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The deployment of fifth-generation mobile network (5G), beyond 5G and sixth-generation mobile network platforms encounters challenges of blockage, interference, and path loss in radio mobile environments. Metasurfaces provide a promising solution to address these limitations. In this paper, we present a methodology for developing ultrathin flexible metasurface-based frequency selective surfaces (FSSs). Our approach combines thermal evaporation for metallic thin films with a macroscopic metasurface mask (something analogous to screen-printing but using thermal evaporation instead of inks). As a proof of concept, we fabricate a sub-6 GHz metasurface-based FSS using gold deposition on a flexible polyethylene terephthalate substrate. Experimental results are validated through numerical full-wave simulations using COMSOL Multiphysics and equivalent-circuit model simulations. The metasurface operates within the primary frequency band utilized in 5G networks (3–5 GHz), indicating its potential applicability across a wide range of flexible, conformal, and wearable devices. The fabricated FSS can be installed on surfaces of any shape, such as flat or curved windows, as well as on walls or other external surfaces. This methodology offers practical solutions for wireless communications and enhancing signal transmission in diverse environments. [ABSTRACT FROM AUTHOR]

Mobile platforms provide important entrepreneurial opportunities by facilitating access to a global market composed of more than a hundred countries. These opportunities are crucial for mobile app startups as they seek to reach a wider audience and accelerate growth. Nevertheless, performing in foreign countries is challenging as mobile app startups face a "foreignness liability" since they are competing against complementors (i.e., app developers) who have experience in their domestic market. We offer a research model that assesses the impact of cultural distance on the app-level performance of mobile app startups in foreign countries. We hypothesize the moderating effects of investor-consumer cultural distance and environmental characteristics within foreign countries. We test the model using a dataset of over 550 mobile app startups operating on the iOS platform. Our findings support our hypotheses and provide insights into whether mobile platforms are level playing fields. Our study sheds light on the interactions between a startup and its investors' cultural distances highlighting configurations of cultural distance that are beneficial for app performance in foreign countries when the environment is stable or uncertain. [ABSTRACT FROM AUTHOR]

Multi-sided platforms must make decisions on both pricing and engineering investment and must continually adjust them as the platform scales over its lifecycle. Engineering investments can be allocated to features that improve a platform's standalone value, social features to take advantage of same-side network effects, or integration tools and boundary resources to facilitate third-party content creation. Guidance in the academic or practitioner literature is not granular. Moreover, relevant normative economic models that consider externalities are rarely dynamic. Hence, there is a gap in knowledge about how to best balance tradeoffs between different strategic decisions throughout the entire platform lifecycle. To begin to address this gap, we explore normative strategies for coordinating pricing and engineering investment decisions on a continuous basis under different ecosystem conditions. We build a simulation model informed by economics and marketing theory and perform extensive sensitivity analyses on key parameters in different ecosystem scenarios over a multi-period lifecycle. We find that pricing and investment strategies must continuously change to perform optimally. In particular, strategies that are most effective at launch often differ from those that are most effective during scaling as well as those most effective at maturity. We also find that the optimal strategy depends strongly on the monetization model and market aversion to price changes. Lastly, we specifically examine four different industry segments: mobile platforms, social media, the sharing economy, and business-to-business. The results provide evidence that the trajectory of platform pricing and investment strategies should greatly differ depending on industrial context. [ABSTRACT FROM AUTHOR]

Portable instruments that can measure the number concentration and size of airborne nanoparticles are very useful for assessing their impacts on human health and climate, mainly because they can enable personal monitoring when carried by individuals, and/or 2- or 3-dimensional mappings when employed onboard mobile platforms. Partector 2 (P2), which is a lightweight and portable instrument manufactured by Naneos Particle Solutions GmbH (Windisch, Switzerland), can determine the concentration (up to 106 #/cm3) and average diameter of aerosol particles having sizes from 10 to 300 nm, making it an excellent candidate for such measurements. Although its performance has been investigated at standard conditions (i.e., ground level pressure and room temperatures), it has not been assessed under reduced pressure and temperature conditions that are typically encountered at higher altitudes; e.g., when employed outdoors in mountainous environments and/or onboard Unmanned Aerial Systems; UASs. Here we assess the counting and sizing capabilities of P2 at temperatures from ca. 22 down to 4 °C, and pressures from 1013 down to 710 hPa that correspond to altitudes from sea level to ca. 3 km. Our results show that the performance of the instrument is not substantially affected when operated at these conditions, remaining within the accuracy thresholds of ±30% reported by the manufacturer. P2, therefore, qualifies for outdoor use at higher altitudes, and can be employed in such environments to determine the number concentration and mean size of sub-300 nm aerosol particles, complementing existing portable optical particle counters that are already employed onboard aerial systems. [ABSTRACT FROM AUTHOR]

Simple Summary: The utilization of computer vision technology and automation for monitoring and collecting pigeon eggs is of significant importance for improving labor productivity and the breeding of egg-producing pigeons. Currently, research both domestically and internationally has predominantly focused on the detection of eggs from poultry such as chickens, ducks, and geese, leaving pigeon egg recognition largely unexplored. This study proposes an effective and lightweight network model, YOLOv8-PG, based on YOLOv8n, which maintains high detection accuracy while reducing the model's parameter count and computational load. This approach facilitates cost reduction in deployment and enhances feasibility for implementation on mobile robotic platforms. In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model's robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model's parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms. [ABSTRACT FROM AUTHOR]

Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n-gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) have diverse functionality, emission sources, and environmental fates. Speciated measurements of their spatiotemporal variability are thus key to understanding their impacts on air quality, health, and climate. Networks of passive samplers can be used to map VOC concentrations, or in situ instruments can be deployed on mobile platforms. Limitations of existing in situ instruments include high cost, identification of non-target species, differentiation of isomeric species, and low time resolution, which limits how quickly an area can be spatially mapped with mobile measurements. This work describes the development of the Multichannel Organics In situ enviRonmental Analyzer (MOIRA), which has been designed for in situ mobile measurements of target and non-target VOCs from the cargo area of a hybrid hatchback wagon vehicle. Staggered sample collection and analysis by four thermal desorption collectors, four miniature gas chromatography (GC) heaters, and two compact residual gas analyzer (RGA) mass spectrometer (MS) detectors enable continuous measurements at a 10 min time resolution. Non-target species and structural isomers can be identified with electron ionization (EI), and species detected include alkanes (from pentane to pentadecane) and aromatics, as well as more oxidized species such as aldehydes, esters, and carboxylic acids. The instrument is characterized in the laboratory under different environmental conditions and in two pilot field studies of indoor air in a single-family residence and of ambient air during a mobile deployment. [ABSTRACT FROM AUTHOR]

Collaborative robots combined with mobile platforms can move freely and have a wide work area. Technological developments in AI and vision have also led to increased research on recognition of objects and surroundings using mobile manipulators. However, even though such trends have resulted in growing demand for research on mobile manipulators, their high prices make them difficult to purchase. We solved this problem using 3D printing. A mobile manipulator was manufactured by replacing more expensive reducers with 3D printed planetary gear and cycloid gear reducers. As well, a mecanum in-wheel motor equipped with a 3D printed cycloid reducer was developed to increase the usability of the interior space of the mobile platform. The study employed a task-space control algorithm, which controls the mobile platform and the manipulator simultaneously to ensure effective control of the mobile manipulator. Task-space motion control performance was verified through Gazebo simulation and experiment. Additionally, the repeatability of the task-space position control was measured through experiments and its performance was compared with a commercial manipulator. Finally, we have developed a mobile manipulator that can perform similar levels of work as a commercial robot at one tenth the price of a general commercial mobile manipulator. [ABSTRACT FROM AUTHOR]

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution. [ABSTRACT FROM AUTHOR]

The underwater environment is filled with various sounds, with its soundscape composed of biological, geographical, and anthropological sounds. Our work focused on developing a novel method to observe and classify these sounds, enriching our understanding of the underwater ecosystem. We constructed a biologging system allowing near-real-time observation of underwater soundscapes. Utilizing deep-learning-based edge processing, this system classifies the sources of sounds, and upon the tagged animal surfacing, it transmits positional data, results of sound source classification, and sensor readings such as depth and temperature. To test the system, we attached the logger to sea turtles (Chelonia mydas) and collected data through a cellular network. The data provided information on the location-specific sounds detected by the sea turtles, suggesting the possibility to infer the distribution of specific species of organisms over time. The data showed that not only biological sounds but also geographical and anthropological sounds can be classified, highlighting the potential for conducting multi-point and long-term observations to monitor the distribution patterns of various sound sources. This system, which can be considered an autonomous mobile platform for oceanographic observations, including soundscapes, has significant potential to enhance our understanding of acoustic diversity. [ABSTRACT FROM AUTHOR]

Mobile manipulator robots that combine mobile platforms and robotic arms have been attracting considerable attention in recent years. In this paper, the motion control problem of the mobile manipulator system is considered with the following proposed control strategy. Firstly, a decoupled dynamic model is created to increase operation safety and to reduce complexity such that the independent controllers can be designed for the mobile system and the manipulator system. Subsequently, an effective reference trajectory generator is proposed to guide the mobile manipulator systems to the proper position for the manipulation being able to grip the target. Thereafter, the fuzzy controllers are designed for the mobile system to eliminate the tracking error and for the manipulator system to accomplish the gripping mission. The stability of the mobile manipulator control system can be guaranteed by the Lyapunov theory. Finally, the numerical simulations are given to demonstrate the effectiveness of the proposed approach. From the simulation results, it can be seen that this paper as well as other compared methods have good control response in case of small controller gain, in which the convergence time are about 4–5 s. However, by increasing the controller gain to improve the control response, the other methods will make the system unstable or the controller output will produce a large amount of chattering. The proposed controller in this paper can not only decrease the convergence time, from 5 to 3 s, but also provide a smooth control response. [ABSTRACT FROM AUTHOR]

The Weight Management Health Note application, developed by the Korean Society for the Study of Obesity (KSSO), was designed to assist individuals in weight management and enhance overall well-being. The Committee of IT-Convergence Treatment of Metabolic Syndrome of the KSSO designed this application. Committee members reviewed and supervised the application’s underlying driving algorithms and scientific rationale. A healthcare-specific application developer subsequently finalized the application. This application encompasses a myriad of features, including a comprehensive food diary, an exercise tracker, and tailor-made lifestyle recommendations aligned with individual needs and aspirations. Moreover, it facilitates connections within a community of like-minded individuals endeavoring to manage their weight, fostering mutual support and motivation. Importantly, the application is rich in evidence-based health content curated by the KSSO, ensuring users access accurate information for effective obesity management. Looking ahead, the KSSO is committed to orchestrating diverse academic research endeavors linked to this application and refining its functionalities through continuous feedback from users. The KSSO aspires for this application to serve as a valuable resource for individuals striving to manage their health and enhance their quality of life. [ABSTRACT FROM AUTHOR]

This study demonstrates robot technology for harvesting edible bird's nests within swiftlet houses. A comprehensive manipulator's movement analysis of harvesting operation with a separating tool is provided for precisely collecting swiftlet nests. A robotic manipulator mounted on a mobile platform with a vision system is also analyzed and evaluated in this study. The actual harvesting or separating the swiftlet nests is performed with visual servo feedback. The manipulator performs the gross motions of separating tools and removing the nests under computer control with velocity and position feedback. The separating principle between the objective nest and wooden frame has been applied to a demonstration removal of nests using a four-degrees-of-freedom manipulator to perform the gross movements of tool. The actual separations using this system are accomplished as fast as the manipulator can be controlled to perform the necessary deceleration and topping at the end of separating. This is typically 2.0 s. This efficiency underscores the system's capability for swift and precise operation in harvesting an edible bird nest task. [ABSTRACT FROM AUTHOR]

Coastal Santa Barbara is among the most exposed communities to wildfire hazards in Southern California. Downslope, dry, and gusty windstorms are frequently observed on the south-facing slopes of the Santa Ynez Mountains that separate the Pacific Ocean from the Santa Ynez valley. These winds, known as "Sundowners," peak after sunset and are strong throughout the night and early morning. The Sundowner Winds Experiment (SWEX) was a field campaign funded by the National Science Foundation that took place in Santa Barbara, California, between 1 April and 15 May 2022. It was a collaborative effort of 10 institutions to advance understanding and predictability of Sundowners, while providing rich datasets for developing new theories of downslope windstorms in coastal environments with similar geographic and climatic characteristics. Sundowner spatiotemporal characteristics are controlled by complex interactions among atmospheric processes occurring upstream (Santa Ynez valley), and downstream due to the influence of a cool and stable marine boundary layer. SWEX was designed to enhance spatial measurements to resolve local circulations and vertical structure from the surface to the midtroposphere and from the Santa Barbara Channel to the Santa Ynez valley. This article discusses how SWEX brought cutting-edge science and the strengths of multiple ground-based and mobile instrument platforms to bear on this important problem. Among them are flux towers, mobile and stationary lidars, wind profilers, ceilometers, radiosondes, and an aircraft equipped with three lidars and a dropsonde system. The unique features observed during SWEX using this network of sophisticated instruments are discussed here. [ABSTRACT FROM AUTHOR]

In this study, the novel mobile dynamometric platform, OREKA, was utilized to perform an extensive analysis of the centre of pressure behaviour during different tilt motion exercises. This platform is based on a parallel manipulator mechanism and can perform rotations around both horizontal axes and a vertical translation. A group of participants took part in an experimental campaign involving the completion of a set of exercises. The aim was to evaluate the platform's potential practical application and investigate the impact of visual on-screen feedback on centre of pressure motion through multiple balance indicators. The use of the OREKA platform enables the study of the impact on a user's balance control behaviour under different rotational perturbations, depending on the availability of real-time visual feedback on a screen. Furthermore, it presented data identifying postural control variations among clinically healthy individuals. These findings are fundamental to comprehending the dynamics of body balance. Further investigation is needed to explore these initial findings and fully unlock the potential of the OREKA platform for balance assessment methodologies. [ABSTRACT FROM AUTHOR]

This article explores the use of drones in physics education, specifically in first-year physics labs. Drones are becoming more accessible and can provide accurate positional data, making them useful for conducting experiments and collecting data. The article highlights the necessary equipment and software, such as the DJI Mavic Mini-2 drone and the PocketLab Voyager data collection device. It presents six experiments that students can conduct using drones, including measuring distance, determining height from shadows, and calculating linear velocity. The article emphasizes the educational benefits of using drones in physics education and provides detailed instructions for conducting the experiments. [Extracted from the article]

Context: The most popular mobile platforms, Android and iOS, are traditionally developed using native programming languages—Java and Kotlin for Android, and Objective‐C followed by Swift for iOS, respectively. Due to their popularity, there is always a demand to convert applications written for one of these two platforms to another. Cross‐platform mobile development is widely used as a solution where an application is written once and deployed on multiple platforms written in several other programming languages. One common cross‐platform approach that has been used recently by some research groups is the Trans‐Compilation approach. They focus on translating a program written in iOS into Android or vice versa. The main problem with their solutions is that library function mapping is not generalized and usually functions constitute most of the parts of any program. Objective: This study aims to introduce an automatic library mapping approach for mobile programming languages. Method: A library function of a source language will be automatically mapped to a corresponding function of the destination language by using the function structure for the two languages. The function structure includes the library to which the function belongs, the return type, parameter types, and the number of parameters. To test our approach, we map from Swift to Java. Results: The results of our experiments show that our automatic library mapping approach achieves an average accuracy of 83.6% when tested on the most used libraries and outperforms current state‐of‐the‐art mapping techniques in terms of mapping accuracy. Conclusion: These findings show that our automatic mapping approach is promising and can help to overcome the limitations of the trans‐compilation approaches. [ABSTRACT FROM AUTHOR]

This article analyzes the practices of the Chinese party newspapers' propaganda in the past decade. Through in-depth interviews with 30 journalists and editors of 12 party newspapers and analysis of investigative reports about 43 party newspapers' propaganda work, this article finds that the party newspapers mainly used three propaganda strategies. Among them, mobile first is a technical strategy that gives priority to the use of mobile communication platforms and technologies in the whole process of news production and dissemination; digitalizing authoritative and localized information is a content strategy that aims to increase the appeal of party newspapers' content to the citizens; and diversified outsourcing, as a service strategy that provides a series of services to the citizens on behalf of government departments, is an innovation of the propaganda practice of the party newspapers. However, there exists a gap in the performance of party newspapers at different levels in the actual propaganda work, and some of them engage in the practice just for the sake of formality. [ABSTRACT FROM AUTHOR]

We have proposed an open-source holonomic mobile manipulator composed of the KUKA youBot holonomic mobile platform with four Swedish wheels and a stationary aboard six-degrees-of-freedom Kinova Jaco Gen 2H manipulator, and we have developed corresponding kinematic problems. We have defined forward and inverse analytic Jacobians and designed Jacobian algorithms of forward and inverse mobile manipulator kinematics. An experimental test conducted with the designed laboratory prototype of the investigated mobile manipulator with the described kinematics was used to verify the obtained theoretical results. The goal of the test was to keep constant the position of the gripper in 3D space while the mobile platform is moving to some extend in the 2D workspace. [ABSTRACT FROM AUTHOR]

Android is the most widely used mobile operating system and responsible for handling a wide variety of data from simple messages to sensitive banking details. The explosive increase in malware targeting this platform has made it imperative to adopt machine learning approaches for effective malware detection and classification. Since its release in 2008, the Android platform has changed substantially and there has also been a significant increase in the number, complexity, and evolution of malware that target this platform. This rapid evolution quickly renders existing malware datasets out of date and has a degrading impact on machine learning-based detection models. Many studies have been carried out to explore the effectiveness of various machine learning models for Android malware detection. Majority of these studies use datasets that have compiled using static or dynamic analysis of malware but the use of hybrid analysis approaches has not been addressed completely. Likewise, the impact of malware evolution has not been fully investigated. Although some of the models have achieved exceptional results, their performance deteriorated for evolving malware and they were also not effective against antidynamic malware. In this paper, we address both these limitations by creating an enhanced subset of the KronoDroid dataset and using it to develop a supervised machine learning model capable of detecting evolving and antidynamic malware. The original KronoDroid dataset contains malware samples from 2008 to 2020, making it effective for the detection of evolving malware and handling concept drift. Also, the dynamic features are collected by executing the malware on a real device, making it effective for handling antidynamic malware. We create an enhanced subset of this dataset by adding malware category labels with the help of multiple online repositories. Then, we train multiple supervised machine learning models and use the ExtraTree classifier to select the top 50 features. Our results show that the random forest (RF) model has the highest accuracy of 98.03% for malware detection and 87.56% for malware category classification (for 15 malware categories). [ABSTRACT FROM AUTHOR]

Bimanual manipulation is valuable for its potential to provide robots in the field with increased capabilities when interacting with environments, as well as broadening the number of possible manipulation actions available. However, for a robot to perform bimanual manipulation, the system must have a capable control framework to localise and generate trajectories and commands for each sub-system to allow for successful cooperative manipulation as well as sufficient control over each individual sub-system. The proposed method suggests using multiple mobile manipulator platforms coupled through the use of an optical tracking localisation method to act as a single bimanual manipulation system. The framework's performance relies on the accuracy of the localisation. As commands are primarily high-level, it is possible to use any number and combination of mobile manipulators and fixed manipulators within this framework. We demonstrate the functionality of this system through tests in a Pybullet simulation environment using two different omnidirectional mobile manipulators, as well a real-life experiment using two quadrupedal manipulators. [ABSTRACT FROM AUTHOR]

Humanoid robots have attracted much attention by virtue of their compatibility with human environments. However, biped humanoids with immense promise still cannot function steadily and reliably in real‐world settings in the current state. Hence, rationally combining a humanoid robot with different stable mobile platforms is a favoured solution for diverse scenarios. Here, a new versatile humanoid robot platform, aiming to provide a generic solution that can be flexibly deployed in diverse scenarios, for example, indoors and fields is presented. Versatile humanoid robot platform incorporates multimodal perception, and extensible interfaces on hardware and software, allowing it to be rapidly integrated with different mobile platforms and end‐effectors, only through easy‐to‐assemble interfaces. Additionally, the platform has achieved impressive integration, lightness, dexterity, and strength in its class, with human‐like size and rich perception, targeted to have human‐intelligent manipulation skills for human‐engineered environments. Overall, this article elaborates on the reasoning behind the design choices, and outlines each subsystem. Lastly, the essential performance of the platform is successfully demonstrated in a set of experiments with precise and dexterous manipulation, and human–robot collaboration requirements. [ABSTRACT FROM AUTHOR]

Privacy and the safety of users are seriously threatened by Android, a mobile operating system that is becoming more and more popular. Due to the single feature selection technique's poor detection effects, the typical machine learning algorithm's ineffective detection efficiency, and the low accuracy caused by inappropriate feature extraction from log files. To overcome these issues, an efficient security testing method is designed based on the behaviour and activities of Android applications. In this designed model, efficient security testing is conducted by collecting the Android log file from the Android database. And the requested permissions are evaluated with the prioritized and categorized permissions for accessing the components of the Android device. If the permission threat level is low, the component permission will be allowed or else the permissions will be denied. The denied permissions are processed for security testing to predict the app status whether malware or benign. For predicting the state of the Android application, the log files are collected for extracting the features related to the behavior and activities using the improved PCA technique. For determining the number of components in the PCA based on the K-fold cross validation technique, the enhanced PCA technique combines PCA with the BES optimization technique. The hybrid DNN-KNN classifier is trained and evaluated with the best set of features from the enhanced PCA for identifying malicious and benign applications. Performance criteria including Accuracy, Recall, Specificity, and Precision are assessed for this created model. The attained performance metrics values for the proposed model are 98%, 97.6%, 99.2% and 97.7%. Using this proposed hybrid model malicious activity in android apps can be detected effectively and the public can be safeguared from serious threats. [ABSTRACT FROM AUTHOR]

Detecting deep-sea megabenthic organisms is of foremost importance for seabed resource surveys, typical habitat protection, and biodiversity surveys. However, the complexity of the deep-sea environment, uneven illumination, and small biological targets that are easily obscured all increase target detection difficulty significantly. To address these, this paper proposes a deep-sea megabenthic detection algorithm, DS-YOLO, based on YOLOv5s. To improve the detection ability of the model for deep-sea megabenthic organisms, the space-to-depth module and the spatial pyramid pooling cross stage partial channel module are introduced in the Backbone layer to enlarge the receptive field and enhance the retention of small-scale features. Then, the space-todepth and normalization-based attention modules and the Add and Concat functions of the bidirectional feature pyramid network are introduced in the Neck layer to increase the multiscale fusion ability of the model and highlight the insignificant features. Finally, the two branches of the decoupling header output the category and location of the target, which causes the model to utilize the feature information to the maximum extent. Experiments showed that DS-YOLO improved mAP0.5 from 89.6% to 92.4% and mAP0.5:0.95 from 65.7% to 72.3% compared to the original YOLOv5s on the homemade dataset and outperformed other algorithms in the YOLO series. DS-YOLO reaches 84.7 FPS for deployment on mobile platforms. In addition, the combined DS-YOLO and DeepSORT algorithm can be used to calculate the abundance and community structure of deep-sea megabenthos. The model outperforms general target detection models for deep-sea megabenthos detection and is suitable for use in complex deep-sea environments. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a mobile application (app) specifically designed for enhancing farm management and performance assessment in fattening beef cattle. Materials and Methods: The development process followed a user-centered design approach, which involved focus group discussions and key informant interviews with 20 participants to design content and features. The app was developed for both mobile and web platforms. After the prototype and launch of the app, the system usability scale (SUS) and user satisfaction were assessed. Results: The assessment findings identify the specific expected functions in the app, with the farm accounting records function being the most desired feature among users, followed by production analysis, production records, and resource inventory. The mean SUS score was calculated to be 75.17, indicating a qualitative assessment of "Good." The assessment of user satisfaction indicated that the mean satisfaction score for all participants was 4.26, suggesting a high level of satisfaction and a favorable perception of the app. Conclusion: This app provides an alternative way to record farm activity, suggest feed and feeding schedules, and provide financial management tools designed explicitly for small-scale beef cattle farmers. [ABSTRACT FROM AUTHOR]

Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts. The extracted frames were converted to grayscale, followed by the application of a georeferencing algorithm to embed coordinate information in to the images. The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system. Next, Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction. After preprocessing, the blob detection algorithm helped detect the vehicles. Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme. Detection was done on the first image of every burst. Then, to track vehicles, the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm. To further improve the tracking accuracy by incorporating motion information, Scale Invariant Feature Transform (SIFT) features have been used to find the best possible match among multiple matches. An accuracy rate of 87% for detection and 80% accuracy for tracking in the A1 Motorway Netherland dataset has been achieved. For the Vehicle Aerial Imaging from Drone (VAID) dataset, an accuracy rate of 86% for detection and 78% accuracy for tracking has been achieved. [ABSTRACT FROM AUTHOR]

In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage. [ABSTRACT FROM AUTHOR]

Online platforms have emerged as a dominant business model in numerous industries in the new millennium. In light of the substantial and burgeoning body of empirical platform research, this article synthesizes extant studies and identifies the evolution of underlying research methodologies and topics. Building upon a database of 860 empirical online platform papers in premier journals during the first two decades of the new millennium, this article presents a categorization framework based on the online platform type (including search platforms, e‐commerce platforms, online communities, and mobile platforms) and research perspective (including platform participants, platform orchestrators, and platform ecosystems). We provide a critical review of noteworthy trends and highlight directions for future research in each category of the proposed framework. A comprehensive bibliometric analysis is then conducted to visualize and track scholarship in empirical online platform research. Lastly, we adopt an interdisciplinary lens to synthesize our critical review of empirical online platform research into lessons and research opportunities that emerge from multiple disciplines. [ABSTRACT FROM AUTHOR]

In this work, we present IoT, a freeware application for Android mobile devices that belongs to the remote laboratory platform called IoT.PhysicsSensor Mobile Edition. It incorporates the potentialities of the IoT to acquire, store, visualize, and analyze data using smartphones. This application, integrated into low‐cost hardware, can be used to conduct online and real‐time laboratory practices. Additionally, hundreds of users can be connected to the lab without limiting their participation due to the physical location or equipment available in the facilities. To test the capabilities of the architecture proposed in this work, a practice to measure the value for the acceleration of gravity from a free‐fall experience was designed. A group of 101 students from the Universidad Nacional de Colombia‐Sede Medellin and another of 63 students from Universidad Nacional de Colombia‐Sede de La Paz, Cesar, participated in the practice, thus obtaining results with a high degree of accuracy and precision and excellent reproducibility. This platform is emerging as a critical tool to face the digital transformation of educational environments, including those generated by the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Since consumers' impulse buying intentions can be easily completed via mobile platforms, impulse buying of travel products has become more common. Hence, this study aimed to understand how mobile tourism applications affect travelers' impulse purchase behaviors. Utilizing the S-O-R framework and distinguishing environmental stimuli and consumer behaviors, this study proposed a conceptual model and hypotheses to investigate the relationship between usability, information effectiveness, emotional arousal, and impulsive purchase intentions in the context of mobile applications. Based on 63 responses, the experiment applied neuroscience methods and found that information effectiveness positively affected impulsive purchase intentions, while usability and emotional arousal did not have significant effects. Hence, results offer practical insights into the development of travel applications and extend the S-O-R framework with the inclusion of emotional arousal. [ABSTRACT FROM AUTHOR]

Product updates have become prevalent on online digital platforms, given the unique market, product, and technological characteristics that underlie digital platforms. Scholars have begun to explore the efficacy of updates on digital platforms, highlighting the merits of implementing frequent updates. Drawing on behavioral decision theory, this paper analyzes the trade-offs that consumers encounter between the benefits and costs associated with updates and challenges an implicit assumption held in extant studies with respect to the linear efficacy of updates. In the context of mobile platforms, this paper, using a multi-method study, identifies the existence of an inverted U-shaped relationship between product updates and complementor product performance. We also find that the moderating efficacy of platform endorsement is positive in that it serves as an amplifier of whatever a complementor offers, tightly intertwined with the complementor's offerings. Our paper expands the platform literature by investigating successful complementor product strategies that take into account unique platform characteristics with a strong grounding in interdependent owner–complementor dynamics. [ABSTRACT FROM AUTHOR]

Environmental perception is crucial for unmanned mobile platforms such as autonomous vehicles and robots. Precise and fast semantic segmentation of the surrounding scene is a key task to enhance this capability. Existing real-time semantic segmentation networks are typically based on convolutional neural networks (CNNs), which have achieved good results, but they still lack control over global context features. In recent years, the Transformer architecture has achieved significant success in capturing global context, which is beneficial for improving segmentation accuracy. However, Transformers tend to ignore local connections, and their computational complexity makes real-time segmentation challenging. We propose a lightweight real-time semantic segmentation network called DTMC-Net, which combines the advantages of CNNs and Transformers. We design a special residual convolution module called the Lightweight Multi-layer Separable Convolution Attention module (LMSCA) to reduce the parameter count and perform multi-scale feature fusion to capture local features effectively. We introduce the Simple Dual-Resolution Transformer (SDR Transformer) that utilizes lightweight attention mechanisms and residual feed forward networks to capture and maintain features, with multiple bilateral fusions between two branches to exchange information. The proposed Anti-artifact Aggregation Pyramid Pooling Module (AAPPM) optimizes the upsampling process, refines features, and performs multi-scale feature fusion again. DTMC-Net only contains 4.2M parameters and achieves good performance on multiple public datasets with different scenarios. [Display omitted] • Emphasizes real-time semantic segmentation for unmanned mobile platforms. • A lightweight model that combines the strengths of CNNs and Transformers is proposed. • Proposes modules to enhance multi-scale feature capture and refinement capabilities. • Competitive results are achieved on several challenging datasets. [ABSTRACT FROM AUTHOR]

The safety, inclusivity, accessibility, and green communities emphasized in the United Nations' Sustainable Development Goals (SDGs) play a vital role in the establishment of child-friendly cities. The governments are actively promoting the development of sustainable, child-friendly cities that prioritize people's needs and aim to enhance the well-being of residents, from children to families. However, there is limited research utilizing GIS analysis techniques and internet big data to analyze spatial equity in children's spatial accessibility. Therefore, this study introduces an innovative approach focusing on the community level. Drawing on data from the popular social networking platform mobile application "Xiaohongshu" and employing network analysis methods based on walking and driving modes, this study analyzed and investigated the accessibility of children's spaces in the city of Hangzhou, China. Regarding spatial characteristics, the distribution of children's space resources in the main urban area of Hangzhou exhibited a "peripheral low and central high" trend, which was closely linked to the distribution of population space. This pattern indicates potential significant disparities in the allocation of children's space resources. Notably, the core area of Hangzhou demonstrated the highest level of accessibility to children's spaces, with Gongshu District exhibiting the best accessibility. Conversely, non-core urban areas generally had relatively poor accessibility. Furthermore, different types of children's spaces, such as indoor cultural spaces, indoor entertainment spaces, outdoor parks, and outdoor nature areas, all exhibited the highest accessibility in the city center, which gradually decreased towards the periphery. Additionally, this study evaluated the convenience of children's spaces in various communities by combining population size and accessibility levels. The findings revealed that communities in the core area had higher accessibility levels in the northwest–southeast direction, while accessibility decreased towards the northeast–southwest direction. Consequently, the relative convenience of these communities tended to be lower. By examining spatial equity, this study provides valuable insights into the promotion of sustainable, child-friendly cities that prioritize people's needs and contribute to the well-being of residents, from children to families. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods have been widely applied to single image super-resolution reconstruction tasks and achieved great improvement in both quantitative and qualitative evaluations. However, most of the existing convolutional neural network-based methods commonly reduce the number of layers or channels to obtain lightweight model. These strategies may reduce the representation ability of informative features and degrade the network performance. To address this issue, we propose a sparse lightweight attention network (SLAN), a novel SISR algorithm to keep informative features between layers. Specially, a sparse attention feature fusion module with lightweight attention and sparse extracting modules is developed to expand feature extracting receptive field and enhance the informative feature extracting ability. To take advantage of the multi-level features and keep fewer multi-adds, cross fusion is used and demonstrated usefully. Extensive experimental results on public datasets demonstrate the superior performance of our proposed SLAN. The average PSNR(dB)/SSIM values of SLAN are about 0.04/0.0004, 0.06/0.0009, and 0.09/0.0018 larger than the competitors under scaling factors of × 2, × 3 and × 4, respectively. Our SLAN benefits from its model size and low computation cost and may be deployed on mobile platforms. [ABSTRACT FROM AUTHOR]

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. [ABSTRACT FROM AUTHOR]

This study proposes a variable-stiffness mechanism for non-pneumatic tires such that can actively adapt to various environments. Non-pneumatic tire is a compliant wheel structure that offers superior robustness and adaptability compared to pneumatic tires. However, the tire designed for certain terrain exhibits relatively high rolling resistance and inadequate suspension. To address these problems, a stiffness-adjustable wheel (SAW) that can modify the force applied to the contact surface is introduced in this study. In addition, the shape of SAW is optimized to maintain a desirable range of stiffness under different conditions. The optimization is conducted with experimental method, because nonlinear response of material and interference between components make it difficult to predict the characteristic of the wheel at large deformation. The SAW has potential for application in various mobile platforms to provide adequate stiffness for a variety of terrains and driving conditions. [ABSTRACT FROM AUTHOR]