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This study conducts a randomised control trial to offer a technical workshop and examine whether providing information about the full range of services on the mobile money platform would increase mobile money usage, by taking a case of the Ashanti Region, Ghana. We find a significant positive impact of mobile money education on the recent usage of mobile money for transactions. However, no significant evidence of the workshop was found on new mobile money account ownership, or on the share of transactions transmitted through mobile money. Furthermore, weak and volatile outcomes were observed as impacts on remittances after the interventions. We discuss potential reasons behind the weak effects found. [ABSTRACT FROM AUTHOR]

This paper presents a mobile robot platform, which performs both indoor and outdoor localization based on an intelligent low-cost depth–inertial fusion approach. The proposed sensor fusion approach uses depth-based localization data to enhance the accuracy obtained by the inertial measurement unit (IMU) pose data through a depth–inertial fusion. The fusion approach is based on feedforward cascade correlation networks (CCNs). The aim of this fusion approach is to correct the drift accompanied by the use of the IMU sensor, using a depth camera. This approach also has the advantage of maintaining the high frequency of the IMU sensor and the accuracy of the depth camera. The estimated mobile robot dynamic states through the proposed approach are deployed and examined through real-time autonomous navigation. It is shown that using both the planned path and the continuous localization approach, the robot successfully controls its movement toward the destination. Several tests were conducted with different numbers of layers and percentages of the training set. It is shown that the best performance is obtained with 12 layers and 80% of the pose data used as a training set for CCN. The proposed framework is then compared to the solution based on fusing the information given by the XSens IMU–GPS sensor and the Kobuki robot built-in odometry solution. As demonstrated in the results, an enhanced performance was achieved with an average Euclidean error of 0.091 m by the CCN, which is lower than the error achieved by the artificial neural network by 56%. [ABSTRACT FROM AUTHOR]

Visual search has become more popular in recent years, allowing users to search by an image they are taking using their mobile device or uploading from their photo library. One domain in which visual search is especially valuable is electronic commerce, where users seek for items to purchase. Despite the increasing popularity of visual search in e-commerce, no comprehensive study has inspected its characteristics compared to traditional search using a text query. In this work, we present an in-depth comprehensive study of visual e-commerce search. We perform query log analysis of one of the largest e-commerce platforms' mobile search application. We compare visual and textual search by a variety of characteristics, with special focus on the retrieved results and user interaction with them. We also examine image query characteristics, refinement by attributes, and segmentation by user types. Additionally, we examine, for the first time, a wide variety of visual pre- and post-retrieval query performance predictors, several of which showing strong results. Our study points out a variety of differences between visual and textual e-commerce search. We discuss the implications of these differences for the design of future e-commerce search systems. [ABSTRACT FROM AUTHOR]

Mobile applications have gained popularity in healthcare in recent years. These applications are an increasingly important pillar of public health care, as they open up new possibilities for data collection and can lead to new insights into various diseases and disorders thanks to modern data analysis approaches. In this context, Ecological Momentary Assessment (EMA) is a commonly used research method that aims to assess phenomena with a focus on ecological validity and to help both the user and the researcher observe these phenomena over time. One phenomenon that benefits from this capability is the chronic condition tinnitus. TrackYourTinnitus (TYT) is an EMA-based mobile crowdsensing platform designed to provide more insight into tinnitus by repeatedly assessing various dimensions of tinnitus, including perception (i.e., perceived presence). Because the presence of tinnitus is the dimension that is of great importance to chronic tinnitus patients and changes over time in many tinnitus patients, we seek to predict the presence of tinnitus based on the not directly related dimensions of mood, stress level, arousal, and concentration level that are captured in TYT. In this work, we analyzed a dataset of 45,935 responses to a harmonized EMA questionnaire using different machine learning techniques. In addition, we considered five different subgroups after consultation with clinicians to further validate our results. Finally, we were able to predict the presence of tinnitus with an accuracy of up to 78% and an AUC of up to 85.7%. [ABSTRACT FROM AUTHOR]

Developing different robotic platforms for farm operations is vital to addressing the increasing world population. A harvesting robot significantly increases a farm's productivity while farmers focus on other relevant farm operations. From the literature, it could be summarized that the design concepts of the harvesting mechanisms were categorized as grasping and cutting, vacuum suction plucking systems, twisting and plucking mechanisms, and shaking and catching. Meanwhile, robotic system components include the mobile platform, manipulators, and end effectors, sensing and localization, and path planning and navigation. The robotic system must be cost-effective and safe. The findings of this research could contribute to the design process of developing a harvesting robot or developing a harvesting module that can be retrofitted to a commercially available mobile platform. This paper provides an overview of the most recent harvesting robots' different concept designs and system components. In particular, this paper will highlight different agricultural ground mobile platforms and their associated mechanical design, principles, challenges, and limitations to characterize the crop environment relevant to robotic harvesting and to formulate directions for future research and development for cotton harvesting platforms. [ABSTRACT FROM AUTHOR]

Vision is an important way for unmanned mobile platforms to understand surrounding environmental information. For an unmanned mobile platform, quickly and accurately obtaining environmental information is a basic requirement for its subsequent visual tasks. Based on this, a unique convolution module called Multi-Scale Depthwise Separable Convolution module is proposed for real-time semantic segmentation. This module mainly consists of concatenation pointwise convolution and multi-scale depthwise convolution. Not only does the concatenation pointwise convolution change the number of channels, but it also combines the spatial features from the multi-scale depthwise convolution operations to produce additional features. The Multi-Scale Depthwise Separable Convolution module can strengthen the non-linear relationship between input and output. Specifically, the multi-scale depthwise convolution module extracts multi-scale spatial features while remaining lightweight. This fully uses multi-scale information to describe objects despite their different sizes. Here, Mean Intersection over Union (MIoU), parameters, and inference speed were used to describe the performance of the proposed network. On the Camvid, KITTI, and Cityscapes datasets, the proposed algorithm compromised between accuracy and memory in comparison to widely used and cutting-edge algorithms. In particular, the proposed algorithm acquired 61.02 MIoU with 2.68 M parameters on the Camvid test dataset. [ABSTRACT FROM AUTHOR]

Air pollution monitoring using mobile ground-based measurement platforms can provide high quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller scale air quality sensors, it is important to address the quality assurance needs for validating these measurements. We explore collocation-based evaluation of air quality instruments in a mobile platform against fixed regulatory sites, both when the mobile platform is parked at the fixed regulatory site and when moving at distances of meters to kilometers from the site. We demonstrate agreement within 4 ppbv (for NO2 and O3+NO2) to 10 ppbv (for NO) when using a running median of 40 hourly differences between the moving mobile platform measurements and stationary site measurements. The comparability is strong when only measurements from residential roads are used but are only slightly diminished when all roads except highways are included in the analysis. We present a method for assessing mobile measurements of ozone (O3), nitrogen dioxide (NO2), and odd oxygen (OX = O3 + NO2) on an ongoing basis through comparisons with fixed regulatory sites. [ABSTRACT FROM AUTHOR]

A healthcare management platform for a personal wearable device can be set up in an app on a mobile phone or on a platform in a smart classroom. However, a large number of Internet of Things (IoT) devices being simultaneously connected to a central management server may negatively affect the server. Combining an IoT platform with deep learning technology facilitates monitoring the state of the input device, allowing for corresponding adjustments. The operation consists of collecting sensor data, optimizing the dataset using deep learning models, and integrating the deep learning model with the IoT platform. To optimize the data, noise data from human factors must be eliminated. Through a re-evaluation of the deep learning results, data with known errors are removed and data with higher accuracy are added. The optimized dataset can be used to construct a higher accuracy model and integrate it into the IoT platform. The IoT platform can analyze outputs through the deep learning model. The results show that the current status of each device can be identified. This is helpful for the user of a healthcare applications system. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs), also known as the Global Goals, were adopted by the United Nations in 2015 as a universal call to end poverty, protect the planet and ensure peace and prosperity for all by 2030. The 17 SDGs have been designed to end poverty, hunger, AIDS and discrimination against women and girls. Despite the clear SDG framework, there is a significant gap in the literature to establish the alignment of systems, projects or tools with the SDGs. In this research work, we assess the SDG alignment of an activity recognition platform for healthcare systems, called ACTIVA. This new platform, designed to be deployed in environments inhabited by vulnerable people, is based on sensors and artificial intelligence, and includes a mobile application to report anomalous situations and ensure a rapid response from healthcare personnel. In this work, the ACTIVA platform and its compliance with each of the SDGs is assessed, providing a detailed evaluation of SDG 7—ensuring access to affordable, reliable, sustainable and modern energy for all. In addition, a website is presented where the ACTIVA platform's compliance with the 17 SDGs has been evaluated in detail. The comprehensive assessment of this novel platform's compliance with the SDGs provides a roadmap for the evaluation of future and past systems in relation to sustainability. [ABSTRACT FROM AUTHOR]

Disasters like forest fires have become a persistent challenge in Himalayan regions due to highly inflammable Pine leaves. Solutions like producing bio-briquettes require involvement of local villagers, which depends on creating socio-economic and social entrepreneurship opportunities. Our research addresses these challenges by developing and evaluating governance interventions to connect villagers with local market through a mobile app platform and shape an ecosystem. We conduct an interpretive case study based on literature on digital platform ecosystems and multi-sided platforms. We use activity theory to analyze case data on implementation of governance interventions and behavior of ecosystem actors towards the challenge of producing and selling pine briquettes through the platform. Our findings suggest that characteristic features of a platform ecosystem running in a socio-economic setting differ substantially from commercial ecosystems. Our research could be one of the first to contribute to digital platforms for development in a local socioeconomic context. [ABSTRACT FROM AUTHOR]

Purpose: The tremendous market growth of mobile platforms for esports underscores the need to understand players' psychological states and consumption behavior. Based on flow theory, this study examines players' psychological states (flow and clutch experiences) and consumption behavior based on the interaction effects of playing frequency, playing duration and players' levels on the PC (LOL – League of Legends) and mobile (LOLWR – League of Legends: Wild Rift) versions of the same esports title. Design/methodology/approach: Data were collected from 930 valid responses and analyzed with confirmatory factor analysis and multiple regression (PROCESS macro, Model 3). Findings: There are two main findings. First, across PC and mobile participants, casual gamers (low playing frequency and duration) have firm purchase intention when they have a clutch experience, but flow experience hinders their purchasing intention. Second, hardcore gamers' (high playing frequency and duration) psychological states are clearly distinguished according to technological platforms. Flow experience is the most effective for their purchase intention in the PC platform, but both flow and clutch states are important in the mobile platform. Flow experience is essential overall for hardcore gamers to intend their in-game item purchasing. Originality/value: This study has two primary originality/values. First, this study explores flow and clutch together to measure psychological states and the impact on the purchase intention of in-game items. Second, the interacting effects of playing frequency, duration, and skill level with technical platforms (i.e. PC and mobile) for esports gaming. [ABSTRACT FROM AUTHOR]

The increase in neural network recognition accuracy is accompanied by a significant increase in the scales of networks and computations. To make deep learning frameworks widely used on mobile platforms, model acceleration has become extremely important in computer vision. In this study, a novel neural network acceleration method based on selective activation is proposed. First, as the algebraic basis for selective activation, mask general matrix multiplication is used to reduce matrix multiplication calculations. Second, to screen and remove activated neurons and reduce the number of calculations, we introduce an Activation Management Unit that includes two different strategies, Selective Activation with Primary Weights (SAPW) and Selective Activation with Primary Inputs (SAPI). SAPW greatly reduces the number of calculations of the fully connected layer and self‐attention and better guarantees detection accuracy. SAPI has the best performance on convolutional architectures, which can significantly reduce the amount of convolutional computation while maintaining the image classification accuracy. We present result of extensive experiments on computational and accuracy tradeoffs and show strong performance for CIFAR‐10 classification and Pascal VOC2012 detection. Compared with the dense method, the proposed selective activation method significantly reduces the number of neural network calculations with equal accuracy. [ABSTRACT FROM AUTHOR]

A novel concept—the contact-based landing on a mobile platform—is proposed in this paper. An adaptive backstepping controller is designed to deal with the unknown disturbances in the interactive process, and the contact-based landing mission is implemented under the hybrid force/motion control framework. A rotorcraft aerial vehicle system and a ground mobile platform are designed to conduct flight experiments, evaluating the feasibility of the proposed landing scheme and control strategy. To the best of our knowledge, this is the first time a rotorcraft unmanned aerial vehicle has been implemented to conduct a contact-based landing. To improve system autonomy in future applications, vision-based recognition and localization methods are studied, contributing to the detection of a partially occluded cooperative object or at a close range. The proposed recognition algorithms are tested on a ground platform and evaluated in several simulated scenarios, indicating the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

BACKGROUND: Along with China entering an aging society, the percentage of people that over 60 will reach 34.9% in 2050, resulted in a significant increase in stroke patients. OBJECTIVE: This paper proposes a rehabilitation robotic walker for walking assistance during the daily life, and a control method for the motor relearning during the gait training. The walker consists of an omni-directional mobile platform (OMP) which ensures the walker can move on the ground, a body weight support system (BWS) which is capable of providing the desired unloading force, and a pelvic assist mechanism (PAM) to provide the user with four degrees of freedom and avoid the rigid impact. The study goal is to gain a better understanding of the assist-as-needed control strategy during the gait training. METHODS: For the man-machine interaction control, the assist-as-needed control strategy is adopted to guide the users' motions and improve the interaction experience. To build the force field in the three-dimensional space, the dynamics of the system is derived to increase the accuracy of force control. RESULTS: The simulation results show that the force field around the motion trajectory was generated in the three-dimensional space. In order to understand the force field, we designed the simulation on sagittal plane and the controller can generate the appropriate force field. The preliminary experiment results were consistent with the simulation results. CONCLUSION: Based on the mathematical simulation and the preliminary test, the results demonstrate that the proposed system can provide the guide force around the target trajectory, the accuracy of force control still remains to be improved. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patient data in current computed tomography scanner machines are transferred through several communication channels, such as WiFi, to the mobile channel platform. Therefore, patient information is an important security concern. Medical imaging must be protected using various methods. OBJECTIVE: The current hardware-dependent method for generating random bits exhibits predictable or inconvenient physical characteristics. Therefore, a more flexible random-bit generation technique is to be devised. METHODS: We propose a deterministic random bit generation algorithm that uses a mathematical periodic function. RESULTS: After randomizing the image using the proposed random bit, the performance is analyzed and compared with that of the processed image. CONCLUSION: The random bit generation method using a mathematical algorithm shows higher entropy than the random bit generated by hardware. [ABSTRACT FROM AUTHOR]

This work investigates the stiffness characteristics of a planar parallel manipulator with variable platforms. The stiffness model is established with shape variables of the base and mobile platforms based on the Jacobian matrix. By virtue of a nondimensionalization of the inhomogeneous stiffness matrix, two homogeneous submatrices are obtained. Upon which, two kinds of performance indices are derived to evaluate the manipulator's stiffness behaviors, one for translation and the other for rotation. Stiffness performance of the considered manipulator with three types of platforms, namely variable base platform, variable mobile platform and both variable base and mobile platforms, is analyzed. To validate the stiffness models, the established analytical models are compared with the FEA based evaluations. In order to find the configurations with the best global stiffness performance, stiffness optimization with consideration of shape variables is conducted with the particle swarm optimization algorithm. Finally, a case study of platforms' shape changing in stiffness adjustment is presented. [ABSTRACT FROM AUTHOR]

Microsoft released a new communication platform, Microsoft Teams, in 2017. Due in part to COVID‐19, the popularity of communication platforms, like Microsoft Teams, increased exponentially. Given its user base and increased popularity, it seems likely that digital forensic investigators will encounter cases where Microsoft Teams is a relevant component. However, because Microsoft Teams is a relatively new application, there is limited forensic research on the application particularly focusing on mobile operating systems. To address this gap, an analysis of data stored at rest by Microsoft Teams was conducted on the Windows 10 operating system as well as on Android and Apple iOS mobile operating systems. Basic functionalities, such as messaging, sharing files, participating in video conferences, and other functionalities that Teams provides, were performed in an isolated testing environment. Cellebrite UFED Physical Analyzer and Magnet AXIOM Examine tools were used to analyze the mobile devices and the Windows device, respectively. Manual or non‐automated investigation recovered, at least partially, the majority of artifacts across all three operating systems. In this study, a total of 77.6% of the populated artifacts were partially or fully recovered in the manual investigation. On the other hand, forensic tools used did not automatically recover many of the artifacts found with the manual investigation. Only 13.8% of artifacts were partially or fully recovered by the forensic tools across all three devices. These discovered artifacts and the results of the investigations are presented in order to aid digital forensic investigations. [ABSTRACT FROM AUTHOR]

• Nonlinear dynamic model of N - links flexible manipulator. • Consider link-joints flexibility and mobile platform. • Investigate the modal analysis of multi-link manipulator. • Demonstrate the dynamic characteristics under harmonic joint forces. • Investigate the stability analysis and bifurcation under resonance conditions. The present work theoretically demonstrates the dynamic analysis of a planar multi-link manipulator considering flexible links and joints mounted on a moving base. A nonlinear model of a multi-manipulator (N -links) has been developed accounting for an equivalent bi-directional mobile base. The nonlinear Euler-Bernoulli beam and spring-inertia/mass system have been introduced to demonstrate the flexibilities of links and joints, respectively. Modal analysis followed by trajectory analysis has been performed to investigate the behaviors of manipulator taking into account of accurate boundary conditions. Further, vibration characteristics, steady-state responses, and their stability at resonant situations due to joint motions and inertial coupling have been studied. Results of exploiting geometric and inertial coupling due to flexible arms on stability analysis of steady-state responses have been investigated and have been subsequently verified with the findings by directly solving the governing equations. Steady-state response characteristics to explore the dynamics of the multi-link manipulators under resonant states have been studied with the varying actuator mass, the flexibility of links, lifted payload, and joint parameters. The results thus obtained, deliver a useful insight into the position analysis and long-term behavior of multi-link flexible manipulators under harmonic hub-joint motions that shall further enable its proper vibration attenuation and control. [ABSTRACT FROM AUTHOR]

Methane (CH 4) is a strong greenhouse gas that has become the focus of climate mitigation policies in recent years. Ethane/methane ratios can be used to identify and partition the different sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We assessed the precision, accuracy, and selectivity of three commercially available laser-based analyzers that have been marketed as measuring instantaneous dry-mole fractions of methane and ethane in ambient air. The Aerodyne SuperDUAL instrument performed the best out of the three instruments, but it is large and requires expertise to operate. The Aeris Mira Ultra LDS analyzer also performed well for the price point and small size, but it required characterization of the water vapor dependence of reported concentrations and careful set-up for use. The Picarro G2210-i precisely measured methane, but it did not detect the 10 ppbv (part-per-billion by volume) increases in ambient ethane detected by the other two instruments when sampling a plume of incompletely combusted natural gas. For long-term tower deployments or those with large mobile laboratories, the Aerodyne SuperDUAL provides the best precision for methane and ethane. The more compact Aeris MIRA can, with careful use, quantify thermogenic methane sources to sufficient precision for mobile and short-term deployments in urban or oil and gas areas. We weighed the advantages of each instrument, including size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument. We recommend the Aerodyne SuperDUAL or the Aeris MIRA Ultra LDS depending on the situation. [ABSTRACT FROM AUTHOR]

Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos. [ABSTRACT FROM AUTHOR]

Objective: To introduce a wireless high‐definition endoscopic system (WHES) and compare it with a Storz high‐definition (HD) system for image resolution, colour resolution, weight, and costs. Materials and Methods: The WHES incorporated a portable light‐emitting diode light source and a wireless camera module, which can be compatible with different types of endoscopes. Images were wirelessly transmitted to a monitor or mobile platform such as smartphone through a receiver. The International Standards Organization 12233 resolution chart image was used for the comparison of image resolution and Munsell Colour Checker Chart for colour resolution. In all, 38 endourologists used a Likert questionnaire to blindly evaluate cystoscopic images from a patient with haematuria. The surgical team was asked about the overall performance of the WHES in 20 laparoscopic adrenalectomies using a unvalidated subjective survey. Results: There was no difference in image resolution between the two systems (5.82 vs 5.89 line pairs/mm). Without lens and respective light sources, there were better purple (ΔE = 21.48 vs 28.73), blue (ΔE = 34.88 vs 38.6) and red colour resolution (ΔE = 29.01 vs 35.45) for the WHES camera (P < 0.05), but orange (ΔE = 43.45 vs 36.52) and yellow (ΔE = 52.7 vs 35.93) resolutions were better for the Storz HD camera (P < 0.05). Comparing the WHES to a Storz laparoscopic system, the Storz system still had better resolution of orange and yellow, while the resolution of purple, blue, and red was similar for the two systems. The expert comments on resolution, brightness, and colour for cystoscopy were not statistically different, but the ergonomics score for the WHES was higher (3.7 vs 3.33, P = 0.038). The overall cost of the WHES was $23 000–25 000 compared with $45 000–50 000 for a Storz system. There were 100% general satisfaction for the WHES in the survey. Conclusion: We developed a new WHES that provides the same resolution images as a Storz laparoscopic system and acceptable colour resolution with the advantages of wireless connection, small volume, low cost, portability, and high‐speed wireless transmission. [ABSTRACT FROM AUTHOR]

Purpose: Staff management is the most cited ART/IVF laboratory inspection deficiency. Small ART/IVF clinics may be challenged to perform these activities by low staff volume; similarly, large ART/IVF networks may be challenged by high staff volume and large datasets. Here, we sought to investigate the performance of an automated, digital platform solution to manage this necessary task. Methods: The ART Compass (ARTC) digital staff management platform was used to assess the clinical decision-making of ART laboratory staff. The survey modules presented standardized instructions to technologists and measured inter- and intra-technologist variability for subjective "clinical decision-making" type questions. Internal and external comparisons were achieved by providing technologists two answers: (1) a comparison to their own lab director and (2) to the most popular response collectively provided by all lab director level accounts. The platform is hosted on HIPAA compliant Amazon web servers, accessible via web browser and mobile applications for iOS (Apple) and Android mobile devices. Results: Here, we investigated the performance of a digital staff management platform for single embryologist IVF practices and for three IVF lab networks (sites A, B, C) from 2020 to 2022. Embryology dish preparation survey results show variance among respondents in the following: PPE use, media volume, timing of oil overlay, and timing of moving prepared dishes to incubators. Surveying the perceived Gardner score and terms in use for early blastocysts reveals a lack of standardization of terminology and fair to poor agreement. We observed moderate inter-technologist agreement for ICM and TE grade (0.47 and 0.52, respectively). Lastly, the clinical decision of choice to freeze or discard an embryo revealed that agreement to freeze was highest for the top-quality embryos, and that some embryos can be highly contested, evenly split between choice to freeze or discard. Conclusions: We conclude that a digital platform is a novel and effective tool to automate, routinely monitor, and assure quality for staff-related parameters in ART and IVF laboratories. Use of a digital platform can increase regulatory compliance and provide actionable insight for quality assurance in both single embryologist practices and for large networks. Furthermore, clinical decision-making can be augmented with artificial intelligence integration. [ABSTRACT FROM AUTHOR]

The mobile phones Operating System like android and IOS finds wide use in smartphones and tablets and is thus suitable for home controllers. This project presents a smart home controller that uses WIFI in smartphone devices to control the operation of an automated security door system. The system idea is to use an Android or IOS app that sends commands to Electronic Relay-connected to home WIFI or internet- to control the opening of the door connected to an electric intercom. The Relay module is installed on the Intercom phone and the intercom phone connected to an electric lock which is installed on the door, the Relay Module receives the commands from the smartphone through WIFI, and passes these commands to the microcontroller that controls the opening of the door. The system can be used in various situations where access to an enclosure, doors, or any other thing needs to be open electronically. With the help of smartphone apps that will illustrate later, we can use smartphones to open the door using a smartphone voice assistant. [ABSTRACT FROM AUTHOR]

The article addresses the issue of mobile robotic platform positioning in GNSS-denied environments in real-time. The proposed system relies on fusing data from an Inertial Measurement Unit (IMU), magnetometer, and encoders. To get symmetrical error gauss distribution for the measurement model and achieve better performance, the Error-state Extended Kalman Filter (ES EKF) is chosen. There are two stages of vector state determination: vector state propagation based on accelerometer and gyroscope data and correction by measurements from additional sensors. The error state vector is composed of the velocities along the x and y axes generated by combining encoder data and the orientation of the magnetometer around the axis z. The orientation angle is obtained from the magnetometer directly. The key feature of the algorithm is the IMU measurements' isolation from additional sensor data, with its further summation in the correction step. Validation is performed by a simulation in the ROS (Robot Operating System) and the Gazebo environment on the grounds of the developed mathematical model. Trajectories for the ES EKF, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) algorithms are obtained. Absolute position errors for all trajectories are calculated with an EVO package. It is shown that using the simplified version of IMU's error equations allows for the achievement of comparable position errors for the proposed algorithm, EKF and UKF. [ABSTRACT FROM AUTHOR]

Smartphones are one of the most affordable and familiar devices available to people that are used for many scientific applications. Thus, implementing a dedicated mobile app for earthquake early warning (EEW) systems is one of the best options to save money and time to achieve dissemination of earthquake warnings to the public at large. This paper describes the development of a Warning Dissemination System comprising field sirens and mobile apps and presents details of developing a smartphone app for Android and iOS operating systems. The app is designed for the EEW system currently operational in the Northwest Himalayan State of Uttarakhand in India. The app named "Uttarakhand Bhookamp Alert" is freely available on the Google Play Store and Apple App Store and has already been downloaded more than 10,000 times. This app uses Firebase Cloud Messaging (FCM) service for sending earthquake early warnings to the users. The user's basic information and FCM token are saved at the warning server in a database during the user registration process. The information will be shared with the disaster response authorities after a damaging earthquake to rescue affected users requiring help. This app also provides online earthquake safety recommendations and guidance, including how to prepare yourself, find a safe hiding place, and do's and don'ts during an earthquake. [ABSTRACT FROM AUTHOR]

A serious game is a type of game that is designed for a primary purpose other than entertainment. Instead, serious games are intended to achieve specific goals, such as education, training, or health promotion. The goal of serious games is to engage players in a way that is both enjoyable and effective in achieving the intended learning or behavior change outcomes. Recently, several systematic reviews on the development and application of serious games and on the application of gamification techniques have been published, which indicate high activity and ongoing progress in this area of research. Such an extensive body of review papers raises the need to analyze and extract the current state and the prevailing trends of the serious games and gamification (SGG) domain by analyzing and summarizing the systematic review articles. This study presents a systematic meta-review, i.e., a review of the 53 survey papers on the domain of serious games and gamification. The systematic review follows the PRISMA guidelines, while constructive and cross-sectional methods are used to analyze and present the results. Finally, this study identifies the future trends and challenges for the domain. As a result, the meta-review helps the reader to quickly assess the present status of SGG and serves as a reference for finding further information on each technology utilized in SGG. Using the criterion of the citations, the meta-review analysis provides insight into the quantity and academic relevance of the published SGG articles. Moreover, 53 articles published in journals were selected as important surveys in the research field. The study found that serious games and gamification techniques are increasingly being used for a wide range of health conditions and the focus is shifting towards the use of mobile and digital platforms, virtual reality, and machine learning to personalize and adapt interventions. The existing research gaps include the lack of standardization in development and evaluation, insufficient understanding of underlying mechanisms of action, limited understanding of integration into existing healthcare systems, limited understanding of specific game mechanics and design elements for promoting health behaviors, and limited research on scalability, adoption, and long-term effects. These research gaps highlight the need for further research to fully understand the potential and limitations of serious games and gamification for health and how to effectively apply them. [ABSTRACT FROM AUTHOR]

Interest in Unmanned Aerial Vehicles (UAVs) has increased due to their versatility and variety of applications, however their battery life limits their applications. Heterogeneous multi-robot systems can offer a solution to this limitation, by allowing an Unmanned Ground Vehicle (UGV) to serve as a recharging station for the aerial one. Moreover, cooperation between aerial and terrestrial robots allows them to overcome other individual limitations, such as communication link coverage or accessibility, and to solve highly complex tasks, e.g., environment exploration, infrastructure inspection or search and rescue. This work proposes a vision-based approach that enables an aerial robot to autonomously detect, follow, and land on a mobile ground platform. For this purpose, ArUcO fiducial markers are used to estimate the relative pose between the UAV and UGV by processing RGB images provided by a monocular camera on board the UAV. The pose estimation is fed to a trajectory planner and four decoupled controllers to generate speed set-points relative to the UAV. Using a cascade loop strategy, these set-points are then sent to the UAV autopilot for inner loop control. The proposed solution has been tested both in simulation, with a digital twin of a solar farm using ROS, Gazebo and Ardupilot Software-in-the-Loop (SiL); and in the real world at IST Lisbon's outdoor facilities, with a UAV built on the basis of a DJ550 Hexacopter and a modified Jackal ground robot from DJI and Clearpath Robotics, respectively. Pose estimation, trajectory planning and speed set-point are computed on board the UAV, using a Single Board Computer (SBC) running Ubuntu and ROS, without the need for external infrastructure. [ABSTRACT FROM AUTHOR]

In today's world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained. [ABSTRACT FROM AUTHOR]

The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path's odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot's pose and position data. [ABSTRACT FROM AUTHOR]

This article presents a method to develop an application based on Augmented Reality technology in a Unity development environment. The article presents two test scenarios that give an insight into the possibilities of augmented reality applications. The first scenario is a presentation of a simple game based on a graphic marker and its generated content in the form of a maze. By physically moving the marker in front of the camera, the maze and the ball inside is moved. The second scenario includes a simulation environment of a collaborative robot in the form of an augmented reality application that offers the possibility of planning the robot's trajectories. The possibilities of this technology are endless and cover a large segment of application development from educational to industrial applications. Marker based Augmented Reality using predefined visual markers embedded within the system, physical world objects are detected for superimposition of virtual elements. The test application was designed using a combination of Vuforia, Unity 3D platforms, and the mobile phone used was an Android based Xiaomi Redmi 10 pro phone. This article focuses on the implementation challenges faced whilst designing Augmented Reality applications on mobile platforms. [ABSTRACT FROM AUTHOR]

The paper aims to examine publicly available vulnerability databases and their systems for assessing the severity of damage that can be caused by malware targeting mobile devices running Android or iOS. The second goal of the study is to evaluate the vulnerability detection capabilities associated with a specific version of a mobile operating system. Methods used: comparative analysis and summary. Based on the results of the application of the comparative analysis, it is established which of the national databases have better capabilities to filter the data and obtain adequate results when searching for vulnerabilities for mobile devices. [ABSTRACT FROM AUTHOR]

In an effort to close the financial gap between the banked and unbanked Nigerian population, mobile money services have been introduced. The alternative banking provides access to financial services for individuals who do not own bank accounts, thereby promoting a cashless society in the country. It provides a platform for small savings for low earners, especially the rural populace, and also facilitates the transfer and receipt of money and payments for goods and services in a more convenient manner. However, many studies have revealed low user acceptance for this alternative banking service in Nigeria. The aim of this study, therefore, was to investigate the determining factors for the acceptance of mobile money services in Nigeria using the self-service technology acceptance model. An online questionnaire was shared among online users of smart phones across the country using survey methodology, from which three hundred and eighty-nine responses were analyzed for the study. The result revealed that perceived usefulness and perceived ease of use have significant influence on user acceptance of mobile money services in Nigeria, while need for interaction and perceived risk of mobile money acceptance were found to have insignificant influence on the acceptance of alternative banking services. The study will guide policymakers regarding mobile money service implementation strategies. The study emphasizes the recommendation that mobile network operators and financial institutions increase their efforts to make mobile money platforms more user-friendly and valuable. It further suggests future research consider more determinants like ethnicity, literacy, trust, security, and others. [ABSTRACT FROM AUTHOR]

Currently, the three-dimensional (3D) distribution and characteristics of air pollution cannot be understood based on the application of any single atmospheric monitoring technology. Long-term, high-precision and large-scale 3D atmospheric monitoring might become practical by combining heterogeneous modern technologies; for this purpose, the Space-Air-Ground integrated system is a promising concept. In this system, optical remote sensing technologies employing fixed or mobile platforms are used as the main means for ground-based observations. Tethered balloons, unmanned aerial vehicles (UAV) and airborne platforms serve as the air-based observation segment. The final part, satellite remote sensing, corresponds to space-based observations. Aside from obtaining the 3D distribution of air pollution, research on emission estimation and pollution mechanisms has been extensively implemented based on the strengths of this system or some portion of it. Moreover, further research on the fusion of multi-source data, optimization of inversion algorithms, and coupling with atmospheric models is of great importance to the realization of this system. [Display omitted] [ABSTRACT FROM AUTHOR]

For some flight missions, such as autonomous landing on mobile platforms, the demands on indicators such as target-tracking accuracy and so on are relatively high. To achieve this, a guidance system with excellent precision is necessary. An asymmetric tracking differentiator based on a tracking differentiator is proposed to establish the guidance system. On the basis of the proposed asymmetric tracking differentiator, an altitudinal and horizontal helicopter guidance system structure is designed. In this paper, a guidance law is designed in order to meet the accuracy and precision requirements in the autonomous landing and transition process. Apart from that, a plane-motion-guidance law is also designed to realize static and dynamic point tracking, linear route tracking and circular route tracking to improve the trajectory smoothness and accuracy. Finally, simulations of the autonomous landing process on moving platforms, including three stages, namely approaching, tracking and landing, are completed. The application effects and precision of the autonomous landing guidance algorithm under different wave heights and period conditions are analyzed through the obtained simulation curves. [ABSTRACT FROM AUTHOR]

Technological developments in recent years have been effective in preferring internet publishing more than traditional publishing. The widespread use of the internet in daily life has accelerated the transition of television broadcasting to this area. Today, broadcasts that reach the audience especially through mobile platforms have brought about qualitative and quantitative diversity and change in program types. The types of programs on the digital platforms broadcasting in Turkey have been restructured formally and contextually in accordance with the consumption culture. This study is important in terms of showing the transformation in the field of publishing that comes with digital publishing platforms. The differences of the series published on digital platforms compared to their traditional counterparts constitute the problematic of the study. All the episodes of the first season of the 'Ten Thousand Steps' series, which was broadcast on the digital platform GAİN, which is an example of the study, were examined in order to determine the structural and narrative differences it contained. In this study, content analysis method was used. This method, which is frequently used in the field of social sciences, has been preferred because it systematically examines the data obtained from highly visual documents such as television series in the integrity of meaning. The findings obtained because of the study show that the new period of publishing has undergone a significant change compared to the traditional period in terms of the use of form, duration, space, story, and character. Although the flexibility provided by digital broadcasting platforms and the ability to do it with lower budgets are considered as advantages, the publication of the produced content without the control mechanisms will pave the way for social and cultural corruption. It is foreseen that the support and supervision of the series contents by the relevant public institutions to ensure cultural continuity will ensure the positive progress of the process in the terms of society. [ABSTRACT FROM AUTHOR]

Shared decision making is a patient-centered clinical decision-making process that allows healthcare workers to share the existing empirical medical outcomes with patients before making critical decisions. This study aims to explore a project in a medical center of developing a mobile SDM in Taiwan. Chi Mei Medical Center developed the mobile SDM platform and conducted a survey of evaluation from healthcare workers. A three-tier platform that based on cloud infrastructure with seven functionalities was developed. The survey revealed that healthcare workers with sufficient SDM knowledge have an antecedent effect on the three perceptive factors of acceptance of mobile SDM. Resistance to change and perceived ease of use show significant effect on behavioral intention. We provided a comprehensive architecture of mobile SDM and observed the implementation in a medical center. The majority of healthcare workers expressed their acceptancem; however, resistance to change still present. It is, therefore, necessary to be eliminated by continuously promoting activities that highlight the advantages of the Mobile SDM platform. In clinical practice, we validated that the mobile SDM provides patients and their families with an easy way to express their concerns to healthcare workers improving significantly their relationship with each other. [ABSTRACT FROM AUTHOR]

The learning customization and monitoring are considered key aspects of the teaching-learning processes. Some works have proposed mobile learning systems that provide teachers and students learning monitoring and personalization services. One of the main requirements of these kinds of systems in terms of software quality is usability; however, few works have addressed the usability issues using laboratory studies with users in real domains. In this work, we present a usability evaluation of the learning monitoring and personalization services of a mobile learning platform based on a laboratory study in which nine teachers and ten students participated. In our usability evaluation, the aspects evaluated were effectiveness, efficiency, and level of user satisfaction as proposed by the ISO/IEC 25000 family of standards. The results show that the teachers presented effectiveness, efficiency, and satisfaction considered satisfactory, while the students presented effectiveness and satisfaction classified as satisfactory and acceptable efficiency. The usability evaluation described in this work can serve as a reference for developers seeking to improve learning monitoring and personalization services development. [ABSTRACT FROM AUTHOR]

Magnetically actuated microrobots showed increasing potential in various fields, especially in the biomedical area, such as invasive surgery, targeted cargo delivery, and treatment. However, it remains a challenge to incorporate biocompatible natural polymers that are favorable for practical biomedical applications. In this work, bilayer magnetic microrobots with an achiral planar design were fabricated using a biocompatible natural polymer and Fe3O4 nanoparticles through the photolithography by applying the layer-by-layer method. The microrobots consisted of a magnetic bottom layer and a photo-crosslinked chitosan top layer. The SEM results showed that the microrobot processed the L-shaped planar structure with the average width, length, and thickness of 99.18 ± 5.11 μm, 189.56 ± 11.37 μm, and 23.56 ± 4.08 μm, respectively. Moreover, microrobots actuated using a three-dimensional (3D) Helmholtz coil system was characterized and reached up to an average maximum velocity of 325.30 μm/s and a step-out frequency of 14 Hz. Furthermore, the microrobots exhibited excellent cell biocompatibility towards L929 cells in the CCK-8 assay. Therefore, the development of bi-layered chitosan-based microrobots offers a general solution for using magnetic microrobots in biomedical applications by providing an easy-to-fabricate, highly mobile microrobotic platform with the incorporation of biocompatible natural polymers for enhanced biocompatibility. [ABSTRACT FROM AUTHOR]

In order to sufficiently protect active personnel and physical environment from hazardous leaks, recent industrial practices integrate innovative multi-modalities so as to maximize response efficiency. Since the early detection of such incidents portrays the most critical factor for providing efficient response measures, the continuous and reliable surveying of industrial spaces is of primary importance. Current study develops a surveying mechanism, utilizing a swarm of heterogeneous aerial mobile sensory platforms, for the continuous monitoring and detection of CH 4 dispersed gas plumes. In order to timely represent the CH 4 diffusion progression incident, the research concerns a simulated indoor, geometrically complex environment, where early detection and timely response are critical. The primary aim was to evaluate the efficiency of a novel multi-agent, closed-loop, algorithm responsible for the UAV path-planning of the swarm, in comparison with an efficient a state-of-the-art path-planning EGO methodology, acting as a benchmark. Abbreviated as Block Coordinate Descent Cognitive Adaptive Optimization (BCD-CAO) the novel algorithm outperformed the Efficient Global Optimization (EGO) algorithm, in seven simulation scenarios, demonstrating improved dynamic adaptation of the aerial UAV swarm towards its heterogeneous operational capabilities. The evaluation results presented herein, exhibit the efficiency of the proposed algorithm for continuously conforming the mobile sensing platforms' formation towards maximizing the total measured density of the diffused volume plume. [ABSTRACT FROM AUTHOR]

Pick-and-place operations are basic, and are currently the most common for robots operating in the industry. Massive applications makes it reasonable to ask whether, and to what extent these operations are realised in a way that guarantees rational energy consumption. In many cases, the answer to such a question is neither positive nor known. Therefore, this paper attempts to present a rational and systematic approach to the low-energy pick-and-place operations performed by robots. This paper describes a new approach for the robot's tool centre point path planning, which enables the minimisation of energy consumption wherein productivity in preserved, and where care is taken for the persistence of the critical mechanical components of the robot cooperating with the autonomous mobile platform. The effectiveness of the described approach has been proven from the results of the theoretical, simulation, experimental and implementation tests carried out using an industrial articulated robot with six degrees of freedom. [ABSTRACT FROM AUTHOR]

Increasingly, many security and privacy-sensitive applications are running on mobile platforms. However, as mobile operating systems are becoming increasingly sophisticated, they are vulnerable to various attacks. In addressing the need of running high assurance mobile apps in a secure environment even though the operating systems are untrusted, this paper presents vTrust, a new mobile app trusted execution environment, which offloads the general execution and storage of a mobile app to a trusted remote server (e.g., a VM running in a cloud) and secures the I/O between the server and the mobile device with the aid of a trusted hypervisor on the mobile device. Specifically, vTrust establishes an encrypted I/O channel between the local hypervisor and the remote server. In this way, any sensitive data flowing through the mobile OS, which the hypervisor hosts, is encrypted from the perspective of the local mobile OS. To enhance the performance of vTrust, we have also designed multiple optimizations, such as output data compression and selective sensor data transmission. We have implemented vTrust, and our evaluation shows that it has limited impact on both user experience and the application performance. [ABSTRACT FROM AUTHOR]

Most of the traditional athletic performance dynamic change prediction platforms use gray correlation and neural networks to establish prediction methods, and the prediction results are inaccurate and time-consuming. Therefore, the construction of a mobile prediction platform for dynamic changes in athletic performance based on screening factors is proposed to improve the accuracy of its prediction results and shorten the prediction time. On the basis of hardware, the main body of students is determined, the athletic performance data of students is collected by the physical education management system of colleges and universities, and the missing data is filled by the criterion function of K-average algorithm. The KNN algorithm is used to make decisions to obtain the most representative predictors of athletic performance data, and the characteristics of students' athletic performance are extracted. The effective factors are selected according to the tenfold cross-validation fold MSE minimum principle of SVM, and the mandatory screening based on SVM is used to assign weights to each reserved factor. According to the weighted result of the obtained factors, the influencing factors of athletic performance are analyzed, and the weighting process is completed for the factors, and the athletic performance prediction is realized by using support vector machine. In the platform established in this paper, the students' 1000-m running results are predicted. The simulation results show that, compared with the traditional platform, the constructed platform has higher prediction accuracy and takes less time. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value. [ABSTRACT FROM AUTHOR]

The Android platform has become the most popular smartphone operating system, which makes it a target for malicious mobile apps. This paper proposes a machine learning-based approach for Android malware detection based on application features. Unlike many prior research that focused exclusively on API Calls and permissions features to improve detection efficiency and accuracy, this paper incorporates applications' contextual features with API Calls and permissions features. Moreover, the proposed approach extracted a new dataset of static API Calls and permission features using a large dataset of malicious and benign Android APK samples. Furthermore, the proposed approach used the Information Gain algorithm to reduce the API and permission feature space from 527 to the most relevant 50 features only. Several combinations of API Calls, permissions, and contextual features were used. These combinations were fed into different machine-learning algorithms to show the significance of using the selected contextual features in detecting Android malware. The experiments show that the proposed model achieved a very high accuracy of about 99.4% when using contextual features in comparison to 97.2% without using contextual features. Moreover, the paper shows that the proposed approach outperformed the state-of-the-art models considered in this work. [ABSTRACT FROM AUTHOR]

An accurate measurement of field-grown wheat traits, including spike number, dimension and volume are essential for crop phenotyping and yield analysis. A high-throughput method to image field-grown wheat in three dimensions is presented with an accompanying unsupervised measuring method to obtain individual wheat spike data. Images are captured using four structured light scanners on a field mobile platform, creating dimensionally accurate sub-millimetre resolution 3D point clouds for a 4.5 m3 volume in less than 10 s. The unsupervised method analyses each trial plot's 3D point cloud, containing hundreds of wheat spikes, calculating the average size of the wheat spike and total spike volume per plot. The analysis utilises an adaptive k -means algorithm with dynamic perspectives, to fit each spike's shape and measures the dimensions with a random sample consensus algorithm. The method generates small cuboids to fit all the wheat spikes and estimate the total spikes volume. Experimental results show that the proposed algorithm is a reliable tool for identifying spikes from wheat crops and identifying individual spikes. Compared with the manual measurement, according to the results of five scenes, the average error rate in the number of spikes, spikes' height and spikes' width in tests were 16.27%, 5.24% and 12.38% respectively. • Laser imaging technology for automatic field phenotyping application. • High-throughput field capture platform was constructed. • Unsupervised algorithm allows for wheat dimensions measurement in 3D point clouds. • Automatic measurement can estimate wheat spike size, volume and number of spikes. • Framework was evaluated by comparing the experiment with manual measurement. [ABSTRACT FROM AUTHOR]

The Sun's ultraviolet radiation, toxic chemicals from industry, and other factors can cause problems to human skin. These factors have boosted the risk of humans contracting skin cancer. In identifying and detecting signals of skin cancer diseases, recent advancements in deep learning techniques significantly improve the accuracy of diagnosis. This work delves into processing techniques for human skin images with MorphologyEx (blackhat), thresholding, semantic segmentation with UNET, and several famous convolutional neural network architectures to improve skin classification performance. If skin cancer can be stopped in the first stage, the survival rate of the patient could be more than 95%, while it drops to just 5% if the detection is carried lately in Stage IV4. Due to its seriousness, the healthcare and research community has received considerable attention, and its most important goal is to be able to diagnose it at an early stage. However, the challenge due to the similarity in melanocytic and non‐melanoma skin lesions is inevitable. We achieved skin classification performances of 0.8560, 0.9672, 0.8063, and 0.8560 measured by overall accuracy, area under curve, Matthews correlation coefficient, and F1‐score, respectively. The proposed approach has been deployed into applications on Mobile and Web platforms. [ABSTRACT FROM AUTHOR]

A previous study investigates the advertising strategies of the platform and App by assuming that the platform's advertisement will increase the number of the App's users, but the App's advertisement will not increase the number of the platform's users; and the platform overcharges the App and takes the advertising fee as a source of its revenue. However, the existing users of the App may recommend the App to the users who have not used it. As a result, the App's advertisement may increase the number of the platform's users. Additionally, it is not reasonable that the platform takes the advertising fee of the App as a source of its revenue. These motivate us to reanalyze the previous work. This paper reanalyzes the previous work by additionally assuming that the App's advertisement will increase the number of the platform's users and assuming that the platform receives its revenue from its users and shares a proportion of the App's sales revenue and the App receives its revenues from its users and a proportion of its advertising cost subsidized by the platform. We find that these new assumptions have some significant effects on the previous results. We use dynamical optimization approaches to analyze a decentralized system and find the two parties' optimal advertisement efforts and proportions. To achieve the efficiency of the integrated system that is proved to be more efficient than the decentralized system, we design a bilateral advertising contact for the decentralized system and show that there exists a unique contact that can coordinate the decentralized system. We find that both parties are better off under some mild conditions and the proportion that the platform bears the App's advertising cost becomes greater with the contract than without the contract. We have gained some managerial insights. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a strong greenhouse gas that has become the focus of climate mitigation policies in recent years. Ethane/methane ratios can be used to identify and partition the difference sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We assessed the precision, accuracy and selectivity of three commercially available laser-based analyzers that have been marketed as measuring instantaneous dry mole fractions of methane and ethane in ambient air. The Aerodyne SuperDUAL instrument performed best of the three instruments but it requires expertise to operate and space for the large footprint. The Aeris Mira Ultra LDS analyzer also performed well for the price point and small footprint but required characterization of the water vapor dependence of reported concentrations and careful setup for use. The Picarro G2210-i precisely measured methane but it did not detect the 10 ppbv increases in ambient ethane detected by the other two instruments when sampling a plume of incompletely combusted natural gas. For long-term tower deployments or those with large mobile laboratories, the Aerodyne SuperDUAL provides the best precision for methane and ethane. For smaller mobile platforms, the Aeris MIRA is a more compact analyzer, and with careful use, can quantify thermogenic methane sources to sufficient precision for short term deployments in urban or oil and gas areas. We weighed the advantages of each instrument, including size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument, and we recommend the Aerodyne SuperDUAL or the Aeris MIRA Ultra LDS depending on the situation. [ABSTRACT FROM AUTHOR]