A causal model of factors affecting azimuth and altitude learning by smart phone in astronomy

Today, using communications devices such as simple and smart mobile phones has led man to face EMFs more. Thus is essential, measuring and comparing the emitted EMFs of simple mobile phones with that of smart phones. The EMFs of 2 simple mobile phones and 2 smart phones were measured by EMFs measurement portable equipment model HI-3603 in a ringing mode. Ultimately, the difference between electric and magnetic fields in simple and smart phones was evaluated by the ANOVA statistical test. The means of the electric fields of simple and smart mobile phones was 2.38±0.18 v/m and 1.9±0.18 v/m respectively. The means of magnetic fields of simple and smart mobile phones was 0.49±0.13 mG and 0.48±0.1 mG, respectively. The ratio of the mean of electric field in simple and smart phones to the standard limit (53.8 v/m) was 4.42% and 3.53% and also the ratio of the mean of electric field in simple and smart phones to the standard limit is 25.12% and 24.61%, respectively. Despite the fact that the mean of electric and magnetic fields of simple mobile phones is more than smart ones, the ANOVA statistical analysis shows that there is no significant difference between these two means (P value > 0.05). Despite the fact that EMFs in simple and smart phones are approximately equal or less than the standard limits, the safety notes shall be considered while using each of these devices.

Smart and cell phone with self-contained sensor such as accelerometer, gyroscopic and digital magnetic compass sensor have been popular. Combining certain algorithm and those sensors, it can estimate user’s activity, situation and even user’s absolute position. However, estimation of user’s activity, situation and user’s absolute position become difficult when once sensors posture and position are changing from original position in user’s motion. Also, according to stored, worn and handheld position and posture of those cell and smart phone are often changed. Therefore, we exclude estimation of user’s position and we focus to only estimation of user’s activity and situation for risk management. Basically, we design special classifier for detecting user’s unusual behavior and apply other user’s position data from internet to the results detected by the classifier which are combined wavelet transform and SVM. We assume that user’s unusual activity and situation can be detected by smart and cell phone with high accuracy.KeywordsActivity recognitionWearable computerSVMnearly fall incidentcell phone and smart phone

Mobile health (mHealth) interventions, which require ownership of mobile phones, are being investigated throughout Africa. We estimate the percentage of individuals who own mobile phones in 33 African countries, identify a relationship between ownership and proximity to a health clinic (HC), and quantify inequities in ownership. We investigate basic mobile phones (BPs) and smartphones (SPs): SPs can connect to the internet, BPs cannot. We use nationally representative data collected in 2017–2018 from 44,224 individuals in Round 7 of the Afrobarometer surveys. We use Bayesian multilevel logistic regression models for our analyses. We find 82% of individuals in 33 countries own mobile phones: 42% BPs and 40% SPs. Individuals who live close to an HC have higher odds of ownership than those who do not (aOR: 1.31, Bayesian 95% highest posterior density [HPD] region: 1.24–1.39). Men, compared with women, have over twice the odds of ownership (aOR: 2.37, 95% HPD region: 1.96–2.84). Urban residents, compared with rural residents, have almost three times the odds (aOR: 2.66, 95% HPD region: 2.22–3.18) and, amongst mobile phone owners, nearly three times the odds of owning an SP (aOR: 2.67, 95% HPD region: 2.33–3.10). Ownership increases with age, peaks in 26–40 year olds, then decreases. Individuals under 30 are more likely to own an SP than a BP, older individuals more likely to own a BP than an SP. Probability of ownership decreases with the Lived Poverty Index; however, some of the poorest individuals own SPs. If the digital devices needed for mHealth interventions are not equally available within the population (which we have found is the current situation), rolling out mHealth interventions in Africa is likely to propagate already existing inequities in access to healthcare.

Mobile health (mHealth) interventions, which require ownership of mobile phones, are being investigated throughout Africa. We estimate the percentage of individuals who own mobile phones in 33 African countries, identify a relationship between ownership and proximity to a health clinic (HC), and quantify inequities in ownership. We investigate basic mobile phones (BPs) and smartphones (SPs): SPs can connect to the internet, BPs cannot. We use nationally representative data collected in 2017-2018 from 44,224 individuals in Round 7 of the Afrobarometer surveys. We use Bayesian multilevel logistic regression models for our analyses. We find 82% of individuals in 33 countries own mobile phones: 42% BPs and 40% SPs. Individuals who live close to an HC have higher odds of ownership than those who do not (aOR: 1.31, Bayesian 95% highest posterior density [HPD] region: 1.24-1.39). Men, compared with women, have over twice the odds of ownership (aOR: 2.37, 95% HPD region: 1.96-2.84). Urban residents, compared with rural residents, have almost three times the odds (aOR: 2.66, 95% HPD region: 2.22-3.18) and, amongst mobile phone owners, nearly three times the odds of owning an SP (aOR: 2.67, 95% HPD region: 2.33-3.10). Ownership increases with age, peaks in 26-40 year olds, then decreases. Individuals under 30 are more likely to own an SP than a BP, older individuals more likely to own a BP than an SP. Probability of ownership decreases with the Lived Poverty Index; however, some of the poorest individuals own SPs. If the digital devices needed for mHealth interventions are not equally available within the population (which we have found is the current situation), rolling out mHealth interventions in Africa is likely to propagate already existing inequities in access to healthcare.

Healthcare workers' mobile phones are easily contaminated with pathogenic bacteria and could be vehicles of transmission. Smart phones are increasingly used in the hospital. The objective of this study was to compare the contamination rate of bacteria with pathogenic potential between smart phones and non-smart phones. We screened mobile phones of healthcare workers in three teaching hospitals in South Korea. The identification of cultivated micro-organisms and assessment of antibiotic susceptibility were performed. One hundred fifteen (56.7%) participants used smart phones, and 88 (43.3%) used non-smart phones. Bacteria with pathogenic potential were isolated from 58 (28.6%) mobile phones, more often from smart phones than from non-smart phones (34.8% vs 20.5%, P=0.03). Multivariate analysis including various characteristics to determine risk factors revealed that only smart phones (vs non-smart phones) were a significant risk factor for contamination by bacteria with pathogenic potential (adjusted odds ratio [OR], 4.02; 95% confidence interval [CI], 1.43-11.31). Also, in a multivariate model including phone size, the smart phone was still a significant risk factor for the pathogen contamination (OR, 4.17; 95% CI, 1.07-16.33; P=0.04). The smart phones of healthcare workers were contaminated with bacteria with pathogenic potential to a greater extent than were non-smart phones.

Globalization has changed our lives in multiples ways, but the most important change in our lives, is due to our way of communication. Mobile phone is popular device since the late 1990s having reached to seven billion users’ worldwide and unique mobile subscription of over 3.5 billion. the recent technological advancements and the innovation in computers and advancement in the field of information and technology brings about the birth of mobile phones and its multi-functions such as voice calls, messaging, data use, games (online and offline) and use of social media apps. Mobile phones have almost become an essential part of our daily lives. Smart phones were invented for the use of adult people, especially probably for people who are working or students of higher level for their better achievements in academics. But now a day’s smart phones are serving the medium of learning to young children/learners. Excessive use of mobile (smart) phone among young learners leads to multiple learning difficulties and behavior disorders, emotional, moral and social development disorders of young children. It has eventually affected the academic performance of young children. The positive and negative effects of the mobile gadgets are affecting the overall development of young children. Educators and psychologist pointed out those using digital devices in the preschooler have negative effect as well, which is mainly linked to using those devices without any time bound.

Assessing the model fit quality of statistical models for network data is an ongoing and under-examined topic in statistical network analysis. Traditional metrics for evaluating model fit on tabular data such as the Bayesian Information Criterion are not suitable for models specialized for network data. We propose a novel self-developed goodness of fit (GOF) measure, the `stratified-sampling cross-validation' (SCV) metric, that uses a procedure similar to traditional cross-validation via stratified-sampling to select dyads in the network's adjacency matrix to be removed. SCV is capable of intuitively expressing different models' ability to predict on missing dyads. Using SCV on real-world social networks, we identify the appropriate statistical models for different network structures and generalize such patterns. In particular, we focus on conditionally independent dyad (CID) models such as the Erdos Renyi model, the stochastic block model, the sender-receiver model, and the latent space model.

Objectives The purpose of this study is to examine trends in research related to teaching and learning methods in university general education. Methods For this study, a total of 107 domestic research papers published between 2012 and July 2023 were selected. Content analysis was employed, and the analysis criteria (including year, teaching and learning methods, academic fields, research objectives, research types, and application environments) were used to conduct fre-quency analysis and cross-analysis. Additionally, word cloud analysis was utilized to analyze the frequency of ap-pearance of keywords in each research paper. Results Firstly, the results of frequency analysis across six domains showed a noticeable recent surge in re-search, particularly in technology-based, discussion-oriented, and problem-based teaching and learning meth-ods, including flipped learning. Humanities emerged as the most frequently studied academic field, with a strong emphasis on effectiveness analysis, application, and case studies. Research types were predominantly mixed or qualitative, and application environments were ranked as offline, blended, and online, in that order. Secondly, cross-domain analysis revealed significant variations in research trends based on research objectives, application environments, and academic fields. Finally, word cloud analysis highlighted keywords such as writing, university, teaching methods, general education, class, English, learner, problem-centered learning, flipped learning, and communication. Conclusions Firstly, it was observed that there is a growing interest and necessity for research in university gen-eral education, particularly in teaching and learning methods. Secondly, with the increasing trend in research based on digital learning environments and technology, it is important for researchers to have a deeper under-standing of these areas. Thirdly, the study indicated the need for extensive follow-up research in academic fields beyond the humanities. Furthermore, it emphasized the importance of balanced and diverse research in terms of research objectives, types, and topics in the context of university general education.

Cell Phones, Internet, and Apps: Who’s Using What in WIC?

The present era is a 'Smart Phone Era'. The mobile phone became an inevitable gadget for everyone and also for farmers to know the latest agricultural technology. But there were many problems which hinder the maximum use of smart phone to get the scientific information of agricultural field. Keeping this in view, the present study was undertaken to identify the constraints faced by farmers in the use of smart phone for agricultural information. This study was undertaken in Porbandar district of Gujarat state. 120 farmers who had minimum three years' experience in the use of smart phone were selected from 12 randomly selected villages of Porbandar district. Results indicated that, unavailability/fluctuation of network connectivity in rural area is the major constraint in the use of smart phone. Some other important constraints were; lack of technical know-how to operate smart phone properly, language barrier i.e. majority of information is available in English not in vernacular language and the cost of smart mobile phones is high. The network service provider should give priority for strong network connectivity in rural areas, farmers should be trained to improve their smart phone use efficiency, agricultural information should be available in local/vernacular language and the government should provide smart phones at low cost to farmers were crucial suggestions given by the respondents to overcome the constraints faced by farmers in the use of smart phone for agricultural information.

Today’s medical students and residents are from the first generation of digital natives: those who grew up with cell phones, text messaging, and the Internet. They are comfortable with e-innovation,1 are increasingly disengaged from traditional teaching methods within medical schools and residency programs, and are quick to embrace new teaching technologies. Students instead stream lectures from home, use electronic devices to access medical journals online, and use “smart” phone applications during rounds. Adapting teaching methods within medical schools and residencies to connect with this generation of e-learners is challenging. Text messaging (short message service) offers a method of bridging the gap between traditional teaching styles and the educational styles of millennial learners because of its ease, familiarity, and asynchronous nature. Among adults aged 18 to 29 years, 94% own a cell phone, and of those, 97% send and receive text messages.2 Some residency programs even provide trainees with smart phones.3 The prevalence, acceptance, and low cost of text messaging make it particularly inviting as a potentially high-yield learning tool in medical education. Texting has already become a well-accepted way to communicate with and engage students in high school and undergraduate settings, with a variety of platforms available for teachers.4–6 Teachers may use text messages to remind students about assignments, to deliver rapid-fire surveys or quizzes during class, or to conduct course-wide discussions (either within or outside the standard classroom period).7 Texting platforms are unique in that they allow participants to ask questions or discuss topics via the comfort of anonymity, which may promote participation and enhance learning.8 This form of feedback direct from one’s mobile phone is popular. Texting that occurs directly between learner and teacher, although it may not afford anonymity to the student from the teacher, … Address correspondence to Matthew A. Broom, MD, Department of Pediatrics, Saint Louis University School of Medicine at SSM Cardinal Glennon Children’s Medical Center, 1465 S Grand Blvd, St Louis, MO 63104. E-mail: broomma{at}slu.edu

Abstract. The purpose of this study is to analyze the driving technical characteristics in product innovation to predict technological trajectories. The analysis is based on hedonic price method and other approaches using empirical data of smartphone technology ( N =738 models over 2008-2018). Results show technological trajectories supporting the evolution of smartphone technology. In particular, critical characteristics of technological evolution in smartphone technology are: RAM in Gb, 1 st and 2 nd camera in Mpx, memory in Gb and resolution in total pixels. Implications of innovation product management are discussed. Keywords. Evolution of technology, Functionality development, Technological innovation, Product innovation management, Mobile phone, Smartphone, Technological change, Hedonic price regression, Economics of technical change. JEL. O30, O31, O32, O33, O38.

In this work, smart phone application for biomedical optic imaging methodology has been written and downloaded into Samsung Galaxy A8 smart cellular phone. Smart phone application is controlling cellular phone’s white Flash light emitting diode (LED) and acquiring the camera pictures simultaneously. In this work Android Studio program development platform has been used. Flash LED light illuminated to the left soft arm tissue. Flash LED has wide wavelength band spectrum which has from 380 to 800 nm. The smart phone white flash LED light has two different Gaussian curve between 380 and 800 nm wavelength. One summit is around 440 nm, the other one is in the 560 nm wavelength. These wavelengths are visible and close to near infrared (NIR). Acquired image data were sent to PC via smart phone USB cable by smart phone Android based application. Image data were analyzed, GPU based Monte Carlo (MC) simulations were run which their executable .exe file was prepared, compiled and built in Microsoft Visual Studio C (MSVSC) code on NVIDIA graphic processor unit. Finally image was reconstructed according to correct tissue type with MatLABTM program.

Recent drastic weather shifts driven by global warming have adversely affected African agriculture, culminating in low crop yields. The purpose of this study is to design decision support algorithms (DSA) that will aid sunflower farmers in the Singida Region in managing the risks associated with weather variations and consequently improve crop yields. A total of 80 respondents, including meteorologists, agricultural extension officers (AEOs), and farmers with feature and smart phones, contributed to the study's designed and empirical validation of the algorithm. The study designed and validated the DSA that assist sunflower growers in the Singida region in making informed decisions to improve productivity amidst adverse weather changes. As revealed, farmers who use both smartphones and feature phones are extremely satisfied with the DSA functions. The decision support algorithm designed in this study integrates smart and feature phone elements that were overlooked in comparable, prior systems and algorithms. Farmers that grow other crops that behave similarly to sunflower in areas with characteristics comparable to the Singida region will find the study's designed and validated algorithm helpful. In order to assist sunflower farmers in making decisions, the DSA interprets and processes data on a predetermined set of daily activities. In order to advance the use of ICT applications in farming activities, the study's findings took into consideration farmers who used feature phones with SMS-based notifications and those who used smartphones. The existing systems mostly concentrated on integrating web-based systems through mobile phones, which is dependent only on internet connectivity being available. Moreover, the use of web-based systems via mobile phones only results in restricted coverage and is out of reach for many farmers. Thus, the innovative element that emphasizes the study's contribution to the field in Tanzania's rural areas is the integration of feature and smart phone.

The use of smart phones is increasing in the world. This excessive use, especially in the last two decades, has created too much concern on the effects of emitted electromagnetic fields and specific absorption rate on human health. In this descriptive-analytical study of the electric field resulting from smart phones of Samsung and Nokia by portable measuring device, electromagnetic field, Model HI-3603-VDT/VLF, were measured. Then, head absorption rate was calculated in these two mobiles by ICNIRP equation. Finally, the comparison of specific absorption rate, especially between Samsung and Nokia smart phones, was conducted by T-Test statistics analysis. The mean of electric field for Samsung and Nokia smart mobile phones was obtained 1.8 ±0.19 v/m and 2.23±0.39 v/m, respectively, while the range of the electric field was obtained as 1.56-2.21 v/m and 1.69-2.89 v/m for them, respectively. The mean of specific absorption rate in Samsung and Nokia was obtained 0.002 ± 0.0005 W/Kg and 0.0041±0.0013 W/Kg at the frequency of 900 MHz and 0.004±0.001 W/Kg and 0.0062±0.0002 W/Kg at the frequency of 1800 MHz respectively. The ratio of mean electronic field to guidance in the Samsung mobile phone at the frequency of 900 MHz and 1800 MHz was 4.36% and 3.34%, while was 5.62% and 4.31% in the Nokia mobile phone, respectively. The ratio of mean head specific absorption rate in smart mobile phones of Samsung and Nokia in the guidance level at the frequency of 900 was 0.15% and 0.25%, respectively, while was 0.23% and 0.38% at the frequency of 1800 MHz, respectively. The rate of specific absorption of Nokia smart mobile phones at the frequencies of 900 and 1800 MHz was significantly higher than Samsung (p value <0.05). Hence, we can say that in a fixed period, health risks of Nokia smart phones is higher than Samsung smart mobile phone.