Frequent Updates Research Articles

ASOD: an adaptive stream outlier detection method using online strategy

In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.

Development and implementation of an injury and illness surveillance system for team USA

BackgroundThe purpose of this report is to provide insight and details regarding the development and implementation of an injury and illness surveillance (IIS) system for the United States Olympic and Paralympic Committee (USOPC).MethodsThe development and deployment of the IIS employed a multiphase approach. First, researchers determined variables to include in the IIS using the recommendations from the 2020 IOC consensus statement for reporting sport epidemiological data. Second, the hosting and deployment platforms were comprehensively evaluated for their suitability, ease of use, flexibility, and backend data structure (for both capture and aggregation). Third, focus groups consisting of the Sports Medicine department leadership and clinicians piloted the IIS system and revisions were made based on their feedback. Pilot testing of the IIS and follow-up focus groups were then conducted among all departmental clinicians to solicit additional feedback and drive further revisions. Finally, the IIS system was piloted among providers working during the 2023 Pan American and Parapan American Games to refine the system for future Games. After reviewing all potential software platform options (electronic medical record [EMR] system, athlete management systems, secure data collection platforms), Qualtrics (Qualtrics, Provo, UT, USA) was selected to host the IIS system. This choice was made due to the inability of the EMR and athlete-management systems to make frequent updates, modify existing questions, and provide the necessary form logic for the variety of scenarios in which the IIS system would be deployed. Feedback from the department’s leadership and clinicians resulted in a number of changes, most notably being the ability to enter multiple diagnoses for a single injury event. Additionally, clinician feedback resulted in the creation of additional diagnostic codes not currently present in the OSIICS v14.0 diagnostic coding system, adding “non-sport” as an additional variable for injury setting, and developing a system for reporting return-to-sport date for time-loss injuries.DiscussionA multi-stage process of extensive planning, stakeholder feedback, and ongoing updates is required in order to successfully develop and implement an IIS system within a National Olympic and Paralynpic Committee. This process can be used to inform the development and implementation of IIS systems in other sporting organizations.

The Impact of Velocity Update Frequency on Time Accuracy for Mantle Convection Particle Methods

AbstractComputing the velocity field is an expensive process for mantle convection codes. This has implications for particle methods used to model the advection of quantities such as temperature or composition. A common choice for the numerical treatment of particle trajectories is classical fourth‐order explicit Runge–Kutta (ERK4) integration, which involves a velocity computation at each of its four stages. To reduce the cost per time step, it is possible to evaluate the velocity for a subset of the four time integration stages. We explore two such alternative schemes, in which velocities are only computed for: (a) stage 1 on odd‐numbered time steps and stages 2–4 for even‐numbered time steps, and (b) stage 1 for all time steps. A theoretical analysis of stability and accuracy is presented for all schemes. It was found that the alternative schemes are first‐order accurate with stability regions different from that of ERK4. The efficiency and accuracy of the alternate schemes were compared against ERK4 in four test problems covering isothermal, thermal, and thermochemical flows. Exact solutions were used as reference solutions when available. In agreement with theory, the alternate schemes were observed to be first‐order accurate for all test problems. Accordingly, they may be used to efficiently compute solutions to within modest error tolerances. For small error tolerances, however, ERK4 was the most efficient.

Effective Automation of End-to-End Testing for OTT Platforms

The rapid evolution of Over-the-Top (OTT) platforms has reshaped the digital media landscape, necessitating robust testing strategies to ensure seamless user experiences and service reliability. Effective automation of end-to-end testing for OTT platforms emerges as a crucial strategy to address the complexities associated with diverse content delivery, user interactions, and varying device compatibilities. This paper explores the challenges and best practices in automating end-to-end testing processes for OTT platforms, focusing on integrating various testing tools, frameworks, and methodologies to achieve comprehensive coverage and efficiency. OTT platforms deliver content directly over the internet, bypassing traditional broadcast and cable channels. This direct-to-consumer approach introduces unique challenges in testing, such as ensuring consistent performance across different devices, network conditions, and content types. Additionally, the dynamic nature of content libraries and frequent updates necessitate rigorous testing to identify and rectify potential issues proactively. Automation, with its capacity to simulate real-world usage scenarios, streamline repetitive tasks, and enhance testing accuracy, plays a pivotal role in addressing these challenges. The paper first outlines the primary challenges in end-to-end testing for OTT platforms. These challenges include the need for cross-platform compatibility, performance under varying network conditions, and the ability to handle diverse content formats and streaming protocols. The complexities are compounded by the requirement for real-time performance metrics and user experience evaluations, which demand a sophisticated testing infrastructure.

Automated Security Testing in DevOps Environments Using AI and ML

The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automated security testing within DevOps environments, offering a comprehensive approach to identifying, predicting, and mitigating security vulnerabilities in real time. Automated security testing leverages AI and ML algorithms to analyze code, detect anomalies, and predict potential security threats. These technologies enable the continuous monitoring of codebases, allowing for the early identification of vulnerabilities before they are exploited. By incorporating AI-driven security testing into the CI/CD pipeline, organizations can ensure that security is not an afterthought but a continuous process integrated into every stage of the software development lifecycle. AI and ML models can be trained to recognize patterns associated with security risks, such as code injection, unauthorized access, and data leakage. These models continuously learn from new data, improving their accuracy over time and adapting to evolving threats. The dynamic nature of AI-driven security testing makes it particularly suited for DevOps environments, where frequent code changes and updates can introduce new vulnerabilities. Moreover, AI and ML can assist in automating complex tasks, such as threat modeling, risk assessment, and the prioritization of security issues, enabling security teams to focus on higher-order tasks that require human expertise.

Predictive Processing During Cue-Outcome Associative Learning in Autistic Children.

Predictive coding theories posit that autism is characterized by an over-adjustment to prediction errors, resulting in frequent updates of prior beliefs. Atypical weighting of prediction errors is generally considered to negatively impact the construction of stable models of the world, but may also yield beneficial effects. In a novel associative learning paradigm, we investigated whether unexpected events trigger faster learning updates in favour of subtle but fully predictive cues in autistic children compared to their non-autistic counterparts. We also explored the relationship between children's language proficiency and their predictive performances. Anticipatory fixations and explicit predictions were recorded during three associative learning tasks with deterministic or probabilistic contingencies. One of the probabilistic tasks was designed so that a fully predictive but subtle cue was overshadowed by a less predictive salient one. Both autistic and non-autistic children based their learning on the salient cue, and, contrary to our predictions, showed no signs of updating in favour of the subtle cue. While both groups demonstrated associative learning, autistic children made less accurate explicit predictions than their non-autistic peers in all tasks. Explicit prediction performances were positively correlated with language proficiency in non-autistic children, but no such correlation was observed in autistic children. These results suggest no over-adjustment to prediction errors in autistic children and highlight the need to control for general performance in cue-outcome associative learning in predictive processing studies. Further research is needed to explore the nature of the relationship between predictive processing and language development in autism.

Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review

The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.

UAV-assisted dependency-aware computation offloading in device–edge–cloud collaborative computing based on improved actor–critic DRL

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become a popular research topic, addressing challenges posed by the pressure of cloud computing and the limited service scope of MEC. However, the limited computing resources of UAVs and the data dependency of specific tasks hinder the practical implementation of efficient computational offloading (CO). Accordingly, a device–edge–cloud collaborative computing model is proposed in this study to provide complementary offloading services. This model considers stochastic movement and channel obstacles, representing the dependency relationships as a directed acyclic graph. An optimization problem is formulated to simultaneously optimize system costs (i.e., delay and energy consumption) and UAV endurance, taking into account resource and task-dependent constraints. Additionally, a saturated training SAC-based UAV-assisted dependency-aware computation offloading algorithm (STS-UDCO) is developed. STS-UDCO learns the entropy and value of the CO policy to efficiently approximate the optimal solution. The adaptive saturation training rule proposed in STS-UDCO dynamically controls the update frequency of the critic based on the current fitted state to enhance training stability. Finally, extensive experiments demonstrate that STS-UDCO achieves superior convergence and stability, while also reducing the system total cost and convergence speed by at least 11.83% and 39.10%, respectively, compared with other advanced algorithms.

Secure Routing and Reliable Packets Transmission In MANET Using Fast Recursive Transfer Algorithm

Mobile Ad-hoc Network (MANET) autonomous operation can be multi-hop it is the infrastructure-less wireless network. Security is one of the biggest challenges in Mobile Adhoc Network. The MANETs security there are considerations must be so that the routing protocol in order to protect the secure data transmission. In the routing and security that is an important aspect for in a MANETs, existing method routing protocol, however, is not enough to security requirements. The proposed method using Fast Recursive Transmission Algorithm (FRTA) used designed to maximize the data security, routing optimization, minimizing the impact of malicious attack using Collision Detection Avoid Algorithm (CDAA) activity over the MANET and select the best path. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. The proposed method shows high performance than other existing evaluations of the most advanced security and routing energy, end-to-end delay, packet transfer rate, packet loss

Isobutane, Isopentane, Butane, and Propane.

The Expert Panel for Cosmetic Ingredient Safety (Panel) first published the Final Report of the safety of Isobutane, Isopentane, Butane, and Propane in 1982. The Panel previously concluded that these ingredients are considered safe as cosmetic ingredients under the present conditions of concentration and use, as described in that safety assessment. Upon re-review in 2002, the Panel reaffirmed the original conclusion, as published in 2005. The Panel reviewed update frequency and concentration of use data again in 2023, in addition to newly available, relevant safety data. Considering this information, as well as the information provided in the original safety assessment and the prior re-review document, the Panel reaffirmed the 1982 conclusion for Isobutane, Isopentane, Butane, and Propane.

Evidence-based unification of potato gene models with the UniTato collaborative genome browser.

Potato (Solanum tuberosum) is the most popular tuber crop and a model organism. A variety of gene models for potato exist, and despite frequent updates, they are not unified. This hinders the comparison of gene models across versions, limits the ability to reuse experimental data without significant re-analysis, and leads to missing or wrongly annotated genes. Here, we unify the recent potato double monoploid v4 and v6 gene models by developing an automated merging protocol, resulting in a Unified poTato genome model (UniTato). We subsequently established an Apollo genome browser (unitato.nib.si) that enables public access to UniTato and further community-based curation. We demonstrate how the UniTato resource can help resolve problems with missing or misplaced genes and can be used to update or consolidate a wider set of gene models or genome information. The automated protocol, genome annotation files, and a comprehensive translation table are provided at github.com/NIB-SI/unitato.

Spatial modelling of present and future groundwater potentials in Nigeria; towards a sustainable water demand and supply

The development of water resources in Nigeria has not risen alongside population increase. Although groundwater is the available source of water at all locations in Nigeria, groundwater aquifer is becoming deeper due to increased groundwater harvest, especially in urban areas. There is a need for continuous groundwater modelling using geological, climatic, environmental data, and spatial tools especially under climate change and intense landcover conversions. The Spatial Multi-Criteria Analysis (SMCA) was adopted in this study to model the current groundwater potential in Nigeria. The multi-criteria analysis tool in ArcGIS was explored to overlay the nine factor maps to model historic and futuristic groundwater potentials. Our groundwater factor maps show an interesting pattern across Nigeria, the southern parts show more potential considering suitability factors like; surface water density, rainfall, temperature, soil, land cover and elevation in the region. Whereas the upland regions even with higher rainfall and lower temperatures suitable for groundwater recharge, are disadvantaged by geomorphological factors. Northern part of Nigeria shows high potentials, considering the geology, soil, lineament density and slope, but disadvantaged by other factors like lower rainfall and higher temperatures. This informed the final groundwater potential maps; results for the historic potential revealed that, no location in Nigeria has optimal (9–10), very poor or no (1–2) groundwater potential. The results further revealed that areas with higher groundwater potentials are largely within the corridors of major rivers in Nigeria (Niger and Benue), covering about 17.6% of the Nigerian landmass, while 2.6%, 33.7, 44% and 2.2% are occupy areas with very low, low, moderate and very high groundwater potentials respectively. Further analysis highlighs locations of concern due to climatic and environmental changes. Interestingly, our groundwater projection results show a persistent increase in groundwater potential from 2021 to 2100 if current landuses and environmental factors are maintained, and if the projected increase in rainfall is true. Despite these groundwater potentials, the recent issues of shallow aquifers have been cautioned by scientists, especially due to groundwater uncertainties in Nigeria especially with intense landcover conversions, combined with accelerated water demands due to increase in population, and incessant groundwater extractions. Therefore, there is a need to seek caution in the pattern of unregulated and incessant groundwater harvest in Nigeria and we recommend frequent updates of the groundwater potential using geospatial tools to inform governing policies on a centralised consolidated sustainable water supply in Nigeria.

Pinning event-triggered sampled-data synchronization of coupled reaction–diffusion neural networks

In this paper, the synchronization problem of coupled reaction–diffusion neural networks (CRDNNs) is considered by using pinning event-triggered sampled-data (PETSD) control method under spatially point measurements. To save the limited transmission channel, a PETSD control mechanism by controlling a small fraction of the nodes is proposed to decrease the update frequency of controller and the unnecessary SD. Then, based on the Lyapunov–Krasovskii functional (LKF) and inequality techniques, sufficient conditions for exponential stability of the synchronization error system presented by linear matrix inequalities (LMIs) can be derived through the designed PETSD controller. Finally, simulation results of one numerical example are presented to demonstrate the effectiveness of the proposed design approach.

Enhanced perturbation measurement range chirped pulse ϕ-OTDR for distributed static measurements using feedback frequency-shift compensation technology

Chirped-pulse phase-sensitive optical time-domain reflectometry (CP-ϕOTDR) is a newly developed distributed optical fiber sensing (DOFS) technology that can measure physical quantities across the length of optical fiber. The measurement of perturbations as strain or temperature changes is performed by comparing the time domain trace of Rayleigh backscattered (RBS) light with a reference trace. However, large strain or temperature fluctuations or laser frequency noises necessitate frequent updates of the reference, and short-term change measurements must be integrated to obtain absolute strain or temperature, which introduces a 1/f noise component due to the combination of noise with each cumulative change. In this study, we propose a novel CP-ϕOTDR system based on frequency-shift compensation (FSC) technology. This system utilizes real-time feedback control of the central frequency of the incident laser to compensate for time domain trace distortion caused by large perturbations. It overcomes the limitations associated with the total chirp bandwidth and eliminates the 1/f noise component by obviating the need for frequent updates of the reference trace. The measuring range for perturbations is only limited to the wavelength range supported by the integrated optical devices, typically on the order of nanometers, corresponding to at least tens of Kelvin in temperature fluctuations or hundreds of micro-strains. To validate the effectiveness, we conducted a temperature measurement experiment involving a 1 K variation, which is about 90 times the maximum measurable perturbation that the original CP-ϕOTDR system can demodulate with the reference unchanged. The results demonstrate that the FSC method significantly expands the perturbation measurement range of the CP-ϕOTDR system.

Potentials of using real-time data to increase the update frequency of production planning and control strategies in MTO: a discrete event simulation study

In recent years, production planning and control strategies have gained increasing importance within manufacturing enterprises to stay competitive by enhancing the ability to meet and quickly adapt to the hyper-dynamic requirements of highly volatile markets. In this context, traditional production planning and control strategies like material requirements planning often result in long lead times and high work in progress due to their weak responsiveness to short-term fluctuations in demand. However, new production planning and control strategies and concepts based on the usage of real-time data, as a fundamental principle of Industry 4.0, may have the potential to compensate for the shortcomings of traditional approaches. By using a discrete event simulation based on the data of an electronics manufacturing company, the potential of using real-time data to increase the update frequency in different production planning and control strategies in make to order production systems is analyzed.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

US commercial health plan coverage dynamics in pulmonary arterial hypertension therapies.

Health plan coverage is central to patient access to care, especially for rare, chronic diseases. For specialty drugs, coverage varies, resulting in barriers to access. Pulmonary arterial hypertension (PAH) is a rare, progressive, and fatal disease. Guidelines suggest starting or rapidly escalating to combination therapy with drugs of differing classes (phosphodiesterase 5 inhibitors [PDE5is], soluble guanylate cyclase stimulators [sGC stimulators], endothelin receptor antagonists [ERAs], and prostacyclin pathway agents [PPAs]). To assess the variation in commercial health plan coverage for PAH treatments and how coverage has evolved. To examine the frequency of coverage updates and evidence cited in plan policies. We used the Tufts Medical Center Specialty Drug Evidence and Coverage database, which includes publicly available specialty drug coverage policies. Overall, and at the drug and treatment class level, we identified plan-imposed coverage restrictions beyond the drug's US Food and Drug Administration label, including step therapy protocols, clinical restrictions (eg, disease severity), and prescriber specialty requirements. We analyzed variation in coverage restrictiveness and how coverage has changed over time. We determined how often plans update their policies. Finally, we categorized the cited evidence into 6 different types. Results reflected plan coverage policies for 13 PAH drugs active between August 2017 and August 2022 and issued by 17 large US commercial health plans, representing 70% of covered lives. Coverage restrictions varied mainly by step therapy protocols and prescriber restrictions. Seven plans had step therapy protocols for most drugs, 9 for at least one drug, and 1 had none. Ten plans required specialist (cardiologist or pulmonologist) prescribing for at least one drug, and 7 did not. Coverage restrictions increased over time: the proportion of policies with at least 1 restriction increased from 38% to 73%, and the proportion with step therapy protocols increased from 29% to 46%, with generics as the most common step. The proportion of policies with step therapy protocols increased for every therapy class with generic availability: 18% to 59% for ERAs, 33% to 77% for PDE5is, and 33% to 43% for PPAs. The proportion of policies with prescriber requirements increased from 24% to 48%. Plans updated their policies 58% of the time annually and most often cited the 2019 CHEST clinical guidelines, followed by randomized controlled trials. Plan use of coverage restrictions for PAH therapies increased over time and varied across both drugs and plans. Inconsistency among health plans may complicate patient access and reduce the proportion who can persist on PAH treatments.

FDM: Effective and efficient incident detection on sparse trajectory data

Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on data mining or machine learning often rely on dense traffic data; for instance, sensors installed in highways provide frequent updates of road information. In this paper, we ask the question: can ID be performed on sparse traffic data (e.g., location data obtained from GPS devices equipped on vehicles)? As these data may not be enough to describe the state of the roads involved, they can undermine the effectiveness of existing ID solutions. To tackle this challenge, we borrow an important insight from the transportation area, which uses trajectories (i.e., moving histories of vehicles) to derive incident patterns. We study how to obtain incident patterns from trajectories and devise a new solution (called Filter-Discovery-Match (FDM)) to detect anomalies in sparse traffic data. We have also developed a fast algorithm to support FDM. Experiments on a taxi dataset in Hong Kong and a simulated dataset show that FDM is more effective than state-of-the-art ID solutions on sparse traffic data, and is also efficient.

Navigating the Frontier: Addressing Artificial Intelligence Challenges in Tourism and Hospitality Education

In this article, we explore artificial intelligence (AI) integration in tourism and hospitality education, analyze its implications, and propose strategies to address challenges. While AI has revolutionized operational processes and customer experiences, its adoption in higher education presents various opportunities and hurdles. The rapid evolution of AI necessitates frequent curriculum updates and faculty development. Moreover, addressing the digital divide and ethical considerations is also crucial. The article categorizes the implications into technological advancements, educational impacts, and future trends. Strategies for addressing AI challenges include curriculum integration, experiential learning, faculty development, interdisciplinary collaboration, and ethical education. By embracing AI responsibly, educational institutions can prepare students for success in the Fourth Industrial Revolution, ensuring positive social and economic impacts.

Well-being of family physicians during COVID-19 pandemic in Slovenia

BackgroundDuring the COVID-19 pandemic, family physicians (FPs) served as the the initial point of contact for patients potentially infected with the virus, necessitating frequent updates to treatment protocols. However, practices also faced organizational challenges in providing care to other patients who also needed their medical attention. The pressure on FPs increased and affected their well-being. The international PRICOV-19 study, titled “Primary care in times of COVID-19 pandemic,” investigated how FPs functioned during the COVID‐19 pandemic. This article examines the correlation between various organizational and structural COVID-19-related variables and the well-being of FPs in Slovenia.MethodsBetween October 2020 and January 2021, we conducted an online cross-sectional survey. The questionnaire was distributed to 1040 Slovenian FPs and 218 family medicine (FM) trainees. Part of the questionnaire assessed the cooperation and well-being of FPs. The Mayo Clinic Well-being Index was used for the assessment. FP’s well-being was also assessed descriptively by asking open-ended questions about maintaining mental health during the pandemic. Potential factors associated with FPs’ well-being were identified using a multivariate linear regression method.ResultsThe final sample comprised 191 participants (response rate 14.1%). The mean value ± standard deviation of the Mayo Well-being Index was 3.3 ± 2.6 points. The FPs with the poorest well-being had 5–15 years of work experience and worked in a practice where work could not be distributed in the absence of a co-worker without compromising the well-being of colleagues. Physical activity was identified as the most common method of maintaining mental health among FPs.ConclusionsThe results of the study suggest that targeted interventions are needed to support FPs mid-career, increase resilience in practice, promote strong team dynamics, and prioritise physical activity in healthcare. Addressing these aspects can contribute to the well-being of individual FPs and the overall health of the healthcare workers.