Effect of Accelerated Laboratory Aging on Selected Electrical Properties of Medium-voltage Heat Shrink Cable Joints

Cross linked polyethylene (XLPE) cables are being used extensively in high-voltage power transmission and distribution networks around the world. However, such joints suffer high failure rates in extreme weather conditions. To investigate this issue, selected properties of 15-kV heat shrinkable XLPE cable joints produced by four manufacturers that are widely being used by electric power utilities in Saudi Arabia were evaluated before and after accelerated multi-factor aging. Aging stresses included high voltage, temperature cycling, and periodic applications of switching impulses while keeping joints in air as well as in water. Partial discharge characteristics, dissipation factor at 60 and 0.01 Hz, and breakdown voltages were measured for un-aged and aged joint samples. The results show that partial discharge characteristics supplemented with a 0.01-Hz dissipation factor provide useful diagnostic information about the condition of such joints. Moreover, these properties also provide useful information about the comparative performance of joints produced by different manufacturers.     

Underlap channel metal source/drain SOI MOSFET for thermally efficient low-power mixed-signal circuits

In this paper it has been shown that employing an underlap channel created by varying the lateral doping straggle in dopant-segregated Schottky barrier SOI MOSFET not only improves the scalability but also suppresses the self-heating effect of this device. Although in strong inversion region the reduced effective gate voltage due to voltage drop across the underlap lengths reduces the drive current, in weak/moderate inversion region defined at I_D=5 μA/μm and V_DS=0.5 V the analog figures of merit such as transconductance, transconductance generation factor and intrinsic gain of the proposed underlap device are improved by 15%, 35% and 20%, respectively over the conventional overlap channel structure. In addition to this, at V_DD=0.5 V the gain-bandwidth product in a common-source amplifier based on proposed underlap device is improved by ～20% over an amplifier based on the conventional overlap channel device. The mixed-mode device/circuit simulation results of CMOS inverter, NAND and the NOR gates based on these devices also show that at V_DD=0.5 V the switching energy, static power dissipation and the propagation delay in the case of proposed underlap device are reduced by ～10%, ～35% and ～25%, respectively, over the conventional overlap device. Thus, significant improvement in analog figures of merit and the reduction in digital design metrics at lower supply voltage show the suitability of the proposed underlap device for low-power mixed-signal circuits.     

A generic low-noise CMOS readout interface for 64?×?64 imaging array with on-chip ADC

This paper presents a low-noise and high dynamic-range CMOS readout-IC (ROIC) for a 64?×?64 array of opto-electrical sensors. The readout chain comprises a pixel preamplifier array, correlated-double-sampling based switched-capacitor gain blocks, class-AB output buffer for driving off-chip loads and a 12-bit pipeline ADC for on-chip digitization. The pixel preamplifiers array, occupying an area of 30???m?×?30???m per pixel, can either be hybridized to a separate IR or UV sensor or can be used as monolithic visible-light active CMOS pixel-array after exposing (by etching the pad) the embedded photodiode under the bonding pads. The ROIC is designed and fabricated in 0.25???m 1P/5?M CMOS technology with 5?mm?×?5?mm of total dimensions. The integrated readout chain, in integrate-then-read mode, demonstrates a dynamic range of 72?dB for electrically emulated sensor currents from 25?pA to 100?nA. It can support a frame rate of 700?fps, with single fully-differential analog as well as 12-bit digital output, at 10?MHz while consuming 17?mW with on-chip biases.     

Nano-crystalline LaFeO3 powders synthesized by the citrate–gel method

A novel sol–gel process was developed for preparing nano-sized, perovskite-type LaFeO₃ powder by the thermal decomposition of the gel-complex of LaFe–(C₆H₈O₇·H₂O). The structural evolution has been systematically investigated by X-ray diffraction (XRD), differential thermal analysis (DTA) and thermogravimetric analysis (TGA). Perovskite powder of  25 nm size could be obtained at a temperature of  600 °C without formation of any secondary phases of La₂O₃ and Fe₂O₃ single oxides and no requirements of high temperature/vacuum/pH control etc. Analysis of the X-ray powder diffraction data showed a decrease in the value of lattice strains with increasing decomposition temperature, whereas the particle size increases with increasing decomposition temperature.     

A comprehensive design and rating study of evaporative coolers and condensers. Part II. Sensitivity analysis

Sensitivity analysis can be used to identify important model parameters, in particular, normalized sensitivity coefficients; by allowing a one-on-one comparison. Regarding design of evaporative coolers, the sensitivity analysis shows that all sensitivities are unaffected by varying the mass flow ratio and that outlet process fluid temperature is the most important factor. In rating evaporative coolers, effectiveness is found to be most sensitive to the process fluid flow rate. Also, the process fluid outlet temperature is most sensitive to the process fluid inlet temperature. For evaporative condensers, the normalized sensitivity coefficient values indicate that the condensing temperature is the most sensitive parameter and that these are not affected by the value of the mass flow ratio. For evaporative condenser design, it was seen that, for a 53% increase in the inlet relative humidity, the normalized sensitivity of the surface area increased 1.8 times in value and, for a 15 °C increase in the condenser temperature, the sensitivity increased by 3.5 times. The performance study of evaporative condensers show that, for a 72% increase in the inlet relative humidity, the normalized sensitivity coefficient for effectiveness increased 2.4 times and, for a 15 °C increase in the condenser temperature, it doubled in value.     

Hydrothermal Synthesis of β-Nb2ZnO6 Nanoparticles for Photocatalytic Degradation of Methyl Orange and Cytotoxicity Study

β-Nb₂ZnO₆ nanoparticles were synthesized by a hydrothermal process and calcined at two temperatures, 500 °C and 700 °C, and assigned as A and B, respectively. X-ray diffraction, together with transmission electron microscopy, revealed that the β-Nb₂ZnO₆ nanoparticles calcined at 700 °C (B) were more crystalline than the β-Nb₂ZnO₆ calcined at 500 °C (A) with both types of nanoparticles having an average size of approximately 100 nm. The physiochemical, photocatalytic, and cytotoxic activities of both types of β-Nb₂ZnO₆ nanoparticles (A and B) were examined. Interestingly, the photodegradation of methyl orange, used as a standard for environmental pollutants, was faster in the presence of the β-Nb₂ZnO₆ nanoparticles calcined at 500 °C (A) than in the presence of those calcined at 700 °C (B). Moreover, the cytotoxicity was evaluated against different types of cancer cells and the results indicated that both types of β-Nb₂ZnO₆ nanoparticles (A and B) exhibited high cytotoxicity against MCF-7 and HCT116 cells but low cytotoxicity against HeLa cells after 24 and 48 h of treatment. Overall, both products expressed similar EC₅₀ values on tested cell lines and high cytotoxicity after 72 h of treatment. As a photocatalyst, β-Nb₂ZnO₆ nanoparticles (A) could be utilized in different applications including the purification of the environment and water from specific pollutants. Further biological studies are required to determine the other potential impacts of utilizing β-Nb₂ZnO₆ nanoparticles in the biomedical application field.     

MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into binary classes, i.e., Normal and Pneumonia. The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models: MobileNet, DenseNet-201, EfficientNet-B0, and VGG-16, which have been finetuned and trained on 5,856 CXR images. The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision, accuracy, recall, AUC-roc, and f1-score. The model effectively decreases training loss while increasing accuracy. The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%, an accuracy of 92.15%, a recall of 90.90%, an auc-roc score of 90.9%, and f-score of 91.49% with minimal data pre-processing, data augmentation, finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.     

Investigating the nonlinear and conditional effects of trust—The new role of institutional contexts in online repurchase

Trust is paramount to developing and maintaining long-term relationships in all stages of the customer lifecycle, including the repurchase stage. This research goes beyond the simple finding documented in the extant trust literature that the effect of trust will diminish. It sheds light on the role of institutional contexts and develops a nuanced understanding of the boundary conditions under which trust operates in the repurchase stage, where knowledge-based trust becomes more predominant. Drawing on a different theoretical tenet, prospect theory, we find that customers exhibit distinctively different transaction intentions in the two perceptual conditions of high and low trust in institutional contexts. Specifically, the nonlinear relationship between trust and repeat online transaction intention is inverted U-shaped curvilinear when trust in institutional contexts is high, but is U-shaped when trust in institutional contexts is low. With data collected from both e-commerce and mobile banking contexts using two different measures of institutional contexts, we employed a new and advanced latent moderated structural (LMS) equations approach for analysis and provided robust results. Our findings largely confirm the hypotheses and offer theoretical, methodological, and practical implications.     

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR de-noising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.