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Computational Design and Fabrication of a Bending-Active Structure Using Fiberglass:A Bioinspired Pavilion Mimicking Marine Microorganism radiolaria

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Journal of Bionic Engineering 2022年第2期19卷 471-482页

作者： Bosheng Liu tanvir r.faisal School of Architecture and Design University of Louisiana at LafayetteLafayetteLA 70503USA Department of Mechanical Engineering University of Louisiana at LafayetteLafayetteLA 70503USA

Bio-inspired architectural designs are often superior for their aesthetics and structural *** forms and loading states of a biological structure is complex as it requires a delicate balance among geometry,material properties,and interacting *** goal of this work is to design a biomimetic,ultra-lightweight,bending-active structure utilizing an informed integral design approach,and thereby constructing a self-supporting cellular pavilion.A bioinspired pavilion has been designed and constructed based on the natural cellular organization observed in radiolaria,a deep-sea *** cellularity was mimicked via Voronoi tessellation in the structure of the pavilion,whose structural performance was evaluated using finite element ***,funicular structure design strategies were studied with a focus on cellular distributions and concentration responding to areas with high structural *** computer aided custom designed pavilion was constructed with engineered,in-house fabricated fiberglass composite *** bending-active lightweight structure was also validated through material performance inquiry,a partial full-scale cellular assembly,and the full-size pavilion *** work contributes to the design approach comprising a bending-active form-finding schematic strategy to construct the elastic bending-active structure physically and simulate computationally within the context of nature inspired innovative lightweight structure design.

关键词： Biomimetic design Double-curved shell pavilion Voronoi tessellation Bending-active structures Finite element analysis

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risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori:A study of a tertiary care hospital in Pakistan

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Artificial Intelligence in Gastroenterology 2023年第1期4卷 10-27页

作者： Shahid Aziz Simone König Muhammad Umer Tayyab Saeed Akhter Shafqat Iqbal Maryum Ibrar Tofeeq Ur-rehman tanvir Ahmad Alfizah Hanafiah rabaab Zahra faisal rasheed Patients Diagnostic Lab Isotope Application DivisionPakistan Institute of Nuclear Science and TechnologyIslamabad 44000Pakistan Department of Microbiology Quaid-i-Azam UniversityIslamabad 45320Pakistan Interdisciplinary Center for Clinical research Core Unit ProteomicsUniversity of MünsterMünster 48149Germany Management Information System Division Pakistan Institute of Nuclear Science and TechnologyIslamabad 44000Pakistan Centre for Liver and Digestive Diseases Holy Family HospitalRawalpindi 46300Pakistan Pakistan Scientific and Technological Information Centre Quaid-i-Azam UniversityIslamabad 45320Pakistan Department of Pharmacy Quaid-i-Azam UniversityIslamabad 45320Pakistan Faculty of Medicine Department of Medical Microbiology and ImmunologyUniversiti Kebangsan MalaysiaCherasKuala Lumpur 56000Malaysia

BACKGrOUND Gastric cancer(GC)is the fourth leading cause of cancer-related deaths *** relies on histopathology and the number of endoscopies is *** pylori(***)infection is a major risk *** To develop an in-silico GC prediction model to reduce the number of diagnostic surgical *** meta-data of patients with gastroduodenal symptoms,risk factors associated with GC,and *** infection status from Holy Family Hospital rawalpindi,Pakistan,were used with machine *** A cohort of 341 patients was divided into three groups[normal gastric mucosa(NGM),gastroduodenal diseases(GDD),and GC].Information associated with socioeconomic and demographic conditions and GC risk factors was collected using a *** infection status was determined based on urea breath *** association of these factors and histopathological grades was assessed statistically.K-Nearest Neighbors and random Forest(rF)machine learning models were *** This study reported an overall frequency of 64.2%(219/341)of *** infection among enrolled *** was higher in GC(74.2%,23/31)as compared to NGM and GDD and higher in males(54.3%,119/219)as compared to *** abdominal pain(72.4%,247/341)was observed than other clinical symptoms including vomiting,bloating,acid reflux and *** majority of the GC patients experienced symptoms of vomiting(91%,20/22)with abdominal pain(100%,22/22).The multinomial logistic regression model was statistically significant and correctly classified 80%of the GDD/GC ***,income level,vomiting,bloating and medication had significant association with GDD and GC.A dynamic rF GC-predictive model was developed,which achieved>80%test *** GC risk factors were incorporated into a computer model to predict the likelihood of developing GC with high sensitivity and *** model is dynamic and will be further improved and validated by including n

关键词： Gastric cancer Gastritis Machine learning Prediction model Helicobacter pylori

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