Tracking Services Research Articles

Vehicle Fixing Recommender System Using User-Based Collaborative Filtering Algorithm

The study refers to vehicle fixing support, an Online Application System for vehicle owners with vehicular problems guided by features on recommending service providers according to their geographical location and performance rating of service providers using the Collaborative Filtering Algorithm. As the number of vehicle owners increases, the tendency for services related to vehicle fixing and maintenance could also increase, providing the chance for vehicle service providers to be in the limelight in the digital world. The system stakeholders include the vehicle owners, vehicle service providers, and the technical support team that administers the registration and approval process among vehicle owners and service providers. Data management with transactions established are properly kept to track and trace services availed by vehicle owners served as the basis for collaborative filtering. The Front-end and back- end of the system are structured using HTML5, Bootstrap 4, CodeIgniter4, JavaScript, and MySQL for its database. The system was found to have advertised the service providers’ skills and services to the customers as vehicle owners who search for and therefore book an appointment with the available service providers online. The desired results were met when the transaction is done from booking appointments through the system to successful services offered on-site. The search results had shown the recommended service providers that precisely fit the vehicle owner's or driver's search parameters criteria. The system's availability has been concluded which obtained a rating of 5 with the quality description of excellent functionality by the end-user reviews by the software requirements specifications

Proposed Marketing Strategy to Increase Sales of Djakarta Transport Jaya

PT Djakarta Transport Jaya faced profit problems five years ago, one of which was the lack of knowledge about the right marketing strategy and limited access to a wide marketing platform. The author conducts quantitative research through surveys and in-depth interviews with new potential market segments that can help PT Djakarta Transport Jaya increase sales in the B2B market. The author also analyzes the factors that can influence the buying interest of new market candidates of PT Djakarta Transport Jaya. The results of this research are expected to be a solution for PT Djakarta Transport Jaya in increasing profits through the implementation of appropriate marketing strategies and optimization of access to extensive marketing platforms. Thus, PT Djakarta Transport Jaya can reach new potential markets and increase its competitiveness in the B2B market. The analysis was conducted internally using: Segmenting, targeting and positioning, Marketing mix, VRIO Framework and Business Model Canvas, as well as external analysis consisting of Porter 5 forces, PESTEL analysis, Competitor analysis and Consumer analysis. The results of the author’s research, PT Djakarta Transport Jaya can emphasize a wide reach to remote areas by upholding the speed and security of shipments on social media platforms. Using social media marketing such as the use of IG ADS, FB ADS and TikTok to reach new consumers by highlighting the completeness of the fleet. Focus on a sophisticated tracking system and service activities carried out by SDM Djakarta transport jaya. Strengthening social media content with interesting topics such as technological innovations such as IoT which is used to monitor the performance and punctuality of vehicle trips to increase customer satisfaction and lastly Keeping prices competitive with competitors in order to attract new customers and retain old customers.

PV-TSC: Learning to Control Traffic Signals for Pedestrian and Vehicle Traffic in 6G Era

Recent advances in traffic signal control have witnessed the success of reinforcement learning. However, most of these approaches have focused on vehicle traffic and lack consideration for pedestrians. This can be attributed in part to the fact that the existing underlying technologies are not yet practical to deploy in real-world environments. Vision technologies, for example, can easily be obscured from view in reality. The direction of movement and position of pedestrians is difficult to estimate accurately. The emergence of 6G localization and tracking services offer new opportunities. With this base service, we intend to improve the efficiency, safety, and scalability of multi-intersection traffic signal control with mixed traffic flows. This problem is challenging for its coordination, scalability, and access of new traffic. To solve these challenges, we propose PV-TSC, a distributed reinforcement learning motivated traffic signal control with pedestrian access. We analyze different behaviors of pedestrian traffic, and integrate pedestrian traffic with the proven traffic signal control scheme for vehicle traffic. Finally, we conduct simulation experiments to illustrate the superiority of PV-TSC against classic methods, and further analyze the effectiveness of PV-TSC design by exploring its variants.

Modeling E-Commerce Website Using QFD

In the last decade, technological developments and advances in the digital economy have changed consumer behavior significantly. In this millennial era, all levels of society can easily use e-Commerce. Meeting customer needs, even exceeding their expectations, is crucial in this intense digital business competition. This study took place at a X Store, which aimed to identify service attributes that would be developed to improve their quality. This study used the Quality Function Deployment (QFD) method, which began with surveys and interviews, then distributed questionnaires to respondents (customers) to find the Voice of Customers, and then processed the data using QFD. The results showed that: 1) the attribute Product Quality had the highest level of importance, with an importance level of 4.63; 2) the attribute Complete Product Type had the top priority for superior performance, with a weight value of 4.6398; 3) the technical response Easy Order Tracking Service is a top priority for improvement, with a priority value of 6.3224 and a normalized contribution of 32.44%.

0948 End-to-End utility of the Cardiopulmonary Coupling Sleep Spectrogram in Sleep Medicine

Abstract Introduction Technology has improved the diagnosis and therapeutic tracking of sleep disorders, but remains fragmented. For example, the laboratory polysomnogram and home sleep apnea test is disconnected from the data available from therapy devices. Remote patient monitoring has focused more on apnea metrics rather than sleep quality. There is some value to a device or system which can be used for diagnosis, risk stratification, and tracking of clinical outcomes across different sleep disorders. Methods A method which offers the potential of end-to-end utility in sleep medicine is the cardiopulmonary coupling (CPC) spectrogram. Originally an ECG-base device, the current form is that of a Ring oximeter (finger) with an integrated activity monitor. The oximeter data is processed via Bluetooth in a smartphone, and then transferred to a Cloud-based analysis (www.sleepimage.com). The CPC analysis integrates heart rate variability and ECG or photoplethysmography-derived respiration. Outputs include a Sleep Quality Index and percentage of stable sleep (high frequency coupling which covaries with delta power on the electroencephalogram and is associated with blood pressure dipping), unstable sleep (low frequency coupling), an apnea-hypopnea index, oxygen desaturation index, heart rate profile across the night (dipping or non-dipping heart rate), total sleep time and sleep efficiency. Data from two clinical systems were used: Empower Sleep (an Internet-based “digital sleep clinic”) and Sleep Continuum (a sleep tracking service offered by Neurocare, Inc, in the Boston area). Results Data from over 500 patients across a range of sleep disorders show end-to-end utility of the SleepImage system. These include diagnosis of sleep apnea including multi-night recording, sleep apnea phenotyping (high loop gain and sleep fragmentation), remote patient monitoring during CPAP or oral appliance use, supportive information for diagnosis of hypersomnia, tracking medication effects (sodium oxybate, sedative-hypnotics, acetazolamide), sleep-wake instability in mood disorders and Long-Covid, impaired sleep quality in chronic fatigue syndrome and restless legs syndrome, off-label therapies for central sleep apnea (oxygen, acetazolamide, buspirone), and sleep fragmentation in Long-Covid and parasomnia. Examples from each of these clinical use cases will be shown. Conclusion The CPC-based SleepImage Ring system can provide complete diagnostic, phenotyping, and tracking functions. Support (if any) The Institute for Personalized Sleep Health, BIDMC, Empower Sleep

College Resources and Event Management & Analysis

This research paper aims to propose a platform for College Resource and Event Management & Analysis. The platform will provide various services such as sharing study content from teachers to students, a one-stop destination for official notices, managing college events and schedules, a platform for sharing career experiences from alumni, BE project tracking service to avoid duplicate project ideas, and a place for internship reference. Additionally, the platform will add an extra advantage for colleges to collect and analyses data of students and college to make decisions for improving the quality of education and sharing experiences. The platform will be implemented using Google’s latest and fastest technology framework Flutter, which supports web and mobile development. The whole application will be implemented in Micro-service Architecture to reduce resource use on mobile devices

OA31 Real life experience of tocilizumab treatment for giant cell arteritis

Abstract Background/Aims Giant cell arteritis (GCA) has been revolutionised by fast track pathways that ensure early secure diagnosis. The GiACTA study provided evidence for the beneficial role of tocilizumab in the treatment of GCA and encouraged us all to reconsider the role of corticosteroids. These data relate to real life experience of the use of tocilizumab, including current status of patients after completing the initial year of treatment. In Scotland, tocilizumab was initially used for patients with relapsing disease or steroid complications. However, since 2018, the Scottish Medicines Consortium has supported use from disease onset and it has become increasingly common to consider adding at diagnosis. Methods The case notes of 23 patients treated with tocilizumab were reviewed back to 2017. Patient demographics were collated including whether cranial (C) or large vessel vasculitis (LVV) variant. The diagnosis of GCA had to be confirmed by biopsy or imaging with GCA rheumatology clinic review. Any patients who discontinued tocilizumab prior to completing one year had the reason recorded. Duration on tocilizumab was intended to be one year. Data were reviewed to capture outcomes after treatment was withdrawn. Results 2 patients with non-relapsing disease discontinued tocilizumab within the first month due to early relative neutropenia (1.4). 5 of 21 patients who completed treatment have since had additional tocilizumab treatment. 1 patient with particularly severe relapsing disease and steroid complications remains on long term tapered tocilizumab monotherapy without attempt to stop (C and LVV). 4 patients (LVV=C) flared within 1-9 months (median 6 months) and resumed with steroids and subsequent tapers; all had commenced for relapsing disease. 1 patient (C), commenced on tocilizumab at diagnosis, opted for MTX when flared. 15 remaining patients are on no steroid, methotrexate or tocilizumab and are free of disease activity as yet, but undergoing monitoring for flare (12C 3 LVV variant). Conclusion Tocilizumab is effective, limits steroid exposure and is well-tolerated by patients. The most common reason for discontinuing tocilizumab was relative neutropenia (neutrophils 1-1.4). The majority of patients discontinued tocilizumab and have not yet flared off all treatment. Cranial variant was less associated with relapse after tocilizumab treatment versus LVV. New diagnoses appear less prone to relapse off tocilizumab than patients already known to have relapsing disease prior to using tocilizumab. Patients discontinuing tocilizumab benefit from ongoing monitoring to recognise the early stages of returning disease; this would need to be provided for at least one year. Patients discontinuing treatment, their GPs and carers need advice on routes to escalate concerns about returning symptoms. Fast track services need to provide a flare service in addition to a diagnosis pathway if there is to be safety, confidence and expert support for this patient group and primary care colleagues. Disclosure L.M.M. Hutton: Other; 2022 Roche educational grant to attend BSR.

A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter

Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories.

A Comparison of Graph-Theoretic Approaches for Resilient System of Systems Design

Abstract System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. SoS can be found across all aspects of modern life such as power grids, supply chains, and disaster monitoring and tracking services. Their resilience (being able to withstand and recover from disruptions) is a critical attribute whose evaluation is nontrivial and requires detailed disruption models. Engineers rely on heuristics (such as redundancy and localized capacity) for achieving resilience. However, excessive reliance on these qualitative guidelines can result in unacceptable operation costs, erosion of profits, over-consumption of natural resources, or unacceptable levels of waste or emissions. Graph-theoretic approaches provide a potential solution to this challenge as they can evaluate architectural characteristics without needing detailed performance simulations, supporting their use in early stage SoS architecture selection. However, no consensus exists as to which graph-theoretic metrics are most valuable for SoS design and how they should be included in the design process. In this work, multiple graph-theoretic approaches are analyzed and compared, on a common platform, for their use as design tools for resilient SoS. The metrics central point dominance, modularity, specialized predator ratio, generalization, vulnerability, and degree of system order are found to be viable options for the development of early stage decision-support tools for resilient SoS design.

Wireless Arduino Controlled Fire Fighter Robot Designed for Residential Area

Fire accidents have become a common problem in human life and may lead to permanent injury or death. The fire fighter robot is developed with the aim to save lives of fire fighters who risk their lives, entering dangerous environments. This paper presents the development of the fire fighter robot, which is suitable to extinguish fire in closed areas and residential areas. The robot’s movement can be manually controlled using Flysky GT2 Controller from outside of the affected area. The robot is in the form of a robust and compact vehicle which allows it to move front, back, left and right in small areas with a steady speed. The robot has a 12V water pressure pump capable of shooting water up to 3 to 5 litres per minute which allows it to control large amounts of fire. The robot also provides live streaming and GPS location tracking services during the movement of the robot. This live recording can be viewed in PC or in the user's mobile phone.

Cyber-physical spare parts intralogistics system for aviation MRO

Spare parts management is a vital supporting function in aviation Maintenance, Repair, and Overhaul (MRO). Spare parts intralogistics (SPI), the operational perspective of spare parts management, significantly affects performance of MRO activities. This paper proposes Cyber-Physical Spare Parts Intralogistics System (CPSPIS) to address the synchronization problems associated with the SPI business process and SPI resources. The proposed system applies Internet-of-Things technologies and unified representations to provide resources and operations traceability and visibility. Further, CPSPIS contributes several services with self-X abilities for real-time synchronization throughout the SPI process. The communication service in the physical layer provides a self-adaptive ability to coordinate between the physical environment and cyberspace. The tracking and tracing service uses self-configured SPI business-related functions to capture accurate real-time resource and operation data through spatial modeling and location-based components for system resource coordination. The self-organized data source integration service consolidates all SPI request sources as the starting point for process analysis. The initialization service has self-awareness capability and models the transformation between the request source and SPI operations. The self-adjusted execution service synchronizes every two operation steps using real-time resource and operation data. In addition, CPSPIS develops applications and visualization tools for real-time cooperation between execution and decision-making. A real-life case study is conducted in one of the largest aviation MRO organizations, and the quantitative and qualitative improvements of CPSPIS are discussed.

RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

Dependency-Aware Vehicular Task Scheduling Policy for Tracking Service VEC Networks

In this paper, we study a tracking service vehicular edge computing (VEC) network that provides computation offloading service for Intelligent vehicles, where computational tasks with different urgency and dependency are required to be completed efficiently within strict time constraints. We consider the actual scenario where the environmental parameters fluctuate randomly and their distributions are unknown, thus, a longterm scheduling policy optimization problem needs to be solved. For this motivation, we first define a queueing criterion to sort the subtasks into a scheduling queue, and then model a specific Markov decision process (MDP) according to the scheduling queue. Furthermore, we propose our vehicular task scheduling policy optimizing (VTSPO) algorithm based on the most advanced policy-based deep reinforcement learning (DRL). The experimental results compared with known value-based DRL algorithms verify the advantages of the proposed VTSPO algorithm.

Early cataloguing of fragments from break-up events

Break-up events represent the dominant source of objects in space catalogues, surpassing half of the overall population. These not so uncommon events include explosions, collisions or anomalous events resulting in fragmentations and their number is estimated to be higher than 630. The early cataloguing of the fragments generated during these events poses a complex challenge for space objects catalogue build-up and maintenance processes. The provision of Space Surveillance and Tracking products and services during the few first days after a break-up event can be crucial to avoid collisions between the fragments and other space objects, particularly in highly congested regimes, such as Low Earth Orbit. In this regard, reducing the time required to accurately estimate the trajectories of the fragments may enable the execution of collision avoidance manoeuvres, in the case of operational space objects with manoeuvre capabilities, and analyse potential collision cascade events, which may endanger the space environment. This paper studies the whole cataloguing process after a break-up event, starting from a catalogue with no fragments from the fragmentation under-analysis, and until a well-established orbit is obtained for all fragments, using a ground-based sensor network. First, the observations enter a multi-sensor multi-target track-to-track association algorithm in charge of grouping observations belonging to the same objects. To resolve the ambiguity, particularly shortly after the event, hypotheses about tracks belonging to the same fragment are generated, scored, pruned, and promoted, only when there is enough confidence, leading to the initialisation of new objects in the catalogue. As soon as the catalogue is populated, a track-to-orbit correlation algorithm is responsible for the correlation of observations and already catalogued orbits. This alleviates the track-to-track association and enables the update of the orbital estimates, required for maintaining the catalogue.

Defense Against Software-Defined Network Topology Poisoning Attacks

Software-Defined Network (SDN) represents a new network paradigm. Unlike conventional networks, SDNs separate control planes and data planes. The function of a data plane is enabled using switches, whereas that of a control plane is facilitated by a controller. The controller learns network topologies and makes traffic forwarding decisions. However, some serious vulnerabilities are gradually exposed in the topology management services of current SDN controller designs. These vulnerabilities mainly exist in host tracking and link discovery services. Attackers can exploit these weak points to poison the network topology information in SDN controllers. In this study, a novel solution is proposed to defend against topology poisoning attacks. By analyzing the existing topology attack principles and threat models, this work constructs legal conditions for host migration to detect host hijacking attacks. The checking of the Link Layer Discovery Protocol (LLDP) source and integrity is designed to defend against link fabrication attacks. A relay-type link fabrication attack detection method based on entropy is also designed. Results show that the proposed solution can effectively detect existing topological attacks and provide complete and comprehensive topological security protection.

Smart Device for Women's Safety Designed Using IoT and Virtual Instrumentation Browser

In the present technological developments, the internet of things (IoT) technology connects all interconnected networks. The attacks on women are increasing daily in Indian countries. If the woman is not carrying a mobile phone or device, she cannot contact the police station or other family members. This proposed method will help the women identify the exact location and inform the police station in such a scenario. The Emergencies, the women, can press the button once the location information is tracked. The purpose of this project is to help women in emergencies. Location tracking and short service messages are also sent to the family members and nearby police stations.

An ICT-based start-up entering maritime logistics networks: facilitation of value cocreation patterns

PurposeThis study aims to explore how a start-up entering maritime logistics networks (MLNs) in the container shipping industry integrates resources underlying value cocreation patterns in these networks.Design/methodology/approachThe paper is based on a single case study of a technological start-up, providing tracking, tracing and other information services to MLN members using internet-based software. An interorganizational theory perspective informs the case study to unveil the resource integration for value cocreation in the network.FindingsThe start-up holds multiple resource interaction roles and the start-up’s involvement enables the creation of new knowledge resources, which facilitate new revenue streams and manage resource dependencies. Hence, the findings indicate that the start-up changes value cocreation patterns in the network by reconfiguring and integrating existing resources so that the service is customized for various customers, including shippers and freight forwarders.Practical implicationsThe results provide insights about how technological start-ups can unlock resources within MLNs.Originality/valueThe study extends previous studies on resource roles in business networks and shows how start-ups can perform multiple roles simultaneously within these networks. In addition, the study contributes to the literature by studying information and knowledge as resources configured in different ways in a unique network setting.

ПРОБЛЕМЫ РАЗВИТИЯ ЛОГИСТИЧЕСКОЙ ИНФРАСТРУКТУРЫ В РЕГИОНАХ АЗЕРБАЙДЖАНА

The formation of logistics infrastructure in the agricultural sector affects economic growth and can contribute to the country’s economy. The experience of developed countries shows that the transportation of agricultural products, storage, customs clearance, expansion of the infrastructure of tracking and logistics services have a positive effect on the expansion of agriculture, production and overall economic growth. Along with this, the magnitude and level of significance of the coefficients for each dimension of the logistics results vary depending on the sectors. Logistics infrastructure is more important for the growth of production and overall economic growth than for the growth of agriculture. The study shows that the importance and scale of the relationship between economic growth and logistics advantages depend on industry and economic policy, as well as other contexts. Thus, investments in logistics services can contribute to the acceleration of both agricultural and industrial growth, as well as overall economic growth.

Telehealth as a public health approach to mitigate the COVID-19 pandemic in Pakistan: A narrative review.

Technology is instrumental in delivering health services, especially telehealth during the COVID-19 pandemic. This article aimed to explore the role of telehealth as a public health approach to support responses to address the COVID-19 pandemic in Pakistan. We developed this article by mapping existing telehealth initiatives developed and implemented during the COVID-19 pandemic in Pakistan. The initiatives were identified searching online portals such as Google Scholar, PubMed, and websites of various governmental and non-governmental agencies. The services are categorized into teleconsultation and follow-ups, online vaccine registration, information dissemination, high-risk subset tracking, virtual Health Care Worker (HCW) and medical student training, and tele-psychological counseling. The teleconsultation category offers online registration and follow-ups. Information dissemination services include federal helpline, SMS alerts, and social media campaigns. The high-risk subset tracking services include app-based COVID-19 checks and online surveys. Virtual HCW and medical student training services include tele-ICU support, COVID-19 critical care courses, and COVID management courses. The tele-psychological counseling services offer helplines for emotional support, proactive counseling for COVID-19 patients, and mental health support and psychiatry services. Telehealth interventions provided novel solutions amid health and social crises such as the COVID-19 pandemic. Health care systems need to expand telehealth services and ensure that health care organizations deliver effective and safe medical care. However, future research should focus on assessing the impact of telehealth on population health.

A novel map matching algorithm for real-time location using low frequency floating trajectory data

The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service.