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The new generation raised in the digital era continues to present unresolved challenges for both parents and teachers. The purpose of this case study is to analyze students' usage of their smartphones in their lives, and understand the success of a new school policy on smartphone use at an arts school in the U.S. For this qualitative research, I interviewed ten participants from the high school. The literary review informs us that smartphones are potentially addictive, with negative effects for healthy mental, emotional and social child development, as well as a source of misleading information. As a consequence, smartphones in the classrooms for instructional purposes are a distraction and present discipline and classroom management problems. The theory that guides my research is the bioecological system with its emphasis both on context (parents, school, smartphones) and on the influence of different systems on the child (proximal processes) where the context is situated. The findings of this study regarding the smartphone use corroborate some of the expectations drawn upon the literary review. The students who received their phones before high school are more attached and spend more hours on social media than students who receive their phones after high school and with boundaries. They are less addictive and more responsible smartphone users as well as appreciative of their parental values. The conclusion of the second research question indicates that a phone policy in schools that includes discipline consequences and a supportive administration is a deterrent of using the phone for non-instructional during class. The implications of this study are: high school is the most appropriate time for young people to receive a smartphone, parental guidance is crucial to develop responsible use, a strict smartphone policy with administrative support is critical to diminish students' distraction in class, and finally, common students' uses of social media highlight the urgent need for digital media literacy to empower new generations as media critical thinkers, especially given the current social and political atmosphere.

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted. ; Sattar, S., Li, S., & Chapman, M. (2018). Road surface monitoring using smartphone sensors: A review. Sensors, 18(11), 3845 ; (This article belongs to the Section Intelligent Sensors)

China's annual Food Safety Publicity Week in June 2019 has, for the first time, included smartphone games in its activities. Food related governmental and non-governmental organizations liaised with Alipay (Alibaba's payment application) launching a smartphone quiz-game with an unprecedented level of public engagement with food information (i.e., food safety, nutrition, and food science and technologies): 12 million participants and 1.7 billion instances of participation within 1 week. This example demonstrates the great potential of smartphone games, and more widely, digital tools, in food-related public education. Given the extent of misinformation among the public on food safety and nutrition issues, food scientists and organizations should be empowered to embrace emerging tools, such as smartphone games, in order to positively shape public opinions. To aid in this task, this commentary article analyzes the factors behind the unprecedented success of Alipay's smartphone quiz-game during China's Food Safety Publicity Week.

The growing smartphone penetration and the integration of smartphones into people's everyday practices offer researchers opportunities to augment survey measurement with smartphone-sensor measurement or to replace self-reports. Potential benefits include lower measurement error, a widening of research questions, collection of in situ data, and a lowered respondent burden. However, privacy considerations and other concerns may lead to nonparticipation. To date, little is known about the mechanisms of willingness to share sensor data by the general population, and no evidence is available concerning the stability of willingness. The present study focuses on survey respondents' willingness to share data collected using smartphone sensors (GPS, camera, and wearables) in a probability-based online panel of the general population of the Netherlands. A randomized experiment varied study sponsor, framing of the request, the emphasis on control over the data collection process, and assurance of privacy and confidentiality. Respondents were asked repeatedly about their willingness to share the data collected using smartphone sensors, with varying periods before the second request. Willingness to participate in sensor-based data collection varied by the type of sensor, study sponsor, order of the request, respondent's familiarity with the device, previous experience with participating in research involving smartphone sensors, and privacy concerns. Willingness increased when respondents were asked repeatedly and varied by sensor and task. The timing of the repeated request, one month or six months after the initial request, did not have a significant effect on willingness.

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

INTRODUCTION: On the world stage unlimited internet access by smartphones have made in social, cultural and economic relations, has transformed the world to be faster and more efficient. In this context, health care requires more attention. Health professionals must be concerned about the majority of the patient's care on the use of smartphone's in this process. Smartphones can be cause distractions and cause poor patient care. Behavioral and psychological changes related to abusive and uncontrolled use in this population may create severe impacts not only on the user's life but also on the community health care. METHOD: A careful research was performed through PubMed, Web of Science and Psycho Info databases using the terms: "Smartphone addiction", "Smartphone dependence", "Smartphone abuse", "physicians", "nurses" and ''health professionals". The search covered the past 5 years up to August 2019. Articles that examine abusive use on smartphones in health professionals were included. We analyzed how this concept evolved over the last five years and hope to contribute to the better understanding of the issue and its impacts on this population and on the health care. RESULTS: There is a lack of specific trial instruments on screening of smartphone addiction or abusive use, tending to adopt different diagnostic criteria by the reports included. There are also a lack of studies discussing etiology of this compulsive behavior. Although known risks, published reports show there is no consciousness of abusive use and possible damages in healthcare by some health professionals. Instead, there are others that can affirm there are unpredictable risks in patient care and tend to stimulate conscience use politics in health settings. Most of them point out smartphone's app benefits. CONCLUSION: Smartphones and its functionalities became part of everyone's life by the various benefits guaranteed. However, an addictive behavior can cause damage, principally in the Health Care setting and health professional's abusive use must ...

Proc. SPIE Conf. on Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV, Baltimore, MD, April 2012 ; A key challenge of sentry and monitoring duties is detection of approaching people in areas of little human traffic. We are exploring smartphones as easily available, easily portable, and less expensive alternatives to traditional military sensors for this task, where the sensors are already integrated into the package. We developed an application program for the Android smartphone that uses its sensors to detect people passing nearby; it takes their pictures for subsequent transmission to a central monitoring station. We experimented with the microphone, light sensor, vibration sensor, proximity sensor, orientation sensor, and magnetic sensor of the Android. We got best results with the microphone (looking for footsteps) and light sensor (looking for abrupt changes in light), and sometimes good results with the vibration sensor. We ran a variety of tests with subjects walking at various distances from the phone under different environmental conditions to measure limits on acceptable detection. We got best results by combining average loudness over a 200 millisecond period with a brightness threshold adjusted to the background brightness, and we set our phones to trigger pictures no more than twice a second. Subjects needed to be within ten feet of the phone for reliable triggering, and some surfaces gave poorer results. We primarily tested using the Motorola Atrix 4G (Android 2.3.4) and HTC Evo 4G (Android 2.3.3) and found only a few differences in performance running the same program, which we attribute to differences in the hardware. We also tested two older Android phones that had problems with crashing when running our program. Our results provide good guidance for when and where to use this approach to inexpensive sensing.

International audience ; Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual dataprocessing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

Many complex systems, such as the product life cycle (PLC), can be described in terms of networks, with elements interacting and based on terms of graph theory. In this article, we intend to analyze the PLC from the point of view of network theory and chaos. To do this we will use the Smarta software, developed by our research group (but not yet in a commercial phase), based on network analysis and that allows us to interpret the virtuous circles that the system possesses, and analyze the causalities by observing the plot of existing relationships between the system's attractors. Smarta allows us to manage the uncertainty of whether or not the product is ecological, and to be proactive, in the sense of designing strategies that anticipate it. A case study is the PLC of a smartphone, where Smarta will act as a filter since it will allow us to associate the concepts of ecological and attractor, so that if the product is recyclable an attractor will be found, and if it is not recyclable it will lack attractors. ; This work was supported in part by the Ministry of Economy and Business, Government of Spain under Grant RTI2018-094653-B-C22, in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) under Grant CloudDriver4Industry TIN2017-89266-R, and in part by the Regional Ministry of Education, Research, Culture, and Sports (Valencian Community) under Grant GV/ 2018//061.

Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals' motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity.

In India, the second-largest sugarcane producing country in the world, accurate mapping of sugarcane land is a key to designing targeted agricultural policies. Such a map is not available, however, as it is challenging to reliably identify sugarcane areas using remote sensing due to sugarcane's phenological characteristics, coupled with a range of cultivation periods for different varieties. To produce a modern sugarcane map for the Bhima Basin in central India, we utilized crowdsourced data and applied supervised machine learning (neural network) and unsupervised classification methods individually and in combination. We highlight four points. First, smartphone crowdsourced data can be used as an alternative ground truth for sugarcane mapping but requires careful correction of potential errors. Second, although the supervised machine learning method performs best for sugarcane mapping, the combined use of both classification methods improves sugarcane mapping precision at the cost of worsening sugarcane recall and missing some actual sugarcane area. Third, machine learning image classification using high-resolution satellite imagery showed significant potential for sugarcane mapping. Fourth, our best estimate of the sugarcane area in the Bhima Basin is twice that shown in government statistics. This study provides useful insights into sugarcane mapping that can improve the approaches taken in other regions.

In Deutschland wird der Umgang mit der zunehmenden Digitalisierung des Alltags von Jugendlichen derzeit kontrovers diskutiert. Während das Bundesbildungsministerin eine Bildungsoffensive mit einem Fünf-Milliarden-Euro Programm für die digitale Vernetzung und den Einsatz digitaler Medien an Schulen fördert, warnt gleichzeitig die Drogenbeauftrage der Bundesregierung vor gesundheitlichen Risiken durch die zunehmende Digitalisierung und intensive Mediennutzung. In dieser Studie soll der Frage nachgegangen werden, wie der "digitale Alltag" von Jugendlichen tatsächlich aussieht. Wie oft sind die Jugendlichen "online" und welche Apps werden dabei vor allem genutzt? Sind Unterschiede zwischen den Geschlechtern, oder unter den von Schüle-rinnen und Schülern besuchten Schularten feststellbar?

Modern smartphones come with high computing power, a variety of sensors and customization abilities that allow creation of a range of practical applications (apps). These features, coupled with the mobility and accessibility in terms of cost that smartphones offer, make them a potential game-changer for smallholder farmers in developing countries, particularly for these farmers' information needs. Smartphones can also make accessing government benefits, land records and crop survey requirements etc. more convenient for farmers. This paper explores how smartphones can be used in different ways, right from production planning to eventual sale of crop. Advanced applications like IoTs, drone use in conjunction with smartphones and design and willingness to pay aspects have also been covered. We believe that given the contemporary trends of robust low-cost devices (e.g., the JioPhone Next etc.) coupled with competitive data tariffs, smartphone use in agriculture will be beneficial for Indian farmers. In fact, the Indian experience can boost similar developments in the developing world, particularly in Africa.

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

This article focuses on contemporary smartphone culture and activism performed by female Indonesian migrant domestic workers in Hong Kong. There are around 330.000 migrant workers in Hong Kong, and wherein 150.000 originated from Indonesia. Most of the Indonesian migrant workers in Hong Kong work in the domestic sectors: housemaid, babysitter, and career. They are obliged to live with their employer and start working from five mornings until late at night for six days a week. The smartphone is their primary medium in keeping a connection with friends and families back home and reduces social isolation. Interestingly, Indonesian migrant workers also use smartphones for activism to support migrant workers' rights issues. Despite the isolation issues and the social limitations experienced by the Indonesian migrant workers', this article aims to explore the following questions: how is smartphone culture developed in Indonesian migrant workers' communication activities in Hong Kong? How is the smartphone, in particular, used as the medium for activism? Using the netnography method, this research explores the smartphone culture and activism performed by Indonesian migrant workers' in their online routine. Smartphone usage has become an interesting phenomenon where this device has changed the cultural habits and further forms a new cultural pattern. Social media platforms and chat applications on smartphones have facilitated Indonesian migrant workers' more comprehensive access to information. It is found that social media access is essential for Indonesian migrant workers to ease loneliness and further used as their medium of existence in the virtual world. Furthermore, they also create virtual identities through social media, share experiences and views through online platforms, and organize protests or other forms of activism. Sulistyaningsih's case is a clear example of how communication access was crucial for Indonesian migrant workers at a time of danger. The diverse forms of communication technology have allowed them to create compelling messages. This activity provides evidence that Indonesian migrant workers still can be creative despite their long working hours.