The effect of learning problem-solving methods on learning to program in the BASIC language

This study was designed to compare learning problem-solving methods versus non problem-solving activity (word-processing) on subsequent learning to program in the BASIC language. It also examined a method to provide students with increased knowledge and skills to enable them to learn how to program;A pretest-posttest control group design was used in this experiment with random assignment of subjects to one of three groups. Experiment groups one (deduction group) and two (induction group) first received the pretest and learning problem-solving methods; then group one received deduction instruction while group two received induction instruction, both followed by learning BASIC language programming instruction, taking midterm test one and two, and then the post-test. The control group first received the pretest and wordprocessing instruction, followed by learning BASIC language programming instruction and taking midterm test one and two, and then the post-test;The results indicated that when female students first study problem-solving methods (induction and deduction) they experience a significant increase in BASIC language programming achievement. Likewise, male students who first learn problem solving (induction) experience a significant increase in BASIC language program achievement;The study also showed that female students who first receive problem-solving instruction in induction subsequently learn BASIC language programming significantly better than female students who first receive problem-solving instruction in deduction and subsequently learn BASIC language programming;Further evidence supports that female students in group one and two on BASIC language programming in design and understanding performed significantly better than female students in the control group. In addition, male students who first learn problem solving (induction) perform significantly better than males who first receive non-problem solving instruction prior to learning BASIC language programming in design and understanding;From this study, the researcher concluded the following: (1) students who first learn problem-solving methods, rather than receiving non problem-solving instruction followed by learning BASIC programming, perform significantly better than their counterparts; and (2) female students who learn problem solving (induction) perform significantly higher than female students who learn problem solving (deduction) followed by learning BASIC language programming. Thus, first learning problem-solving skills enhances the ability to learn a programming language

Based on MIPv6 with Support to Improve the Mobile Commerce Transaction

Mobile Commerce is anticipated to be the next business revolution. Under the trend of mobile age, a person begins to realize the benefits of transaction by mobility operations. We can access information, shop and bank on line, work from home and speak and send messages via mobile appliances throughout all over the world. The research that is mobile transaction managing on database has begun since 1950 and skips the Link and Network Layer with support to improve mobile commerce. This paper focus on how effectually to make the new generation of mobile network protocol apply on mobile commerce and improve the mainly four properties required by mobile transactions. The four properties are respectively atomicity, consistency, isolation and durability. The purpose based on the mobile commerce environment and making mobile transactions complete and personal by means of the Destination Extension Header based on IPv6 and the Java Transaction Service. After experiment and testing, this paper verify that we improve the mobile commerce environment and make the mobile transaction more complete with the optimization of the Destination Extension Header based on IPv6 and the Java Transaction Service under the comparison with the environment on IPv4

A Comparative Study on Machine Learning Algorithms for Network Defense

Network security specialists use machine learning algorithms to detect computer network attacks and prevent unauthorized access to their networks. Traditionally, signature and anomaly detection techniques have been used for network defense. However, detection techniques must adapt to keep pace with continuously changing security attacks. Therefore, machine learning algorithms always learn from experience and are appropriate tools for this adaptation. In this paper, ten machine learning algorithms were trained with the KDD99 dataset with labels, then they were tested with different dataset without labels. The researchers investigate the speed and the efficiency of these machine learning algorithms in terms of several selected benchmarks such as time to build models, kappa statistic, root mean squared error, accuracy by attack class, and percentage of correctly classified instances of the classifier algorithms

Sampling Neural Radiance Fields for Refractive Objects

Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing methods usually assume the scene is a homogeneous volume so that a ray is cast along the straight path. In this work, the scene is instead a heterogeneous volume with a piecewise-constant refractive index, where the path will be curved if it intersects the different refractive indices. For novel view synthesis of refractive objects, our NeRF-based framework aims to optimize the radiance fields of bounded volume and boundary from multi-view posed images with refractive object silhouettes. To tackle this challenging problem, the refractive index of a scene is reconstructed from silhouettes. Given the refractive index, we extend the stratified and hierarchical sampling techniques in NeRF to allow drawing samples along a curved path tracked by the Eikonal equation. The results indicate that our framework outperforms the state-of-the-art method both quantitatively and qualitatively, demonstrating better performance on the perceptual similarity metric and an apparent improvement in the rendering quality on several synthetic and real scenes.Comment: SIGGRAPH Asia 2022 Technical Communications. 4 pages, 4 figures, 1 table. Project: https://alexkeroro86.github.io/SampleNeRFRO/ Code: https://github.com/alexkeroro86/SampleNeRFR

A comprehensive functional map of the hepatitis C virus genome provides a resource for probing viral proteins.

UnlabelledPairing high-throughput sequencing technologies with high-throughput mutagenesis enables genome-wide investigations of pathogenic organisms. Knowledge of the specific functions of protein domains encoded by the genome of the hepatitis C virus (HCV), a major human pathogen that contributes to liver disease worldwide, remains limited to insight from small-scale studies. To enhance the capabilities of HCV researchers, we have obtained a high-resolution functional map of the entire viral genome by combining transposon-based insertional mutagenesis with next-generation sequencing. We generated a library of 8,398 mutagenized HCV clones, each containing one 15-nucleotide sequence inserted at a unique genomic position. We passaged this library in hepatic cells, recovered virus pools, and simultaneously assayed the abundance of mutant viruses in each pool by next-generation sequencing. To illustrate the validity of the functional profile, we compared the genetic footprints of viral proteins with previously solved protein structures. Moreover, we show the utility of these genetic footprints in the identification of candidate regions for epitope tag insertion. In a second application, we screened the genetic footprints for phenotypes that reflected defects in later steps of the viral life cycle. We confirmed that viruses with insertions in a region of the nonstructural protein NS4B had a defect in infectivity while maintaining genome replication. Overall, our genome-wide HCV mutant library and the genetic footprints obtained by high-resolution profiling represent valuable new resources for the research community that can direct the attention of investigators toward unidentified roles of individual protein domains.ImportanceOur insertional mutagenesis library provides a resource that illustrates the effects of relatively small insertions on local protein structure and HCV viability. We have also generated complementary resources, including a website (http://hangfei.bol.ucla.edu) and a panel of epitope-tagged mutant viruses that should enhance the research capabilities of investigators studying HCV. Researchers can now detect epitope-tagged viral proteins by established antibodies, which will allow biochemical studies of HCV proteins for which antibodies are not readily available. Furthermore, researchers can now quickly look up genotype-phenotype relationships and base further mechanistic studies on the residue-by-residue information from the functional profile. More broadly, this approach offers a general strategy for the systematic functional characterization of viruses on the genome scale

Correlation of Month of Birth and Socioeconomic Status with Autism Spectrum Disorder: a Nationwide Study

The aim of this study was to investigate whether autism spectrum disorder (ASD) is associated with birth in certain months in Taiwan, as has been found in other countries. A case–control study (1:4) matched according to sex and age was conducted. The study population comprised 4.3% of the population of Taiwan, using the National Health Insurance Research Database (NHIRD) from 1996 through 2008. Multiple logistic regressions were performed after adjusting for socioeconomic factors of urbanization level and income level. A total of 965 people with ASD and 3,860 controls were recruited. In comparison with a March birth, a higher risk of ASD was found for June and August births. After adjusting for level of urbanization and income, the risk of developing ASD was still higher for June, July, and August births over the year. There was higher risk of ASD in urban area when comparing with rural area. A higher risk of ASD was found in the highest income level. A higher risk of ASD was identified among children born in summer months, and a higher risk of ASD in urban area and high socioeconomic status suggested the presence of social-environmental causes of ASD

Pencil lead graphite electrochemically modified with polyglutamic acid as a sensor for detection of enrofloxacin in aqueous media

This study investigates the modification of pencil lead graphite electrodes with polyglutamic acid using an effective and fast static method to develop a sensor for the detection of enrofloxacin (ENR). The successful fabrication of pGA on the electrode surface was confirmed by scanning electron microscopy, energy dispersive X-ray analysis, and Fourier-transform infrared spectroscopy. The conditions of electrochemical modification, including the applied potentials and number of cycles in the potentiostatic process, were systematically investigated to determine their effects on the ENR electrochemical response. The pH of the electrolyte media was also explored to elucidate the electrochemical reaction mechanism of ENR. The developed electrochemical sensor was evaluated using square wave stripping voltammetry for ENR detection. Under optimal conditions, the sensor demonstrated good reproducibility with a relative standard deviation of 4.3% (from five measurements) for ENR signal detection. A linear relationship between ENR concentration and its peak current was observed in the concentration range of 0.1 to 5 µM, with a high correlation coefficient of 0.9988. The limit of detection for ENR using the sensor was 0.12 µM. Our findings provide valuable insights into the design and optimisation of pencil lead graphite electrode-based sensors for ENR detection in aqueous media