Effects of variable resistance on smart structures of cubic reconnaissance satellites in various thermal and frequency shocking conditions

Piezoelectric materials are widely used as smart structures in cubic reconnaissance satellites because of their sensing, actuating, and energy-harvesting abilities. In this study, an analytical model is developed in specific mechanical thermal shocking conditions. A special circuit and apparatus is designed for experimentation on the basis of the inverse piezoelectric effect. An equivalent circuit method is used to establish the relationship between the resistance and peak-to-peak voltage of lead zirconate titanate used as smart materials for cubic reconnaissance satellites. Various frequencies and resistance were applied in different mechanical thermal shocking conditions. Moreover, numerical simulations are conducted in various mechanical loading conditions to determine the accumulative effect. The model provides a novel mechanism to characterize the smart structures in cubic reconnaissance satellites. A rise in temperature increases peak-to-peak voltage; a rise in frequency decreases peak-to-peak voltage; and intensified resistance decreases peak-to-peak voltage. Based on experimentation and simulation, the optimum resistance is predicted for the various frequencies and temperatures. The various conditions may correspond to the different applications of smart structures for cubic reconnaissance satellites. The analytical calculations are in good agreement with experimental and numerical calculations. © 2017, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Generation of electrical energy using lead zirconate titanate (PZT-5A) piezoelectric material: Analytical, numerical and experimental verifications

Energy harvesting is the process of attaining energy from the external sources and transforming it into usable electrical energy. An analytical model of piezoelectric energy harvester has been developed to determine the output voltage across an electrical circuit when it is forced to undergo a base excitation. This model gives an easy approach to design and investigate the behavior of piezoelectric material. Numerical simulations have been carried out to determine the effect of frequency and loading on a Lead zirconate titanate (PZT-5A) piezoelectric material. It has been observed that the output voltage from the harvester increases when loading increases whereas its resonance frequency decreases. The analytical results were found to be in good agreement with the experimental and numerical simulation results

An area-optimized N-bit multiplication technique using N/2-bit multiplication algorithm

A unique design for an optimized N-bit multiplier is proposed and implemented which utilizes a modified divide-and-conquer technique. The conventional technique requires four N/2-bit multipliers to perform N-bit multiplication, whereas the proposed design uses only one multiplier module in hardware to perform the functionality of four modules. It uses Dadda algorithm in its multiplier module. It has been implemented using Verilog HDL, and a good accuracy of results was observed in simulations which effectively verify its functionality. Design was also synthesized on various FPGAs including Spartan 3E, Virtex-5 and Virtex-7. Performance summary, after place and route, showed that the proposed approach significantly reduces hardware utilization. Furthermore, the proposed design is almost 75% more efficient in terms of resources utilization and operating frequency as compared to the conventional design

DO DIFFERENT SECTORS AFFECT EQUITY RISK PREMIUMS IN EMERGING MARKETS? EVIDENCE FROM ASIA

This paper explores intricacies of the higher equity risk premia of emerging Asian economies within the context of industrial composition. The conventional ex-post empirical analysis is executed to scrutinize the impact of industries on the country's stock performance, diverging from the contemporary literature on finance, which was restricted to "total market indexes." By utilizing the DataStream’s Total Return Indices (TRIs) data of emerging market industries, this study highlights the contribution of various industries towards higher equity risk premiums across prominent Asian emerging countries. The study unearths several salient empirical findings. Primarily, the study confirms the "high-volatile highperformance nature" in conjunction with the time-varying dynamics of excess returns for emerging markets at the industry level. Secondly, the study's findings identify the industries accountable for the most significant contribution to higher stock premia of emerging markets at both the country and dynamic context levels. Thirdly, we observe that certain industries demonstrate greater exposure to global factors than others. It is, therefore, argued that these observations provide a crucial indication for international portfolio diversification. The investigation of diversification opportunities due to the impact of global factors on country indexes, and the existence of some industries that offer little but advantageous insurance components provide valuable insights for the higher equity premia of emerging markets. The overall study findings suggest that foreign portfolio investors must not only diversify across countries but also across industries to generate augmented returns in emerging stock markets

Hematology and serum chemistry reference values of stray dogs in Bangladesh

Hematology and serum chemistry values were obtained from 28 male and 22 female stray dogs in Chittagong Metropolitan area, Bangladesh. The goal of the study was to establish reference value for hematology and serum chemistry for these semi wild animals in relation to age, sex, reproductive stage and body condition. No significant differences were found for mean values of hemoglobin, packed cell volume, mean corpuscular hemoglobin concentration, white blood cell, differential leukocyte count, total protein, albumin, glucose, cholesterol, phosphorus and potassium among or between sexes, ages, reproductive states or body conditions. Significant differences were noted for erythrocyte sedimentation rate (p<0.02) between sexes. Among different age groups significant differences were found for total red blood cell count (p<0.001). Different body conditions have significant differences in red blood cell count, mean corpuscular volume and mean corpuscular hemoglobin (p<0.001). Pregnant and non-pregnant females differed significantly in their red blood cell count, mean corpuscular volume and mean corpuscular hemoglobin (p<0.001)

The chemical composition and in vitro digestibility evaluation of almond tree (Prunus dulcis D. A. Webb syn. Prunus amygdalus; var. Shokoufeh) leaves versus hulls and green versus dry leaves as feed for ruminants

The current study aimed to evaluate the chemical composition and in vitro digestibility of almond tree (Prunus dulcis D. A. Webb syn. Prunus amygdalus; var. Shokoufeh) leaves versus hulls, and green versus dry leaves as feed for ruminants. The fresh green almond hulls (GAH) and leaves (GAL) were harvested and spread under a shade to dry. Dry almond leaves (DAL) were collected from under the trees where as dry almond hulls (DAH) were collected 4 weeks after harvesting the fresh samples. The chemical composition of substrates was determined using standard approaches and the metabolisable energy (ME), in vitro dry matter (DMD) and in vitro organic matter (OMD) digestibility were measured using the in vitro gas production (GP) technique. The GAL contained 81 g crude protein (CP) kg-1 DM while DAH contained 103 g CP kg-1 DM. The CP was higher (P = 0.0003) in dry (leaves and hulls) than in green (leaves and hulls) samples. The ash content ranged from 99.2 to 181.5 g kg-1 DM in DAH and DAL, respectively, (P = 0.0041). The ether extract content ranged from 27 for DAH to 65 g kg-1 for DAL (P = 0.0018). The acid detergent fibre and neutral detergent fibre content ranged from 185 to 304 and 444 to 620 g kg-1 DM (P = 0.04), for GAL and DAH, respectively. The DAH had the highest (P = 0.0001) GP24 and GP96. The DAH had the highest (P = 0.0001) potential GP (i.e., b), while the GP rate was highest for GAL and GAH (P = 0.034), ME was highest for DAH (P = 0.0001), and in vitro OMD was highest for DAH (P = 0.0001). The highest DMD (P = 0.0001) values were obtained with DAH followed by GAL, DAL and GAH, respectively. It can be concluded that almond hulls and leaves have a good nutritional potential to cover the maintenance nutrient requirements of small ruminants. Almond hulls and leaves can also be used as supplement to low quality mature pasture and/or crop residues. However, more studies are warranted to better characterize these feeds in in vivo animal feeding trials

The chemical composition and in vitro digestibility evaluation of almond tree (Prunus dulcis D. A. Webb syn. Prunus amygdalus; var. Shokoufeh) leaves versus hulls and green versus dry leaves as feed for ruminants

Almond, (Prunus dulcis D. A. Webb syn. Prunus amygdalus) is a species belonging to the Rosaceae family (Sfahlan et al. 2009). The state of California in the USA is the major producer of almond; however, its production is widely distributed (Wijerante et al. 2006) and there is increasing interest to produce almond and its by-products worldwide. Approximately 2,112,815 metric ton of almonds with shell is produced globally with Iran contributing about 110,000 metric ton (FAO 2007). Almond is a midsize tree with fruit that can grow up to ten meters tall (Chen et al. 2010). The fruit is made of hulls, shell and kernel. Drying almond hulls results in approximately (kg-1 DM) 250 g nut, 500 g hulls and 250 g shell (Aguilar et al. 1984; Fadel 1999).The current study aimed to evaluate the chemical composition and in vitro digestibility of almond tree (Prunus dulcis D. A. Webb syn. Prunus amygdalus; var. Shokoufeh) leaves versus hulls, and green versus dry leaves as feed for ruminants. The fresh green almond hulls (GAH) and leaves (GAL) were harvested and spread under a shade to dry. Dry almond leaves (DAL) were collected from under the trees where as dry almond hulls (DAH) were collected 4 weeks after harvesting the fresh samples. The chemical composition of substrates was determined using standard approaches and the metabolisable energy (ME), in vitro dry matter (DMD) and in vitro organic matter (OMD) digestibility were measured using the in vitro gas production (GP) technique. The GAL contained 81 g crude protein (CP) kg-1 DM while DAH contained 103 g CP kg-1 DM. The CP was higher (P = 0.0003) in dry (leaves and hulls) than in green (leaves and hulls) samples. The ash content ranged from 99.2 to 181.5 g kg-1 DM in DAH and DAL, respectively, (P = 0.0041). The ether extract content ranged from 27 for DAH to 65 g kg-1 for DAL (P = 0.0018). The acid detergent fibre and neutral detergent fibre content ranged from 185 to 304 and 444 to 620 g kg-1 DM (P = 0.04), for GAL and DAH, respectively. The DAH had the highest (P = 0.0001) GP24 and GP96. The DAH had the highest (P = 0.0001) potential GP (i.e., b), while the GP rate was highest for GAL and GAH (P = 0.034), ME was highest for DAH (P = 0.0001), and in vitro OMD was highest for DAH (P = 0.0001). The highest DMD (P = 0.0001) values were obtained with DAH followed by GAL, DAL and GAH, respectively. It can be concluded that almond hulls and leaves have a good nutritional potential to cover the maintenance nutrient requirements of small ruminants. Almond hulls and leaves can also be used as supplement to low quality mature pasture and/or crop residues. However, more studies are warranted to better characterize these feeds in in vivo animal feeding trials