General Data Research Articles

Design characteristics of integrated surveillance of antimicrobial resistance at the human, animal, and environment interfaces

Abstract Efforts have been made to strengthen national and global antimicrobial resistance (AMR) surveillance programs by integrating data collection across the human, animal, and environmental sectors. We searched the literature to review published studies reporting the implementation of integrated AMR surveillance approaches, and we identified a total of 96 articles from 36 countries published from 2000 to 2022, which met our inclusion criteria. Standard review protocols were applied in our study. Out of 96 studies, 47 (49%) articles integrated AMR analysis from human and animal (HA) populations, 24 (25%) considered human, animal, and environmental (HAE) samples together, 11 (12%) studies included animal and environment (AE) samples, and 12 (13%) integrated human and environmental (HE) surveillance. Human isolate-based and animal isolate-based surveillance were the most common study designs (38, 52.8%), with the remainder applying human case-based and isolate-based for animal populations (32, 44.4%). Finally, our results demonstrate that AMR studies on E. coli revealed a lower correlation between human and animal AMR prevalence compared to Salmonella spp. and Campylobacter spp. studies. Different aspects of surveillance design were associated with the level of correlation of AMR prevalence between sectors. Our study found that while global efforts for integrated AMR surveillance have increased in the past 10 years, significant variation exists between studies with regard to the epidemiological and laboratory aspects of their surveillance designs. Our findings indicate that to enable the generation of comparable epidemiological data across countries and sectors, there is a need for the development of a global protocol to support the design of surveillance programs that aim to conduct integrated surveillance of AMR.

Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.

Generative adversarial network model to classify human induced pluripotent stem cell-cardiomyocytes based on maturation level

Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification by incorporating synthetic data. This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability.

Long-Term Risk of Autoimmune and Autoinflammatory Connective Tissue Disorders Following COVID-19

Few studies have investigated the association between COVID-19 and autoimmune and autoinflammatory connective tissue disorders; however, research with long-term observation remains insufficient. To investigate the long-term risk of autoimmune and autoinflammatory diseases after COVID-19 over an extended observation period. This retrospective nationwide population-based study investigated the Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service (K-COV-N) cohort. Individuals with confirmed COVID-19 from October 8, 2020, to December 31, 2022, and controls identified among individuals who participated in the general health examination in 2018 were included in the analysis. Confirmed COVID-19. Incidence and risk of autoimmune and autoinflammatory connective tissue disorders in patients after COVID-19. Various covariates, such as demographic characteristics, general health data, socioeconomic status, and comorbidity profiles, were balanced using inverse probability weighting. A total of 6 912 427 participants (53.6% male; mean [SD] age, 53.39 [20.13] years) consisting of 3 145 388 with COVID-19 and 3 767 039 controls with an observational period of more than 180 days were included. Alopecia areata (adjusted hazard ratio [AHR], 1.11 [95% CI, 1.07-1.15]), alopecia totalis (AHR, 1.24 [95% CI, 1.09-1.42]), vitiligo (AHR, 1.11 [95% CI, 1.04-1.19]), Behçet disease (AHR, 1.45 [95% CI, 1.20-1.74]), Crohn disease (AHR, 1.35 [95% CI, 1.14-1.60]), ulcerative colitis (AHR, 1.15 [95% CI, 1.04-1.28]), rheumatoid arthritis (AHR, 1.09 [95% CI, 1.06-1.12]), systemic lupus erythematosus (AHR, 1.14 [95% CI, 1.01-1.28]), Sjögren syndrome (AHR, 1.13 [95% CI, 1.03-1.25]), ankylosing spondylitis (AHR, 1.11 [95% CI, 1.02-1.20]), and bullous pemphigoid (AHR, 1.62 [95% CI, 1.07-2.45]) were associated with higher risk in the COVID-19 group. Subgroup analyses revealed that demographic factors, including male and female sex, age younger than 40 years, and age 40 years and older, exhibited diverse associations with the risk of autoimmune and autoinflammatory outcomes. In addition, severe COVID-19 infection requiring intensive care unit admission, the Delta period, and not being vaccinated were associated with higher risk. This retrospective cohort study with an extended follow-up period found associations between COVID-19 and the long-term risk of various autoimmune and autoinflammatory connective tissue disorders. Long-term monitoring and care of patients is crucial after COVID-19, considering demographic factors, disease severity, and vaccination status, to mitigate these risks.

Integrating Blockchain Technology with Internet of Things and Edge Computing to Achieve Security

The last decade has seen many advancements and emergence in computing technologies; Internet of Things (IoT) being one of the most successful among them. Huge data generation in IoT can be utilized for analysis and monitoring the behaviour of various patterns. In spite of wide adaptability, IoT has various challenges such as privacy, security and computational constraints. To address the later challenge, Edge computing can be a potential solution with great computational resources leading towards the improvement in performance in terms of latency, throughput and response time. However, privacy and security remain largely unaddressed along with the traditional limitations of a centralized approach. This brings into the usage of another technology viz. Blockchain that handles the issue of privacy and security through its inherent features such as immutability, transparency and decentralization. In this research, we intend to explore the option of possible integration of Blockchain technology with IoT to address these challenges.

Identifying genomic data use with the Data Citation Explorer

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

Cervical Cancer Prediction Based on Imbalanced Data Using Machine Learning Algorithms with a Variety of Sampling Methods

Cervical cancer affects a large portion of the female population, making the prediction of this disease using Machine Learning (ML) of utmost importance. ML algorithms can be integrated into complex, intelligent, agent-based systems that can offer decision support to resident medical doctors or even experienced medical doctors. For instance, an experienced medical doctor may diagnose a case but need expert support that related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influence on predictions made using ML algorithms. Cancer data, in general, and cervical cancer data, in particular, are frequently imbalanced. For this study, we chose a messy, real-life cervical cancer dataset available in the Kaggle repository that includes large amounts of missing and noisy values. To identify the best imbalanced technique for this medical dataset, the performances of eleven important resampling methods are compared, combined with the following state-of-the-art ML models that are frequently applied in predictive healtchare research: K-Nearest Neighbors (KNN) (with k values of 2 and 3), binary Logistic Regression (bLR), and Random Forest (RF). The studied resampling methods include seven undersampling methods and four oversampling methods. For this dataset, the imbalance ratio was 12.73, with a 95% confidence interval ranging from 9.23% to 16.22%. The obtained results show that resampling methods help improve the classification ability of prediction models applied to cervical cancer data. The applied oversampling techniques for handling imbalanced data generally outperformed the undersampling methods. The average balanced accuracy for oversampling was 77.44%, compared to 62.28% for undersampling. When detecting the minority class, oversampling achieved an average score of 60.80%, while undersampling scored 41.36%. The logistic regression classifier had the greatest impact on balanced techniques, while random forest achieved promising performance, even before applying balancing techniques. Initially, KNN2 outperformed KNN3 across all metrics, including balanced accuracy, for which KNN2 achieved 53.57%, compared to 52.71% for KNN3. However, after applying oversampling techniques, KNN3 significantly improved its balanced accuracy to 73.78%, while that of KNN2 increased to 63.89%. Additionally, KNN3 outperformed KNN2 in minority class performance, scoring 55.72% compared to KNN2’s 33.93%.

Assessment of genotoxicity biomarkers in neotropical fish species from streams of the Ivinhema River basin located in sugarcane cultivation areas

The Ivinhema River basin has experienced the greatest expansion of sugarcane cultivation in the state of Mato Grosso do Sul, Brazil. Theassessment of water quality through physical and chemical parameters and ecotoxicological studies, employing both bioindicators collected in situ and laboratory tests, provides more robust results for this region. In this context, the objective of this study was to analyze the composition and structure of the landscape around the sampling sites located in three streams (Vitória, Rosário, and Piravevê) belonging to the Ivinhema River basin to evaluate the impacts of sugarcane cultivation expansion on water quality and genotoxicity biomarkers in situ and ex situ using native fish species. Inthe sampling sites in the Rosário and Vitória streams, sugarcane is the main land use, while in the Piravevê stream, pasture areas represent the main land use. The acidic pH observed in the Vitória and Rosário streams seems to be related to the application of fertilizers and the fertigation in the sugarcane crop. Two species of detritivorous and five of omnivorous fishes were collected. A higher frequency of genotoxic damage was identified in detritivorous fish species. In the genotoxicity tests with Astyanax lacustris, the most frequent nuclear alteration induced by the water samples from all sampling sites was nuclear invagination. Our results indicated that the expansion of the sugarcane ridge in the Ivinhema River basin may cause negative impacts on the aquatic environment and native biota. These results contribute to the generation of data and information that can be used for public hearing requests that aim to discuss and review many aspects of legislation regarding agricultural activities around streams, as well as the need for restoration and management programs in these areas in order to conserve biodiversity.

Rapid lithological mapping using multi-source remote sensing data fusion and automatic sample generation strategy

ABSTRACT The advancement of remote sensing technology aids geologists in obtaining lithological maps more quickly, comprehensively, and accurately. However, key challenges in lithological mapping include the limited spectral information from individual sensors and the difficulties in visually interpreting lithological samples. In this study, we integrated 241 scenes of optical data and 106 scenes of radar data on the Google Earth Engine (GEE) platform, proposing a rapid lithological identification framework that combines an automatic lithological sample data generation strategy with multi-source data. Using various machine learning algorithms, we evaluated the classification capabilities of heterogeneous predictive factors, feature optimization algorithms, and object-based algorithms. Results indicate that: (1) Combining optical and radar data improves prediction accuracy, with terrain data further enhancing mapping capabilities; (2) Terrain factors contribute most to classification, but SWIR and TIR bands of optical data are critical for lithological identification; (3) The feature optimization algorithm reduces feature redundancy and efficiency issues from multi-source data, achieving 96.51% accuracy with the optimal feature model, an improvement of 0.1%−2.02% over original features; (4) Object-based algorithms show significant potential in mapping areas with large rock outcrops. This study offers new insights for medium- to large-scale lithological maps and provides essential data support for geological work.

Using spatial video and deep learning for automated mapping of ground-level context in relief camps

BackgroundThe creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).MethodsWe trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.ResultsThe initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.ConclusionsThe results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Monitoring materialities: Anthropocene landscapes of environing technologies in the High North

An increased interest in Arctic regions as places of data generation to inform climate models, reports, and policies on environmental change has created a distinct kind of landscape, transformed by the material legacy of short and long-term monitoring and other scientific activities. Drawing from fieldwork in four key areas for environmental monitoring in the High North of Norway, Sweden, and Greenland, we investigate the intentional and unintentional materialities of environing technologies and other anthropogenic impacts that form a particular kind of Anthropocene landscape related to knowing global environmental change. We conclude that such distinctive monitoring landscapes contribute to work investigating shifting conceptualisations of heritage and the emergence of unintentional landscapes in the Anthropocene, and suggest that as co-creators of the global environment, they provide an interesting insight into the material legacies of natural scientific knowledge production.

Diagnostics-LLaVA

The recent advancements in the area of Large language models (LLMs) has opened horizons for conversational assistant-based intelligent models capable of interpreting images, and providing textual response, also known as Visual language models (VLMs). These models can assist equipment operators and maintenance technicians in the complex Prognostics and Health Management (PHM) tasks such as diagnostics of faults, root cause analysis and repair recommendation. Significant open source contributions in the area of VLMs have been made. However, models trained on general data fail to perform well in complex tasks in specialized domains such as diagnostics and repair of industrial equipment. Therefore, in this paper we discuss our work on development of Diagnostics-LLaVA, a VLM suitable for interpreting images of specific industrial equipment and provide better response than existing open source models in PHM tasks such as fault diagnostics and repair recommendation. We introduce Diagnostics-LLaVA based on the architecture of LLaVA, and created one instance of the Diagnostics-LLaVA for the automotive repair domain, referred to as Automotive-LLaVA. We demonstrate that our proposed Automotive-LLaVA model performs better than the state-of-the-art open source visual language models such as mPlugOWL and LLaVA in both qualitative and quantitative experiments.

Landslide Studies in the Context of Disaster Management in Bangladesh—A Systematic Literature Review

Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches.

Academics’ Experiences during Horrible Times: Online Teaching, Learning, and COVID-19

E-Learning has become the new normal in Higher Educational Institutions across South Africa. Hence, intergration of the new learning pedagogies has initiated a shift of the norms on professional development. This paper seeks to unpack the strategies used by lecturers in the face of adversities experienced during the E-learning space pre & post Covid-19. There are strategies advised by teacher autonomy which need to be explored. It might have been better if teacher autonomy was merely being against pressures exerted on the individual teachers. But, it is a means for teachers to advance their personal and professional capability to adapt to the rapidly changing conditions and situations in learning spaces. This paper seeks to unravel an unacquainted new path on the drive of lecturers in their professional development that has not been evident in their training. In socio-politically transforming societies such as South Africa, the absence of adequate structures to implement a range of changing policies, may leave lecturers in despair when trying to access development opportunities. This paper adopts analytic theory and semi-structured interviews to give light to the constructivist views of lecturers who have developed methods during the times of adversity. Purposive sampling of 6 lecturers and 10 students will help the generation of data for this study because even though lectures had to adjust to new methods but students were in the receiving end of all the pressure. This study argues for implementation of best practices for the successful professional development of academics in line with E-learning. The University of Limpopo will be the centre of data generation with ethical considerations requested as well as literature visited. Collected data will be thematically analysed in order to sieve and arrange the necessary information for this study. The newly shared practices will form part of the resolutions to the professional development of academic staff.

Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

Overcoming the Challenges of Including Learners with Visual Impairments Through Teacher Collaborations

In this article we report on a study undertaken with 255 teachers working with learners with visual impairments. The focus of our discussion is teachers’ implementation of inclusive education policies with learners with visual impairments in full-service schools in South Africa. We foreground the ways in which the teacher participants relied on teacher collaborations to overcome some of the challenges they faced as a result of limited resource provisions in schools in this country. We implemented an instrumental case study design and followed the approach of participatory reflection and action (PRA). The sample included teachers (n = 255) from seven full-service and ten special schools from five provinces in South Africa. In addition, 50 expert stakeholders who work in the field of visual impairment were involved. For data generation and documentation, we utilised PRA-based workshops, the observation-as-context-of-interaction method, audio-visual techniques, field notes, and reflective journals. The findings of our research confirm that full-service schools face distinct challenges regarding limited resources as well as teachers that are inexperienced to accommodate learners with visual impairments. Even though the teachers in our study were initially reluctant to implement inclusive education practices, their collaboration with fellow teachers and other informed stakeholders enabled them to address some of the challenges they experienced and implement inclusive practices. They subsequently formed a team and learnt from one another to facilitate positive changes through the implementation of inclusive practices, thereby following a socio-ecological approach to inclusive practices in full-service schools in South Africa.

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

Traffic Flow Outlier Detection for Smart Mobility Using Gaussian Process Regression Assisted Stochastic Differential Equations

Current methods for detecting outliers in traffic streaming data often struggle to capture real-time dynamic changes in traffic conditions and differentiate between genuine changes and anomalies. This study proposes a novel approach to outlier detection in traffic streaming data that effectively addresses stochasticity and uncertainty in observations. The proposed method utilizes Stochastic Differential Equations (SDEs) and Gaussian Process Regression (GPR). By employing SDEs, we can capture drift and diffusion estimates in traffic streaming data, providing a more comprehensive modeling of the data generation process. Integrating GPR allows precise Bayesian posterior inferences for outlier detection within the SDE framework. To improve practicality, we introduce a flexible threshold-setting mechanism using statistical testing to control the false positive rate. This adaptability helps strike a balance between model fitting and complexity in outlier detection. Compared to traditional SDE-based methods, our SDE-GPR outlier detection method demonstrates enhanced robustness and better adaptability to the complexities of traffic systems. This is evidenced through an empirical study using time series data collected in California, USA. Overall, this study introduces a more advanced and accurate approach to outlier detection in traffic streaming data, paving the way for improved real-time traffic condition monitoring and management.

Factors associated with general practitioner-led diagnosis of long COVID: an observational study using electronic general practice data from Victoria and New South Wales, Australia.

To investigate associations between sociodemographic factors, pre-existing chronic comorbidities, and general practitioner-led diagnosis of long COVID. We conducted a retrospective observational case-control study using de-identified electronic general practice data, recorded between January 2020 and March 2023, from 869 general practice clinics across four primary health networks in Victoria and New South Wales. Sociodemographic factors and pre-existing chronic comorbidities associated with general practitioner-led diagnosis of long COVID. A total of 1588 patients had a recorded general practitioner-led long COVID diagnosis. Females exhibited a higher likelihood of general practitioner-led long COVID diagnosis (adjusted odds ratio [aOR], 1.58; adjusted confidence interval [aCI], 1.35-1.85) compared with males. Patients aged 40-59 years had a higher likelihood of general practitioner-led long COVID diagnosis (aOR, 1.68; aCI, 1.40-2.03) compared with patients aged 20-39 years. The diagnosis was more likely in patients of high socio-economic status (aOR, 1.37; aCI, 1.05-1.79) compared with those of mid socio-economic status. Mental health conditions (aOR, 2.69; aCI, 2.25-3.21), respiratory conditions (aOR, 2.25; aCI, 1.85-2.75), cancer (aOR, 1.64; aCI, 1.15-2.33) and musculoskeletal conditions (aOR, 1.50; aCI, 1.20-1.88) were all significantly associated with general practitioner-led long COVID diagnosis. Female sex, middle age, high socio-economic status and pre-existing comorbidities, including mental health conditions, respiratory conditions, cancer and musculoskeletal conditions, were associated with general practitioner-led long COVID diagnosis among general practice patients. These factors largely parallel the emerging international evidence on long COVID and highlight the patient characteristics that practitioners should be cognisant of when patients present with symptoms of long COVID.

Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.