Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states'' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.     

Stabilising and Trimming 3D Woven Fabrics for Composite Preforming Applications

The work presented here focusses on the developments in the stabilising and trimming of 3D woven preforms. Dry fibre preforms are notoriously difficult to trim; once a fabric is cut, it loses its edge stability and consequently the fabric frays. The result is an unstable fabric which can easily be displaced/ distorted prior to composite manufacturing. In this work, three stabilisation and three trimming techniques were investigated. Of the stabilisation techniques these included powder binder, thermoplastic binder yarn (activated to give fabric stabilization); and polyester stitching. The stabilised fabrics were trimmed to near-net-shape using different trimming techniques. The trimming techniques investigated were laser, clicker press and ultrasonic knife. Each stabilisation method was trialled with each trimming method to assess the most suitable combination. The assessment of quality and suitability was made by observing the level of stabilisation, amount of fraying fibres, quality of the cut, ease of application and repeatability of the process. This paper details the assessments made for each combination alongside practical application conclusions. The key findings were; cutting by means of a laser is capable of sealing the fabric edges, producing high edge quality. Stitching as a method of stabilising is not sufficient in preventing fibres from moving during the cutting process, hence producing an unclean cut.     

Random Alloyed versus Intermetallic Nanoparticles: A Comparison of Electrocatalytic Performance

As synthetic methods advance for metal nanoparticles, more rigorous studies of structure–function relationships can be made. Many electrocatalytic processes depend on the size, shape, and composition of the nanocatalysts. Here, the properties and electrocatalytic behavior of random alloyed and intermetallic nanoparticles are compared. Beginning with an introduction of metallic nanoparticles for catalysis and the unique features of bimetallic compositions, the discussion transitions to case studies of nanoscale electrocatalysts where direct comparisons of alloy and intermetallic compositions are undertaken for methanol electrooxidation, formic acid electrooxidation, the oxygen reduction reaction, and the electroreduction of carbon dioxide (CO₂). Design and synthesis strategies for random alloyed and intermetallic nanoparticles are discussed, with an emphasis on Pt–M and Cu–M compositions as model systems. The differences in catalytic performance between alloys and intermetallic nanoparticles are highlighted in order to provide an outlook for future electrocatalyst design.     

Cancer-Targeting Nanoparticles for Combinatorial Nucleic Acid Delivery

Nucleic acids are a promising type of therapeutic for the treatment of a wide range of conditions, including cancer, but they also pose many delivery challenges. For efficient and safe delivery to cancer cells, nucleic acids must generally be packaged into a vehicle, such as a nanoparticle, that will allow them to be taken up by the target cells and then released in the appropriate cellular compartment to function. As with other types of therapeutics, delivery vehicles for nucleic acids must also be designed to avoid unwanted side effects; thus, the ability of such carriers to target their cargo to cancer cells is crucial. Classes of nucleic acids, hurdles that must be overcome for effective intracellular delivery, types of nonviral nanomaterials used as delivery vehicles, and the different strategies that can be employed to target nucleic acid delivery specifically to tumor cells are discussed. Additonally, nanoparticle designs that facilitate multiplexed delivery of combinations of nucleic acids are reviewed.     

RETRACTED ARTICLE: Tolerance rough set firefly-based quick reduct

In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.

     

Peptoid Efficacy against Polymicrobial Biofilms Determined by Using Propidium Monoazide‐Modified Quantitative PCR

Biofilms containing Candida albicans are responsible for a wide variety of clinical infections. The protective effects of the biofilm matrix, the low metabolic activity of microorganisms within a biofilm and their high mutation rate, significantly enhance the resistance of biofilms to conventional antimicrobial treatments. Peptoids are peptide‐mimics that share many features of host defence antimicrobial peptides but have increased resistance to proteases and therefore have better stability in vivo. The activity of a library of peptoids was tested against monospecies and polymicrobial bacterial/fungal biofilms. Selected peptoids showed significant bactericidal and fungicidal activity against the polymicrobial biofilms. This coupled with low cytotoxicity suggests that peptoids could offer a new option for the treatment of clinically relevant polymicrobial infections.     

SPT Hammer Energy Ratio versus Drop Height

Automatic trip hammers have advantages for standard penetration test (SPT) of consistent drop height and low friction loss during hammer fall. These advantages, however, generate high energy transfer ratios (ER), typically about 90%. This efficiency causes lower sensitivity and higher energy correction coefficients, CE. To reduce ER and CE and to increase the sensitivity of SPT conducted at the Wildlife Liquefaction Array (WLA) and the Garner Valley Downhole Array, instrumented Network for Earthquake Engineering Simulation sites, a 127?mm (5.00?in.) long sleeve was placed in the hammer mechanism to reduce the drop height from 762?mm (30?in.) to 635?mm (25in.). To calibrate the energy for these drop heights, measurements were made for a series of SPT tests in Borehole X2 at WLA on November 21, 2003. For these SPT, sleeves were inserted with lengths of 50?mm (2?in.), 127?mm (5?in.) 177?mm (7?in.), and no sleeve. Resulting drop heights were 762?mm (30?in.), 711?mm (28?in.), 635?mm (25?in.), and 584?mm (23?in.). Results indicate that: (1) ER increases with rod length as expected; (2) corrections for rod length, CR, increased with rod length in accordance with CR published in 2001 by Youd et al.; and (3) for lengths greater than 6?m, ER increased approximately linearly with drop height. Average ER30 [ER based on a 762?mm (30?in.) drop height] were 43% for a 584?mm (23?in.) drop, 60% for a 635?mm (25?in.) drop, 84% for 711?mm (28?in.) drop, and 89% for a 762?mm (30?in.) drop.     

An Investigation into Parameters Affecting Crystal Purity of Urea in a Stirred Tank and an Oscillatory Baffled Crystallizer

Confidential industrial trials showed that higher purity crystals were produced in an oscillatory baffled crystallizer (OBC) compared to a stirred tank crystallizer (STC) when operating at similar conditions. This trend is verified in this work using urea as the test compound that is different from that was employed in industrial tests. We have observed that a higher supersaturation level at nucleation coupled with a lower nucleation temperature was measured in the STC at all investigated conditions compared to that in the OBC. This led to a higher nucleation rate in the STC, consequently producing smaller crystals. Crystal size distributions and imaging analyses suggest that these smaller crystals were more likely to form agglomerates in the STC, trapping either mother liquor or impurity or both and leading to the lower purity observed when compared to that in the OBC.     

Twin-free uniform epitaxial GaAs nanowires grown by a two-temperature process

We demonstrate vertically aligned epitaxial GaAs nanowires of excellent crystallographic quality and optimal shape, grown by Au nanoparticle-catalyzed metalorganic chemical vapor deposition. This is achieved by a two-temperature growth procedure, consisting of a brief initial high-temperature growth step followed by prolonged growth at a lower temperature. The initial high-temperature step is essential for obtaining straight, vertically aligned epitaxial nanowires on the (111)B GaAs substrate. The lower temperature employed for subsequent growth imparts superior nanowire morphology and crystallographic quality by minimizing radial growth and eliminating twinning defects. Photoluminescence measurements confirm the excellent optical quality of these two-temperature grown nanowires. Two mechanisms are proposed to explain the success of this two-temperature growth process, one involving Au nanoparticle-GaAs interface conditions and the other involving melting-solidification temperature hysteresis of the Au-Ga nanoparticle alloy.