Recovering vector displacement estimates in quasistatic elastography using sparse relaxation of the momentum equation

We consider the problem of estimating the 2D vector displacement field in a heterogeneous elastic solid deforming under plane stress conditions. The problem is motivated by applications in quasistatic elastography. From precise and accurate measurements of one component of the 2D vector displacement field and very limited information of the second component, the method reconstructs the second component quite accurately. No a priori knowledge of the heterogeneous distribution of material properties is required. This method relies on using a special form of the momentum equations to filter ultrasound displacement measurements to produce more precise estimates. We verify the method with applications to simulated displacement data. We validate the method with applications to displacement data measured from a tissue mimicking phantom, and in-vivo data; significant improvements are noticed in the filtered displacements recovered from all the tests. In verification studies, error in lateral displacement estimates decreased from about 50% to about 2%, and strain error decreased from more than 250% to below 2%.     

Degradation Pathway of Ozone Oxidation of Phenols and Chlorophenols as Followed by LC-MS-TOF

ABSTRACT

This contribution summarizes the temporal intermediates produced during the ozonation of phenol, 2-chlorophenol, 4-chlorophenol and 2,4-dichlorophenol, as followed by liquid chromatography coupled to a time of flight mass spectrometer (LC-MS-TOF). A 2 × 10^?3 M solution of the phenolic compounds was ozonated in a sintered glass reactor at an ozone dose of 0.14 mg/min (O₂/O₃ flow 10 mL/min). Identified intermediates showed that catechol pathway was the predominant route for phenol oxidation with acrylic acid being the end product. Hydroxylative dechlorination of 2-chlorophenol also gave catechol, and the reaction products were similar to that of phenol. Hydroxylation and ring opening via the 4-chlorocatechol pathways were the predominant route for the ozonation of 4-chlorophenol, while 2,4-dichlorophenol followed both hydroxylation and hydroxylative dechlorination mechanism. Several novel intermediates and coupling products were identified and reaction schemes leading to breakdown products are provided for each phenolic compound.     

Functional properties of protein concentrates and isolates produced from cashew (Anacardium occidentale L.) nut

Protein isolates and concentrates were obtained from defatted cashew nut powder by two methods: alkaline extraction-isoelectric precipitation (IP) and alkaline extraction-methanol precipitation (MP). The functional properties of cashew nut protein isolates, concentrates and powder were significantly different (p < 0.05). Cashew nut protein isolate (CNPI) had higher water and oil absorption capacities (2.20 ml/g and 4.42 ml/g, respectively), emulsifying stability index (447%), foam capacity and stability (45% and 55%, respectively), and least gelation capacity (13.5%) than cashew nut protein concentrate (CNPC), which was also higher than that of defatted cashew nut powder (DCNP). However, emulsifying activity index (12.45%) and bulk density (0.31) of CNPI were lower than that of CNPC, which were also lower than that of DCNP. The water solubility of CNPI (95%) and CNPC (95%) was not significantly different (p > 0.05) among the samples, but was significantly different (p < 0.05) from that of DCNP (75%). The CNPI, CNPC and DCNP showed decreasing solubility with decreasing pH, with the minimum solubility being observed at a pH range of 4.0–4.5, confirming the isoelectric point of cashew proteins. However, higher water solubility, emulsifying activity, and foaming property were observed at an alkaline pH than at an acidic pH in all samples.     

Parametric studies of mixed convective fluid flow around cylinders of different cross-sections

A numerical study of mixed convective heat transfer in a lid-driven square enclosure containing a hot elliptic cylinder is conducted. The impacts of the Grashof number $({10}^3\le \mathit{Gr}\le 10^6)$, Reynolds number $(1.0\le Re\le 100)$, cylinder tilt angle $(0^{°}\le \varphi \le {90}^{°})$, and aspect ratio $(1.0\le AR\le 3.0)$ have been examined for a fluid of $Pr$ of 0.71. The horizontal enclosure walls are insulated, while its vertical walls are restricted to a nonvarying temperature T_c, whereas a sinusoidal temperature of $T_h+∆T\sin (\pi x/L)$ is imposed on the wall of the elliptical cylinder. The governing equations are solved using COMSOL Multiphysics 5.6 software. The fluid dynamic and the heat transport profiles between the enclosure and the elliptical cylinder walls are represented by the stream function, isothermal contours, and average Nusselt number. Results established that for all the considered aspect ratios, the thermal heating range of ${10}^3\le \mathit{Gr}\le 10^4$ is predominantly a conduction mechanism. The critical position of the ellipse where the inclination effect becomes insignificant is determined by the Grashof number and aspect ratio when the Re = 100. The strength of vortices and cell numbers are significantly influenced by the aspect ratio, particularly when the $\mathit{Gr}=10^4$. When $AR=1.0$, the average heat transfer from the cylinder remains the same regardless of the cylinder's orientation. The impact of cylinder orientation on heat transfer from the cylinder wall is minimal for $1.5\le AR\le 2.0$. For AR values of $2.5\le AR\le 3.0$, increasing the inclination angle does not result in improved heat transfer. The influence of the increasing inclination angle on the right wall diminishes as the angle increases, except when the Grashof number is greater than 10⁵, where the rate of heat transfer is enhanced for inclination angles beyond 45°.     

A lightweight deep learning with feature weighting for activity recognition

With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state-of-the-art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models proposed to address this have often recorded high overall recognition accuracy, while low recall and precision are often recorded on some individual activities due to the complexity of their patterns. Some existing models that have focused on tackling these issues are always bulky and complex. Since most embedded systems have resource constraints in terms of their processor, memory and battery capacity, it is paramount to propose efficient lightweight activity recognition models that require limited resources consumption, and still capable of achieving state-of-the-art recognition of activities, with high individual recall and precision. This research proposes a high performance, low footprint deep learning model with a squeeze and excitation block to address this challenge. The squeeze and excitation block consist of a global average-pooling layer and two fully connected layers, which were placed to extract the flattened features in the model, with best-fit reduction ratios in the squeeze and excitation block. The squeeze and excitation block served as channel-wise attention, which adjusted the weight of each channel to build more robust representations, which enabled our network to become more responsive to essential features while suppressing less important ones. By using the best-fit reduction ratio in the squeeze and excitation block, the parameters of the fully connected layer were reduced, which helped the model increase responsiveness to essential features. Experiments on three publicly available datasets (PAMAP2, WISDM, and UCI-HAR) showed that the proposed model outperformed existing state-of-the-art with fewer parameters and increased the recall and precision of some individual activities compared to the baseline, and the existing models.     

Numerical investigation of mixed convection of non-Newtonian fluid in a vented square cavity with fixed baffle

This paper numerically investigates mixed convective heat transfer in a vented square cavity incorporated with a baffle that is subjected to external non-Newtonian fluids (NNFs). Adiabatic conditions are imposed on the top and bottom walls, while cold temperature conditions are applied to the right and left solid boundaries. Heated NNF enters the cavity through the inlet and goes out through the outlet at three different locations, and it passes on a vertical baffle fixed at the base placed at different lengths. To examine the impact of the inlet and outlet positions, three different shapes of the outlet port located on the right wall and the inlet port on the left bottom wall were investigated. The impacts of Reynolds number (Re) of 100 ≤ Re ≤ 1000, Richardson number (Ri) of 0.1 ≤ Ri ≤ 3, power law index (n) of 0.6 ≤ n ≤ 1.4, length of baffle (L_b) of 0.2 ≤ L_b ≤ 0.6 and the outlet hole positions (S) of $0\le S\le 0.9$ on the thermal and flow distributions in the cavity are taken into consideration in this paper. The results demonstrated that the flow's intensity and heat transfer increase with improvement in the Re and n at any baffle length. When the Ri increased from 0.1 to 3, $N{u}_{\mathrm{avg}}$ increased by 23.3% at $n=0.6$, and 13.8% at $n=1.2$. Also, the Ri increment results in the augmentation of the average heat transfer.     

Synthesis of Reduced Graphene Oxide/Copper Tin Sulfide (Cu2SnS3) Composite for the Photocatalytic Degradation of Tetracycline

Reduced graphene oxide (rGO)-copper tin sulfide (Cu₂SnS₃), (rGO-CTS), composites were successfully synthesized through a facile ex-situ process and the obtained composites were utilized as photocatalysts for the degradation of tetracycline (TCE) under a UV-LED irradiation. Physicochemical and morphological characterization of the composites confirmed the incorporation of CTS unto rGO. The optical study of the composites, using absorption spectroscopy, showed a red shift to lower energy with increase in the percentage of rGO in the composites. This reduction in band gap suggests a possible enhancement in photocatalytic potency due to enhanced charge carrier generation. The photocatalytic degradation study showed an increase in TCE degradation with increase in rGO content of the composite. This enhanced photocatalytic activity could be ascribed to: (i) enhanced adsorption properties due to the increased presence of oxygenated functional groups on rGO, and (ii) increased charge carrier generation and separation due to modification of the band edge potentials of the composites by the incorporation of rGO. Radical scavenging studies of the degradation process showed that photogenerated holes played the most significant role in the degradation process. A plausible mechanism was proposed for the degradation process based on the radical scavenging experiment and charge carrier characteristics of the catalyst.