Mobile Platform Research Articles

Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment

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

Technology and communal culture of sharing and giving: implications on household savings behavior in Fiji

PurposeThis study examines how the introduction of mobile money transfers, while making it efficient and convenient to access funds, has affected rural households’ savings behavior and the banking sector.Design/methodology/approachThis study utilizes Fiji’s most recent agricultural census data to model the agricultural household’s saving decision. The study estimates an probit model to examine rural households' savings behavior. Furthermore, it utilizes time series secondary data to examine how funds transfer has been channeled to rural households in Fiji.FindingsFirstly, the results demonstrate that with the mobile money transfer platform launch, the banking sector has lost substantial money previously used to pass through its system, thus losing service fees and interest income. Furthermore, the findings demonstrate that those using mobile wallet platforms to receive money are more likely not to have a savings account with the bank. Noting the cultural systems and social settings of the native households and the ease of payments via the mobile platform, they tend to spend more on consumption rather than saving, thus making these households more vulnerable during shocks such as natural disasters.Originality/valueWhile mobile money transfer is hailed as a revolution, no research has yet picked up the downside to it, that of undermining the very effort by policymakers to get low-income rural households to save. Secondly, this study also highlights how mobile money transfer deprives the banking system of a significant transfer fee income and a source of funds to pool and lend to earn interest income. Furthermore, this study brings to the forefront a dichotomy about how a rural indigenous community sees the welfare and prosperity of their community much differently than what economics textbooks portray.

Development of an IoT based 3D printed Mobile Robot Platform for training of Mechatronics Engineering Students

The advent of the Internet of Things (IoT) has opened up new opportunities in education, particularly in the field of mobile robotics. Mobile robotics is a fast-growing field, and with the increasing demand for skilled robotics engineers, it is essential to provide students with hands-on experience in designing and developing robots. In this regard, an IoT-based 3D printed mobile robot platform has been developed for students training. STEM (Science, Technology, Engineering, and Mathematics) education has been gaining attention as a means to improve science and engineering education. STEM education aims to foster critical thinking, problem-solving skills, creativity, and innovation among students. It involves an educational policy that aims to develop the next generation of skilled professionals by integrating the fields of Science, Technology, Engineering and Mathematics. The goal of this approach is to provide students in mechatronics, who are assigned to the robotics lab during their studies, an up to date knowledge and experience in research and development activities. Thus, this paper aims to provide education that focuses on the acquisition and application of engineering knowledge, including control engineering, mechanical engineering, electrical and electronic engineering, information engineering, among others, with the main focus on mobile robot research.

An Element Perception and State Recognition Method Oriented to Complex Assembled Product

Abstract Quality inspection during assembly process has an important impact on the performance and service life of the final product. Automated inspection solutions based on machine vision and artificial intelligence greatly improve the efficiency of inspection. However, the current mainstream large-scene inspection solutions, such as mobile vision platforms, are difficult to apply widely due to cost issues. Therefore, portable vision inspection solutions have gradually gained attention. However, the bottleneck technologies that limit this solution are full-element perception and occlusion judgment under free viewing angle, robust object matching method. To address the above problems, This paper proposes a free-viewpoint-based element perception and state recognition scheme. An improved structure estimation model is adopted to establish a bridge between virtual and actual mapping under the support of small-sample data, and all elements are located and occlusion analyzed by a 3D model-constrained view evolution algorithm. In addition, a hierarchy matching and optimization method is established to complete visual analysis. Experimental results show that this scheme performs well in structure estimation, element localization, occlusion judgment, state recognition, and work efficiency, demonstrating high application value.

An evaluation of the impact of social and structural determinants of health on forgone care during the COVID-19 pandemic in Baltimore, Maryland.

Evidence suggests that reductions in healthcare utilization, including forgone care, during the COVID-19 pandemic may be contributing towards excess morbidity and mortality. The objective of this study was to describe individual and community-level correlates of forgone care during the COVID-19 pandemic. We conducted a cross-sectional, secondary data analysis of participants (n = 2,003) who reported needing healthcare in two population-representative surveys conducted in Baltimore, MD in 2021 and 2021-2022. Abstracted data included the experience of forgone care, socio-demographic data, comorbidities, financial strain, and community of residence. Participant's community of residence were linked with data acquired from the Baltimore Neighborhood Indicators Alliance relevant to healthcare access and utilization, including walkability and internet access, among others. The data were analyzed using weighted random effects logistic regression. Individual-level factors found to be associated with increased odds for forgone care included individuals age 35-49 (compared to 18-34), female sex, experiencing housing insecurity during the pandemic, and the presence of functional limitations and mental illness. Black/African American individuals were found to have reduced odds of forgone care, compared to any other race. No community-level factors were significant in the multilevel analyses. Moving forward, it will be critical that health systems identify ways to address any barriers to care that populations might be experiencing, such as the use of mobile health services or telemedicine platforms. Additionally, public health emergency preparedness planning efforts must account for the unique needs of communities during future crises, to ensure that their health needs can continue to be met. Finally, additional research is needed to better understand how healthcare access and utilization practices have changed during versus before the pandemic.

Robust Radar Inertial Odometry in Dynamic 3D Environments

Millimeter-Wave Radar is one promising sensor to achieve robust perception against challenging observing conditions. In this paper, we propose a Radar Inertial Odometry (RIO) pipeline utilizing a long-range 4D millimeter-wave radar for autonomous vehicle navigation. Initially, we develop a perception frontend based on radar point cloud filtering and registration to estimate the relative transformations between frames reliably. Then an optimization-based backbone is formulated, which fuses IMU data, relative poses, and point cloud velocities from radar Doppler measurements. The proposed method is extensively tested in challenging on-road environments and in-the-air environments. The results indicate that the proposed RIO can provide a reliable localization function for mobile platforms, such as automotive vehicles and Unmanned Aerial Vehicles (UAVs), in various operation conditions.

Utilization of bis-MPA Dendrimers for the Calibration of Ion Mobility Collision Cross Section Calculations.

Ion mobility-mass spectrometry (IM-MS) has become increasingly popular with the rapid expansion of available techniques and instrumentation. To enable accuracy, standardization, and repeatability of IM-MS measurements, the community requires reliable and well-defined reference materials for calibration and tuning of the equipment. To address this need, synthetic dendrimers of high chemical and structural purity were tested on three ion mobility platforms as potential calibrants. First, synthesized dendrimers were characterized by drift tube ion mobility (DTIMS), using an Agilent 6560 IM-qTOF-MS to assess their drift tube collision cross section (DTCCS) values. Then, assessment of obtained CCS values on trapped ion mobility (TIMS) and traveling wave ion mobility (TWIMS) ion mobility platforms were compared to those found by DTIMS. Across all three systems, dendrimers were found to have high potential for m/z and ion mobility calibration in the CCS range of 160-1700 Å2. To further validate their use as calibrants, drift tube calculated CCS values for dendrimers were utilized to calibrate calculations of CCS for known standards including Agilent Tuning mix, the CCS Major mix from Waters, and SPLASH LIPIDOMIX. Additionally, structures of sodiated dendrimers were computated along with theoretical CCS values which showed good agreement with the experimental CCS values. On the basis of the results presented, we recommend the use of dendrimers as alternatives and/or complementary compounds to commonly used calibrants for ion mobility platforms.

Examining the Role of Edu-Cartoons in Imparting Reading Skills to Children. A Case of Selected Pre-Primary Schools in Tabora – Tanzania

Educational cartoons have emerged as a prominent factor in developing pre-primary children. Video and mobile platforms facilitate the acquisition of various skills and promote early childhood preparedness. This study focuses on examining the role of Edu-cartoons in imparting necessary reading skills to pre-primary children while identifying the significance of Edu-cartoons in imparting reading skills to children as the specific objective of this study. The study is informed by the Social Learning Theory by Albert Bandula; Krejcie and Morgan's formula was used to obtain a sample size of 200 participants, and a computer-aided koboTool program with cartoons giving instructions to children was used to solicit information on Edu-cartoons reading skills from 150 children. Further, 25 pre-primary teachers and 25 parents were also exposed to semi-structured interviews on Android phones aided with KoboTool programs. Data collected from Temihill, St. Francis, Westland, Greenlane, and Shauri Jema pre & primary schools were analysed using SPSS version 27.1. The findings reveal that, apart from website sources such as Ubongo Kids and Kilimani Sesame, children access the same content from local Television and TV cables. Moreover, the study finds that Edu-cartoons help improve children's pronunciation skills during reading, develop reading comprehension and increase vocabulary to children. The study recommends that parents and teachers guide children on where they can access relevant information resources to enhance reading competencies

Design and Development of an Air–Land Amphibious Inspection Drone for Fusion Reactor

This paper proposes a design method for a miniature air–land amphibious inspection drone (AAID) to be used in the latest compact fusion reactor discharge gap observation mission. Utilizing the amphibious function, the AAID realizes the function of crawling transportation in the narrow maintenance channel and flying observation inside the fusion reactor. To realize miniaturization, the mobile platform adopts the bionic cockroach wheel-legged system to improve the obstacle-crossing ability. The flight platform adopts an integrated rotor structure with frame and control to reduce the overall weight of the AAID. Based on the AAID dynamic model and the optimal control method, the control strategies under flight mode, hover mode and fly–crawl transition are designed, respectively. Finally, the prototype of the AAID is established, and the crawling, hovering, and fly–crawling transition control experiments are carried out, respectively. The test results show that the maximum crawling inclination of the AAID is more than 20°. The roll angle, pitch angle, and yaw angle deviation of the AAID during hovering are all less than 2°. The landing success rate of the AAID during the fly–crawl transition phase also exceeded 77%, proving the effectiveness of the structural design and dynamic control strategy.

Modeling Positions and Orientations of Cantaloupe Flowers for Automatic Pollination

An automatic system for cantaloupe flower pollination in greenhouses is proposed to meet the requirements of automatic pollination. The system consists of a mobile platform, robotic manipulator, and camera that reaches the flowers to detect and recognise their external features. The main task of the vision system is to detect the position and orientation of the flower in Cartesian coordinates, allowing the manipulator to reach the pose and perform pollination. A comprehensive method to ensure the accuracy of the pollination process is proposed that accurately determines the position and orientation of cantaloupe flowers in real environments. The vision system is used to capture images, detect the flower, and recognise its state according to its external features, such as size, colour, and shape, thereby providing appropriate nozzle access during pollination. The proposed approach begins with a segmentation method designed to precisely locate and segment the target cantaloupe flowers. Subsequently, a mathematical model is used to determine the key points that are important for establishing the growth orientation of each flower. Finally, an inverse-projection method is employed to convert the position of the flower from a two-dimensional (2D) image into a three-dimensional (3D) space, providing the necessary position for the pollination robot. The experimental process is conducted in a laboratory and proves the efficacy of the cantaloupe flower segmentation method, yielding precision, recall, and F1 scores of 87.91%, 90.76%, and 89.31%, respectively. Furthermore, the accuracy of the growth-orientation prediction method reaches approximately 86.7%. Notably, positional errors in 3D space predominantly fall within the allowable range, resulting in a successful pollination rate of up to 83.1%.

Direct and Inverse Kinematics of a 3RRR Symmetric Planar Robot: An Alternative of Active Joints

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

Plant Disease Detection Using Convolutional Neural Network

Plant disease detection is critical in agriculture, significantly impacting crop yield and food supply. This paper introduces an innovative approach utilizing deep learning and computer vision techniques to detect and classify plant diseases. The proposed method involves image processing to analyze leaf health conditions using a dataset of varied leaf images exhibiting disease manifestations. The model is trained to recognize distinct patterns associated with different diseases, enabling early detection and intervention. This AI-based system aims to enhance agricultural productivity, minimize crop losses, and contribute to food security. Furthermore, it has potential application in mobile platforms, providing farmers with a user-friendly tool for managing plant diseases. Nonetheless, challenges such as image condition variability and the necessity for comprehensive, diverse datasets for model training require further research and development.

Value‐centric analysis of user adoption for sustainable urban micro‐mobility transportation through shared e‐scooter services

AbstractMicro‐mobility services, which are considered a sustainable alternative to traditional transportation modes, have gained substantial popularity due to advancements in mobile technology. As one of those modes of transportation, shared e‐scooter services have encouraged several startups in urban areas, allowing them to reach massive numbers of consumers in a highly competitive environment. This study aims to explore gains and barriers that affect the intention of consumers to use shared e‐scooter services, all within the framework of sustainability‐driven considerations. The Latent Dirichlet Allocation (LDA) Algorithm was used to analyse 24.798 reviews from the Google Play Store, uncovering eight topics. Those topics were used to discover customer value perceptions in the shared e‐scooter context and compare them with the related literature on perceived value. Besides, their impact on user ratings on the mobile application platform was measured using machine learning algorithms. The study's findings are expected to contribute to developing regulations for shared e‐scooter services, which have gained popularity as an eco‐friendly mode of transportation sustainability in urban areas by introducing a novel perspective.

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

The mobile revolution has influenced students’ preferences for various educational platforms in the new digital era, especially regarding young learners’ utilization of mobile devices such as smartphones, iPads, and other gadgets for mobile learning (m-learning). Because of this, universities that implement learning management systems (LMS) through standard web-based platforms should explore the potential for integrating mobile devices and technologies into m-learning platforms. Through the utilization of this technology, LMS can facilitate continuous user interaction and enhance user awareness of any revisions made to the material. To reach the majority of LMS users, mobile applications must be developed for all major mobile platforms. By utilizing the Web View API, this research combined native mobile and web technologies to develop the mobile application. This strategy was adopted in anticipation of the requirement to create and maintain the application across multiple mobile platforms. It was anticipated that this approach would reduce the time needed for creation, maintain a consistent interface, and enable the use of platform-specific features. It also makes sense to provide mobile device access to some of the LMS virtual classroom’s functions. The K-means algorithm is used for analyzing course material and learning. Nevertheless, achieving this goal might not be an easy process. To enable this form of connection between the LMS and the m-learning applications, this chapter assesses the challenges involved in achieving that goal and presents various common interchange designs and related research and development efforts.

Behavioral Involvement, Technology Acceptance, and Failure in Mobile Learning: A Systematic Review

As we move further into the digital age, machine learning algorithms have become increasingly popular in E-learning for their ability to predict learner failure and assess behavioral engagement, particularly in mobile learning environments. This paper reports on the systematic review conducted by the most relevant research in the literature that uses machine learning algorithms to predict failure, verify acceptance of mobile technology, and analyze behavioral engagement in mobile learning platforms. The search was performed using research papers extracted from four commonly used databases and published between 2010 and 2023; the last database access was on 15/05/2023. Guided by the PRISMA checklist, the review followed a structured approach to select, analyze, and report relevant studies. Studies were selected based on strict inclusion and exclusion criteria, focusing on peer-reviewed articles that empirically test the application of machine learning in mobile learning contexts. Of the initial 332 screened articles, 20 were eligible for inclusion. The results highlight the transformative role that machine learning is playing in revolutionizing online mobile learning experiences.

HOME AUTOMATION SYSTEM

The project presents the development of an advanced home automation system aimed at revolutionizing residential living through smart technologies. Leveraging Internet of Things (IoT) principles, the system integrates various sensors, actuators, and communication protocols to enable remote monitoring and control of home appliances and devices. Through a user-friendly interface accessible via mobile or web platforms, homeowners can effortlessly manage their home environment, ensuring convenience, security, and energy efficiency. The project emphasizes scalability and interoperability, paving the way for future expansion and integration with emerging smart technologies. Key Words: Home Automation System, Internet of Things (IOT), Sensors, Actuators, Remote Monitoring

An mHealth Intervention for Gay and Bisexual Men's Mental, Behavioral, and Sexual Health in a High-Stigma, Low-Resource Context (Project Comunică): Protocol for a Randomized Controlled Trial.

The World Health Organization reported that 80% of new HIV diagnoses in Europe in 2014 occurred in Central and Eastern Europe. Romania has a particularly high HIV incidence, AIDS prevalence, and number of related deaths. HIV incidence in Romania is largely attributed to sexual contact among gay and bisexual men. However, homophobic stigma in Romania serves as a risk factor for HIV infection for gay and bisexual men. The Comunică intervention aims to provide a much-needed HIV risk reduction strategy, and it entails the delivery of motivational interviewing and cognitive behavioral therapy skills across 8 live text-based counseling sessions on a mobile platform to gay and bisexual men at risk of HIV. The intervention is based on the information-motivation-behavior and minority stress models. There is preliminary evidence suggesting that Comunică holds promise for reducing gay and bisexual men's co-occurring sexual (eg, HIV transmission risk behavior), behavioral (eg, heavy alcohol use), and mental (eg, depression) health risks in Romania. This paper describes the protocol for a randomized controlled trial designed to test the efficacy of Comunică in a national trial. To test Comunică's efficacy, 305 gay and bisexual men were randomized to receive Comunică or a content-matched education attention control condition. The control condition consisted of 8 time-matched educational modules that present information regarding gay and bisexual men's identity development, information about HIV transmission and prevention, the importance of HIV and sexually transmitted infection testing and treatment, heavy alcohol use and its associations with HIV transmission risk behavior, sexual health communication, finding social support, and creating sexual health goals. Participants undergo rapid HIV and syphilis testing and 3-site chlamydia and gonorrhea testing at baseline and the 12-month follow-up. Outcomes are measured before the intervention (baseline) and at the 4-, 8-, and 12-month follow-ups. The study was funded in September 2018, and data collection began in May 2019. The last participant follow-up was in January 2024. Currently, the data analyst is cleaning data sets in preparation for data analyses, which are scheduled to begin in April 2024. Data analysis meetings are scheduled regularly to establish timelines and examine the results as analyses are gradually being conducted. Upon completion, a list of manuscripts will be reviewed and prioritized, and the team will begin preparing them for publication. This study is the first to test the efficacy of an intervention with the potential to simultaneously support the sexual, behavioral, and mental health of gay and bisexual men in Central and Eastern Europe using motivational interviewing support and sensitivity to the high-stigma context of the region. If efficacious, Comunică presents a scalable platform to provide support to gay and bisexual men living in Romania and similar high-stigma, low-resource countries. ClinicalTrials.gov NCT03912753; https://clinicaltrials.gov/study/NCT03912753. DERR1-10.2196/52853.

The Integration and Utilization of Mobile Application in Healthcare Consultation Services for Quirino Province

The use of mobile devices has been rapidly increasing because of its versatile and useful applications. As a result, its high-tech features and Users have ready-made and customizable alternatives thanks to open source platforms., making it particularly helpful for healthcare services at any time and from any location. Healthcare services for the rural areas are limited since hospitals are also limited and their location is also one of the problems in availing healthcare service in the province. The main objective of this study focuses on the integration and utilization of mobile application as an alternative solution for healthcare consultation services. The descriptive and survey method was used in the data gathered from the respondents which comprised a number of people from the six municipalities of the province specifically rural areas. A questionnaire-checklist was administered to determine the possibility of the integration of mobile application for healthcare consultation services. Statistical Package for the Social Sciences (SPSS) a statistical tool used to determine the number of interested respondents to avail such mobile platform and to determined economic aspect of the study. The study proves that there is a possibility for the integration of mobile application as an online platform for healthcare consultation services. This will be beneficial to Quirinians and nearby provinces.

Control of a three-wheeled omnidirectional mobile robot via a mixed FTB/$$\mathcal H_\infty $$ approach

AbstractIn this paper, the problem of guidance and motion control of mobile robots is addressed and solved within the novel framework of the mixed finite-time/$$\mathcal H_\infty $$ H ∞ control theory of nonlinear quadratic systems (NLQSs). Starting from a NLQS describing the dynamics of omnidirectional mobile platforms, the main tasks performed for controlling in closed loop the motion of omnidirectional robots can be conveniently formulated as a mixed finite-time/$$\mathcal H_\infty $$ H ∞ control problem. A robust motion controller, which can effectively rejects disturbances deviating the robot platform from a planned path, can be designed after choosing a linear state-feedback structure for the controller. The synthesis problem is solved through some sufficient conditions contemplating both norm-bounded disturbances and sets constraining initial and terminal conditions, together with a finite-time bound on the output transient. Therefore, for all the allowable uncertainties, in presence of nonzero initial conditions and exogenous disturbance inputs which are possible within an unstructured environment, the motion control tasks can be accomplished through optimal $$\mathcal H_\infty $$ H ∞ performance by simultaneously guaranteeing that the NLQS, which governs in closed loop the robot platform, is finite-time bounded. Finally, the applicability and control performance of the design approach have been evaluated through numerical simulations.

The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases

ObjectiveTo develop a machine-learning visual diagnostic algorithm for penile diseases to address disparities in access to sexual health services. Patients and MethodsWe developed an image data set using original and augmented images for 5 penile diseases: herpes lesions, syphilitic chancres, balanitis, penile cancer, and genital warts. We used U-Net architecture model for semantic pixel segmentation into background or subject image, Inception-ResNet version 2 neural architecture to classify each pixel as diseased or nondiseased, and a salience map using GradCAM++. We trained the model on a random 91% sample of the images and evaluated the model on the remaining 9%, assessing recall (or sensitivity), precision, specificity, and F1-score. The model is currently in use via a mobile application platform; we assessed application usage between July and October 1, 2023. ResultsOf 239 images in the validation data set, 45 (18.8%) were of genital warts, 43 (18%) were of herpes simplex virus infection (ranging from early vesicles to ulcers), 29 (12.1%) were of penile cancer, 40 (16.7%) were of balanitis, 37 (15.5%) were of syphilitic chancres, and 45 (18.8%) were nondiseased images. The overall accuracy of the model for correctly classifying images was 0.944. There were 2640 unique submissions to the mobile platform; among a random sample (n=437), 271 (62%) were from the United States, 64 (14.6%) from Singapore, 41 (9.4%) from Canada, 40 (9.2%) from the United Kingdom, and 21 (4.8%) from Vietnam. ConclusionWe report on the development of a machine-learning model for classifying 5 penile diseases, which exhibited excellent performance.