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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]

Android is the most targeted mobile operating system for malware attacks. Most modern anti-malware solutions largely incorporate deep learning or machine learning techniques to detect malwares. In this paper, we conduct a comprehensive analysis on 10 deep learning and 5 machine learning classifiers in their abilities to identify Android malware applications. We used 1-gram dataset, 2-gram dataset and image dataset generated from the system call co-occurrence matrix for our experiments. Among the machine learning classifiers, XGBoost with 2-gram dataset showed the highest F1-score of 0.98. Also, the deep learning classifiers such as extreme learning machine with the system call images demonstrated the best F1-score of 0.952. We experimented using Gabor filters to investigate classifier performance on textures extracted from system call images. We observed an F1-score of 0.953 using the extreme learning machine with the Gabor images. We generated the Gabor image dataset by combining the images generated by passing system call images through 25 different Gabor configurations. In addition, to enhance the performance of the baseline classifiers, we considered the combination of autoencoders with machine learning classifiers. We observed that the amalgam of autoencoder with Random Forest displayed the best F1-score of 0.98. To evaluate the effectiveness of the aforesaid classifiers with diverse features on adversarial examples, we simulated a black-box based attack using a Generative Adversarial Network. The True Positive Rate of XGBoost on the 1-gram dataset, Random Forest on the 2-gram dataset and the Extreme Learning Machine on the system call image dataset significantly dropped to 0 from 0.98, 0.001 from 0.99 and 0 from 0.984 after the attack. Our experiments exposed a crucial vulnerability in classifiers used in modern anti-malware systems. A similar event in a real-world system could potentially render grave catastrophes. To defend against such probable attacks, we should continue further research and develop adequate security mechanisms. [ABSTRACT FROM AUTHOR]

Existing direct and inverse kinematic models of planar parallel robots assume that the robot's active joints are all at the bases. However, this approach becomes excessively complex when modeling a planar parallel robot in which the active joints are within one single kinematic chain. To address this problem, our article unveils an alternative for a 3RRR symmetric planar robot modeling technique for the derivation of the robot workspace and the analysis of its direct and inverse kinematics. The workspace was defined using a system of inequalities, and the direct and inverse kinematics models were generated using vectorial analysis and an optimized geometrical approach, respectively. The resulting models are systematically presented and validated. Two final model renditions are delivered supplying a thorough equation analysis and an applicability discussion based on the importance of the robot's mobile platform orientation. The advantages of this model are discussed in comparison to the traditional modeling approach: whereas conventional techniques require the solution of complex eighth-degree polynomials for the analysis of the active joint configuration of these robots, these models provide an efficient back-of-the-envelope analysis approach that requires the solution of a simple second-degree polynomial. [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]

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 need to assess these measurements in a scalable manner. We explore the collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches: a standard collocation assessment technique, in which the mobile platform is parked near the fixed regulatory site for a period of time, and an expanded approach, which uses measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed-reference-site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, CO, USA, and in the San Francisco Bay Area, CA, USA. Our 1-month Denver dataset includes both parked collocation periods near the fixed regulatory sites and general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, to the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (yearlong) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40 h running median converges to within ± 4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be employed during large-scale monitoring campaigns using small sensors to identify potential measurement biases. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

• Motorised transport is contributing to the decline of environmental and human health. • A greater understanding of the types of behavioural interventions used in active travel research is required. • Personalised travel plans and mobile applications were the most common behavioural interventions identified. • There is a need for more robust, theory-driven study designs to enable research translation. The societal reliance on motorised transport is negatively affecting the health of humans and the environment. Active travel provides a unique solution to both issues by removing the number of motorised vehicles on the road, while also increasing physical activity rates. Interventions that encourage greater uptake of active travel are considered to be highly beneficial to society. This systematic review of the literature sought to provide evidence to answer the question which behavioural interventions increase commuter cycling? Five databases were searched to identify articles that tested a behavioural intervention to increase commuter cycling. Article parameters were defined from the literature and in consultation with the research team. Study selection, quality appraisal and data extraction were undertaken using Covidence. The final 25 articles revealed that personalised travel plans and mobile phone-based platforms were the most common interventions. Unvalidated travel surveys were the most widely used mode of data collection. Modest increases in commuter cycling were observed across studies; however, the heterogeneity of study designs and reporting of results did not allow aggregation of findings. This review highlights the need for high quality behavioural science research into active travel. This will assist us to better understand the most effective interventions that increase levels of commuter cycling, subsequently improving environmental and human health. [ABSTRACT FROM AUTHOR]

This paper proposes a multiple unmanned aerial vehicles (UAVs) cooperative landing algorithm based on deep reinforcement learning. First, to solve the partial observation problem, we propose the recurrent neural network to predict the moving platform trajectory. Afterwards, with the centralized multiagent framework, we present a parameter sharing method to realize multi-UAV cooperation. Finally, focusing on the sensor noise problem of the actual UAV flight, we propose a noise compensation recurrent proximal policy optimization (NC-RPPO) algorithm to extract images' features to compensate for inertial measurement unit (IMU) and GPS errors. We utilize AirSim to construct a simulated 3D environment resembling an offshore oil development zone. In this setting, we evaluate the effectiveness of our proposed multi-UAV cooperative landing algorithm while considering the presence of sensor noise. Through experimental trials, we demonstrate that our NC-RPPO algorithm enables UAVs to accurately predict the trajectory of a mobile platform and successfully land on it cooperatively in real time. Notably, the experimental outcomes obtained through our image-assisted noise correction method closely align with those obtained from the ground truth experiment. [ABSTRACT FROM AUTHOR]

BACKGROUND: The monitoring of fetal heart rate (FHR) before intrapartum has been crucial in modern obstetrics. FHR has been used for about 300 years to determine fetal status, leading to the development of monitoring devices to prevent fetal death during gestation. While medical devices like fetal electrocardiograms exist for disease detection, their size and cost limit individual use. OBJECTIVE: To address cardiovascular issues during pregnancy, a mobile system is developed to display heart rates and blood pressure on mobile devices. The system is generated from a medical device with Bluetooth communication, supplementing traditional monitoring. METHOD: The study focuses on creating a mobile system that connects to mobile operating systems, enhancing treatment, diagnosis, and patient monitoring. The mobile system displays cardiovascular data obtained from the medical device. RESULTS: The results are expected to have an immediate impact on cases where abnormal measurement parameters of the monitoring system occur during pregnancy. The use of mobile systems or applications on smartphones is seen as beneficial in distributing processing and census of embedded health systems. CONCLUSION: The study highlights the potential benefits of mobile systems in distributing processing for health systems, particularly in addressing cardiovascular problems during pregnancy. The creation of a mobile system for displaying cardiovascular data could significantly improve monitoring and early detection. [ABSTRACT FROM AUTHOR]

Large lightweight slender links mounted on mobile robots enlarge their workspace. This work presents one of these robots that has been endowed with 4 legs to improve its static stability—which is put at risk by the large link—and help in the positioning of the link. The efficiency of this system depends on its accuracy in positioning and orientating the end-effector placed at the tip of the link, which is compromised by vibrations that appear in the link during the movement and permanent deflections caused by gravity. A relatively simple control scheme that combines a feedback control of the pose of the mobile platform with a feedforward term that orientates the link is proposed. It uses three legs as actuators; and feeds back measurements of the extensions of the three legs and the orientation of the platform given by an inertial sensor placed in the upper part of the mobile platform. Based on the flatness property of a simplified dynamic model of the link, its dynamics can be easily inverted, being used to implement the proposed feedforward term. Since such feedforward term is used, extra sensors to measure the deflection of the link are not needed, and since the legs are used to move the link, extra actuators mounted on the link are neither needed. All this allows us to reduce the weight and the volume of the payload carried by the mobile platform. Results obtained through a finite elements software and experimentation of the real prototype show the good performance attained with the proposed control strategy. [ABSTRACT FROM AUTHOR]

Submarine recognition plays a critical role in maritime security and military defense. However, traditional submarine recognition algorithms face limitations in feature representation capability and robustness. Additionally, deploying deep learning methods on embedded and mobile platforms presents a bottleneck. To address these challenges, we propose an innovative and practical approach—an improved YOLOv5-based lightweight submarine automatic recognition detection algorithm. Our method leverages the Feature Pyramid based on MobileNetV3 and the C3_DS module to reduce computation and parameter complexity while ensuring high precision in submarine recognition. The integration of the adaptive neck from the SA-net strategy further mitigates missed detections, significantly enhancing the accuracy of submarine target detection and recognition. We evaluated our improved model on a submarine dataset, and the results demonstrate remarkable advancements in Precision, Recall, and mAP0.5, with respective increases of 8.54%, 6.02%, and 3.36%. Moreover, we achieved a notable reduction of 34.1% in parameter quantity and 67.9% in computational complexity, showcasing its lightweight effects. Overall, our proposed method introduces novel improvements to submarine recognition, addressing existing limitations and offering practical benefits for real-world deployment on embedded and mobile platforms. The enhanced performance in precision and recall metrics, coupled with reduced computational requirements, emphasizes the significance of our approach in enhancing maritime security and military applications. [ABSTRACT FROM AUTHOR]

The global proportion of eco-friendly vehicles continues to increase; however, regarding hybrid vehicles, the vehicle powertrains in most countries include internal combustion engines. Therefore, research on reducing the carbon emissions from internal combustion engines must be conducted. Carbon capture technology must be developed for e-fuel, which has recently attracted attention, to achieve carbon neutrality. In this study, a turbo compound system capable of recovering waste exhaust gas energy was selected as the most appropriate energy supply system to operate a mobile carbon capture system. The feasibility was reviewed by analyzing the turbo compound speed, pressure drop, power generation, etc., using a one-dimensional simulation method. The maximum power generation of the configured turbo compound system was approximately 9 kW, and approximately 1–3 kW of energy could be recovered under medium speed and load conditions, which are the optimal operating conditions for a test engine with the displacement of a 4 L. [ABSTRACT FROM AUTHOR]

Air pollution, in terms of particulate matter (PM10, PM2.5, PM1.0) and gaseous pollutants (H2S and VOC), has serious health effects, particularly in cities. The evaluation of outdoor air quality was carried out in the vicinity of a small paintshop operating in the city of Łódź, Poland. The concentrations of pollutants in the vertical profiles (up to 47 m a.g.l.) of ambient air were measured using a mobile platform (unmanned aerial vehicle with measuring equipment) during three measurement campaigns in September 2021. The vertical structure of the pollutant concentrations indicated the occurrence of different types of layers with an almost constant concentration near the land surface, a strong decrease (up to ca. 10–15 m a.g.l.), and significant fluctuations in concentrations to higher levels (above 25 m a.g.l.). Particulate matter concentrations (PM10, PM2.5, PM1.0) did not exceed 39 µg m−3, with stable levels on the surface. The maximum value of particulate matter concentrations (up to 38.5 µg m−3) in the vertical profiles was recorded at ca. 35–40 m a.g.l. The average concentrations of H2S and VOC varied between 0.07 and 0.12 ppm and 0.01 and 0.27 ppm, respectively. The highest H2S concentrations were observed at ca. 18–23 m a.g.l., reaching 0.14 ppm. A rapid increase in VOC concentrations, reaching 0.29 ppm, was measured in vertical profiles from 20 m a.g.l. and up to about 40 m a.g.l. In situ measurement approaches were combined with interpolation methods in the GIS system to investigate the spatial variability of pollution levels from a specified source in the urban atmosphere. Based on the survey results, the kriging interpolation method was well suited for generating spatially distributed pollution maps for individual measurement campaigns. [ABSTRACT FROM AUTHOR]

Purpose: This study examined and compared the diagnostic accuracy and correlation levels of the acoustic parameters of the audio recordings obtained from smartphones on two operating systems and from dynamic and condenser types of external microphones. Method: The study included 87 adults: 57 with voice disorder and 30 with a healthy voice. Each participant was asked to perform a sustained vowel phonation (/a/). The recordings were taken simultaneously using five microphones AKG-P220, Shure-SM58, Samson Go Mic, Apple iPhone 6, and Samsung Galaxy J7 Pro microphones in an acoustically insulated cabinet. Acoustic examinations were performed using Praat version 6.2.09. The data were examined using Pearson correlation and receiver-operating characteristic (ROC) analyses. Results: The parameters with the highest area under curve (AUC) values among all microphone recordings in the time-domain analyses were the frequency perturbation parameters. Additionally, considering the correlation coefficients obtained by synchronizing the microphones with each other and the AUC values together, the parameter with the highest correlation coefficient and diagnostic accuracy values was the jitter-local parameter. Conclusion: Period-to-period perturbation parameters obtained from audio recordings made with smartphones show similar levels of diagnostic accuracy to external microphones used in clinical conditions. [ABSTRACT FROM AUTHOR]

Studies involving acoustic telemetry typically use stationary acoustic receivers arranged in an array or grid. Unmanned surface vehicle (USV)-based mobile receivers offer advantages over the latter approach: the USV can be programmed to autonomously carry a receiver to and from target locations, more readily adapting to a survey's spatial scope and scale. This work examines the acoustic detection performance of a low-cost USV developed as a flexible sensing platform. The USV was fitted with an acoustic receiver and operated over multiple waypoints set at increasing distances from the transmitter in two modes: drifting and station-keeping. While drifting, the USV was allowed to drift from the waypoint; while station-keeping, the USV used its thruster to hold position. Detection performance of the USV was similar to that of stationary receivers while drifting, but significantly worse while station-keeping. Noise from the USV thruster was hypothesized as a potential cause of poor detection performance during station-keeping. Detection performance varied with the depth of the tethered receiver such that detection range was greater during the deepest (4.6 m) trials than during shallower (1.1 and 2.9 m) trials. These results provide insight and guidance on how a USV can be best used for acoustic telemetry, namely, navigating to a planned waypoint, drifting and lowering the receiver to a desired depth for listening, and then navigating to the next waypoint. [ABSTRACT FROM AUTHOR]

As a convenient learning tool in the We Media era, mobile apps have been paid more and more attention by college students because of their accompanying timeliness and practicality. With the increasing number of English learning apps, many such apps provide college students with new ways to obtain learning resources and diversified learning modes. The related research in the field of mobile-assisted language learning at home and abroad has developed over nearly 20 years, basically following the route from theory to application in practice, but there have been few process studies on learners' individual language skill learning behaviors based on mobile platform data. In this study, the time series clustering method was adopted, and the learning behavior of college students in an English vocabulary learning app in China was selected for data mining. Firstly, taking the "single-day memorization amount" as the measurement index, the memorization records of college students in the whole use cycle were extracted and processed into trajectory data, and the KmL algorithm was used to cluster the trajectory of the memorization amount in the time series. According to the intra-class average trajectory, the characteristics of learning behavior changes among the different college students are summarized, and two learning modes are depicted. Secondly, through the experimental analysis, it was found that adopting the English learning model three weeks before an exam can effectively stimulate college students and improve their willingness to learn and continue using the app. [ABSTRACT FROM AUTHOR]

This research paper investigates the integration of blockchain technology to enhance the security of Android mobile app data storage. Blockchain holds the potential to significantly improve data security and reliability, yet faces notable challenges such as scalability, performance, cost, and complexity. In this study, we begin by providing a thorough review of prior research and identifying critical research gaps in the field. Android's dominant position in the mobile market justifies our focus on this platform. Additionally, we delve into the historical evolution of blockchain and its relevance to modern mobile app security in a dedicated section. Our examination of encryption techniques and the effectiveness of blockchain in securing mobile app data storage yields important insights. We discuss the advantages of blockchain over traditional encryption methods and their practical implications. The central contribution of this paper is the Blockchain-based Secure Android Data Storage (BSADS) framework, now consisting of six comprehensive layers. We address challenges related to data storage costs, scalability, performance, and mobile-specific constraints, proposing technical optimization strategies to overcome these obstacles effectively. To maintain transparency and provide a holistic perspective, we acknowledge the limitations of our study. Furthermore, we outline future directions, stressing the importance of leveraging lightweight nodes, tackling scalability issues, integrating emerging technologies, and enhancing user experiences while adhering to regulatory requirements. [ABSTRACT FROM AUTHOR]

High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring. Traditional phenotype monitoring methods for field crops relying on artificial sampling and measurement have some disadvantages including low efficiency, strong subjectivity, and single characteristics. To solve these problems, the rapid monitoring, acquisition, and analysis of phenotyping information of field crops have become the focus of current research. The research explores the systematic framing of phenotype monitoring systems for field crops. Focusing on four aspects, namely phenotyping sensors, mobile platforms, control systems, and phenotyping data preprocessing algorithms, the application of the sensor technology, structural design technology of mobile carriers, intelligent control technology, and data processing algorithms to phenotype monitoring systems was assessed. The research status of multi-scale phenotype monitoring products was summarized, and the merits and demerits of various phenotype monitoring systems for field crops in application were discussed. In the meantime, development trends related to phenotype monitoring systems for field crops in aspects including sensor integration, platform optimization, standard unification, and algorithm improvement were proposed. [ABSTRACT FROM AUTHOR]

Background: Poor medication adherence is a major barrier to HIV control among youth living with HIV (Y-PLWH). The PEERNaija application (app) is an adapted smartphone app grounded in social cognitive and contigency management theories and designed to harness peer-based social incentives and conditional financial incentives to promote medication adherence. The app delivers a multifaceted medication adherence intervention including (1) peer-based social incentives, (2) financial incentives, (3) virtual peer social support, and (4) early clinic-based outreach for non-adherent Y-PLWH. A pilot trial of the app will be conducted in Nigeria, Africa's most populous country with the 4th largest HIV epidemic, and home to 10% of the world's four million Y-PLWH. Methods: In this randomized controlled trial, we will compare implementation outcomes (feasibility, acceptability, appropriateness measured via validated scales, enrollment and application installation rates, feedback surveys and focus group discussions with participants, and back-end application data), and preliminary efficacy (in improving medication adherence and viral suppression) of the PEERNaija app at 6 months. Participants in Arm 1 (PEERNaija) will receive daily medication reminders, peer-based social incentives, and virtual peer social support. Participants in Arm 2 (PEERNaija +) will additionally receive a conditional financial incentive based on their adherence performance. Eligibility for Y-PLWH includes (1) being aged 14–29 years, (2) being on ART, (3) owning a smartphone, (4) being willing to download an app, and (5) being able to read simple text in English. Discussion: This study will serve as the basis for a larger intervention trial evaluating the PEERNaija app (and the integration of mHealth, incentive, and peer-support-based strategies) to improve HIV outcomes in a critically important region of the world for Y-PLWH. Trial registration: ClinicalTrials.gov. NCT04930198. First submitted date: May 25, 2021. Study start: August 1, 2021, https://clinicaltrials.gov/. Protocol version: January 21, 2022. [ABSTRACT FROM AUTHOR]

With the rapid development and wide popularity of Internet technology, mobile application services have become an indispensable part of people's daily life. As a typical mobile application service, mobile office can provide an anytime, anywhere platform, enabling users to use fragmented time and fully improve office efficiency. However, it differs from the traditional one in that the user's identity cannot be directly identified in real space. Therefore, the need to ensure the authenticity of the user's identity is not negligible. Authentication technology is one of the effective technologies to verify the identity of users. However, there are few authentication schemes for the mobile office. Therefore, this paper proposes a trusted authentication scheme based on a super SIM card for the mobile office. Based on the Generic Bootstrapping Architecture (GBA) authentication technology of the super SIM card, the authentication between the terminal device and the operator is realized. The authentication credentials are generated to provide trusted authentication for mobile office applications. We also have a comprehensive security analysis and proof that the scheme satisfies two-way authentication, anti-replay attack, anti-forgery attack, anti-man-in-the-middle attack and non-repudiation. [ABSTRACT FROM AUTHOR]

Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy. [ABSTRACT FROM AUTHOR]