, Duke, Graeme, Dunts, Pavel, Dybwik, Knut, Dykyy, Maksym, Eckert, Philippe, Efe, Serdar, Elatrous, Souheil, Elay, Gülseren, Elmaryul, Abubaker S, Elsaadany, Mohamed, Elsayed, Hany, Elsayed, Samar, Emery, Malo, Ena, Sébastien, Eng, Kevin, Englert, Joshua A, Erdogan, Elif, Ergin Ozcan, Perihan, Eroglu, Ege, Escobar, Miguel, Esen, Figen, Esen Tekeli, Arzu, Esquivel, Alejandro, Esquivel Gallegos, Helbert, Ezzouine, Hanane, Facchini, Alberto, Faheem, Mohammad, Fanelli, Vito, Farina, Maria Fernanda, Fartoukh, Muriel, Fehrle, Lutz, Feng, Feng, Feng, Yufeng, Fernandez, Irene, Fernandez, Borja, Fernandez-Rodriguez, Maria Lorena, Ferrando, Carlos, Ferreira da Silva, Maria João, Ferreruela, Mireia, Ferrier, Janet, Flamm Zamorano, Matias Jesús, Flood, Laura, Floris, Leda, Fluckiger, Martin, Forteza, Catalina, Fortunato, Antonella, Frans, Eric, Frattari, Antonella, Fredes, Sebastian, Frenzel, Tim, Fumagalli, Roberto, Furche, Mariano Andres, Fusari, Maurizio, Fysh, Edward, Galeas-Lopez, Juan Luis, Galerneau, Louis-Marie, Garcia, Analía, Garcia, María Fernanda, Garcia, Elisabet, Garcia Olivares, Pablo, Garlicki, Jaroslaw, Garnero, Aude, Garofalo, Eugenio, Gautam, Prabha, Gazenkampf, Andrey, Gelinotte, Stéphanie, Gelormini, Domenico, Ghrenassia, Etienne, Giacomucci, Angelo, Giannoni, Robert, Gigante, Andrea, Glober, Nancy, Gnesin, Paolo, Gollo, Yari, Gomaa, Dina, Gomero Paredes, Rosita, Gomes, Rui, Gomez, Raúl Alejandro, Gomez, Oscar, Gomez, Aroa, Gondim, Louise, Gonzalez, Manuel, Gonzalez, Isabel, Gonzalez-Castro, Alejandro, Gordillo Romero, Orlando, Gordo, Federico, Gouin, Philippe, Graf Santos, Jerónimo, Grainne, Rooney, Grando, Matilde, Granov Grabovica, Sanja, Grasso, Salvatore, Grasso, Rinaldo, Grimmer, Lisa, Grissom, Colin, Gu, Qing, Guan, Xiang-Dong, Guarracino, Fabio, Guasch, Neus, Guatteri, Luca, Gueret, Renaud, Guérin, Claude, Guerot, Emmanuel, Guitard, Pierre-Gildas, Gül, Fethi, Gumus, Ayca, Gurjar, Mohan, Gutierrez, Patricia, Hachimi, Abdelhamid, Hadzibegovic, Adi, Hagan, Samantha, Hammel, Clare, Han Song, Joo, Hanlon, Gabrielle, Heines, Serge, Henriksson, Johanna, Herbrecht, Jean-Etienne, Heredia Orbegoso, Gabriel Omar, Hermon, Andrew, Hernandez, Rosana, Hernandez, Carmen, Herrera, Luis, Herrera-Gutierrez, Manuel, Heunks, Leo, Hidalgo, Juan, Hill, Dianne, Holmquist, Dagmar, Homez, Marcela, Hongtao, Xia, Hormis, Anil, Horner, Daniel, Hornos, M Carmen, Hou, Meihong, House, Stacy, Housni, Brahim, Hugill, Keith, Humphreys, Sally, Humbert, Louis, Hunter, Stephanie, Hwa Young, Lee, Iezzi, Nicolas, Ilutovich, Santiago, Inal, Volkan, Innes, Richard, Ioannides, Panagiotis, Iotti, Giorgio Antonio, Ippolito, Mariachiara, Irie, Hiromasa, Iriyama, Hiroki, Itagaki, Taiga, Izura, Javier, Izza, Santiago, Jabeen, Rakhshanda, Jamaati, Hamidreza, Jamadarkhana, Sunil, Jamoussi, Amira, Jankowski, Milosz, Jaramillo, Luis Alberto, Jeon, Kyeongman, Jeong Lee, Seok, Jeswani, Deepak, Jha, Simant, Jiang, Liangyan, Jing, Chen, Jochmans, Sébastien, Johnstad, Bror Anders, Jongmin, Lee, Joret, Aurélie, Junhasavasdikul, Detajin, Jurado, Maria Teresa, Kam, Elisa, Kamohara, Hidenobu, Kane, Caroline, Kara, Iskender, Karakurt, Sait, Karnjanarachata, Cherdkiat, Kataoka, Jun, Katayama, Shinshu, Kaushik, Shuchi, Kelebek Girgin, Nermin, Kerr, Kathryn, Kerslake, Ian, Khairnar, Prakash, Khalid, Abidi, Khan, Akram, Khanna, Ashish K, Khorasanee, Reza, Kienhorst, Dieneke, Kirakli, Cenk, Knafelj, Rihard, Kol, Mark Kol, Kongpolprom, Napplika, Kopitko, Csaba, Korkmaz Ekren, Pervin, Kubisz-Pudelko, Agnieszka, Kulcsar, Zoltan, Kumasawa, Junji, Kuriyama, Akira, Kutchak, Fernanda, Labarca, Eduardo, Labat, Françoise, Laborda, César, Laca Barrera, Manuel Alberto, Lagache, Laurie, Landaverde Lopez, Antonio, Lanspa, Michael, Lascari, Valeria, Le Meur, Matthieu, Lee, Su Hwan, Lee, Young Ju, Lee, Jinwoo, Lee, Won-Yeon, Lee, Jarone, Legernaes, Terje, Leiner, Tamaas, Lemiale, Virginie, Leonor, Tiago, Lepper, Philipp M, Li, Dahuan, Li, Hongbin, Li, Oleg, Lima, Ana Raquel, Lind, Dan, Litton, Edward, Liu, Ning, Liu, Ling, Liu, Jialin, Llitjos, Jean-François, Llorente, Beatriz, Lopez, Rodolfo, Lopez, Claudia Elizabeth, Lopez Nava, Claudia, Lovazzano, Pablo, Lu, Min, Lucchese, Francesca, Lugano, Manuela, Lugo Goytia, Gustavo, Luo, Hua, Lynch, Ceri, Macheda, Sebastiano, Madrigal Robles, Victor Hugo, Maggiore, Salvatore Maurizio, Magret Iglesias, Mònica, Malaga, Peter, Mallapura Maheswarappa, Harish, Malpartida, Guillermo, Malyarchikov, Andrey, Mansson, Helena, Manzano, Anaid, Marey, Ismael, Marin, Nathalie, Marin, Maria del Carmen, Markman, Eliana, Martin, Felix, Martin, Alex, Martin Dal Gesso, Cristina, Martinez, Felipe, Martínez-Fidalgo, Conchita, Martin-Loeches, Ignacio, Mas, Arantxa, Masaaki, Sakuraya, Maseda, Emilio, Massa, Eleni, Mattsson, Anna, Maugeri, Jessica, McCredie, Victoria, McCullough, James, McGuinness, Shay, McKown, Andrew, Medve, László, Mei, Chengqing, Mellado Artigas, Ricard, Mendes, Vitor, Mervat, Mohamed Khalaf Ebraheim, Michaux, Isabelle, Mikhaeil, Michael, Milagros, Olga, Milet, Igor, Millan, Maria Teresa, Minwei, Zhang, Mirabella, Lucia, Mishra, Sanghamitra, Mistraletti, Giovanni, Mochizuki, Katsunori, Moghal, Arif, Mojoli, Francesco, Molin, Alexandre, Montiel, Raquel, Montini, Luca, Monza, Gianmario, Mora Aznar, Maria, Morakul, Sunthiti, Morales, Maria, Moreno Torres, Daniel, Morocho Tutillo, Diego Rolando, Motherway, Catherine, Mouhssine, Doumiri, Mouloudi, Eleni, Muñoz, Tapia, Munoz de Cabo, Carlos, Mustafa, Mohamed, Muthuchellappan, Radhakrishnan, Muthukrishnan, Muraleekrishnan, Muttini, Stefano, Nagata, Isao, Nahar, Dick, Nakanishi, Misuzu, Nakayama, Izumi, Namendys-Silva, Silvio Antonio, Nanchal, Rahul, Nandakumar, Sivakumar, Nasi, Alessandra, Nasir, Kamal, Navalesi, Paolo, Naz Aslam, Tayyba, Nga Phan, Thuy, Nichol, Alistair, Niiyama, Shuhei, Nikolakopoulou, Sofia, Nikolic, Elena, Nitta, Kenichi, Noc, Marko, Nonas, Stephanie, Nseir, Saad, Nur Soyturk, Ayse, Obata, Yukako, Oeckler, Richard, Oguchi, Moe, Ohshimo, Shinichiro, Oikonomou, Marina, Ojados, Agueda, Oliveira, Maria Teresa, Oliveira Filho, Wilson, Oliveri, Carlo, Olmos, Aitor, Omura, Kazuya, Orlandi, Maria Cristina, Orsenigo, Francesca, Ortiz-Ruiz De Gordoa, Laura, Ota, Kei, Ovalle Olmos, Rainier, Öveges, Nándo, Oziemski, Peter, Ozkan Kuscu, Ozlem, Pachas Alvarado, Fernando, Pagella, Gonzalo, Palaniswamy, Vijayanand, Palazon Sanchez, Eugenio Luis, Palmese, Salvatore, Pan, Guojun, Pan, Wensen, Papanikolaou, Metaxia, Papavasilopoulou, Theonymfi, Parekh, Ameet, Parke, Rachael, Parrilla, Francisco J, Parrilla, Dácil, Pasha, Taha, Pasin, Laura, Patão, Luis, Patel, Mayur, Patel, Grisma, Pati, Basanta Kumar, Patil, Jayaprakash, Pattnaik, Saroj, Paul, Daniel, Pavesi, Maurizio, Pavlotsky, Vanesa Alejandra, Paz, Graciela, Paz, Enrique, Pecci, Elisabetta, Pellegrini, Carlos, Peña Padilla, Andrea Gabriela, Perchiazzi, Gaetano, Pereira, Tiago, Pereira, Vera, Perez, Manuel, Perez Calvo, Cesar, Perez Cheng, Meisy, Perez Maita, Ronald, Pérez-Araos, Rodrigo, Perez-Teran, Purificación, Perez-Torres, David, Perkins, Gavin, Persona, Paolo, Petnak, Tananchai, Petrova, Marina, Pham, Tai, Philippart, François, Picetti, Edoardo, Pierucci, Elisabetta, Piervincenzi, Edoardo, Pinciroli, Riccardo, Pintado, Maria-Consuelo, Piraino, Thomas, Piras, Stephanie, Piras, Claudio, Pirompanich, Pattarin, Pisani, Luigi, Platas, Enrique, Plotnikow, Gustavo, Porras, Willy, Porta, Virginia, Portilla, Mariana, Portugal, José, Povoa, Pedro, Prat, Gwenael, Pratto, Romina, Preda, Gabriel, Prieto, Isidro, Prol-Silva, Estefania, Pugh, Richard, Qi, Yupeng, Qian, Chuanyun, Qin, Tiehe, Qu, Hongping, Quintana, Teobaldo, Quispe Sierra, Rosari, Quispe Soto, Rocio, Rabbani, Raihan, Rabee, Mohamed, Rabie, Ahmed, Rahe Pereira, Maria Augusta, Rai, Ashish, Raj Ashok, Sundar, Rajab, Mostafa, Ramdhani, Navin, Ramey, Elizabeth, Ranieri, Marco, Rathod, Darshana, Ray, Banambar, Redwanul Huq, Shihan Mahmud, Regli, Adrian, Reina, Rosa, Resano Sarmiento, Natalia, Reynaud, Faustine, Rialp, Gemma, Ricart, Pilar, Rice, Todd, Richardson, Angus, Rieder, Marcelo, Rinket, Martin, Rios, Fernando, Risso Vazquez, Alejandro, Riva, Ivano, Rivette, Monaly, Roca, Oriol, Roche-Campo, Ferran, Rodriguez, Covadonga, Rodriguez, Gabriel, Rodriguez Gonzalez, Daniel, Rodriguez Tucto, Xandra Yanina, Rogers, Angela, Romano, María Elena, Rørtveit, Linda, Rose, Alastair, Roux, Damien, Rouze, Anahita, Rubatto Birri, Paolo Nahuel, Ruilan, Wang, Ruiz Robledo, Aldana, Ruiz-Aguilar, Antonio Luis, Sadahiro, Tomohito, Saez, Ignacio, Sagardia, Judith, Saha, Rajnish, Saha, Rohit, Saiphoklang, Narongkorn, Saito, Shigeki, Salem, Maie, Sales, Gabriele, Salgado, Patricia, Samavedam, Srinivas, Sami Mebazaa, Mhamed, Samuelsson, Line, San Juan Roman, Nandyelly, Sanchez, Patricia, Sanchez-Ballesteros, Jesus, Sandoval, Yazcitk, Sani, Emanuele, Santos, Martin, Santos, Carla, Sanui, Masamitsu, Saravanabavan, Lakshmikanthcharan, Sari, Sema, Sarkany, Agnes, Sauneuf, Bertrand, Savioli, Monica, Sazak, Hilal, Scano, Riccardo, Schneider, Francis, Schortgen, Frédérique, Schultz, Marcus J, Schwarz, Gabriele Leonie, Seçkin Yücesoy, Faruk, Seely, Andrew, Seiler, Frederik, Seker Tekdos, Yasemin, Seok Chan, Kim, Serano, Luca, Serednicki, Wojciech, Setten, Mariano, Shah, Asim, Shah, Bhagyesh, Shang, You, Shanmugasundaram, Pradeep, Shapovalov, Konstantin, Shebl, Eman, Shiga, Takuya, Shime, Nobuaki, Shin, Phil, Short, Jack, Shuhua, Chen, Siddiqui, Sughrat, Silesky Jimenez, Juan Ignacio, Silva, Daniel, Silva Sales, Betania, Simons, Koen, Sjøbø, Brit Ågot, Slessor, David, Smiechowicz, Jakub, Smischney, Nathan, Smith, Paul, Smith, Tim, Smith, Mark, Snape, Sarah, Snyman, Lindi, Soetens, Filiep, Sook Hong, Kyung, Sosa Medellin, Miguel Ángel, Soto, Giovanna, Souloy, Xavier, Sousa, Elsa, Sovatzis, Stefania, Sozutek, Didem, Spadaro, Savino, Spagnoli, Marco, Spångfors, Martin, Spittle, Nick, Spivey, Mike, Stapleton, Andrew, Stefanovic, Branislava, Stephenson, Lorraine, Stevenson, Elizabeth, Strand, Kristian, Strano, Maria Teresa, Straus, Slavenka, Sun, Chenliang, Sun, Rongqing, Sundaram, Venkat, SunPark, Tai, Surlemont, Elisabeth, Sutherasan, Yuda, Szabo, Zsuzsanna, Tainter, Christopher, Takaba, Akihiro, Tallott, Mandy, Tamasato, Tamasato, Tang, Zhanhong, Tangsujaritvijit, Viratch, Taniguchi, Leandro, Taniguchi, Daisuke, Tarantino, Fabio, Teerapuncharoen, Krittika, Temprano, Susana, Terragni, Pierpaolo, Terzi, Nicolas, Thakur, Anand, Theerawit, Pongdhep, Thille, Arnaud W, Thomas, Matt, Thungtitigul, Poungrat, Thyrault, Martial, Tilouch, Nejla, Timenetsky, Karina, Tirapu, Juna, Todeschini, Manuel, Tomas, Roser, Tomaszewski, Christian, Tonetti, Tommaso, Tonnelier, Alexandre, Trinder, John, Trongtrakul, Konlawij, Truwit, Jonathon, Tsuei, Betty, Tulaimat, Aiman, Turan, Sema, Turkoglu, Melda, Tyagi, Sanjeev, Ubeda, Alejandro, Vagginelli, Federica, Valenti, María Florencia, Vallverdu, Imma, Van Axel, Alisha, van den Hul, Ingrid, van der Hoeven, Hans, Van Der Meer, Nardo, Vanhoof, Marc, Vargas-Ordoñez, Mónica, Vaschetto, Rosanna, Vascotto, Ettore, Vatsik, Maria, Vaz, Ana, Vazquez-Sanchez, Antonia, Ventura, Sara, Vermeijden, Jan Wytze, Vidal, Anxela, Vieira, Jocyelle, Vilela Costa Pinto, Bruno, Villagra, Ana, Villegas Succar, Cristina, Vinorum, Ole Georg, Vitale, Giovanni, Vj, Ramesh, Vochin, Ana, Voiriot, Guillaume, Volta, Carlo Alberto, von Seth, Magnus, Wajdi, Maazouzi, Walsh, Don, Wang, Shouhong, Wardi, Gabriel, Ween-Velken, Nils Christian, Wei, Bi-Lin, Weller, Dolf, Welsh, Deborah, Welters, Ingeborg, Wert, Michael, Whiteley, Simon, Wilby, Elizabeth, Williams, Erin, Williams, Karen, Wilson, Antoinette, Wojtas, Jadwiga, Won Huh, Jin, Wrathall, David, Wright, Christopher, Wu, Jian-Feng, Xi, Guo, Xing, Zheng-Jiang, Xu, Hongyang, Yamamoto, Kotaro, Yan, Jie, Yáñez, Julio, Yang, Xiaobo, Yates, Elliot, Yazicioglu Mocin, Ozlem, Ye, Zhenglong, Yildirim, Fatma, Yoshida, Norifumi, Yoshido, Hector Higo Leon, Young Lee, Bo, Yu, Rongguo, Yu, Gong, Yu, Tao, Yuan, Boyun, Yuangtrakul, Nadwipa, Yumoto, Tetsuya, Yun, Xie, Zakalik, Graciela, Zaki, Ahmad, Zalba-Etayo, Begoña, Zambon, Massimo, Zang, Bin, Zani, Gianluca, Zarka, Jonathan, Zerbi, Simone Maria, Zerman, Avsar, Zetterquist, Harald, Zhang, Jiuzhi, Zhang, Hongwen, Zhang, Wei, Zhang, Guoxiu, Zhang, Weixin, Zhao, Hongsheng, Zheng, Jia, Zhu, Bin, Zumaran, Ronald, Pham, Tài, Madotto, Fabiana, Goligher, Ewan C, Mancebo, Jordi, Peñuelas, Oscar, Pesenti, Antonio, Wunsch, Hannah, van Haren, Frank, and Laffey, John G

Published
2023
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27. A cross-sectional survey on the lung ultrasound training and practice of respiratory therapists in mainland China

Author

Kai Liu, Yu-long Yao, Yu-xian Wang, Bi-lin Wei, Liu-cun Li, Qi-xing Wang, Hui-qing Ge, Er-shan Wang, Li-min Yang, Huan Chen, Yun-qing Yang, Hao Qin, Wen-jun Zhai, Shen-ji Yu, Xiao-ting Wang, Zhe Luo, and Guo-wei Tu

Subjects
Lung ultrasound, Mainland China, Practice, Respiratory therapist, Training, Diseases of the respiratory system, RC705-779
Abstract

Abstract Purpose This national study aimed to investigate the lung ultrasound (LUS) training and practice of respiratory therapists (RTs) in mainland China. Methods A cross-sectional multicenter survey was conducted from May 22, 2021 to August 12, 2021, through online platforms. This survey included RTs in mainland China. The survey was divided into four sections: (1) demographic characteristics and basic information; (2) basic information about LUS training and practice; (3) LUS practice details; and (4) Other ultrasound training and practice. Results A total of 514 responses were received, and 494 valid responses were included in the analysis. 81.2% (401/494) participants’ highest degree of education was a bachelor’s degree, and 43.1% (213/494) participants were at level II in terms of job ranking. 99.2%(490/494) participants agreed that the RTs needed to learn lung ultrasound, but only 12.3% (61/494) participants had received a LUS training course. Further, 66.2% (327/494) experienced participants responded to Sect. 3. Most of RTs used LUS when the patient had hypoxia (265/327, 81%) or dyspnea (260/317, 79.5%); they also used it during spontaneous breathing trial(SBT) (191/327, 58.4%) or in prone position (177/327, 54.1%). The A-line (302/327, 92.4%), B-line (299/327, 91.4%), lung slide (263/327, 80.4%), and bat sign (259/327, 79.2%) were well known as LUS signs. Also, 30.6% (100/327) participants did not use the LUS protocol in their clinical practice, and only 25.4%(83/327) participants said they had used LUS scores. Moreover, 55.7% (182/327) participants frequently changed the respiratory therapy strategy according to LUS results. Conclusions We should improve the number and workplace of RTs in mainland China in the future. We should also standardize the application of LUS practice and training for RTs in mainland China and establish corresponding certification pathways.

Published
2022
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36. An Efficient and Automatic Simplification Method for Arbitrary Complex Networks in Mine Ventilation.

Author

Zhong, Deyun, Wen, Lixue, Bi, Lin, and Liu, Yulong

Subjects
MINE ventilation, AIR resistance, DISCRETE mathematics, NP-hard problems, ENERGY conservation
Abstract

The simplification of complex networks is a research field closely related to graph theory in discrete mathematics. The existing methods are typically limited to simplifying the series sub-networks, parallel sub-networks, diagonal sub-networks, and nested simple sub-networks. From the current perspective, there are no available methods that can handle complex sub-networks and nested complex sub-networks. In this paper, we innovatively propose an efficient and automatic equivalence simplification method for arbitrary complex ventilation networks. The method enables, for the first time, the maximum possible equivalence simplification of nested simple sub-networks and nested complex sub-networks. In order to avoid the NP-hard problem caused by the searching of simplifiable sub-networks, it is necessary to analyze the intrinsic topology relationship between simplifiable sub-networks and spanning sub-graphs to optimize the searching process. One of our main contributions is that we present an efficient searching method for arbitrarily nested reducible sub-networks based on the bidirectional traversal process of a directed tree. The method optimizes the searching process for simplifiable node pairs by combining the characteristics of a directed tree with the judgment rules of simplifiable sub-networks. Moreover, by deriving the formula of an equivalent air resistance calculation for complex sub-networks, another one of our main contributions is that we present an equivalent calculation and simplification method for arbitrarily complex sub-networks based on the principle of energy conservation. The basic idea of the method is to calculate the equivalent air resistance using the ventilation network resolution of the constructed virtual sub-networks. We realize the simplification method of arbitrarily complex mine ventilation networks, and we validate the reliability of the simplification method by comparing the air distribution results using the network solution method before and after simplification. It can be determined that, with appropriate modifications to meet specific requirements, the proposed method can also be applicable to equivalent simplification instances of other types of complex networks. Based on the results analysis of several real-world mine ventilation network examples, the effectiveness of the proposed method is further verified, which can satisfactorily meet the requirements for simplifying complex networks. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology.

Author

Bi, Lin, Bai, Dewei, and Chen, Boxun

Subjects
SURFACE reconstruction, PREDICTION models, PRIOR learning, ORES, SHOVELS
Abstract

To address the problem of incomplete perception of limited viewpoints of ore blocks in future remote and intelligent shoveling-dominated mining scenarios, a method of using new view generation technology to predict ore blocks with limited view based on a latent diffusion model is proposed. Initially, an ore block image-pose dataset is created. Then, based on prior knowledge, the latent diffusion model undergoes transfer learning to develop an intelligent ore block shape prediction model (IOBSPM) for rock blocks. During training, structural similarity loss is innovatively introduced to constrain the prediction results and solve the issue of discontinuity in generated images. Finally, neural surface reconstruction is performed using the generated multi-view images of rock blocks to obtain a 3D model. Experimental results show that the prediction model, trained on the rock block dataset, produces better morphological and detail generation compared to the original model, with single-view generation time within 5 s. The average PSNR, SSIM, and LPIPS values reach 23.02 dB, 0.754, and 0.268, respectively. The generated views also demonstrate good performance in 3D reconstruction, highlighting significant implications for future research on remote and autonomous shoveling. [ABSTRACT FROM AUTHOR]

Published
2024
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38. An explicit multilevel power turbulent wall function based on the de-thresholding Douglas–Peucker algorithm.

Author

Wang, ZhiPing, Bi, Lin, Luo, CanYan, Zhang, Hui, Yuan, Xianxu, and Tang, Zhigong

Subjects
*ENGINEERING standards, *BOUNDARY layer (Aerodynamics), *STRUCTURAL dynamics, *IRON & steel plates, *ENGINEERING simulations
Abstract

Wall functions are extensively applied in engineering simulations with turbulence. They facilitate a significant increase in the scale of the grids next to the wall, which in turn reduces the total number of grids needed. This optimization enhances computational efficiency, making the simulation process more effective and streamlined. However, the current commonly used wall functions, such as the Spalding wall function, are an implicit expression that needs to be solved iteratively, which affects the computational efficiency, and the multilayer segmented wall function is not smoothly articulated, which affects the fidelity. In this study, based on flat plate direct numerical simulation (DNS) data, combined with structural ensemble dynamics theory, the de-thresholding Douglas–Peucker algorithm is introduced to construct an explicit wall function expression in the form of multilevel power exponential concatenated multiplication. The comparison of the new wall function against DNS data reveals that it demonstrates superior fitting accuracy in contrast to the traditional ones, and eliminates the need for manual calibration, reduces subjective influence, and enhances reliability. The numerical simulation outcomes for the flat plate boundary layer and a series of airfoils showcase the new wall function's exceptional accuracy, which not only meets but also surpasses the demanding standards of engineering practice. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Optimization Method of Mine Ventilation Network Regulation Based on Mixed-Integer Nonlinear Programming.

Author

Wen, Lixue, Zhong, Deyun, Bi, Lin, Wang, Liguan, and Liu, Yulong

Subjects
MINES & mineral resources, NONLINEAR programming, COAL mining, MATHEMATICAL optimization, ENERGY consumption, MINE ventilation, NATURAL ventilation
Abstract

Mine ventilation is crucial for ensuring safe production in mines, as it is integral to the entire underground mining process. This study addresses the issues of high energy consumption, regulation difficulties, and unreasonable regulation schemes in mine ventilation systems. To this end, we construct an optimization model for mine ventilation network regulation using mixed-integer nonlinear programming (MINLP), focusing on objectives such as minimizing energy consumption, optimal regulation locations and modes, and minimizing the number of regulators. We analyze the construction methods of the mathematical optimization model for both selected and unselected fans. To handle high-order terms in the MINLP model, we propose a variable discretization strategy that introduces 0-1 binary variables to discretize fan branches' air quantity and frequency regulation ratios. This transformation converts high-order terms in the constraints of fan frequency regulation into quadratic terms, making the model suitable for solvers based on globally accurate algorithms. Example analysis demonstrate that the proposed method can find the optimal solution in all cases, confirming its effectiveness. Finally, we apply the optimization method of ventilation network regulation based on MINLP to a coal mine ventilation network. The results indicate that the power of the main fan after frequency regulation is 71.84 kW, achieving a significant energy savings rate of 65.60% compared to before optimization power levels. Notably, ventilation network can be regulated without adding new regulators, thereby reducing management and maintenance costs. This optimization method provides a solid foundation for the implementation of intelligent ventilation systems. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires.

Author

Bi, Lin, Liu, Yulong, Zhong, Deyun, and Wen, Lixue

Subjects
MINE ventilation, CIVILIAN evacuation, MINES & mineral resources, DYNAMIC programming, CONSTRAINT programming, BUILDING evacuation, FIRE detectors
Abstract

When a fire occurs underground, pre-existing emergency escape routes may become ineffective. Such scenarios necessitate the real-time planning of escape routes that consider evolving conditions and prioritize safety. This paper proposes a multi-objective real-time search method for emergency escape routes using dynamic programming to address these challenges. Firstly, this paper discusses how the evacuation of an underground mine fire caused by external factors depends on the fire monitoring system, personnel positioning system, and emergency evacuation system, as well as the modeling and solution methods of evacuation route planning. Then, based on fire smoke, CO, and temperature sensor data from the mine ventilation network structure and monitoring system, the possible smoke spread in the event of an underground fire was calculated. The objectives are to optimize both the shortest equivalent length of the path and the shortest time to walk through the smoke flow while ensuring that temporary escape time does not exceed the rated protection time of the self-rescuer. A mathematical model for emergency escape route planning is established under these conditions. A labeling algorithm based on dynamic programming is employed to find the Pareto optimal solution set of emergency evacuation routes that meet emergency requirements. Finally, two path evaluation indicators, namely "escape target priority" and "personnel temporary escape time", are introduced to re-rank the solutions in the Pareto optimal set, thereby obtaining disaster evacuation routes with different priorities. Example verification shows that the algorithm can quickly solve the disaster evacuation routes that meet the actual disaster evacuation needs in complex networks. Example verification shows that the algorithm can quickly solve the emergency escape routes in complex networks that meet actual emergency escape needs. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Recent advances in lanthanide-doped up-conversion probes for theranostics

Author

Danyang Xu, Chenxu Li, Wenjing Li, Bi Lin, and Ruichan Lv

Subjects
lanthanide-doped, probes for theranostics, up-conversion, up-conversion luminescence, biomedicine, Chemistry, QD1-999
Abstract

Up-conversion (or anti-Stokes) luminescence refers to the phenomenon whereby materials emit high energy, short-wavelength light upon excitation at longer wavelengths. Lanthanide-doped up-conversion nanoparticles (Ln-UCNPs) are widely used in biomedicine due to their excellent physical and chemical properties such as high penetration depth, low damage threshold and light conversion ability. Here, the latest developments in the synthesis and application of Ln-UCNPs are reviewed. First, methods used to synthesize Ln-UCNPs are introduced, and four strategies for enhancing up-conversion luminescence are analyzed, followed by an overview of the applications in phototherapy, bioimaging and biosensing. Finally, the challenges and future prospects of Ln-UCNPs are summarized.

Published
2023
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48. Wetting and Diffusion of Water on Pristine and Strained Phosphorene

Author

Zhang, Wei, Ye, Chao, Bi, Lin, Yang, Zaixing, and Zhou, Ruhong

Subjects
Condensed Matter - Soft Condensed Matter, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

Phosphorene, a newly fabricated two-dimensional (2D) nanomaterial, have exhibited promising application prospect in biology. Nonetheless, the wetting and diffusive properties of bio-fluids on phosphorene are still elusive. In this study, using molecular dynamics (MD) simulations, we investigated the structural and dynamic properties of water on pristine and strained phosphorene. The MD simulations illustrated that the diffusion of water molecules on the phosphorene surface is anisotropic, while strain-enhanced diffusion is clearly present which arises from strain-induced smooth of the energy landscape. The contact angle of water droplet on phosphorene exhibited a nonmonotonic variation with the transverse strain. The structure of water on transverse stretched phosphorene was demonstrated to be different from that on longitudinal stretched phosphorene. Moreover, we discovered that the contact angle of water on strained phosphorene is proportional to the quotient of longitudinal and transverse diffusion coefficients of interfacial water. These findings would offer helpful insights in potential ways of manipulating the wetting and transport of water at nanoscale, and in future bio-applications of phosphorene., Comment: 8 pages, 6 figures

Published
2015

49. Prediction of Part Shrinkage for Injection Molded Crystalline Polymer via Cavity Pressure and Melt Temperature Monitoring

Author

Shia-Chung Chen, Bi-Lin Tsai, Cheng-Chang Hsieh, Nien-Tien Cheng, En-Nien Shen, and Ching-Te Feng

Subjects
crystalline polymer, enthalpy change, completion time of crystallization, response surface methodology, time-averaged solidification pressure, part shrinkage, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

During an injection molding process, different parts of the molded material are subjected to various thermal–mechanical stresses, such as variable pressures, temperatures, and shear stresses. These variations form different pressure–temperature paths on the pressure–volume–temperature diagram. If these paths cannot converge at a specific target volume value during ejection, it often leads to different levels of shrinkage and associated warping, which pose a significant challenge for molders during mold trials and part quality control. The situation is particularly complicated when molding crystalline polymers because the degree of crystallinity depends on the processing conditions and may vary across different locations. In this study, we propose an innovative and practical approach to improving part shrinkage when molding crystalline polymers. For the first time, we utilized melt temperature profile monitoring rather than the previous mold temperature measurement to detect the crystallization process and determine the time taken to complete the crystallization at different melt and mold temperatures. In addition, we used response surface methodology to build a crystallization time prediction model. The feasibility of the prediction model was verified by determining the warpage of parts molded at various cooling times. Based on this model, we varied the packing pressure, packing time, and melt temperatures to determine the correlation with part shrinkage. Through regression analysis, the time-averaged solidification pressure values can accurately control part shrinkage. Two prediction models provide reasonable accuracy and efficiency for part shrinkage control, as demonstrated by subsequent verification experiments.

Published
2023
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50. Improving grain yield, nitrogen use efficiency and radiation use efficiency by dense planting, with delayed and reduced nitrogen application, in double cropping rice in South China

Author

You-qiang FU, Xu-hua ZHONG, Jia-huan ZENG, Kai-ming LIANG, Jun-feng PAN, Ying-feng XIN, Yan-zhuo LIU, Xiang-yu HU, Bi-lin PENG, Rong-bing CHEN, Rui HU, and Nong-rong HUANG

Subjects
grain yield, resource use efficiencies, indica rice, planting density, nitrogen application strategy, Agriculture (General), S1-972
Abstract

Improving both grain yield and resource use efficiencies simultaneously is a major challenge in rice production. However, few studies have focused on integrating dense planting with delayed and reduced nitrogen application to enhance grain yield, nitrogen use efficiency (NUE) and radiation use efficiency (RUE) in rice (Oryza sativa L.) in the double rice cropping system in South China. A high-yielding indica hybrid rice cultivar (Yliangyou 143) was grown in field experiments in Guangxi, South China, with three cultivation managements: farmers’ practice (FP), dense planting with equal N input and delayed N application (DPEN) and dense planting with reduced N input and delayed N application (DPRN). The grain yields of DPRN reached 10.6 and 9.78 t ha–1 in the early and late cropping seasons, respectively, which were significantly higher than the corresponding yields of FP by 23.9–29.9%. The grain yields in DPEN and DPRN were comparable. NUE in DPRN reached 65.2–72.9 kg kg–1, which was 61.2–74.1% higher than that in FP and 24.6–30.2% higher than that in DPEN. RUE in DPRN achieved 1.60–1.80 g MJ–1, which was 28.6–37.9% higher than that in FP. The productive tiller percentage in DPRN was 7.9–36.2% higher than that in DPEN. Increases in crop growth rate, leaf area duration, N uptake from panicle initiation to heading and enhancement of the apparent transformation ratio of dry weight from stems and leaf sheaths to panicles all contributed to higher grain yield and higher resource use efficiencies in DPRN. Correlation analysis revealed that the agronomic and physiological traits mentioned above were significantly and positively correlated with grain yield. Comparison trials carried out in Guangdong in 2018 and 2019 also showed that DPRN performed better than DPEN. We conclude that DPRN is a feasible approach for simultaneously increasing grain yield, NUE and RUE in the double rice cropping system in South China.

Published
2021
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