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وثائق مكافحة كوفيد-19
بين يدي القارئ كتاب يجمع بين دفتيه الترجمة العربية لوثائق مكافحة كوفيد 19- التي أصدرتها لجنة الصحة الوطنية الصينية، ومـن بـين هذه الوثائق النسخ الست مـن آليات الوقاية مـن الالتهاب الرئوي الناجم عـن فيروس كـورونا المستجد ومكافحته، والنسخة التجريبية السابعة لآليات تـشخيص الالتـِهاب الـرئوي الناجِم عـن فـيروس كـورونا المستجد وعلاجه وغـيرها مـن الـمرفقات. وعـمل على ترجمة النـسخة الـعربية لوثائق مكافحة كـوفيد 19- الصينية فريـق ترجمة به أكـثر من عشريـن أستاذا وطالبا مـن قسم اللغة العربية بكلية اللغات الأجنبية بجامعة بكين بـالتعاون مع كلية الآداب في جامعة القاهرة والمعهد العالي للغات بتونس في جامعة قرطاج، في الفترة مـن مارس وحتى مايو 2020، قام خلالها فـريق الترجمة بترجمة قرابة 100 ألـف رمز صيني.
BOOK
Governance on the Drug Supply Chain via Gcoin Blockchain
As a trust machine, blockchain was recently introduced to the public to provide an immutable, consensus based and transparent system in the Fintech field. However, there are ongoing efforts to apply blockchain to other fields where trust and value are essential. In this paper, we suggest Gcoin blockchain as the base of the data flow of drugs to create transparent drug transaction data. Additionally, the regulation model of the drug supply chain could be altered from the inspection and examination only model to the surveillance net model, and every unit that is involved in the drug supply chain would be able to participate simultaneously to prevent counterfeit drugs and to protect public health, including patients.
Journal Article
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of
1
,
643
observations, the proposed approach yields a mean absolute error (MAE) of
0.07
eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.
Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions with high accuracy.
Journal Article
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.
Artificial intelligence and machine learning can greatly enhance materials property prediction and discovery. Here the authors propose cross-property transfer learning to build accurate models for dozens of properties with limited data availability.
Journal Article
Metal-free three-dimensional perovskite ferroelectrics
The perovskite structure accommodates many different combinations of elements, making it attractive for use in a wide variety of applications. Building perovskites out of only organic compounds is appealing because these materials tend to be flexible, fracture-resistant, and potentially easier to synthesize than their inorganic counterparts. Ye et al. describe a previously unknown family of all-organic perovskites, of which they synthesized 23 different family members (see the Perspective by Li and Ji). The compounds are attractive as ferroelectrics, including one compound with properties close to the well-known inorganic ferroelectric BaTiO 3 . Science , this issue p. 151 ; see also p. 132 A family of all-organic perovskites has attractive ferroelectric properties. Inorganic perovskite ferroelectrics are widely used in nonvolatile memory elements, capacitors, and sensors because of their excellent ferroelectric and other properties. Organic ferroelectrics are desirable for their mechanical flexibility, low weight, environmentally friendly processing, and low processing temperatures. Although almost a century has passed since the first ferroelectric, Rochelle salt, was discovered, examples of highly desirable organic perovskite ferroelectrics are lacking. We found a family of metal-free organic perovskite ferroelectrics with the characteristic three-dimensional structure, among which MDABCO ( N -methyl- N' -diazabicyclo[2.2.2]octonium)–ammonium triiodide has a spontaneous polarization of 22 microcoulombs per square centimeter [close to that of barium titanate (BTO)], a high phase transition temperature of 448 kelvins (above that of BTO), and eight possible polarization directions. These attributes make it attractive for use in flexible devices, soft robotics, biomedical devices, and other applications.
Journal Article
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as
ElemNet
; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of
ElemNet
enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds.
Journal Article
An organic-inorganic perovskite ferroelectric with large piezoelectric response
Piezoelectric materials allow conversion between electricity and mechanical stresses. The most efficient piezoelectric materials are ceramics such as BaTiO 3 or PbZrO 3 , which are also extremely stiff. You et al. identified an organic perovskite structured piezoelectric material that is far more pliable yet has a piezoelectric response similar to that of traditional ceramics. This material may be a better option to use as a mechanical sensor for flexible devices, soft robotics, biomedical devices, and other micromechanical applications that benefit from a less stiff piezoelectric material. Science , this issue p. 306 Trimethylchloromethyl ammonium trichloromanganese(II) may be a flexible material competitive for piezoelectric applications. Molecular piezoelectrics are highly desirable for their easy and environment-friendly processing, light weight, low processing temperature, and mechanical flexibility. However, although 136 years have passed since the discovery in 1880 of the piezoelectric effect, molecular piezoelectrics with a piezoelectric coefficient d 33 comparable with piezoceramics such as barium titanate (BTO; ~190 picocoulombs per newton) have not been found. We show that trimethylchloromethyl ammonium trichloromanganese(II), an organic-inorganic perovskite ferroelectric crystal processed from aqueous solution, has a large d 33 of 185 picocoulombs per newton and a high phase-transition temperature of 406 kelvin (K) (16 K above that of BTO). This makes it a competitive candidate for medical, micromechanical, and biomechanical applications.
Journal Article
A White Random Laser
Random laser with intrinsically uncomplicated fabrication processes, high spectral radiance, angle-free emission, and conformal onto freeform surfaces is in principle ideal for a variety of applications, ranging from lighting to identification systems. In this work, a white random laser (White-RL) with high-purity and high-stability is designed, fabricated, and demonstrated
via
the cost-effective materials (
e.g
., organic laser dyes) and simple methods (
e.g
., all-solution process and self-assembled structures). Notably, the wavelength, linewidth, and intensity of White-RL are nearly isotropic, nevertheless hard to be achieved in any conventional laser systems. Dynamically fine-tuning colour over a broad visible range is also feasible by on-chip integration of three free-standing monochromatic laser films with selective pumping scheme and appropriate colour balance. With these schematics, White-RL shows great potential and high application values in high-brightness illumination, full-field imaging, full-colour displays, visible-colour communications, and medical biosensing.
Journal Article
A molecular perovskite solid solution with piezoelectricity stronger than lead zirconate titanate
Piezoelectric materials produce electricity when strained, making them ideal for different types of sensing applications. The most effective piezoelectric materials are ceramic solid solutions in which the piezoelectric effect is optimized at what are termed morphotropic phase boundaries (MPBs). Ceramics are not ideal for a variety of applications owing to some of their mechanical properties. We synthesized piezoelectric materials from a molecular perovskite (TMFM)ₓ(TMCM)1–x
CdCl₃ solid solution (TMFM, trimethylfluoromethyl ammonium; TMCM, trimethylchloromethyl ammonium, 0 ≤ x ≤ 1), in which the MPB exists between monoclinic and hexagonal phases. We found a composition for which the piezoelectric coefficient d
33 is ∼1540 picocoulombs per newton, comparable to high-performance piezoelectric ceramics. The material has potential applications for wearable piezoelectric devices.
Journal Article
Retrotransposon activation contributes to neurodegeneration in a Drosophila TDP-43 model of ALS
Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two incurable neurodegenerative disorders that exist on a symptomological spectrum and share both genetic underpinnings and pathophysiological hallmarks. Functional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in ~40% of FTLD cases, but the cascade of events leading to cell death are not understood. We have expressed human TDP-43 (hTDP-43) in Drosophila neurons and glia, a model that recapitulates many of the characteristics of TDP-43-linked human disease including protein aggregation pathology, locomotor impairment, and premature death. We report that such expression of hTDP-43 impairs small interfering RNA (siRNA) silencing, which is the major post-transcriptional mechanism of retrotransposable element (RTE) control in somatic tissue. This is accompanied by de-repression of a panel of both LINE and LTR families of RTEs, with somewhat different elements being active in response to hTDP-43 expression in glia versus neurons. hTDP-43 expression in glia causes an early and severe loss of control of a specific RTE, the endogenous retrovirus (ERV) gypsy. We demonstrate that gypsy causes the degenerative phenotypes in these flies because we are able to rescue the toxicity of glial hTDP-43 either by genetically blocking expression of this RTE or by pharmacologically inhibiting RTE reverse transcriptase activity. Moreover, we provide evidence that activation of DNA damage-mediated programmed cell death underlies both neuronal and glial hTDP-43 toxicity, consistent with RTE-mediated effects in both cell types. Our findings suggest a novel mechanism in which RTE activity contributes to neurodegeneration in TDP-43-mediated diseases such as ALS and FTLD.
Journal Article