Common Platform Research Articles

Trends in real-time artificial intelligence methods in sports: a systematic review

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings.

Discrete-Event Simulation Integrates an Improved NEH Algorithm for Practical Flowshop Scheduling Problems in the Satellite Industry

The production of multiple types of satellites based on a common manufacturing platform represents a permutation flowshop scheduling problem (PFSP) with complex constraints. This is a highly complex scheduling problem, yet there is still a gap between theoretical research and practical application, particularly in the satellite industry. Therefore, we propose a more practical method that integrates discrete-event simulation modelling and an improved NEH algorithm to solve a more realistic PFSP. The discrete-event simulation-based method includes the following three main components: a flexible PFSP simulation modelling approach, an improved NEH algorithm, and an interaction mechanism between the simulation model and the optimisation algorithm. The proposed method allows automatic and flexible simulation modelling according to the characteristics of the actual satellite manufacturing workshop, which determines the practical nature of the approach proposed in this paper and then achieves excellent scheduling results based on the special interaction mechanism. The computational results demonstrate that this is a 9.18% improvement over the initial NEH algorithm and a 1.40% improvement over the best current improved NEH algorithm.

Assessment of YouTube Videos Related to Radioactive Iodine Therapy for Thyroid Cancer for Patient Education.

Thyroid cancer is the most common cancer diagnosis among those aged 15-29, with a noticeable increase in cases over the last decade. YouTube, one of the most frequented websites on the internet, serves as a common platform to obtain health information. This study uses a systematic approach to characterize YouTube videos related to radioactive iodine (RAI) therapy for thyroid cancer. The first 50 videos across four distinct YouTube search terms related to RAI therapy for thyroid cancer were identified after applying exclusion criteria. A previously validated video assessment tool was utilized to evaluate the videos. Two independent reviewers assessed a random sample of 10 videos, while the remaining 40 videos were evaluated by one reviewer as there were minimal discrepancies in coding. Among the identified videos, 26 videos were published within the past 3years, with a median video length of 4min and 53s. The three most common publishers' affiliations were non-profits (15 videos), personal accounts (12 videos), and health care facilities/organizations (11 videos). Most of the videos originated in the United States, used an interview format, featured physician presenters, and were directed towards patients. Highly covered topics included "side effects and risks" and "RAI therapy overview". The findings underscore the significant role YouTube plays in aiding patient comprehension of RAI therapy for thyroid cancer based on video parameters and content. These results can inform physicians' discussions with patients regarding YouTube and contribute to the development of new, high-quality YouTube videos to support thyroid cancer education.

Directional Coupling to a λ/5000 Nanowaveguide.

Silicon-based microdevices are considered promising candidates for consolidating several terahertz technologies into a common and practical platform. The practicality stems from the relatively low loss, device compactness, ease of fabrication, and wide range of available passive and active functionalities. Nevertheless, typical device footprints are limited by diffraction to several hundreds of micrometers, which hinders emerging nanoscale applications at terahertz frequencies. While metallic gap modes provide nanoscale terahertz confinement, efficiently coupling to them is difficult. Here, we present and experimentally demonstrate a strategy for efficiently interfacing subterahertz radiation (λ = 1 mm) to a waveguide formed by a nanogap, etched in a gold film, that is 200 nm (λ/5000) wide and up to 4.5 mm long. The design principle relies on phase matching dielectric and nanogap waveguide modes, resulting in efficient directional coupling between them when they are placed side-by-side. Broadband far-field terahertz transmission experiments through the dielectric waveguide reveal a transmission dip near the designed wavelength due to resonant coupling. Near-field measurements on the surface of the gold layer confirm that such a dip is accompanied by a transfer of power to the nanogap, with an estimated coupling efficiency of ∼10%. Our approach efficiently interfaces millimeter waves with nanoscale waveguides in a tailored and controllable manner, with important implications for on-chip nanospectroscopy, telecommunications, and quantum technologies.

A qualitative exploration of policy interventions to improve the health-related quality of life of people living with HIV AIDS and co-morbidities of hypertension and/or diabetes in Ghana.

The intersection of infectious diseases, such as HIV, with chronic conditions like hypertension and diabetes poses a significant challenge in global health. While advancements in antiretroviral therapy have transformed HIV into a manageable chronic condition, a growing number of individuals with HIV now grapple with coexisting non-communicable diseases, impacting their Health-Related Quality of Life (HRQoL). Despite strides in HIV care, there is a notable policy gap that undermines efforts to address HIV-associated co-morbidities, particularly hypertension and diabetes, especially efforts to improve access, early detection, and ultimately HRQoL for individuals with HIV and co-morbidities. This study seeks to explore policy interventions aimed at improving the quality of life of HIV patients with hypertension or diabetes. The study utilized a qualitative descriptive design to explore the experiences and perspectives of healthcare professionals and support staff regarding policy interventions for managing HIV patients with hypertension and/or diabetes co-morbidities in three regions of Ghana. The research was conducted in the Upper West, Ashanti, and Greater Accra regions among 11 participants, chosen purposively from professions involved in HIV patient care to understand their views on the implementation of policy interventions to HRQoL for individuals with HIV and co-morbidities. In-depth interviews were conducted face-to-face and tape-recorded. Thematic analysis approach was used to analyze the data. The study involved 11 participants from three regions with varied years of experience. Implemented policies that potentially improve the HRQoL for individuals with HIV and co-morbidities involve support groups, home visits, provision of free drugs, and counselling. Barriers to policy implementation included non-adherence to medication, stigma, cost of non-communicable diseases (NCDs) medications, accessibility issues to NCDs services, lack of interest or understanding among implementers, and high staff turnover. Facilitators encompassed in-service training, guidelines in common platforms, knowledge sharing, external resources, regular check-ups, and motivational packages for patients. Individuals with HIV and comorbidities face complex challenges impacting their HRQoL, including emotional and financial dimensions. The study identifies critical policies and barriers, underscoring the need for tailored, patient-centered approaches. Facilitators like in-service training and regular check-ups offer actionable insights for effective policy implementation, emphasizing improved health outcomes for those with comorbid conditions. The study recommends integrated care approach and adherence support programs that address the unique challenges faced by people living with HIV.

Capturing the Benefits of Autonomous Vehicles in Ride Hailing: The Role of Market Configuration

We develop an economic model of autonomous vehicle (AV) ride-hailing markets, in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and a flexible pool of human drivers (HVs). Dispatch efficiencies increase with scale because of density effects. We analyze market outcomes in this setting under four market configurations, defined by two dispatch platform structures (common platform versus independent platforms) and two levels of supply competition (monopoly AV versus competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform structure and the level of AV supply competition. In the extreme case, we show that if AVs and HVs operate on independent dispatch platforms, there is a monopoly AV supplier, and labor supply elasticity is sufficiently high, then prices are even higher than in a pure-HV market. Indeed, to guarantee consistently lower prices (relative to a pure HV market) in all scenarios and under all supply and density elasticities, a common dispatch platform between AVs and HVs is required. Furthermore, competitive AVs lead to lower prices than monopoly AVs in every such scenario. Our results illustrate the critical role that market configuration plays in realizing potential welfare gains from AVs. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03112 .

Access to 1,2- and 1,3-Amino Alcohols via Cu-Catalyzed Enantioselective Borylations of Allylamines.

We report Cu-catalyzed enantioselective borylation/oxidation sequences that afford either enantioenriched 1,2- or 1,3-amino alcohols starting from N,N-protected allylamines as a common platform. The approach is based on the mechanistic specificities of catalytic hydroboration and protoboration reactions, which formally install a boron unit and a hydrogen atom at opposite positions of a C═C bond. The nature of the substituents of the carbon-carbon double bond also exerts a determining influence on the regioselectivity of insertion of the catalytically active Cu species. Both protocols are regio- and enantioselective and applicable to a broad range of substrates. Homoallylamines are also competent substrates, providing access to 1,3- and 1,4-amino alcohols.

Data Moves as a Focusing Lens for Learning to Teach with CODAP

Innovative dynamic data tools afford opportunities for K-12 students and teachers to explore multivariate data and create linked data representations. These tools also support engagement in data moves, which are transnumerative actions to process, organize, and visualize data. The current study sought to understand how prospective K-12 mathematics teachers (PMTs) use data moves in the Common Online Data Analysis Platform (CODAP) to create and interpret visualizations and statistical measures to make sense of state-level data about education in the United States. Extending the work of Erickson et al. (2019), a framework is presented to characterize data moves and provide examples of actions within CODAP that illustrate each data move. Based on analysis of thirty screencasts created by PMTs, four examples highlight PMTs’ use of data moves to investigate data in CODAP.

PLATFORMS OF EXPANSIONISM: A COMPARISON OF RUSSIA'S NARRATIVES FOR UKRAINE AND SERBIA'S NARRATIVES FOR KOSOVO

Following the invasion of Crimea by Russia, there has been a significant shift in academic focus towards the study of Ukraine. Russia's efforts to establish a new warfront in the Western Balkans have drawn attention to the developments in the region. This paper offers a comparative analysis of the narratives put forth by Russia concerning Ukraine and Serbia's perspectives on Kosovo. Both Russia and Serbia have constructed fake narratives that present their geopolitical disputes as issues of historical and national identity and territorial integrity. In the case of Russia, the narratives regarding Ukraine underscore a shared history, the protection of ethnic Russians, while contesting Ukrainian sovereignty. Similarly, Serbia's narratives about Kosovo emphasize the cultural and historical significance of Kosovo, drawing its fake narrative for Kosovo as integral to Serbian identity and rejecting its independence as illegitimate and imposed by foreign powers. By delving into official discourse and media representations, this study aims to explore three core narratives used as expansionist platforms: historical and national identity narratives, the narrative of victimization and the narratives on the legitimacy of statehood and sovereignty and how these narratives are utilized within domestic and international spheres to gain internal and international support, legitimize policies, and perpetuate a sense of victimhood. The paper contends that although the contexts differ, the strategic deployment of historical grievances, ethnic unity, and anti-Western sentiment forms a common platform in the expansionist approaches of Russia and Serbia.

We Don't Talk About X(Twitter): A Cross-Sectional Analysis of Social Media Utilization Among Neurologists.

Medical professionals use social media for career development, education, clinical outreach, or advocacy. Prior studies estimate that 25% to 65% of health care providers use social media professionally; however, the number of users and platforms are rapidly changing. Therefore, as part of a broader study, we set out to assess platform preferences and social media usage among neurologists. This was a multisite cross-sectional analysis consisting of a REDCap survey of clinicians, residents, and medical students. Faculty, trainees, or clinical year medical students interested in child neurology or adult neurology residency or fellowship programs within the United States were eligible to participate. Recruitment methods were broad to encompass as diverse and extensive participation as possible. Results were analyzed using descriptive statistics. Data are presented according to the STROBE guidelines. Of the 226 neurology respondents, 55% (n = 124) were child neurology and 45% (n = 102) were adult neurology across all career stages, including students. Of the 70% who reported using social media in a professional capacity, the most commonly reported reasons were for networking and collaboration (n = 95, 60%), self-directed medical learning (n = 90, 57%), and brand building and reputation (n = 62, 39%). Twitter and Facebook were the most common and versatile platforms used by neurologists. Medical students had the highest documentation of social media scholarships on their curriculum vitae (37%, P = .016) and the most interest (33%, P = .016) in learning how to document social media scholarships if they were not already. Early faculty shared this interest more than residents, fellows, or mid-late career faculty. In all groups except for mid-late career faculty, a majority of respondents (>75%) showed interest in learning how to leverage social media for career development. Social media is used professionally by a majority of neurologists, most commonly for networking, self-directed learning, and building individual brands. Opportunities exist to better understand platform preferences and ways to optimize their use for various professional activities as well as to provide education on effective professional use of social media including documentation for promotion.

Modernisation of Fire Protection Education in Elementary Schools

The article deals with the issue of teaching fire protection in elementary schools in the Czech Republic. The area of fire protection is one of the key areas that everyone should be familiar with and it is important to teach children how to prevent fires and how to face fires in case of danger. Thus, this article analyses the current approaches of elementary schools to teaching fire protection and compares the data obtained with approaches in teaching abroad. The obtained comparison is complemented by conducting a questionnaire survey, which underpins the current trends and possibilities of modernising the teaching itself. The results are presented in the form of graphs and the design of a potential application based on the presented mobile applications available on common mobile platforms. The conclusions of the study are summarised in the final chapter, which points to the need to expand fire protection education and the use of modern technologies.

Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring.

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the "BeeTogether" dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives' extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony's belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.

BLOCKCHAIN BASED TRANSACTIONS BETWEEN INDIAN AIR FORCE AND ITS AEROSPACE DEFENCE COMPANY

The use of Blockchain technology in various fields like cryptocurrencies, supply chain, healthcare, finance, etc has found widespread acceptance in general and has proven to be pioneering when implemented in the Supply Chain industry. Various use cases where blockchain has brought various stakeholders in a large supply chain on a common platform and has ensured transparency and established a trustless environment among them have been studied and implemented by multiple organizations. This is facilitated by having a shared distributed, immutable ledger which facilitates the process of recording transactions and tracking the assets as they move in a business network. In this project, we propose the application of Blockchain technology on the transactions between the Indian Air Force (IAF) and Hindustan Aeronautics Limited (HAL). Various aircraft components move from IAF to HAL for repair and maintenance purposes and change hands as they move along the various departments involved in servicing and repairing those components. The proposed architecture of Blockchain based transactions between IAF and HAL would bring transparency, and better security and establish a trustless environment among all the stakeholders involved, and would also empower the stakeholders to optimize their working capabilities thereby saving the time for which an aircraft is on the ground owing to want of an unserviceable component.

Improving Responsiveness: Our Journey from Manual Yearly Updates to Automated Linkage for Near Real-Time Understanding of Outcomes and Modelling Future Service Demand

ObjectiveDemand for real-time data during the COVID-19 pandemic revealed a need to increase efficiencies in manual linkage processes to respond to events in near real-time. In response, our jurisdictional linkage agency transitioned from yearly to daily, weekly and monthly linkage practices through increasing automation and improving process flows. ApproachOur linkage agency transitioned to a fully automated process utilising scalable cloud infrastructure. Source data is now provided directly to a common data platform. This data is split into linkage and content, cleansed and quality assured in Python and set to automatically run via Azure data pipelines. The data is then linked via a combination of deterministic and probabilistic criteria, with data quality checks automatically performed along the way. Researchers can analyse this data in a secure virtual machine that only they can access and retrieve data from. Results This infrastructure expedites the data linkage process allowing daily linkage results to select datasets, and enables advanced research such as a predictive micro-simulation model, which leverages the platform to predict and intervene on outcomes influenced by governmental policies. This model relies on timely administrative data to build targeted interventions for groups with poor future outcomes, tests these interventions, and monitors outcomes in near real-time. ConclusionOur linkage agency has transitioned from manual to automated linkage processes in response to increasing need for timely data. By embracing cloud infrastructure and leveraging automation, we have streamlined our operations, enabling responsive linkage depending on need and expediting the provision of de-identified, linked data to researchers.

Small independent retailers: digitalize or disappear? An empirical study based on dynamic capabilities

PurposeThis study analyzes how small French retailers are adapting their front-office to the digitalization of their business environment.Design/methodology/approachThe qualitative study focuses on dynamic capabilities of 27 independent French retailers, in a wide variety of sectors.FindingsThe digitalization of small retailers does not date from the pandemic health crisis. Small retailers are willing, agile and organized to make controlled progress, ranging from the visibility on social networks to online sales with its specific logistical constraints. Even if their presence on marketplaces is trickier to implement, it represents the culmination of the digitalization process, once their online store has been launched. The digital transformation of independent retailers should be less radical than for large retailers.Research limitations/implicationsBy distinguishing between the concepts of adaptive, absorptive and innovative capabilities, this research highlights strong differences between small retailers, that is SMEs, and larger companies. In terms of adaptive capabilities, it confirms that small retailers are not embracing digitalization as a fad, but because of real changes in the market, and particularly in demand. In contrast to large companies, small retailers drive it more around external objectives linked to their intimate knowledge of changing customer behavior (customer centricity). In terms of absorptive capabilities, the success or failure of digital transformation weighs directly on the entrepreneur's shoulders, but is less hampered by technological legacy. Despite interviews only conducted in the Paris region, it converges with professional studies carried out on a larger scale in France. Its widespread use is certainly easier in countries at the same stage of commercial development.Practical implicationsIn terms of innovative capabilities, independent retailers need to focus on four key areas: reinventing the in-store experience; increasing visibility on social networks; creating an online store; being present in one or two marketplaces or creating a common platform with other local merchants.Originality/valueThis research is one of the first to analyze the digital transformation experienced by small structures. It draws on the concept of dynamic capabilities, well-suited to technologically and commercially dynamic markets. It puts into perspective studies carried out in other countries on less diversified types of shops. Unlike other studies examining the front office, it does not exclude stores and SEO in marketplaces.

Implementing Blockchain Technology for Optimized Supply Chain and Enhanced Sustainability

Supply chain efficiency, transparency, and sustainability can be enhanced using blockchain technology. Blockchain enables a company to accurately track raw material origin to finished products, which ensures standard quality and sustainability. Furthermore, blockchain improves partnerships between supply chain stakeholders by providing a confident, common platform for sharing data. The influence of implementing blockchain technology on supply chain sustainability includes reduced wastage, increased resource transparency, monitoring of social standards, and reduced operational costs. The challenges of blockchain implementation include scalability, incorporation with present systems, lack of expertise, safety and confidentiality, and regulatory uncertainty. Companies can tackle these challenges through collaborative approaches and technical improvements. In general, blockchain technology significantly enhances supply chain sustainability and efficiency, which provides opportunities for creative business solutions.

The Environmental Enteric Dysfunction Biopsy Initiative (EEDBI) Consortium: mucosal investigations of environmental enteric dysfunction

Environmental enteric dysfunction (EED) is an asymptomatic acquired disorder characterized by upper small bowel inflammation, villus blunting, and gut permeability. It is a major contributor to poor growth in childhood as well as other highly consequential outcomes such as delayed neuorcognitive development. After decades of intermittent interest in this entity, we are now seeing a resurgence in the field of EED. However, recent studies have been hampered by a lack of investigation of the target tissue—the upper small bowel. In 2016, the EEDBI (Environmental Enteric Dysfunction Biopsy Initiative) Consortium was established as a common scientific platform across 3 independent EED biopsy cohort studies in Bangladesh, Pakistan, and Zambia. Two centers in the United States recruited comparison groups of children undergoing endoscopy for clinical indications. The EEDBI Consortium goal was to augment the contributions of the individual centers and answer high-level questions amenable to analysis and interpretation across the studies. Here, we describe the Consortium and its cohorts and recruitment procedures across studies. We also offer details applicable to all papers in this supplement, which describe EED mucosal histology, morphometry, immunohistochemistry, and transcriptomics as well as histology relationship to pathogens and biomarkers.

Learning and Control Platform “Kahoot!”: Theory and Usage Features in ASIPCS

The study examined the place of modern information technologies in education and the importance of “gamification” technology in the organization of secondary and higher education. It was revealed that the most common educational monitoring platform, “Kahoot,” has significant advantages compared to traditional approaches to organizing the educational process. The use of this platform in the process of training bachelors and masters, as well as in advanced training courses for coaches at the Armenian State Institute of Physical Culture and Sport (ASIPCS), showed great interest among students in the “Kahoot” service, a high assessment of the capabilities of this platform in training, consolidation, and control of theoretical knowledge.

Towards benchmarking VR sickness: A novel methodological framework for assessing contributing factors and mitigation strategies through rapid VR sickness induction and recovery

Virtual Reality (VR) sickness remains a significant challenge in the widespread adoption of VR technologies. The absence of a standardized benchmark system hinders progress in understanding and effectively countering VR sickness. This paper proposes an initial step towards a benchmark system, utilizing a novel methodological framework to serve as a common platform for evaluating contributing VR sickness factors and mitigation strategies. Our benchmark, grounded in established theories and leveraging existing research, features both small and large environments. In two research studies, we validated our system by demonstrating its capability to (1) quickly, reliably, and controllably induce VR sickness in both environments, followed by a rapid decline post-stimulus, facilitating cost and time-effective within-subject studies and increased statistical power, (2) integrate and evaluate established VR sickness mitigation methods — static and dynamic field of view reduction, blur, and virtual nose — demonstrating their effectiveness in reducing symptoms in the benchmark and their direct comparison within a standardized setting. Our proposed benchmark also enables broader, more comparative research into different technical, setup, and participant variables influencing VR sickness and overall user experience, ultimately paving the way for building a comprehensive database to identify the most effective strategies for specific VR applications.

Investigations into optimum non‐periodic bursts scheme in radar pulse train designed for fixed‐wing drone detections by multi‐function radar

AbstractDrone surveillance with multi‐function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub‐RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non‐periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub‐RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X‐band MFR platform called ENAVI was developed in‐house. The proposed non‐periodic burst scheme optimisation approach was validated with in‐field tests with the ENAVI radar model. The in‐air target was a 3‐metre fixed‐wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low‐profile dual‐polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell‐averaging technique which proved to correctly detect drones within a 7‐km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights.