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Air pollution is a lethal global threat. To mitigate the effects of air pollution, we must first understand it, find its patterns and correlations, and predict it in advance. Air pollution is highly dependent on spatial and temporal correlations of prior meteorological, wildfire, and pollution structures. We use the advanced deep predictive Convolutional LSTM (ConvLSTM) model paired with the cutting-edge Graph Convolutional Network (GCN) architecture to predict spatiotemporal hourly PM2.5 across the Los Angeles area over time. Our deep-learning model does not use atmospheric physics or chemical mechanism data, but rather multisource imagery and sensor data. We use high-resolution remote-sensing satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA Terra+Aqua satellites and remote-sensing data from the Tropospheric Monitoring Instrument (TROPOMI), a multispectral imaging spectrometer onboard the Sentinel-5P satellite. We use the highly correlated Fire Radiative Power data product from the MODIS instrument which provides valuable information about the radiant heat output and effects of wildfires on atmospheric air pollutants. The input data we use in our deep-learning model is representative of the major sources of ground-level PM2.5 and thus we can predict hourly PM2.5 at unparalleled accuracies. Our RMSE and NRMSE scores over various site locations and predictive time frames show significant improvement over existing research in predicting PM2.5 using spatiotemporal deep predictive algorithms.

AimsTo explore the correlation of local macular ganglion cell/inner plexiform layer (GC/IPL) thickness measurements with sensitivity at individual test locations on the central 10-2 visual fields (VFs) in patients with glaucoma.MethodsOne hundred thirty-seven eyes of 125 patients with spectral domain optical coherence tomography (OCT) and 10-2 VFs were included. The exported thickness matrices (200×200) of GC/IPL measurements were centred on the fovea. Total deviation values at each test location were correlated with the 20 000 GC/IPL thickness measurements in the corresponding inferior or superior hemiretina, and areas of highest correlation were plotted. Macular structure–function relationships were also examined between six wedge-shaped GC/IPL sectors and the corresponding VF clusters. A multivariate model was built to identify the 10-2 VF test locations associated with each GC/IPL sector thickness.ResultsAverage mean deviation on 10-2 VFs was −9.2±6.1 dB. The 10-2 VF test points demonstrated correlations with GC/IPL thickness in localised arcuate patterns mostly limited within the central 4.8×4.0 mm measurement ellipse (ρ=0.43–0.74, p<0.05 for all). Twenty-one test points of the 10-2 VF were the best predictors of sectoral GC/IPL thickness. Sectoral VF-OCT correlations were high (ρ=0.53–0.66, p<0.001) and did not significantly change after adjusting for retinal GC displacement (p>0.05).ConclusionsMacular OCT/VF relationships have localised arcuate characteristics in the central region of the macula. Given the overlapping nature of structure–function relationships, a smaller number of VF test locations may be used to summarise macular functional damage.Trial registration numberNCT01742819.

Waveform inversion of vertical seismic profile (VSP) data is studied in this paper. In this study, finite-difference acoustic wave modelling in the frequency-domain is used for seismic forward modelling and a classical iterative Gauss-Newton algorithm is used for inversion. The inversion algorithm in the frequency-domain allows a multiscale approach in which individual frequency components from low to high are inverted. This reduces nonlinearity of the inverse problem and decreases the computational costs by converging to a favourable solution via a limited number of frequency components. Here, the algorithm is applied to synthetic and real VSP datasets. The synthetic model includes both smooth and sharp features. It is observed that although satisfactory results can be obtained without including the higher-frequency components, these are essential in preserving the edges of the sharp discontinuities in the velocity model. Seismic preconditioning was applied to the real VSP dataset prior to inversion to mitigate the effects of noise and unwanted wave modes. By comparing the results of inversion with the sonic log data, it is shown that waveform inversion is capable of capturing the small scale variations in the velocity model, whereas traveltime inversion fails to capture the true range of velocity variations.

An absorbing boundary condition is necessary in seismic wave simulation for eliminating the unwanted artificial reflections from model boundaries. Existing boundary condition methods often have a trade-off between numerical accuracy and computational efficiency. We proposed a local absorbing boundary condition for frequency-domain finite-difference modelling. The proposed method benefits from exact local plane-wave solution of the acoustic wave equation along predefined directions that effectively reduces the dispersion in other directions. This method has three features: simplicity, accuracy and efficiency. Numerical simulation demonstrated that the proposed method has higher efficiency than the conventional methods such as the second-order absorbing boundary condition and the perfectly matched layer (PML) method. Meanwhile, the proposed method shared the same low-cost feature as the first-order absorbing boundary condition method.

The morphological characteristic of electrospun polyacrylamide/multi-walled carbon nanotube (PAAm/MWCNTs) nanocomposite nanofibers is optimized in this work using Taguchi’s experimental design. The optimization is performed considering the effect of PAAm concentration, MWCNTs content, flow rate, and applied voltage on average nanofibers diameter. The reasonable dispersion of MWCNTs in PAAm solution is first ascertained via optical microscopy method. The experimental data required for the optimization process are then provided by statistical calculations on field-emission scanning electron microscopy images of the samples formulated based on a designed L 9 orthogonal array. PAAm concentration is found to have the most contribution on final fibers morphology according to the results obtained from simultaneous implementation of the analysis of variance and mean effect assessment. Therefore, PAAm concentration, which is in consistence with solution viscosity and surface tension parameter, is found to have the most contribution to forming nanofibers including the finest fiber diameter. On the contrary, the flow rate of solution among the selected parameters shows the least effect on average nanofiber diameter.

In this work, polyacrylamide/multi-walled carbon nanotubes (MWCNT) solution is electrospun to nanocomposite nanofibrous membranes for acetylcholinesterase enzyme immobilization. A new method for enzyme immobilization is proposed, and the results of analysis show successful covalent bonding of enzymes on electrospun membrane surface besides their non-covalent entrapment. Fourier transform infrared spectroscopy, mechanical and thermal investigations of nanofibrous membrane approve successful cross-linking and enzyme immobilization. The enzyme relative activity and kinetic on both pure and nanocomposite membranes is investigated, and the results show proper performance of designed membrane to even improve the enzyme activity followed by immobilization compared to free enzyme. Scanning electron microscopy images show nanofibrous web of 3D structure with a low shrinkage and hydrogel structure followed by enzyme immobilization and cross-linking. Moreover, the important role of functionalized carbon nanotubes on final nanofibrous membrane functionality as a media for enzyme immobilization is investigated. The results show that MWCNT could act effectively for enzyme immobilization improvement via both physical (enhanced fibers’ morphology and conductivity) and chemical (enzyme entrapment) methods. Figure Mechanism for APTS surface modification of nanofibrous nanoweb for enzyme immobilization

Nanofibers have a great potential for enzyme immobilization application due to their large surface area to volume ratio besides their porous structure. In this work, we produce polymethyl methacrylate (PMMA) nanofibers via electrospinning method in dimethylformamide (DMF) as solvent. Thereafter, we employ a chemical method on final PMMA nanofiberous web to covalently immobilize acetylcholinesterase (AChE) enzyme on membrane surface. Morphology and tensile properties of nanofibers are studied as first steps of characterization to make sure of obtaining a properly stable membrane for enzyme carrying application. Thereafter, the stability and activity of immobilized enzymes as two main characteristic parameters are tested and reported for different applications such as biosensor manufacturing.

The proliferation of ubiquitous sensing devices along with advances in low power wireless communication technology have resulted in the extensive use of wireless body area networks (WBANs) as the building blocks of the emerging field of wireless health. In these battery-operated WBANs, the sensor devices are strategically placed in/on the human body and the short/mid/long wireless communications are conducted on/off the surface of the body. As the battery energy does not follow Moore's law, energy-efficiency is always one of the design challenges of wireless health-monitoring systems, impacting usability, security, and cost. The idea of transmission power control (TPC) is to automatically reduce the radio amplifier's output power when the transmission power is more than required. Reduced transmission power translates into more energy savings and reduced interference problems. TPC techniques have been used in abundance in cellular networks and wireless LANs. TPC schemes for WBANs, however, are still in their infancy. For example, current IEEE 802.15.4 specifications do not differentiate between mobile and static settings, thus leaving WBAN transmitters in the dark as to what transmission power level they should utilize. In this dissertation, we have investigated the potential benefits and limitations of TPC as a means to extend the battery lifetime in WBANs at the first three abstraction levels. Physical and MAC layers' approach to TPC perform a local optimization, whereas network layer TPC is capable of a global optimization. At the network layer, we analytically solve an optimization problem whose solution determines an important parameter, i.e., energy-efficient cluster size, for a class of routing/MAC protocols in WBANs. Assuming that the routes are established in an energy-efficient manner, we then experimentally profile the 2.4 GHz on/off-body radio channel under several scenarios regarding mobility states and environments, and we showed that fixed transmission power either wastes energy or hinders reliability. Finally, we devote our attention to an ambulatory medical monitoring WBAN system, which is tied up with different characteristics in terms of mobility, periodicity, and `unforgivingness' of the wireless channel as a result of proximity to the ground as well as to human's body. The target ambulatory WBAN system encompasses a pair of wireless instrumented insoles (known as smart insoles) for gait data collection, plantar pressure monitoring, and gait analysis. We design a sensor-assisted TPC scheme that augments in-network information with information from built-in sensors. To this end, multiple mobility states are defined for the smart insoles and the mobility states are incorporated into transmission power control policies. Available sensor information is leveraged to detect the mobility states, based on which the TPC scheme switches strategies. We validate this new idea of switching transmission power control strategies by implementing and evaluating the sensor-assisted scheme and comparing it against a frame-based TPC scheme, which adjusts the transmit power solely based on recent information about packet transmission successes and failures. Our testbed experiments involving mixed mobility scenarios show that our TPC scheme obtains up to 50% increase in the battery lifetime, enabling the smart insoles to be used in uncontrolled environments. Such an improvement in battery longevity (from 4.0 hours to 7.8 hours) is made by reducing the average energy consumed for communication of a single packet from 4.51 mJ/pkt to 2.27 mJ/pkt. Although designed for the smart insoles as a severely energy-constrained device, the sensor-assisted TPC technique is readily deployable on a variety of today's commodity devices to make a connection between the sensing subsystem and the communication subsystem of such devices. In addition, as the underlying mobility state detection methods place relaxed requirements on how the device should be worn in terms of orientation and position, they can be used for a variety of purposes, such as improving the patient's compliance with medical treatments and therapies.

The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient's OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.

One-layer wound dressings cannot meet all the clinical needs due to their individual characteristics and shortcomings. Therefore, bilayer wound dressings which are composed of two layers with different properties have gained lots of attention. In the present study, polycaprolactone/gelatin (PCL/Gel) scaffold was electrospun on a dense membrane composed of polyurethane and ethanolic extract of propolis (PU/EEP). The PU/EEP membrane was used as the top layer to protect the wound area from external contamination and dehydration, while the PCL/Gel scaffold was used as the sublayer to facilitate cells’ adhesion and proliferation. The bilayer wound dressing was investigated regarding its microstructure, mechanical properties, surface wettability, anti-bacterial activity, biodegradability, biocompatibility, and its efficacy in the animal wound model and histopathological analyzes. Scanning electron micrographs exhibited uniform morphology and bead-free structure of the PCL/Gel scaffold with average fibers’ diameter of 237.3 ± 65.1 nm. Significant anti-bacterial activity was observed against Staphylococcal aureus (5.4 ± 0.3 mm), Escherichia coli (1.9 ± 0.4 mm) and Staphylococcus epidermidis (1.0 ± 0.2 mm) according to inhibition zone test. The bilayer wound dressing exhibited high hydrophilicity (51.1 ± 4.9°), biodegradability, and biocompatibility. The bilayer wound dressing could significantly accelerate the wound closure and collagen deposition in the Wistar rats’ skin wound model. Taking together, the PU/EEP-PCL/Gel bilayer wound dressing can be a potential candidate for biomedical applications due to remarkable mechanical properties, biocompatibility, antibacterial features, and wound healing activities.