Physics-informed UNets for Discovering Hidden Elasticity in Heterogeneous Materials

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance, both in terms of accuracy and computational cost, by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.Comment: 25 pages, 9 figure

Preparation of nanodiamonds from carbon nanoparticles at atmospheric pressure.

A route for producing diamond nanocrystals is reported in this paper. Li2CO3 containing carbon nanostructures synthesised in molten LiCl were transformed to nanodiamonds by simple heating at atmospheric pressure, far less severe conditions than conventional processes. The method presented offers the possibility of bulk production.This is the author accepted manuscript. The final version is available from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C5CC00233

Large-scale preparation of graphene by high temperature insertion of hydrogen into graphite.

Experimental evidence for high temperature diffusion of hydrogen into the interlayer space of graphite is provided. This process is discussed as a possible method for the rapid production of high-quality, inexpensive graphene in large quantities, which could lead to the widespread application of graphene. It was found that hydrogen cations, dissolved in molten LiCl, can be discharged on cathodically polarized graphite rods, which then intercalate into the graphite structure, leading to the peeling of graphite to produce graphene. The graphene nanosheets produced displayed a single-crystalline structure with a lateral size of several hundred nanometers and a high degree of crystallinity and thermal stability. The method introduced could be scaled up to produce industrial quantities of high-quality graphene.This is the author accepted manuscript. The final version is available from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C5NR01132

Comparison of effects of propofol and ketofol (Ketamine-Propofol mixture) on emergence agitation in children undergoing tonsillectomy

Background: The aim of this study was to compare the effect of propofol and ketofol (ketamine-propofol mixture) on EA in children undergoing tonsillectomy.Method: In this randomized clinical trial, 87 ASA class I and II patients, aged 3-12 years, who underwent tonsillectomy, were divided into two groups to receive  either propofol 100 µg/kg/min (group p, n=44) or ketofol : ketamine 25 µg/kg/min + propofol 75 µg/kg/min (group k, n= 43). Incidence and severity of EA was evaluated using the Pediatric Anesthesia Emergence Delirium (PAED) scales on arrival at the recovery room, and 10 and 30 min after that time.   Results: There was no statistically significant difference in demographic data between the two groups. In the ketofol group, the need for agitation treatment and also mean recovery duration were lower than in the propofol group (30 and 41%, and 29.9 and 32.7 min), without statistically significant difference (P value=0.143 and P value=0.187). Laryngospasm or bronchospasm occurred in 2 patients in each group and bleeding was observed in only one individual in the ketofol group.Conclusion: Infusion of ketofol in children undergoing tonsillectomy provides shorter recovery time and lower incidence of EA despite the non significant difference with propofol.Keywords:  Emergence agitation, ketofol, propofol

A unit-based symbolic execution method for detecting memory corruption vulnerabilities in executable codes

Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound and complete in theory, it faces some challenges, such as path explosion, when applied to real-world complex programs. In this paper, we present a method for improving the efficiency of symbolic execution and detecting four classes of memory corruption vulnerabilities in executable codes, i.e., heap-based buffer overflow, stack-based buffer overflow, use-after-free, and double-free. We perform symbolic execution only on test units rather than the whole program to avoid path explosion. In our method, test units are considered parts of the program's code, which might contain vulnerable statements and are statically identified based on the specifications of memory corruption vulnerabilities. Then, each test unit is symbolically executed to calculate path and vulnerability constraints of each statement of the unit, which determine the conditions on unit input data for executing that statement or activating vulnerabilities in it, respectively. Solving these constraints gives us input values for the test unit, which execute the desired statements and reveal vulnerabilities in them. Finally, we use machine learning to approximate the correlation between system and unit input data. Thereby, we generate system inputs that enter the program, reach vulnerable instructions in the desired test unit, and reveal vulnerabilities in them. This method is implemented as a plugin for angr framework and evaluated using a group of benchmark programs. The experiments show its superiority over similar tools in accuracy and performance

Using curcumin to prevent structural and behavioral changes of medial prefrontal cortex induced by sleep deprivation in rats

Sleep Deprivation (SD) is known to result in a range of neurological consequences in chronically-afflicted subjects. Curcumin, a natural substance, has neuroprotective properties. This study aimed to evaluate the effects of curcumin on the medial Prefrontal Cortex (mPFC) of SD rats. Male rats were arbitrarily assigned to nine groups, including control, curcumin (100 mg/kg/day), olive oil, SD, SD+curcumin, SD+olive oil, grid, grid+curcumin, and grid+ol- ive oil groups. SD was induced by a multiplatform box containing water. After a period of 21 days, the learning and memory of the rats were tested in an eight-arm radial maze. Afterwards, their brains were evaluated using stereological methods. Concomitant treatment of curcumin during SD caused fewer errors during evaluation of the working and reference memory errors in the acquisition and retention phases. The overall volume of the mPFC, Infralimbic Cortex (ILC), Prelimbic Cortex (PLC), Anterior Cingulate Cortex (ACC) and the total number of neurons and glial cells reduced by 20 %-40 % on average in the SD animals in comparison to the control group. This indicated atrophic changes and cell loss in these areas (p < 0.01). The dendrites’ length and the number of spines per dendrite also reduced by 35 %-55 % in the SD rats compared to the ones in the control group (p < 0.01). Yet, treatment of the SD animals with curcumin prevented the atrophic changes of the mPFC, cell loss, and den- dritic changes (p < 0.05). SD induced structural changes in the mPFC and memory impairment in the rats. However, curcumin could protect their PFC

Effect of Temperature on Demographic Parameters of the Hawthorn Red Midget Moth, Phyllonorycter corylifoliella, on Apple

The hawthorn red midget moth, Phyllonorycter corylifoliella (Hübner) (Lepidoptera: Gracillariidae), is one of the most serious pests of apple and pear orchards in Iran, however little is known about its biology and relationship with environmental factors. The reproduction and population growth parameters of P. corylifoliella were examined at six constant temperatures (15, 20, 25, 30, 33 and 35° C) on apple var. golden delicious. At 35° C, P. corylifoliella failed to develop beyond the first instar. The lowest (13%) and highest (64%) mortality rates of immature stages occurred at 25 and 33° C, respectively. The life expectancies (ex) decreased with increasing of age and the life expectancies of one-day-old larvae were estimated to be 38.68, 33.34, 35.11, 26.28 and 16.11 days at 15, 20, 25, 30 and 33° C, respectively. The highest intrinsic rate of natural increase (rm), net reproductive rate (Ro) and finite rate of increase (λ) at 25° C were 0.100 ± 0.003, 47.66 ± 5.47 and 1.11 ± 0.00, respectively. The mean generation time (T) decreased with increasing temperatures from 86.86 ± 0.53 days at 15° C to 33.48 ± 0.16 days at 30° C. Doubling time (DT) varied significantly with temperature and the shortest doubling time was obtained at 25° C. The results of this study provide direction for future research on evaluating the performance of P. corylifoliella and the efficiency of its natural enemies in apple orchards under variable environmental conditions