Mobile Device Applications Research Articles

Competing technologies: determining the geographical origin of strawberries (Fragaria × ananassa) using laboratory based near-infrared spectroscopy compared to a simple portable device.

The application and development of fast and simple screening methods for the authentication of foods has increased continuously in recent years. A widely used analytical technique is Fourier transform near-infrared spectroscopy (FT-NIR). Despite the simple application of FT-NIR analysis, the analyses are usually carried out on benchtop devices in the laboratory. However small, inexpensive and mobile NIR devices could be used on-site. Despite the simple use of FT-NIR analysis, the examinations are usually carried out on a stationary benchtop device in a laboratory. However, in order to be able to perform the application directly on site, the application of small, cost-effective and mobile NIR devices for food analysis is crucial. In this study, both, a benchtop NIR instrument and a handheld NIR device with a lower resolution and analyzed wavenumber range were applied for the differentiation of strawberries from different geographical origins. Distinguishing German and non-German strawberries using linear discriminant analysis (LDA) yielded an accuracy of 91.9% and 84.0% using the benchtop and the handheld devices, respectively. Relevant variables could be assigned to lipids, carbohydrates and proteins. Overall, our study demonstrated for the first time that analyzing the geographical origin of strawberries using NIR spectroscopy is also possible by means of a handheld device.

Lightweight Mulberry Fruit Detection Method Based on Improved YOLOv8n for Automated Harvesting

Aiming at the difficulty of feature extraction in complex environments during mulberry detection and the need for embedded devices to lighten the model, this study carries out lightweight improvements on the basis of the YOLOv8n model. First, the CSPPC module incorporates lightweight partial convolution (PConv) within its bottleneck structure, replacing the C2f module to enhance feature extraction efficiency. Secondly, the ADown module is used to replace the traditional downsampling module and the P-Head module is used to replace the traditional convolutional detector head with the partial convolutional detector head. Finally, a knowledge distillation technique is used to compensate for the loss of accuracy due to parameter reduction. Ablation experiments are conducted to evaluate the impact of each module on the model’s performance. The experimental results show that the improved YOLOv8 model has a precision of 88.9%, a recall of 78.1%, and an average precision of 86.8%. The number of parameters is 1.29 × 106, the model size is 2.6 MB, the floating-point computation is 2.6 GFLOPs, and the frame rate reaches 19.84 FPS on the edge end. Therefore, this model provides theoretical and technical support for the deployment and application of mobile detection devices, such as automatic mulberry harvesting in practical scenarios.

The relationship between dynamic capacity and innovation: the case of startup hand talk

This theoretical essay explores the relationship between dynamic capacity and innovation based on the case study of the startup Hand Talk published by the Revista de Ciências Empresariais da UNIPAR, in 2022. The company's business is to use technology to overcome barriers between deaf and hearing people with innovation and digital accessibility. A qualitative approach was used, with bibliographical research on the concepts of dynamic capabilities and single case study analysis, aiming to explore and describe observable characteristics of the theories of dynamic capabilities, effectuation and frugal innovation in the period 2012 to 2022. The result showed that the company identified an opportunity and through dynamic capacity, created a business model capable of absorbing user demands and implementing continuous innovations in its services to enable digital accessibility. of deaf people to the market, through an application for mobile devices that converts texts, images and audio into Brazilian Sign Language (LIBRAS). The company was able to realize its ideas and put effectuation into practice, in addition to adopting a dynamic capacity approach, through organizational reconfiguration and skills development, observing the environment and organizational resources.

Implementation of a Home Automation System

This report presents an innovative home automation system using Arduino and a variety of components, such as sensors, motors, and a Bluetooth module. Through the mobile device application developed through the MIT App Inventor, users can easily manage and monitor the various aspects of their home. The project not only demonstrates the technical capabilities of the system but also highlights the importance of smart technologies in enhancing convenience and security in the home environment.

Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, improved network usage, and certain privacy improvements, split learning, where deep learning models are split away from the mobile device and computed in a distributed manner, has become an extensively explored topic. Incorporating compression-aware methods (where learning adapts to compression level of the communicated data) has made split learning even more advantageous. This method could even offer a viable alternative to traditional methods, such as federated learning techniques. In this work, we develop an adaptive compression-aware split learning method (“deprune”) to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources. This method is also extended (“prune”) to very quickly train deep learning models through a transfer learning approach, which tradesoff little accuracy for much more network-efficient inference abilities. We show that the “deprune” method can reduce network usage by 4× when compared with a split-learning approach (that does not use our method) without loss of accuracy, while also improving accuracy over compression-aware split-learning by up to 4 percent. Lastly, we show that the “prune” method can reduce the training time for certain models by up to 6× without affecting the accuracy when compared against a compression-aware split-learning approach.

Development of the intelligent traffic light system based on image processing and fuzzy control techniques

In Viet Nam's current traffic conditions, congestion and jams—especially at intersections during peak hours—present major challenges. Traditional traffic light systems, which rely on fixed timing principles, often fail to manage traffic flow efficiently, particularly when vehicle density varies significantly across different directions. This research aims to develop an intelligent traffic light system where the signal timings automatically adjust based on the vehicle density at intersections. The study uses an object recognition algorithm to identify, classify, and count vehicles. The data was then fed into a fuzzy logic model to calculate the optimal signal timings. Experimental results demonstrate an accuracy of approximately 88% in vehicle detection. The fuzzy logic model and the programmable logic controller were able to effectively compute reasonable signal timings based on real-time vehicle density. Future developments include expanding the system's functionalities, creating a user-friendly interface, and developing a management application for mobile devices.

Interactive advertising as a new factor of influence on consumer behaviour of youth

The article is devoted to the analysis of interactive advertising as a relatively new factor influencing the consumer behaviour of Moscow youth. The empirical basis of the work are statistical and sociological research data, including the authors’ own research conducted using the methods of mass survey and indepth interview. As the study has showed, the Moscow youth are quite familiar with the interactive advertising, most often encountering it on mobile devices and in gaming applications. The article analyses several types of the interactive advertising, differing in the degree of their impact on the consumer behaviour of young people. The authors come to the conclusion that the most effective in this regard are playable and video advertising placed in mobile device applications. As advantages of the interactive advertising, in comparison with the traditional format, the authors highlight its innovation and playful nature. At the same time, many young Muscovites express a negative attitude towards the interactive advertising, mainly because of its intrusiveness. Moreover, young men demonstrate greater loyalty to this format of advertising influence in comparison with young ladies. The authors come to the conclusion that, despite the noted shortcomings, the interactive advertising has great potential for development in modern society.

Evaluation of the Effect of a Mobile Application on Glycated Hemoglobin in Older Adults with Type 2 Diabetes Mellitus-Protocol of a Randomized Clinical Trial.

Digital educational technologies in health have been an important instrument for promoting learning, self-care, self-esteem, and security regarding prevention and health promotion actions that lead to changes in behavior, mainly for non-communicable disease patients, such as type 2 Diabetes Mellitus (DM 2). This study aimed to describe a protocol for evaluating the effect of an app for cell phones and tablets on the blood glucose of older adults with DM 2. The protocol will be used to compare the effectiveness of an application for mobile devices concerning the educational booklet in reducing Glycated Hemoglobin in older adults with DM 2 in Primary Health Care. This protocol is part of a Randomized Clinical Trial project entitled Effectiveness of a Mobile Device Application on Glycated Hemoglobin in Elderly People with Type 2 Diabetes Mellitus: a Randomized Clinical Trial. The protocol was structured in the following phases: (i) sample calculation, (ii) invitation to participate in the study according to the eligibility criteria; (iii) participant registration; (iv) randomization and allocation of participants into groups (double blinding); (v) application of the intervention; (vi) post-intervention procedures (post-test); (vii) data analysis. It is expected that encouraging studies on the impact of a mobile application will improve and enhance health education focused on self-care for older adults with DM 2, potentially influencing the local health system by reducing hospitalizations due to conditions that are sensitive to primary care, since health promotion and prevention of DM-related illnesses will be the main focus of the application and booklet developed.

An application for mobile devices as a decision support for the treatment and prevention of bovine fasciolosis - A survey among organic dairy farms in Bavaria

Based on health applications used in human medicine for the self-management of chronic diseases, the aim of this study was to evaluate a mobile app in veterinary medicine using the example of a decision-making aid for the control and prevention of bovine fasciolosis on dairy cattle farms. The study was carried out on 37 organic dairy cattle farms in Bavaria. The farms were divided into two groups: one group received a mobile app (n=17) as a decision-making aid, while the other group received a printed brochure (n=20) with identical content. At the beginning of the study, all participants were questioned by telephone concerning their farm, among other things. After using the respective tool, the participants were again interviewed by telephone. At the beginning of the study, the majority of participants were aware of the options for drainage (n=28; 75.7%) and fencing off wet areas (n=36; 97.3%). 37.8% (n=14) stated that they were familiar with a pasture rotation system. After using the respective aid, the majority of participants described the tool as helpful (94.1% app; 80% brochure). Many participants (app group: n=10 (58.8%); brochure group: n=16 (80%)) stated that they wanted to change their approach based on the new knowledge gained, e. g. introducing a pasture rotation system (app group: n=9 (52.9%); brochure group: n=13 (65.0%)). Both study groups enjoyed working with the tool they used. They viewed the knowledge imparted by the mobile app or brochure as helpful and useful. Most participants expressed a desire to continue using these tools in animal health management on farms in the future. The use of tools such as mobile apps may improve veterinary advice, e. g. in the management of parasitoses in cattle herds.

Mobile Devices in Forest Mensuration: A Review of Technologies and Methods in Single Tree Measurements

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection.

Usability of an at-home tablet-based cognitive test in older adults with and without cognitive impairment

BackgroundMobile device-based cognitive screening has the potential to overcome the limitations in diagnostic precision and efficiency that characterize conventional pen and paper cognitive screening. Several mobile device-based cognitive testing platforms have demonstrated usability, but the usability of take-home mobile device-based cognitive screening in typical adult primary care patients requires further investigation.MethodsThis study set out to test the usability of a prototype mobile device-based cognitive screening test in older adult primary care patients across a range of cognitive performance. Participants completed the St. Louis University Mental Status Examination (SLUMS) and then used a study-supplied mobile device application at home for 5 days. The application presented 7 modules lasting approximately 15 min. Participants completed the System Usability Scale (SUS) after using the application.ResultsA total of 51 individuals participated, with a median (IQR) age of 81 (74–85) years. Cognitive impairment (SLUMS score < 27) was present in 30 (59%) of participants. The mean (95% Confidence Interval [CI]) SUS score was 76 (71–81), which indicates good usability. Usability scores were similar across ranges of cognitive impairment. A Lower SLUMS score predicted early withdrawal from the study with an area under the receiver operating characteristic curve (95% CI) of 0.78 (0.58–0.97).ConclusionTake-home mobile device-based cognitive testing is a usable strategy for many older adult primary care patients. Depending on patient preferences and abilities, it could be part of a flexible cognitive testing and follow-up strategy that includes mobile device-based testing in healthcare settings and pen-and-paper cognitive testing.

EngageHealth: a mobile device application designed to deliver stroke rehabilitation exercises using asynchronous video recordings

BackgroundStroke survivors who receive more rehabilitation therapy achieve better functional outcomes. The amount of rehabilitation that patients receive is, however, limited due to constraints of the healthcare system.ObjectiveTo assess whether EngageHealth, a mobile device application designed to deliver stroke rehabilitation exercises using asynchronous video recordings, increases the amount of outpatient rehabilitation in stroke patients and improves their upper extremity function and quality of life.DesignProspective single-arm study consisting of a 2-week pre-intervention phase without EngageHealth followed by a 4-week intervention period with EngageHealth.SettingAmbulatory care.ParticipantsTwenty-four stroke patients with upper extremity impairment were recruited at the Stanford Stroke Center outpatient clinic.InterventionsParticipants were instructed to use the EngageHealth application daily.Main outcome measuresAdherence, user experience, and change in the upper extremity Fugl-Meyer (UE-FM), Quality of Life in Neurological Disorders (Neuro-QoL), and Stroke Impact Scale (SIS).ResultsOf 23 participants, five (22%) used the application for 17 days, six (26%) used the application for 9–16 days, and 12 (52%) used it &amp;lt; 9 days. Sixty-three percent of participants would recommend the application to other stroke survivors, with fifty percent indicating they would continue using the application, if available. During the pre-intervention phase, there were no changes in hand function. During the intervention period, participants improved by 4 points on the UE-FM (P &amp;lt; 0.01), and 15 points in the hand-function domain of SIS (P = 0.03). Videos of participants' exercises were successfully recorded, allowing the clinician to review videos of the participants' completed tasks asynchronously. In-depth interviews revealed that participants viewed the EngageHealth application favorably, and that their perceived usefulness of the exercises affected their motivation.ConclusionsUse of the EngageHealth application in the home environment may improve upper extremity function in subacute/chronic stroke patients. Additional support strategies should be implemented in future studies to improve adherence. These findings from a prospective single-arm study, support the design of a randomized controlled trial to determine the efficacy of long-term use of the EngageHealth application.

User Sentiment Analysis Based on Securities Application Elements.

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.

Features of Bioluminescence Dynamics of Photobacterium phosphoreum IMV B-7071

The problem of the prolonged and stable intensity of bioluminescent signals is relevant in the development of any test systems that use biological objects. The aim of this work was to study the features of bioluminescence dynamics of Photobacterium phosphoreum IMV B-7071 in a liquid and on different stationary media. Methods. Bioluminescence studies were performed in liquid, agarose, and cellulose-cotton media. Bacterial suspensions were cultivated at 21°C in the mediums with standard composition. We studied both the background glow and its dynamics under conditions of mixing a liquid medium. Bioluminescence was recorded using digital photography with subsequent image processing of the samples. The measurements of luminescence were made by digital photo or video recording using Olympus digital camera SP560UZ, CANON 700D, and mobile device camera Samsung Galaxy 9 Note with specialized applications for mobile devices "Colorimeter (Lab Tools Apps)" and Camera Color Counter (Keuwsoft) at maximum light sensitivity in the automatic white balance mode at a fixed distance from the sample. Image processing was carried out using ImageJ and Origin Pro. Spectra of bacterial luminescence and its dynamics over time were measured using an LOMO MDR-23 spectrometer in the range of 200–750 nm. Results. The results of the study prove that in aqueous or solid agar and also on cellulose cotton medium, the intensity of bioluminescence of P. phosphoreum gradually increases, reaching a maximum within approximately 2 days, after which it slowly fades. It was established that the bioluminescence of photobacterium P. phosphoreum is a non-stationary process and has characteristic features of temporal dynamics associated with both the dynamics of the oxygen concentration in the environment of bacterial suspensions and the dynamics of the bacterial population density. Analysis of the luminescence spectra of bacteria shows that luminescence occurs mainly in the blue and green regions of the spectrum with luminescence maxima in the range of 460–520 nm, but the ultra-weak glow is also registered in the UV and red spectral ranges. The variability of photobacterial luminescence spectra over time in the spectral ranges of the main luminophores causes color fluctuations between the blue and green ranges. Conclusions. The key parameters of multi-day background and short-term induced bioluminescence dynamics of photobacteria in different environments were clarified, and the certain variability of the spectral characteristics of luminescent radiation over time was shown. The revealed features of the dynamics of the bioluminescence of P. phosphoreum must be taken into account in practical application to assess the toxicity of substances of various nature, as well as in environmental monitoring.

Realizing computer vision rehabilitation assessment tests & evaluation applications for mobile devices

Upper extremity impairments are a common consequence of stroke, necessitating thorough rehabilitation monitoring and kinematic assessments to facilitate motor recovery. The Box and Block Test (BBT) and Sollerman Hand Function Test (SHFT) are two widely utilized and recommended tools for objectively measuring upper limb dexterity and evaluating fine motor skill rehabilitation in patients. However, these tests rely on specific equipment and therapist attendance, making the process time-consuming and clinic-dependent. This paper introduces a computer vision-based hand rehabilitation assessment suite specifically designed for mobile devices, such as smartphones and tablets, which serves as a virtual alternative to traditional methods while also incorporating an interactive exergame. Our application faithfully integrates the original tests’ guidelines and procedures into an engaging computer vision experience, utilizing advanced technologies like MediaPipe Hands for precise hand and finger tracking. This innovative solution obviates the need for additional computer peripherals such as smart gloves or VR headsets, as well as physical equipment like wooden boxes and blocks, relying solely on the built-in camera of everyday mobile devices. In addition, we address several technical challenges encountered in our approach and outline future directions for score normalization and feature expansion, ensuring the continued improvement and efficacy of our hand rehabilitation assessment suite.

Wideband Patch Antenna with Modified L-Probe Feeding for mmWave 5G Mobile Applications

This paper presents a wideband low-profile dual-polarized patch antenna with helical-shaped L-probe feeding (HLF) for mmWave 5G mobile device applications. Parametric studies on the HLF structure are performed to identify the optimal specifications. As a result, the optimized antenna achieves a wide bandwidth of 5.4 GHz (24.2–29.6 GHz), good isolation &gt; 18 dB between ports, and 5.1 dBi of good peak realized gain, which is experimentally verified with a 10× upscaled antenna. In addition, various one × four phased arrays with different port configurations and beamform capabilities are designed and simulated for the peak realized gain. The designed antenna array shows a high peak realized gain of 10 dBi, high isolation of 15 dB between the ports, and a small substrate thickness of 0.048λ0 (λ0 is the wavelength of 24.25 GHz). Compared to the state-of-the-art antennas, the designed dual-polarized antenna can operate in the frequency ranges of 24.25–29.6 GHz, including n257, n258, and n261 of the 5G new radio frequency range 2.

Optimization Method of SiC MOSFET Switching Trajectory Based on Variable Current Drive

Silicon carbide (SiC) MOSFETs exhibit superior performance compared to traditional silicon (Si) MOSFETs, characterized by faster switching speeds, lower on-resistance, higher breakdown voltage, and greater operational temperature tolerance. These attributes make SiC MOSFETs highly suitable for applications in electric vehicles, charging stations, and mobile devices. However, their rapid switching speed can intensify current and voltage overshoot and oscillations during device switching, leading to increased device losses or potential damage. To address this issue, this paper proposes a current-type active gate drive (AGD) circuit. The circuit first detects the rate of change in the drain current and drain-source voltage. Subsequently, it employs an analog amplifier circuit and adjustable drive resistors to decelerate the rate of change in the drain-source voltage and drain current. As a result, overshoot and oscillation in the drain-source voltage and drain current are mitigated. Experimental results demonstrate that the proposed AGD circuit can reduce drain current overshoot by 60%, drain-source voltage overshoot by 15.38%, and waveform oscillations. Additionally, the AGD circuit decreases conduction and turn-off losses by 24% and effectively mitigates electromagnetic interference (EMI) issues within the frequency range of 0.1 to 3 MHz.

Tooth Anatomy Inspector: A comprehensive assessment of an extended reality (XR) application designed for teaching and learning of root canal anatomy by students.

To develop and evaluate a suitable software application for mobile devices designed for teaching root canal anatomy to undergraduate students in an informative and engaging manner. Extracted human teeth were scanned by μCT and digitized by converting into STL files. An extended reality (XR) application illustrating the root canal anatomy of the scanned teeth was developed. Prior to deployment, undergraduate dental students were voluntarily asked about their expectations regarding an educational application on tooth anatomy. After a testing phase of the application on a mobile device and within a virtual reality environment, a subsequent evaluation was conducted to assess their overall experience in relation to their initial expectations. Data were analysed using Kolmogorov-Smirnov test and Mann-Whitney U test. The level of significance was set to .05 (p = .05). The application was able to meet the expectations of the students in all categories (p < .466-.731). Furthermore, it was evaluated as user-friendly (98.2%) and highly motivating for the purpose of learning more on root canal anatomy (100%). Given the overwhelmingly positive reception from undergraduate dental students, the application emerges to be a promising supplementary teaching method for the endodontic curriculum.

Mid-Infrared Spectrometer Based on Tunable Photoresponses in Pdse2

Mid-infrared (mid-IR) photodetection is important for various applications, including biomedical diagnostics, security, chemical identification, and free-spacing optical communications. However, conventional “photon” mid-IR photodetectors require liquid nitrogen cooling (i.e., MCT). Furthermore, acquiring mid-IR spectra usually involves a complex and expensive Fourier Transform Infrared spectrometer, a tabletop instrument consisting of a meter-long interferometer and MCT detectors, which is not suitable for mobile and compact device applications. In this work, we present tunable photoresponsivity in the mid-IR wavelength in palladium diselenide (PdSe2) – molybdenum disulfide (MoS2) heterostructure field-effect transistors (FETs), operating at room temperature. Furthermore, we applied a tunable membrane cavity to modulate the Fabry–Pérot resonance to modulate the absorption spectrum of the device layer. We used a robust polyetherimide (PEI) membrane with CVD-grown graphene to electrically tune the membrane structure. For the next step, we will integrate the PdSe2-based photodetector and tunable membrane to increase detection sensitivity and spectrum tunability to realize the ‘learning’-based spectroscopy.

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.