Miniaturized Ka-Band Dual-Channel Radar

Smaller (volume, mass, power) electronics for a Ka-band (36 GHz) radar interferometer were required. To reduce size and achieve better control over RFphase versus temperature, fully hybrid electronics were developed for the RF portion of the radar s two-channel receiver and single-channel transmitter. In this context, fully hybrid means that every active RF device was an open die, and all passives were directly attached to the subcarrier. Attachments were made using wire and ribbon bonding. In this way, every component, even small passives, was selected for the fabrication of the two radar receivers, and the devices were mounted relative to each other in order to make complementary components isothermal and to isolate other components from potential temperature gradients. This is critical for developing receivers that can track each other s phase over temperature, which is a key mission driver for obtaining ocean surface height. Fully hybrid, Ka-band (36 GHz) radar transmitter and dual-channel receiver were developed for spaceborne radar interferometry. The fully hybrid fabrication enables control over every aspect of the component selection, placement, and connection. Since the two receiver channels must track each other to better than 100 millidegrees of RF phase over several minutes, the hardware in the two receivers must be "identical," routed the same (same line lengths), and as isothermal as possible. This level of design freedom is not possible with packaged components, which include many internal passive, unknown internal connection lengths/types, and often a single orientation of inputs and outputs

Internal evaluation of speech therapy department of Semnan University of Medical Sciences

Introduction: Using students' view for teacher evaluation is a common method. This study was designed to investigate the view of faculties and medical students about faculty teaching experiences. Materials and Methods: 100 medical students and 35 faculties from Jahrom University of Medical Sciences were participated in this study. Two separate questionnaires were designed for this purpose and after determining validity and reliability completed by teachers and students. Results: 70.9 percent of faculties reported that they are satisfied with evaluation by students. 48.6 percent of them reported that feedback from this evaluation improved their teaching. 48.8 percent of them thought that some students behave spitefully. 60 percent reported self assessments as a useful method for evaluation of their own teaching. The majority of medical students (76.6%) reported that teachers' communication skills are one of the important factors in teacher evaluation. 67.4 percent of them reported that they completed the teachers' evaluation forms carefully and 60.9 percent of them asserted that teachers, who take difficult examinations, have lower grades in evaluation forms. Conclusion: In general, most teachers are agreed with teacher evaluation. Since students' opinion about their teachers is influenced by some factors which have no close relationship with the evaluation subject and is merely related to other factors, using other evaluation methods such as self evaluation and peer evaluation seems to be necessary. In addition, we should establish a single national and standard method for teacher evaluation all over the country

Process-Property Linkages Construction for Inkjet Printing with Machine Learning

Printed electronics are emerging technologies that can potentially revolutionize the manufacturing of electronic devices. One promising technology for printed electronics is inkjet printing. Inkjet printing offers both low-cost processing and high resolution. Being a subset of additive manufacturing, inkjet printing minimizes waste and is compatible with a wide range of inks. However, inkjet printing of electronic devices is still in its infancy. One major challenge for inkjet printing is the complexity of the process optimization and uncertain high throughput production. To achieve a high-quality print, there is a complex parameter space of materials and processing parameters that needs to be optimized. To address this challenge, in this thesis work, we develop a machine learning algorithm to connect the processing parameters to print morphology for inkjet processes. To achieve this goal, we developed more than 200 experimental samples and processed the print images automatically with OpenCV-based codes. Finally, we correlated the morphology specifications, i.e., print line width, overspray, and roughness to the processing parameters, i.e., cartridge height, nozzle voltage, and drop spacing, via a neural network model. The order of machine learning model accuracy from high to low is for line width, roughness, and overspray, respectively. The model\u27s low predictability of overspray can be attributed to our limited dataset, Dimatix unreliable performance, or the low dependency of overspray on the processing parameters of this study

Placenta previa after prior abortion: a meta-analysis

There is controversy regarding the role of prior abortion on placenta previa in subsequent pregnancies. We conducted an updated, comprehensive meta-analysis of placenta previa after prior abortion. The search was conducted from PubMed, Web of Science and Scopus databases from the database inception to January 31, 2017. The heterogeneity across studies was evaluated by Q-test and I2 statistical test. Publication bias was assessed by Begg's test and Egger's test. Results of odds ratio (OR) estimates with their corresponding 95% confidence intervals (CI) were pooled using random-effects modeling. The literature search included 872 articles up until January 2017 with 2,134,529 participants. Based on OR estimates obtained from case-control and cohort studies, we found a significant association between prior spontaneous abortions and placenta previa (1.77; 95% CI: 1.60, 1.94) and between prior induced abortions and placenta previa (1.36; 95% CI: 1.02, 1.69). The meta-analysis study herein showed that prior abortion is a risk factor for placenta previa.</jats:p

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Size-dependent nonlinear analysis of composite laminated micro skew plates reinforced with functionally graded graphene sheets

The high mechanical strength and superior physical properties of graphene and its derivatives have made them an ideal choice for modern engineering structures. The use of graphene as nanofillers in reinforced polymer composites has led to the development of sustainable high performance composite materials. Such materials can be utilized in engineering structures not only to improve their structural performance but also to reduce their environmental impact as well. Skew plates are commonly used in aerospace structures and ship hulls. In this paper, a size-dependent nonlinear model is presented for bending analysis of composite laminated micro skew plates reinforced with functionally graded graphene sheets. The modified Halpin-Tsai micromechanical model and rule of mixture are considered for the effective mechanical properties of graphene sheets which vary continuously throughout the thickness of the skew plate. The Schapery's model is considered for the effective thermal properties. The skew plate is assumed in thermal environments while transversely loaded. The governing equations of the problem are derived based on the Mindlin plate theory and the modified coupled stress theory. Using the generalised differential quadrature method, the nonlinear governing equations are first converted into a set of linear algebraic equations and then solved to obtain the bending moment of the skew plate under different loading conditions. Results show that reinforcing composite laminated micro skew plates with functionally graded graphene sheets increases the overall stiffness and bending strength of the plate, enhancing its performance under large deflections. It has also been observed that the bending performance of the skew plate further enhances through changing the distribution pattern of the functionally graded graphene reinforcement as well as with an increase in the angle of the skew plate

The 100 Prisoners Problem: Parallel Execution Using Graphics Processing Unit

An existing optimal strategy to solve the 100 prisoners problem is to assume that its success probability is independent of the number of prisoners. However, the execution time depends on the size of the problem. For this strategy, both sequential and parallel implementations are applicable. In this paper, we compared the execution times of the sequential and parallel algorithms to see how they vary when the problem size increases. This paper posits that in spite of the parallel nature of this strategy, it will not fully benefit from the GPU implementation. The results show that in spite of the GPU's high memory latency overhead, the parallel implementation will outperform the sequential of larger problem sizes. For the problem size of 100, the GPU implementation using global memory yields a speedup of 0.012. The achieved speedup reaches 1.652, as the problem size increases to 100,000. For the problem size of 100, the implementation using GPU's shared memory runs 8 times faster than the one using global memory