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Objective: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. Approach: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. Conclusion: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of mobile learning technologies on student engagement and learning outcomes in higher education. A pre-test/post-test control group design was employed to compare the engagement and learning outcomes of students who used a mobile learning platform with those who did not. The study was conducted with undergraduate students at a large public university. Results showed that the intervention group reported a more positive experience with the platform, used it more frequently, found it more useful, and were more satisfied with it than the control group. Additionally, the intervention group outperformed the control group in terms of both course grades and standardized test scores. The findings suggest that mobile learning technologies can positively impact student engagement and learning outcomes in higher education. Future research could explore ways to optimize the platform further and investigate the scalability and sustainability of implementing mobile learning technologies in higher education. [ABSTRACT FROM AUTHOR]

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• The use of patient-generated data collected and shared with a CF app was feasible and acceptable in clinical settings. • Use of the CF app was associated with improved measures of patient-centered care, including shared decision-making and satisfaction with care. • Study results support wider use of the CF app (Genia) in clinical settings. Previously, we adapted a mobile health platform (Genia) to the needs of patients and families in a pediatric CF center in the United States. In this feasibility study, we tested the impact of Genia on measures of patient-centered care. In a one-group pre-post study with adolescents with CF and caregivers of children with CF, we tested Genia's effect over 6 months on patient satisfaction with chronic illness care (PACIC) and shared decision-making (CollaboRate). Feasibility and acceptability were assessed with exit interviews and app analytics. The intervention included 40 participants: 30 caregivers of children with CF age ≤14 years and 10 patients with CF age ≥15 years. The use of Genia was associated with increased satisfaction with care (p = 0.024), including delivery system and decision support (p = 0.017), goal setting (p = 0.006), and shared decision-making (p <0.001). The use of Genia was associated with nominal improvements in all QOL domains and symptom scales. The platform was feasible, with participants recording more than 4,400 observations (mean 84.2) and submitting 496 weekly reports (mean 13.8) and 70 quarterly reports (mean 1.8), and acceptable (95% retention rate). For participants, the most useful app feature was pre-visit reports (66.7%), and the top symptom trackers were those for cough (23.7%), appetite (21.1%), energy (18.4%), and medicines (18.4%). The use of Genia over 6 months was feasible, acceptable, and associated with improved measures of patient-centered care. Study results support wider use of Genia in clinical settings. Efficacy for clinical outcomes should be assessed in a randomized clinical trial. [ABSTRACT FROM AUTHOR]

In this paper, we propose a comparison of open-source LiDAR and Inertial Measurement Unit (IMU)-based Simultaneous Localization and Mapping (SLAM) approaches for 3D robotic mapping. The analyzed algorithms are often exploited in mobile robotics for autonomous navigation but have not been evaluated in terms of 3D reconstruction yet. Experimental tests are carried out using two different autonomous mobile platforms in three test cases, comprising both indoor and outdoor scenarios. The 3D models obtained with the different SLAM algorithms are then compared in terms of density, accuracy, and noise of the point clouds to analyze the performance of the evaluated approaches. The experimental results indicate the SLAM methods that are more suitable for 3D mapping in terms of the quality of the reconstruction and highlight the feasibility of mobile robotics in the field of autonomous mapping. [ABSTRACT FROM AUTHOR]

Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver–vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver–vehicle interfaces, multimodal interaction, and intelligent vehicles. [ABSTRACT FROM AUTHOR]

At the nanoscale, metals exhibit special electrochemical and optical properties, which play an important role in nanobiosensing. In particular, surface plasmon resonance (SPR) based on precious metal nanoparticles, as a kind of tag-free biosensor technology, has brought high sensitivity, high reliability, and convenient operation to sensor detection. By applying an electrochemical excitation signal to the nanoplasma device, modulating its surface electron density, and realizing electrochemical coupling SPR, it can effectively complete the joint transmission of electrical and optical signals, increase the resonance shift of the spectrum, and further improve the sensitivity of the designed biosensor. In addition, smartphones are playing an increasingly important role in portable mobile sensor detection systems. These systems typically connect sensing devices to smartphones to perceive different types of information, from optical signals to electrochemical signals, providing ideas for the portability and low-cost design of these sensing systems. Among them, electrochemiluminescence (ECL), as a special electrochemically coupled optical technology, has good application prospects in mobile sensing detection due to its strong anti-interference ability, which is not affected by background light. In this review, the SPR is introduced using nanoparticles, and its response process is analyzed theoretically. Then, the mechanism and sensing application of electrochemistry coupled with SPR and ECL are emphatically introduced. Finally, it extends to the relevant research on electrochemically coupled optical sensing on mobile detection platforms. [ABSTRACT FROM AUTHOR]

We design a two-stage federated learning incentive mechanism by including both a "combinatorial auction" stage and a "bargaining" stage, referred as Combinatorial Auction and Bargaining (CAB). We demonstrate that the CAB-based federated learning incentive mechanism not only maximizes the overall utility of both the federated learning platform and mobile users, but also generates greater profits for the platform than other mechanisms. Further, we apply both theoretical and numerical analysis to prove the effectiveness of the proposed mechanism and show that it is individually rational and incentive compatible, and outperforms other baseline mechanisms. [ABSTRACT FROM AUTHOR]

A robust and high-resolution deconvolution algorithm based on coordinate correction is developed for a compact conformal array on a three-dimensional (3D) moving platform such as underwater glider. First, the coordinate-correcting conventional beamforming is derived to directly estimate the azimuth of the long-range targets in the geodesic coordinate system for a 3D moving platform array. Then, we improve the extended Richardson–Lucy deconvolution (Ex-RL-dCv) beamforming algorithm utilizing coordinate correction to simplify the bearing estimation model from two-dimensional (2D) to one-dimensional (1D). The improved algorithm corrects the deconvolution point spread function (PSF) dictionary mismatch caused by the platform's 3D motion, and has lower sidelobes compared with the Ex-RL-dCv algorithm without coordinate correction. Finally, simulations and results from field-trial data processing are presented. The results demonstrate that the improved Ex-RL-dCv beamforming based on coordinate correction can significantly suppress the high sidelobes caused by deconvolution model mismatch, and successfully realize the robust and high-resolution detection for targets using the compact conformal array on a 3D mobile platform. [ABSTRACT FROM AUTHOR]

This paper investigates the teleoperation problem of networked disturbed mobile manipulators (NDMMs), in which the human operator remotely controls multiple slave mobile manipulators through a master manipulator. Each individual of the slave ones consisted of a nonholonomic mobile platform and a holonomic constrained manipulator that is mounted on the nonholonomic mobile platform. The cooperative control objective of the considered teleoperation problem includes: (1) synchronizing the states of the slave manipulators to the human-controlled master one; (2) forcing the mobile platforms of the slave ones to form a user-defined formation; (3) controlling the geometric center of all the platforms to track a reference trajectory. We present a hierarchical finite-time cooperative control (HFTCC) framework to achieve the cooperative control goal in a finite time. The presented framework includes the distributed estimator, the weight regulator and the adaptive local controller, where the estimator generates the estimated states of the desired formation and trajectory, the regulator selects the slave robot that the master one needs to track, as well as the presented adaptive local controller guarantees the finite-time convergence of the controlled states with model uncertainties and disturbances. Additionally, for improving the telepresence, a novel super twisting observer is presented to reconstruct the interaction force between the salve mobile manipulators and the remote operating environment on the master (i.e., the human) side. Finally, the effectiveness of the proposed control framework is demonstrated by several simulation results. • This paper solves the teleoperation problem of NDMMs in a distributed manner. • A novel HFTCC framework is proposed to achieve the finite-time cooperative control of the NDMMs in the teleoperation system. • A super twisting observer is designed for reconstructing the interaction force between the slave mobile manipulators and the task environment on the human operator. [ABSTRACT FROM AUTHOR]

This paper explores collaborative spatial data as an alternative data source for hydrodynamic model calibration, specifically concerning flood risk mapping. This study is motivated by the multiple uncertainties in the modeling process, especially in the relevant calibration and validation stages. Furthermore, information shared collaboratively can play a fundamental role when data are scarce, which is a usual modeling limitation. This paper tackled aspects like developing a collaborative platform (a mobile app) to collect flood marks; identifying strategies to motivate and engage potential collaborators; developing tools to analyze data consistency; and assessing the capability and restrictions in collaborative information use. Results validate the hypothesis that collaborative spatial data can be an alternative in hydrodynamic model calibration. Developing mechanisms to assess data consistency was crucial for achieving a good performance in the results, as simulations using collaborative data presented errors (RMSE) below 10 cm. Finally, regarding public motivation and engagement, elementary school students performed the best in data collection, highlighting the importance of direct actions, such as face-to-face workshops. Thus, the study hopes to reduce uncertainty in hydrological risk mapping, focusing specifically on observed data scarcity and its influence on model calibration and validation. [ABSTRACT FROM AUTHOR]

Emergencies can unpredictably arise, posing severe threats to individuals' lives. Swift and effective communication plays a crucial role in promptly relaying vital information to relevant response agencies and loved ones, aiming to minimize or eliminate the impact of such disasters. Existing systems often require manual triggering of distress signals through button presses or image captures. However, these methods can be ineffective, particularly when victims are under the surveillance of wrongdoers, rendering them unable to discreetly use their devices. To address this issue, this study introduces the Dual-Activation Emergency Situation Notification System (DA-ESNS) model, which ensures that potential wrongdoers remain unaware of the user's attempt to seek help by employing an efficient communication and alerting system during emergencies. Through a review of previous models, the DA-ESNS model was developed to address identified gaps. The Feature Driven Development software development process model was selected due to its agile and incremental nature, enabling focused development and integration of the notification feature. ReactJS and NodeJS were utilized for frontend and backend development, respectively. The integration of Twilio API and Geolocation API facilitated SMS notifications and precise location sharing with emergency response teams and predefined contacts. The DA-ESNS model automatically relays distress signals by persistently listening for predefined keywords or manually triggered by clicking on corresponding emergency cards, notifying both the emergency response team and the victim's family members. The model was implemented across various mobile platforms, providing a centralized and interactive interface. By offering quick notifications and offline accessibility, the DA-ESNS model streamlines the emergency alert process, significantly improving the effectiveness of emergency response teams in reducing the impact of crises through rapid reaction times. As a result, the DA-ESNS model emerges as a valuable and effective tool in emergency management. [ABSTRACT FROM AUTHOR]

Moblie platform is an online store for distributing mobile apps and designed to connect mobile app developers and users. Mobile platform launches in-app advertisement for their advertisers to monetize the mobile app market. Unlike traditional advertising, the profitability of in-app advertising is not only affected by the decision of the platform, but also by the decision of the app developer, and they will share the advertising revenue. We develop an app advertising revenue sharing model with two competitive app developers and one mobile platform. The mobile platform is the leader, and the two app developers are the follower. Our analysis shows that the two app developers may choose to publish or not publish adds. If publishing, app developers may choose to offer a free app with in-app ads, or choose to offer a paid app with in-app ads. These results can be categorized into seven ranges for the platform's given revenue share of advertisement. In addition, we find the platform's optimal choice of ad revenue share is affected by the quality of advertisement. With the improvement of advertising quality, the revenue sharing ratio will first remain unchanged, then decrease, and finally increase. This is caused by the platform's subsidization strategy for app developers. [ABSTRACT FROM AUTHOR]

In the case of recently released smartphones, they support high resolution of 200 million pixels or more and provide functions comparable to the performance of expensive DSLR cameras. The video taken by the camera provides a variety of high-quality video contents due to the technical processing of very fast video editing. In addition, due to the development of mobile media platforms, PC-based long-form contents are changing to short-form contents. This study analyzes and implements the characteristics of various video editing applications provided by mobile devices by dividing them into four stages: scene change, sound control, text insertion, and graphic effect, using data captured by high-resolution mobile cameras. The detail and sophistication of mobile video editing and the development of various templates allow easy video editing without the support of professional video editing equipment. It is expected that changes in the video editing environment will reduce video editing time and increase the efficiency of producing various short-form video contents. [ABSTRACT FROM AUTHOR]

In recent decades, enthusiasts and researchers from all over the world have invested a lot of energy and time in trying to develop linear propulsion systems using the inertial force generated by two masses revolving in opposite directions. The authors present an original concept for the propulsion system of a mobile platform by using the inertial force generated by two eccentric bodies that are revolving in opposite directions. Based on this approach, a wheeled vehicle was designed, built using additive manufacturing techniques, and finally tested. Validation of the concept and verification of the analytically derived kinematic parameters were carried out by 3D modeling and motion simulation performed in SolidWorks 2023. It was concluded that the initial position of the eccentric bodies determines the direction of movement. Moreover, a favorable correlation of the eccentric bodies' masses with the entire platform mass is able to ensure the movement at an oscillating but permanently positive speed. [ABSTRACT FROM AUTHOR]

Resonators are at the core of optical sensors enhancing light–analyte interaction and leading to higher sensitivities. Maximizing the sensitivity is an obvious objective function for the resonator design. However, high sensitivity does not guarantee sufficient detectivity. When the optical energy budget is limited, as in sensors on mobile platforms, a higher sensitivity usually leads to lower detectivity for nanophotonic sensors. In such scenarios, resonator design requires balancing the trade-off between the sensitivity and detectivity of the resonant sensor. Here, we show the direct dependence of detectivity on the Q-factor and the trade-off between the Q-factor and sensitivity. We study this trade-off in an array of plasmonic resonators. We choose plasmonic resonators because of their high sensitivity arising from large local field enhancements. Then, we show that the detectivity of this sensor may be boosted for limited energy budget applications by making an array of resonators supporting a surface lattice resonance (SLR). We experimentally demonstrate sensing and detection of antimouse IgG protein in a gold nanodisk array–based SLR sensor for various energy budgets. [ABSTRACT FROM AUTHOR]

This study adopts an ethnographic approach to examine how digital technology used at home ‐ mobile platforms and smart devices ‐ shaped the intimacy of ageing adults (aged 55 to 65) in Beijing. China's fast-ageing population and its ambition to be a high‐tech superpower are some of the conditions that have necessitated the mobilisation of governmental ideas that link population management to technology development. This study built on Ara Wilson's (2016) essay "The Infrastructure of Intimacy" by including critical infrastructure studies to examine how practices of intimacy are translated and operationalised through daily technology use at home. Data analysis was conducted using three frameworks: (1) making intimate: familiarising and reconnecting; (2) materialising intimacy: gifts, bonding, and avoidance; and (3) self‐intimacy (re)structured: choices, self‐love, and empowerment. The ethnographic data unveils the pivotal role of intimacy in the use of technologies and platforms by the ageing informants in managing their relationships between the self and their family and social relations. With its profound functionality in broadening the practices of intimacy, such as strengthening self-worth and self‐realisation, the daily use of digital technology at home also made it easier for the ageing informants in this study to deter, replace, and lessen their need for physical interactions. The findings suggest that normalising older adults' active use of technology at home will further advance the technologisation of Chinese society while enhancing intimacy in the ageing population. [ABSTRACT FROM AUTHOR]

Personalization is an emerging digital strategy to engage users across different business domains. It is defined as the capability to match content, products, and services to individual users based on the knowledge of their past behaviors and revealed preferences. It has shown its great potential across a variety of contexts, including search engines, recommender systems, targeted marketing, and more. In this study, we examine personalization on third-party mobile app platforms, which account for a $36 billion market in 2021. We develop a comprehensive structural framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action margin. A series of policy experiments highlights the value of personalization to various extent. Remarkably, personalized rankings at the individual level outperform the current practice by 16.73%. This cost-efficient approach showcases how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution. The ease of customer data collection has enabled the widespread personalization of content and services in digital platforms. We examine personalization in a hitherto unaddressed context: that of mobile app distribution. Specifically, we develop a comprehensive framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action (CPA) margin, which is the revenue earned by the platform per app installation. To this end, we specify a structural model of click and installation choices, jointly estimated as a function of a comprehensive set of numerical (screen rank, quality, and popularity) and textual (titles, descriptions, and reviews) covariates. Our novel data set is at the granular user-impression level and uniquely includes app CPA margins paid to the platform. We conduct a series of policy experiments to quantify the value of personalization. Specifically, we show that a personalized hybrid margin and utility margin ranking scheme outperforms other personalized methods, including those based on utilities alone or a combination of utilities and margins. Overall, our analysis demonstrates how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution. History: Param Singh, Senior Editor; Juan Feng, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1156. [ABSTRACT FROM AUTHOR]

As platform owners interact with end users and complementors, their demand-side characteristics and performance affect the overall value creation of ecosystems. This research investigated how the emergence of popular complements on a mobile communication platform impacts the usage of other complements by the platform's end users and how platform owners can benefit from such demand spillovers. Using individual user-level app usage data, we empirically demonstrated how the presence of a popular app alters the demand structure of a platform through changes in the usage of other apps operating within it. The findings reveal that popular app adoption by users increases the app usage, excluding the usage of popular apps, only within the platform offering a popular app, supporting the existence of positive spillovers from popular complement adoption on a platform. Such positive within-platform spillovers occur through the increased usage of both existing and new apps, with stronger positive spillovers for new apps. These findings imply that platform owners can reap benefits by coordinating the launch of new complements and the promotion of less-known counterparts to end users with the emergence of a popular app. All these results shed light on how platform owners can manage their complements and create value from popular complements. As platform owners interact with end users and complementors, their demand-side characteristics and performance affect the overall value creation of ecosystems. This research investigated how the emergence of popular complements on a mobile communication platform impacts the usage of other complementary products by the platform's end users and how platform owners can benefit from such demand spillovers. We identified two different forms of demand spillovers (i.e., backward and forward) and conceptualized how each subsequently affects platform expansion. On the basis of individual user-level app usage data, we empirically demonstrated how the presence of a popular app alters the demand structure of a platform through changes in the usage of other apps operating within it. The findings reveal that popular app adoption by users increases the number of apps used and the duration of app usage, excluding the usage of popular apps, only within the platform offering a popular app. These results support the existence of positive spillovers from popular complement adoption on a platform. Such positive within-platform spillovers are derived from both backward spillovers onto existing apps adopted before popular app adoption and forward spillovers onto new apps to be adopted after the uptake of favored apps. These patterns suggest that positive spillovers of popular app adoption occur through both the increased retrieval of existing apps and reduced uncertainty about newly released apps. Furthermore, forward spillover is considerably stronger than backward spillover, implying that platform owners can reap benefits by coordinating the launch of new complements and the promotion of less-known counterparts to end users with the emergence of a popular app. These results shed light on how platform owners can manage their complements and create value beyond direct contributions from popular complements. History: Yulin Fang, Senior Editor; Yan Huang, Associate Editor. [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, or low time resolution, which limits how quickly an area can be spatially mapped with mobile measurements. This work describes the development of the M ultichannel O rganics I n situ envi R onmental A nalyzer (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 analyser (RGA) mass spectrometer (MS) detectors enable continuous measurements at 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]

There are many real-world applications that require high-performance mobile computing systems for onboard, real-time processing of gathered data due to latency, reliability, security, or other application constraints. Unfortunately, most existing high-performance mobile computing systems require a prohibitively high power consumption in the face of the limited power available from the batteries typically used in these applications. For high-performance mobile computing to be practical, alternative hardware designs are needed to increase the computing performance while minimizing the required power consumption. This article surveys the state-of-the-art in high-efficiency, high-performance onboard mobile computing, focusing on the latest developments. It was found that more research is needed to design high-performance mobile computing systems while minimizing the required power consumption to meet the needs of these applications. [ABSTRACT FROM AUTHOR]

Traditional methods often ignore the direct influences of vehicle vibration on the flywheel battery system, which leads to an inaccurate analysis of the dynamic performance of the flywheel battery system and its control effect. Therefore, to make up for the deficiencies of existing studies, a more accurate dynamic performance analysis method and efficient control parameter adjustment algorithm for flywheel batteries based on automotive direct action are proposed in this study. First, the influence of road conditions and vehicle driving conditions on the stability of a vehicle is analyzed primarily. Then, the vibration signal generated by the vehicle is transmitted to the vehicle's magnetic flywheel battery system for analysis, and the accuracy of the analysis process is realized. Then, according to the stability analysis results for the direct action of the vehicle and the actual PID controller, the control parameter adjustment algorithm is summarized using the curve-fitting method. Finally, a performance test is carried out on the mobile experimental platform. Good experimental results show that the flywheel can quickly return to its equilibrium position and effectively reduce the influence of interference from road conditions and different working conditions and improve the robustness. Therefore, the correctness of the theoretical analysis and parameter adjustment method proposed in this paper was effectively verified. [ABSTRACT FROM AUTHOR]

The recent advancementsin autonomous driving technology have led to an increased utilization of mobile robots across various industries. Notably, four-wheel steering robots have gained significant attention due to their robustness and agile maneuvering capabilities. This paper presents a novel four-wheel steering robot platform for research purposes and an adaptive four-wheel steering control algorithm for efficient manual operation. The proposed robot platform is specifically designed as a simple and compact research-oriented platform for developing navigation and manual operation of four-wheel steering robots. The compact design of the robot platform allows for additional space utilization, while the horizontal independent steering system provides precise control and enhanced maneuverability. The adaptive four-wheel steering control algorithm aims to offer efficient and intuitive manual operation of the four-wheel steering robot, aligning with the intentions of the human operator. It enables the platform to utilize front-wheel steering under normal circumstances and efficiently reduce the turning radius by employing rear wheel steering when additional steering input is required. Experimental results demonstrated the accurate steering performance of the robot platform and effectiveness of the adaptive steering algorithm. The developed four-wheel steering robot platform and the adaptive steering control algorithm serve as valuable tools for further research and development in the fields of autonomous driving and steering algorithms. [ABSTRACT FROM AUTHOR]

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking. Measurement(s) Particulate matter, gaseous pollutants Technology Type(s) Low-cost environmental sensing platform (City Scanner) Sample Characteristic - Environment City Sample Characteristic - Location Global [ABSTRACT FROM AUTHOR]

The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses. [ABSTRACT FROM AUTHOR]

Purpose: Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms. Design/methodology/approach: A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction. Findings: Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes. Practical implications: The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms. Originality/value: A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research. [ABSTRACT FROM AUTHOR]

The integration of mobile devices into different sectors of society is an increasingly common process; however, their integration as part of medical monitoring for treatments carried out at home is still new, especially their application to the motor development of premature children. This work determines the elements that a mobile application must have to support a hospital-home intervention program aimed at improving motor development in premature infants. The application is designed to work in Android and iOS platforms for mobile devices and to support the hospital-home strategy developed at the Universidad de La Sabana. In this study, an evolutionary device was created, developed and validated along with itsUnity3D tool. The result is an intuitive application that allows parents to strengthen the motor development of their children through a series of 10 exercises designed to stimulate the baby. It consists of a registry of babies, fundamentals, exercise modules and follow-up procedures. The conclusions indicate that although the application is intuitive, some parents have difficulty accessing a mobile device, so it is necessary to create a web version of the application. [ABSTRACT FROM AUTHOR]

There are various potential applications of biomimetic adhesive solutions including climbing robotic systems, mobile sensor platforms, and biomedical applications such as patches for external use. Achieving resistance to both normal and tangential loads, however, is a critical issue that still needs to be addressed. Some animals have developed exceptional attachment mechanisms based on combined fibrillar elements of different shapes and functions. Experimental investigation of combined biomimetic adhesive micro-textures on tribological performances such as adhesion, friction, and peeling resistance is needed to apply this idea to the design of an artificial texture having similar "biomimetic" properties. In the present study, we demonstrate that combinations of different shapes of biomimetic adhesive micro-textures show increased efficiency under different contact environments and enable long-term adhesive solutions. Our work sheds light on combinations of different element shapes inspired by nature and their adhesive efficiency as a function of the ratio of each biomimetic element, as well as their spatial repartition. [ABSTRACT FROM AUTHOR]

In recent years, there has been a remarkable surge in the development and research of tethered aerial systems, thus reflecting a growing interest in their diverse applications. Long-term missions involving aerial vehicles present significant challenges due to the limitations of current battery solutions. Tethered vehicles can circumvent such restrictions by receiving their power from an element on the ground such as a ground station or a mobile terrestrial platform. Tethered Unmanned Aerial Vehicles (UAVs) can also be applied to load transportation achieved by a single or multiple UAVs. This paper presents a comprehensive systematic literature review, with a special focus on solutions published in the last five years (2017–2022). It emphasizes the key characteristics that are capable of grouping publications by application scope, propulsion method, energy transfer solution, perception sensors, and control techniques adopted. The search was performed in six different databases, thereby resulting in 1172 unique publications, from which 182 were considered for inclusion in the data extraction phase of this review. Among the various aircraft types, multirotors emerged as the most widely used category. We also identified significant variations in the application scope of tethered UAVs, thus leading to tailored approaches for each use case, such as the fixed-wing model being predominant in the wind generation application and the lighter-than-air aircraft in the meteorology field. Notably, the classical Proportional–Integral–Derivative (PID) control scheme emerged as the predominant control methodology across the surveyed publications. Regarding energy transfer techniques, most publications did not explicitly describe their approach. However, among those that did, high-voltage DC energy transfer emerged as the preferred solution. In summary, this systematic literature review provides valuable insights into the current state of tethered aerial systems, thereby showcasing their potential as a robust and sustainable alternative to address the challenges associated with long-duration aerial missions and load transportation. [ABSTRACT FROM AUTHOR]

Hate speech, characterized by intentional expressions of dissatisfaction, is a prevalent phenomenon on social media platforms, including Twitter. Its continual occurrence can foster divisions, misunderstandings, and even acts of violence between individuals and groups, particularly due to the resulting prejudice. This study investigates the occurrence of hate speech within Indonesian content on Twitter, employing a deep learning approach to detect and analyze such expressions. The Long Short-Term Memory (LSTM) method, coupled with the GloVe word embedding technique, is utilized on a dataset comprising 13,169 Indonesian tweets flagged for hate speech. Four distinct model architectures were developed through the integration of LSTM and GloVe. The findings reveal model 1 to exhibit superior performance, achieving a precision of 89%, a recall of 99%, an F-1 score of 94%, and an overall accuracy of 94.24%. It is suggested that future research explore the potential deployment of this model in web or mobile platforms for real-time analysis, thereby enhancing the capacity for immediate hate speech detection and mitigation. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the effects of two visual design principles, repetition and compositional lines, in a food image on purchase intention in the context of a mobile food delivery app and test the effect of crossmodal correspondences between vision and taste as a processing mechanism. Design/methodology/approach: In this study, two experiments were conducted using burgers and iced tea as stimuli. Findings: The results demonstrate that repetition of an identical food product increases visual appeal for both burgers and iced tea. However, the optimal level of repetition was different between the two products. The findings show that different compositional lines generate different levels of visual appeal and the effects of compositional lines vary between burgers and iced tea. The results also validate the serial mediation effects of vision and taste between design principles and purchase intention. Originality/value: The findings of this study add substantially to the understanding of visual information processing in food retailing by demonstrating how design principles such as repetition and compositional lines facilitate crossmodal responses between vision and taste and influence purchase decisions in a mobile platform. Also this study provides guidance as to how food retailers use design principles (e.g. repetition and compositional lines) for different products effectively when the food retailers develop visual digital content for a mobile app. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology allows users to view and interact in real-time with virtual information layered on top of the physical environment. Interaction technique development is based on the AR application context. In some cases, using an AR basic interaction technique is unsuitable as it only allows translation and rotation of the virtual object. For example, kitchen design requires various manipulation functions, especially in changing the features of a virtual kitchen cabinet. Existing AR tangible interaction techniques specified to the kitchen design context enable alteration of the features of virtual furniture despite requiring extra input and output devices, such as a stylus, head-mounted displays (HMDs), and projectors. Such devices are not suitable for AR as they are rarely accessible in daily life. Therefore, this study aimed to develop an AR tangible interaction technique on a mobile platform with authoring capabilities for the kitchen design process. The technique uses a cube and a card as the physical medium and features manipulation functions, such as virtual object placement, position changing, deletion, and changing kitchen cabinet features. The tangible interaction technique recorded a faster mean task completion time than the AR basic interaction technique. The tangible interaction technique also recorded higher usability than the basic interaction technique. Younger and experienced users performed better. Gender did not impact the interaction technique. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the "capture, mark and recapture" technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem. [ABSTRACT FROM AUTHOR]

Background: Telemetry methods that allow low-cost, offshore tracking are required for the effective conservation and sustainable use of migratory fish in large lakes with vast and deep offshore areas. Surface drifting buoys, or drifters, are promising platforms for acoustic receiver attachment that have few operational constraints. However, the low recovery rate of drifters in the open ocean presents a challenge. In contrast, closed waters, such as lakes, are more suitable for operating drifters since drifting objects eventually strand on the shore. In this study, we examined the effectiveness of a single GPS-tracked drifter for telemetry surveys of the common carp Cyprinus carpio, which migrates throughout the large offshore area of a large lake, Lake Biwa, in Japan. Its performance was assessed regarding the success rate of drifter recovery, detection frequency of tagged individuals, search area of the drifter, and accuracy of detection locations. Results: We successfully recovered the drifter during all eight deployments. In 6 deployments, tagged individuals were detected 777 times. The drifter successfully detected 48% (10 of 21) of the tagged individuals, exhibiting higher performance (10–33%) than fixed receivers used in other telemetry studies conducted on highly migratory fish species in the lake. The drifter scanned an area of 5.5–8.0 km2 per day. Based on the data obtained from the field trials, the horizontal location accuracy of detected individuals was estimated to be approximately 400 m in shallow coastal areas and 600 m in deep offshore areas. Furthermore, the horizontal distance of tagged individuals traveled was estimated to be between 2 and 9 km within a day. Conclusion: Our newly developed method deploys and retrieves a single GPS drifter that allows low-cost, wide-ranging telemetry surveys in offshore areas of large lakes. The lack of boat noise, moderate moving speed, and continuity of observation range of the drifter seemingly enabled the efficient detection of tagged individuals. Thus, the drifter method offers a promising novel approach for acoustic telemetry in offshore waters that can operate in combination with large receiver arrays and other mobile platforms. [ABSTRACT FROM AUTHOR]

Today's desktop rendering platforms typically use GPUs, which have become the most powerful computing chip to meet the growing visual needs, especially in ray tracing. However, ray tracing is challenging for mobile platforms because mobile GPUs need to accommodate insufficient computing power, hardware resources, and memory bandwidth. This paper presents a novel architecture for the mobile domain called Mobile Multiple stacks Ray Tracing (MMsRT). The most complicated calculations in ray tracing are completed through lightweight embedded design. MMsRT has three key features: First, we set multiple stacks to ensure multiple rays are parallel in the system. Second, it sets a stack cache to store the data in stacks when the storage space of multiple stacks is insufficient. Third, we adopt the data prefetching mechanism to set caches to improve the cache hit rate and performance. An accurate simulator test proves that our design can be applied to mobile devices. We calculate the performance of about 82.9 Million Rays Per Second (MRPS), the chip area is about 0.856 mm2, and 96.85 MRPS/mm2. [ABSTRACT FROM AUTHOR]

In this paper, the theoretical model of the coupling efficiency of BG beams to few-mode fibers (FMFs) in the case of perfect alignment, lateral or vertical offsets, and random angular jitter is investigated and compared with that of sew-mode fibers (SMFs). Under the same conditions, FMF obtains a higher coupling efficiency with the BG beam and flatter response characteristics to deviations from ideal conditions compared with SMF. At the same time, the effects of the half-cone angle and topological charge of the BG beam on the coupling efficiency and response characteristics during nonideal conditions are studied. According to the precision of the coupling system, a higher coupling efficiency can be obtained by adjusting the half-cone angle, topological charge, and focal length. That makes them a more practical choice for information transmissions, such as mobile platform communication and the transmission of pedestrian data collected during transportation. The research results could help apply the BG beam and other orbital angular momentum (OAM) beams coupled with FMF. [ABSTRACT FROM AUTHOR]

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level. [ABSTRACT FROM AUTHOR]

The wireless network is composed of multiple micronetwork nodes that are difficult to replace. These network nodes are usually located in areas with poor environmental conditions, so the energy consumption and security of nodes have become the main limitations and challenges. Therefore, this paper discusses the network topology, data transmission and node trust mechanism, and proposes a cloud computing technology algorithm for intelligent regional wireless network. According to the data of arrival delay difference in intelligent regional wireless network environment, the approximate location of mobile system is set as different positioning results. The dynamic position of the mobile system is obtained through multiple calculations. With the support of cloud computing technology, this paper also studies the color rendering system of 3D animation. 3D animation can not only express emotions and feelings, but also color render through the facial expressions of characters in the animation, so as to enhance the entertainment and interest visible to the audience. However, due to the great demand for computing resources and color rendering resources, at present, the main research and implementation are carried out on the desktop platform. There are also solutions to use high-end infrared devices for 3D animation color rendering on the mobile system. However, because this method has high cost in use and does not have universality on the mobile device, this paper proposes a rendering system suitable for the general mobile platform. This paper studies the 3D animation color rendering system based on wireless network cloud computing technology, hoping to realize the universality of the system with the support of technology. [ABSTRACT FROM AUTHOR]

In a household setting, a wheelchair-mounted robotic arm (WMRA) can be useful for assisting elderly and disabled individuals. However, the current WMRA can only perform movement and grasping tasks through joystick remote control. This method results in low efficiency due to poor coordination between the mobile platform and the robotic arm as well as the numerous operational steps required. To improve the efficiency and success rate of the robot in task execution, this paper proposes a parking location optimization method that combines the occupied grid map (OGM) and the inverse reachability map (IRM). Firstly, the SLAM algorithm is used to collect environment information, which is then stored in the form of an occupied grid map. The robotic arm workspace is then gridded, and the inverse reachability map is calculated based on the grasping pose of the target object. Finally, the optimal position of the mobile platform is obtained by comparing the optimal location point in the inverse reachability map and the obstacle information in the occupied grid map. This process achieves base placement optimization based on the grasping pose. The experimental results demonstrate that this method reduces the user operation time by 97.31% and overall task completion time by 40.57% when executing household environment tasks compared with the joystick control, increasing the range of executable tasks compared with the algorithm of the EL-E robot and reducing task completion time by 23.48% for the same task. This paper presents a parking location optimization method that can improve the grasping efficiency of the robotic arm and achieve parking location position selection for the WMRA in a household environment. [ABSTRACT FROM AUTHOR]

The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works. [ABSTRACT FROM AUTHOR]

Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair. [ABSTRACT FROM AUTHOR]

To explore the visual experience characteristics and influencing factors of college students' visual attention intervention in the interface of mobile learning platform by using eye-tracking technology, and to summarize and summarize the visual experience pattern of platform interface design and its design inspiration. Methods: Using the head-mounted eye-tracking technology, 28 images from 6 groups of typical elements in the interface layout of CGTN learning platform were selected as the test samples, and the eye-movement indexes of the subjects browsing the interface were recorded. Results: There were significant differences in the attention time, number of times of attention, visual attention rate and visual recall rate of different areas and topics of the interface (P < 0. 001). Conclusion: In the platform interface design, the analysis of the factors influencing visual attention can be found that people's attention and visual experience is mainly influenced by color, text, and typography, and secondary areas and layout also play an important role in visual communication. The color and text areas in the interface design, as well as the innovative design of typography can effectively enhance the visual attention of college students and better communicate the information of the platform. [ABSTRACT FROM AUTHOR]

Asparagus harvesting presents unique challenges, due to the variability in spear growth, which makes large-scale automated harvesting difficult. This paper describes the development of an asparagus harvesting robot system. The system consists of a delta robot mounted on a mobile track-based platform. It employs a real-time asparagus detection algorithm and a sensory system to determine optimal harvesting points. Low-level control and high-level control are separated in the robot control. The performance of the system was evaluated in a laboratory field mock-up and in the open field, using asparagus spears of various shapes. The results demonstrate that the system detected and harvested 88% of the ready-to-harvest spears, with an average harvesting cycle cost of 3.44 s ± 0.14 s . In addition, outdoor testing in an open field demonstrated a 77% success rate in identifying and harvesting asparagus spears. [ABSTRACT FROM AUTHOR]

The MRI process can be a challenging experience for young children. Therefore, general anaesthesia is often used to ensure paediatric patient's compliance. A recent attempt by researchers and practitioners has been the development of mobile applications that are designed to provide a virtual real-like MRI experience. The paper aims to review and provide insight into some of the available MRI mobile applications regarding their functionality and use as well as providing practical suggestions for future mobile application developers in the field. MRI mobile applications that are available on the two platforms of IOS and Android were downloaded and tested to gain the user experience in terms of functionality and overall use. The findings revealed that the available MRI mobile applications designed for the paediatric community lack features and functionalities in terms of appealing to the age groups they had been designed for. Such features include the lack of availability of interactive games, aesthetic layouts, localisation to suit the culture, lack of dual language, etc. The review of the available MRI mobile applications that are designed for paediatric users clearly showed that more work should be done and there is a room for improvement in terms of their designs and functionality. [ABSTRACT FROM AUTHOR]

Objective: Analyze the elements related to the satisfaction of the m-commerce consumer residing in the city of Tijuana. Methodology: Review about the concept of m-commerce. A questionnaire was applied to 600 m-commerce consumers residing in the city of Tijuana. Results: By applying the statistical technique of multiple linear regression, it was found that there is a positive association between the independent variables (efficiency of the purchasing process, optimization and design of the website or mobile app, monitoring during and after the sale, security of the platform or mobile app), with the dependent variable (m-commerce satisfaction). Limitations: The study focuses on the experience of m-commerce consumers in the city of Tijuana. It is necessary in a new investigation to identify the coincidences and differences with the strategies used to satisfy the needs of users by micro and small entrepreneurs in Tijuana who use m-commerce as one of their main sales channels. Conclusions: The contemporary consumer has been adapting to new purchasing modalities and systems, resolving opportunity, speed, convenience, exploration, and diversity from their mobile device. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present development results a technological solution supported by artificial intelligence (AI), for the company Servicios Amma S.A.S. This technological solution has the function to allow the correlation of the socio-behavioral characteristics of the customer, with the profile of the operational staff. This will provide the detection of patterns and prediction of consumption behaviors for the service that leads to the identification of opportunities, reduction of dissatisfaction risks, and improvement of the user's service experience. The methodology used in this study is quantitative, therefore, the demographic, social, psychographic, behavioral and lifestyle characteristics of customers and operating personnel of the company, as well as the beneficiary companies, are the variables defined for the development of this research. Additionally, the development of mathematical algorithms will be used to gather information for data correlation and prediction. The results obtained from this research allowed us to develop and program a mobile application and Land Page that allows data entry for the creation of user profiles for customers, employees, and platform administrators. In this way, Servicios Amma S.A.S. will be able to enhance the services it offers with the help of Artificial Intelligence. [ABSTRACT FROM AUTHOR]