The current studies entail systematic quality by design (QbD)-based development of simple, precise, cost-effective and stability-indicating high-performance liquid chromatography method for estimation of ketoprofen. Analytical target profile was defined and critical analytical attributes (CAAs) were selected. Chromatographic separation was accomplished with an isocratic, reversed-phase chromatography using C-18 column, pH 6.8, phosphate buffer-methanol (50 : 50v/v) as a mobile phase at a flow rate of 1.0 mL/min and UV detection at 258 nm. Systematic optimization of chromatographic method was performed using central composite design by evaluating theoretical plates and peak tailing as the CAAs. The method was validated as per International Conference on Harmonization guidelines with parameters such as high sensitivity, specificity of the method with linearity ranging between 0.05 and 250 µg/mL, detection limit of 0.025 µg/mL and quantification limit of 0.05 µg/mL. Precision was demonstrated using relative standard deviation of 1.21%. Stress degradation studies performed using acid, base, peroxide, thermal and photolytic methods helped in identifying the degradation products in the proniosome delivery systems. The results successfully demonstrated the utility of QbD for optimizing the chromatographic conditions for developing highly sensitive liquid chromatographic method for ketoprofen.
Environmental stress and advancing age is considered as the main cause of skin aging. However, environmental stress (especially UV radiations) accelerates the process of skin aging by manifolds. Coenzyme Q10 (CoQ10), an essential compound of cellular bioenergetics also acts as a strong antioxidant and protects the body against aging. High molecular weight and structure specific lipophilic nature of this molecule is a bottle neck in effective delivery through topical route. Preparation of a novel proniosomal (PN) gel formulation of CoQ10 employing systematic design of experiment (DoE) approach is a step ahead in transcending the constraints of the topical delivery. I-optimal mixture design was employed for systematic optimization of proniosomal formulation and evaluation of experimental data was performed for entrapment efficiency and in vitro release. Hydration of PN gel formulation with phosphate buffer (pH 7.5) results in submicron niosomes vesicles of spherical shape, which appeared dark against bright surroundings in TEM study. Animal skin was treated with UV radiations followed by treatment of PN gel CoQ10 and conventional CoQ10 present in a gel base. The effectiveness of the treatment was evaluated on the basis of biochemical estimation and histopathological studies. By using CoQ10 PN gel formulation, levels of superoxide dismutase (SOD), catalase (CA), glutathione (GSH) and total proteins were restored by 81.3%, 72.1%, 74.8 and 77.1%, respectively to that of control group. Histopathological studies revealed better protection of skin treated with CoQ10 PN gel compared to free CoQ10. Prepared PN gel was found undisturbing with the normal histology hence, tolerated by animal skin compare to conventional gel.
Convolutional neural networks (CNNs) based deep learning algorithms require high data flow and computational intensity. For real-time industrial applications, they need to overcome challenges such as high data bandwidth requirement and power consumption on hardware platforms. In this work, we have analyzed in detail the data dependency in the CNN accelerator and propose specific pipelined operations and data organized manner to design a high throughput CNN accelerator on FPGA. Besides, we have optimized the kernel operations to obtain a high power efficiency. The proposed CNN accelerator supports image classification and real-time object detection with high accuracy. The evaluation results show that our CNNbased FPGA accelerator can achieve 740 Giga operations per second (GOPS) at 200 MHz with kernel power of 12.2 watts on Intel Arria 10 FPGA. For object detection tasks, our system can achieve 105 fps with 56.5 mAP or 25 fps with 73.6 mAP on VOC dataset. Since we use the mixed fixed-point data representation, the detection accuracy is comparable with the GPU-based YOLO V2 framework. The power efficiency of our system is ∼ 3.3× better than Titan X GPU and ∼ 418× better than Intel E5-2620 V4 CPU.
Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). Existing state-of-the-art SRAM architectures for AI computing are highly accurate and can provide high throughput. However, these SRAMs have problems that they consume high power and occupy a large area to accommodate complex AI models. A carbon nanotube field-effect transistors (CNFET) device has been reported as a potential candidates for AI devices requiring ultra-low power and high-throughput due to their satisfactory carrier mobility and symmetrical, good subthreshold electrical performance. Based on the CNFET and FinFET device’s electrical performance, we propose novel ultra-low power and high-throughput 8T SRAMs to circumvent the power and the throughput issues in Artificial Intelligent (AI) computation for autonomous vehicles. We propose two types of novel 8T SRAMs, P-Latch N-Access (PLNA) 8T SRAM structure and single-ended (SE) 8T SRAM structure, and compare the performance with existing state-of-the-art 8T SRAM architectures in terms of power consumption and speed. In the SRAM circuits of the FinFET and CNFET, higher tube and fin numbers lead to higher operating speed. However, the large number of tubes and fins can lead to larger area and more power consumption. Therefore, we optimize the area by reducing the number of tubes and fins without compromising the memory circuit speed and power. Most importantly, the decoupled reading and writing of our new SRAMs cell offers better low-power operation due to the stacking of device in the reading part, as well as achieving better readability and writability, while offering read Static Noise Margin (SNM) free because of isolated reading path, writing path, and greater pull up ratio. In addition, the proposed 8T SRAMs show even better performance in delay and power when we combine them with the collaborated voltage sense amplifier and independent read component. The proposed PLNA 8T SRAM can save 96%, while the proposed SE 8T SRAM saves around 99% in writing power consumption compared with the existing state-of-the-art 8T SRAM in FinFET model, as well as 99% for writing operation in CNFET model.
Aggressive technology scaling has inevitably led to reliability becomes a major concern for modern high-speed and high-performance integrated circuits. The major reliability concerns in nanoscale very-large-scale integration design are the time-dependent negative bias temperature instability (NBTI) degradation. Owing to increasing vertical oxide field and higher operating temperature, the threshold voltage of P-channel MOS transistors increases with time under NBTI. This study presents a novel subthreshold Darlington pair-based NBTI degradation sensor under the stress conditions. The proposed sensor provides the high degree of linearity and sensitivity under subthreshold conditions. The Darlington pair used in the circuit provides the stability and the high-input impedance of the circuit makes it less affected by the process variations. Owing to high sensitivity, the proposed sensor is best suited for sensing of temperature variation, process variation, and temporal degradation during measurement. The sensitivity of the proposed sensor at room temperature is 0.239 mV/nA under subthreshold conditions. The proposed sensor is less affected by the process variation and has the maximum deviation of 0.0011 mV at standby leakage current of 30 nA.
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