Online Data Sources Research Articles

Rib Stress Fractures and a Novel Muscle-Sparing Rib Fixation Plating in an Elite Tennis Player: A Systematic Review of the Literature and Case Report.

A nationally ranked Division One female collegiate tennis player presented with pain in the chest and right posterior rib region after feeling a popping sensation during a routine overhead movement. The patient was eventually diagnosed with 2 lower rib stress fractures. After unsuccessful conservative management, the player underwent an open reduction and internal fixation and autologous bone grafting of the sixth and seventh ribs with a muscle-sparing approach and was able to return to full competition. We present this case along with a systematic review of the literature regarding rib stress fractures, which included 6 separate online data sources (PubMed, EMBASE, Cochrane, CINAHL, SportDiscus, and Medline). The successful surgical intervention demonstrates a significant option for elite athletes who wish to return to competition but have been unable to with standard of care conservative management of rib stress fractures.

Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.

Bipol: A novel multi-axes bias evaluation metric with explainability for NLP

We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to classify bias using SotA architectures, we evaluate two popular NLP datasets (COPA and SQuADv2) and the WinoBias dataset. As additional contribution, we created a large English dataset (with almost 2 million labeled samples) for training models in bias classification and make it publicly available. We also make public our codes.

Automatic Face Recognition System Using Deep Convolutional Mixer Architecture and AdaBoost Classifier

In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban security. This is a difficult task due to face occlusion, which makes it hard to capture effective features. Existing work focuses on improving performance while ignoring issues like a small dataset, high computational complexity, and a lack of lightweight and efficient feature descriptors. In this paper, face recognition (FR) using a Convolutional mixer (AFR-Conv) algorithm is developed to handle face occlusion problems. A novel AFR-Conv architecture is designed by assigning priority-based weight to the different face patches along with residual connections and an AdaBoost classifier for automatically recognizing human faces. The AFR-Conv also leverages the strengths of pre-trained CNNs by extracting features using ResNet-50, Inception-v3, and DenseNet-161. The AdaBoost classifier combines these features’ weighted votes to predict labels for testing images. To develop this system, we use the data augmentation method to enhance the number of datasets using human face images. The AFR-Conv method is then used to extract robust features from images. Finally, to recognize human identity, an AdaBoost classifier is utilized. For the training and evaluation of the AFR-Conv model, a set of face images is collected from online data sources. The experimental results of the AFR-Conv approach are presented in terms of precision (PR), recall (RE), detection accuracy (DA), and F1-score metrics. Particularly, the proposed approach attains 95.5% PR, 97.6% RE, 97.5% DA, and 98.5% of F1-score on 8500 face images. The experimental results show that our proposed scheme outperforms advanced methods for face classification.

Hipsters and drunks, tourists and locals

Abstract This article uses linguistic and semiotic landscapes as tools to analyze the ideological work required for rendering Calle Loíza, an urban street in San Juan, Puerto Rico, as successfully revitalized. Linguistic Landscapes provide insight on discursive chains that circulate logics and produce values of places, and therefore form an intrinsic part of capital-driven urban change. I aim to show how perspectives of places can be structured, and how values of places are naturalized and embedded in the neoliberal political economy. Drawing from ethnographic and online sources of data, I argue that Calle Loíza is a site of ideological contestation and that the processes of rhematization and erasure are required for Calle Loiza’s indexical relation to progress and its articulation as a successfully revitalized urban neighborhood. The findings demonstrate that online spaces are also material, and that language is essential in the production and circulation of political economic values of places.

Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification.

A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community.

Frequency and specificity of health issues in local political campaigns

PurposeThis study investigates the frequency and specificity of health-related issues discussed during the East Java Indonesia Gubernatorial Election Campaign 2018.Design/methodology/approachThe research utilized a mixed-methods approach, combining topic modeling and qualitative thematic analysis, to analyze 4,023 online news articles from March to June 2018. From the data sources obtained in terms of time frame, this research can be categorized as a retrospective study because it examines origins from events that have already occurred.FindingsThe study found that health issues accounted for only 6% of all gubernatorial election news in each selected online media portal. The health issues identified were categorised into eight groups: health financing and facilities, health workforce, malnutrition and stunting, leprosy, cigarettes and tobacco, healthy lifestyles, pregnancy and breastfeeding, and disability. The study also reveals a need for increased attention to health-related issues in political campaigns and media. By focusing on the health-related issues raised during the campaign, the study provides valuable insights into the gaps and priorities in addressing the health needs of the East Java population. The research framework used in this study offers a valuable approach for analyzing online data sources using qualitative analysis capacity. The study can improve health policies and outcomes in the local election campaign by raising awareness of health issues and promoting informed decision-making among voters.Research limitations/implicationsThis research limitation is a local political campaign case in Indonesia. The research indicates that health issues receive limited coverage during election campaigns, suggesting a lack of emphasis on health as a critical issue among East Java's electorate.Originality/valueThe study can improve health policies and outcomes in the local election campaign by raising awareness of health issues and promoting informed decision-making among voters.

Information theory and headache triggers.

This secondary analysis evaluated the information content exhibited by various measurement strategies of commonly perceived causes, or "triggers," of headache attacks. When evaluating triggers of primary headache attacks, the variation observed in trigger candidates must be measured to compare against the covariation in headache activity. Given the numerous strategies that could be used to measure and record headache trigger variables, it is useful to consider the information contained in these measurements. Using previously collected data from cohort and cross-sectional studies, online data sources, and simulations, the Shannon information entropy exhibited by many common triggers was evaluated by analyzing available time-series or theoretical distributions of headache triggers. The degree of information, reported in bits, was compared across trigger variables, measurement strategies, and settings. A wide range of information content was observed across headache triggers. Due to lack of variation, there was little information, near 0.00 bits, in triggers like red wine and air conditioning. Most headache triggers exhibited more information when measured using an ordinal scale of presence/degree (e.g., absent, mild, moderate, severe) versus a present/absent binary coding. For example, the trigger "joy" exhibited 0.03 bits when assessed using binary coding but 1.81 bits when coded using an ordinal scale. More information was observed with the use of count data (0.86 to 1.75 bits), Likert rating scales (1.50 to 2.76 bits), validated questionnaires (3.57 to 6.04 bits), weather variables (0.10 to 8.00 bits), and ambulatory monitoring devices (9.19 to 12.61 bits). Although commonly used, all binary-coded measurements contain ≤1.00 bit of information. Low levels of information in trigger variables make associations with headache activity more difficult to detect. Assessments that balance information-rich measurements with reasonable participant burden using efficient formats (e.g., Likert scales) are recommended to enhance the evaluation of the association with headache activity.

Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study

One of the barriers to performing geospatial surveillance of mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property level and matched to important risk factor information (e.g., anthropogenic, environmental, and climate) that enables the spatial risk prediction of mosquito occupancy or infestation. Such data are invaluable pieces of information for academics, policy makers, and public health program managers operating in low-resource settings in Africa, Latin America, and Southeast Asia, where mosquitoes are typically endemic. The reality is that such data remain elusive in these low-resource settings and, where available, high-quality data that include both individual and spatial characteristics to inform the geospatial description and risk patterning of infestation remain rare. There are many online sources of open-source spatial data that are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are threefold: (1) to highlight where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial predictions for mosquito occupancy in general; (2) to use Brazil as a case study to demonstrate how these datasets can be combined to predict the presence of arboviruses through the use of ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke applications beyond these open-source online data sources, demonstrating for how they can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this article was mainly limited to a Brazilian context because it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the states of Pernambuco and Paraiba. The analysis presented in this article was also limited to a specific mosquito species, i.e., Aedes aegypti, due to its endemic status in Brazil.

Science paper or big data? Assessing invasion dynamics using observational data

Non-native species are spreading at an unprecedented rate over large spatial scales, with global environmental change and growth in commerce providing novel opportunities for range expansion. Assessing the pattern and rate of spread is key to the development of strategies for safeguarding against future invasions and efficiently managing existing ones. Such assessments often depend on spatial distribution data from online repositories, which can be spatially biased, imprecise, and lacking in quantity. Here, the influence of disparities between occurrence records from online data repositories and what is known of the invasion history from peer-reviewed published literature on non-native species range expansion was evaluated using 6693 records of the Pacific oyster, Magallana gigas (Thunberg, 1793), spanning 56 years of its invasion in Europe. Two measures of spread were calculated: maximum rate of spread (distance from introduction site over time) and accumulated area (spatial expansion). Results suggest that despite discrepancies between online and peer-reviewed data sources, including a paucity of records from the early invasion history in online repositories, the use of either source does not result in significantly different estimates of spread. Our study significantly improves our understanding of the European distribution of M. gigas and suggests that a combination of short- and long-range dispersal drives range expansions. More widely, our approach provides a framework for comparison of online occurrence records and invasion histories as documented in the peer-reviewed literature, allowing critical evaluation of both data sources and improving our understanding of invasion dynamics significantly.

Power Apps pada Sistem Pemodelan Pengambilan Obat di Klinik Melia PT. POMI, Paiton

Digitalization is one of the important keys in increasing productivity, namely by building digital technology that improves process efficiency, product and service quality, and optimizes resource allocation, so as to reduce processing time for faster, streamlined operations, and greater customer satisfaction. as good as Power Apps. Power Apps is a service for building and deploying custom business applications that connect to your data and work across the web and mobile - without the time and expense of specialized software development. Power Apps are a number of applications, services, and connectors, as well as a data platform, that provide a fast development environment for building custom applications for business needs. Using Power Apps, users can quickly create custom business applications that connect to data stored on the base data platform (Microsoft Dataverse) or across various online and on-premises data sources (such as SharePoint, Microsoft 365, Dynamics 365, SQL Server, and so on). The purpose of this study is to implement and analyze the use of Power Apps in modeling drug retrieval at the Melia Clinic PT POMI, Paiton with the aim of improving the quality of Melia clinic services through acceleration and ease of drug taking. The output of this research is an accredited national journal.

A Protocol to Retrieve and Curate Spatial and Climatic Data from Online Biodiversity Databases Using R.

Ecological and evolutionary studies often require high quality biodiversity data. This information is readily available through the many online databases that have compiled biodiversity data from herbaria, museums, and human observations. However, the process of preparing this information for analysis is complex and time consuming. In this study, we have developed a protocol in R language to process spatial data (download, merge, clean, and correct) and extract climatic data, using some genera of the ginseng family (Araliaceae) as an example. The protocol provides an automated way to process spatial and climatic data for numerous taxa independently and from multiple online databases. The script uses GBIF, BIEN, and WorldClim as the online data sources, but can be easily adapted to include other online databases. The script also uses genera as the sampling unit but provides a way to use species as the target. The cleaning process includes a filter to remove occurrences outside the natural range of the taxa, gardens, and other human environments, as well as erroneous locations and a spatial correction for misplaced occurrences (i.e., occurrences within a distance buffer from the coastal boundary). Additionally, each step of the protocol can be run independently. Thus, the protocol can begin with data cleaning, if the database has already been compiled, or with climatic data extraction, if the database has already been parsed. Each line of the R script is commented so that it can also be run by users with little knowledge of R.

Barriers and enablers to implementing clinical practice guidelines in primary care: an overview of systematic reviews

ObjectivesTo identify the barriers and enablers to implementing clinical practice guidelines (CPGs) recommendations in primary care and to provide recommendations that could facilitate the uptake of CPGs recommendations.DesignAn overview of...

Audit partner characteristics, career advancement and audit quality in the USA

PurposeThis study aims to explore the relation among US audit partners’ characteristics, their career advancement and audit quality.Design/methodology/approachThis study uses data from Public Company Accounting Oversight Board Form AP, Auditor Reporting of Certain Audit Participants, and publicly available online data sources. The hand-collected data on audit partners’ personal characteristics include gender, work experience and educational background. The measures for audit quality include restatements and audit fees.FindingsThe authors find that audit partner characteristics matter for the time it takes an individual to reach partnership after completing a bachelor’s degree. There are significant differences in work experience and educational background between partners in the largest (Big N) audit firms and smaller (non-Big N) audit firms. Audit partner traits are related to audit quality, and the effects differ between Big N and non-Big N partners.Originality/valueThe literature has examined audit partners’ career paths using international data. However, little empirical academic research has examined the career advancement of US audit partners. This study provides initial insights on the career advancement of US partners on a large scale and complements the recent research that examines audit partner characteristics and audit quality in the US market.

Sentiment Analysis on Inflation after COVID-19

We implement traditional machine learning and deep learning methods for global tweets from 2017-2022 to build a high-frequency measure of the public’s sentiment index on inflation and analyze its correlation with other online data sources such as google trend and market-oriented inflation index. We use manually labeled trigrams to test the prediction performance of several machine learning models (logistic regression, random forest etc.) and choose Bert model for final demonstration. Then, we sum daily tweets’ sentiment scores gained from Bert model to obtain the predicted inflation sentiment index, and we further analyze the regional and pre/post covid patterns of these inflation indexes. Lastly, we take other empirical inflation-related data as references and prove that twitter-based inflation sentiment analysis method has an outstanding capability to predict inflation. The results suggest that Twitter combined with deep learning methods can be a novel and timely method to utilize existing abundant data sources on inflation expectations and provide daily indicators of consumers’ perception on inflation.

Rethinking Workplace Ecosystem during Crisis Recovery Period: Lessons from COVID-19

Purpose: The aim of the study was to rethink how workplace ecosystem should be modelled during crisis recovery period with lessons from COVID-19 pandemic.
 Methodology: The study adopted a desktop review of literature on publications, interviews and surveys by major global consultancy firms. This method established an extensive data research using online data sources. Only resources relating to the topic under study were considered for inclusion.
 Results: The analysis of the data resulted in a process that led to a core category that showed how the workplace can be re-imagined, re-improved and re-invented, during and after the COVID-19 pandemic, where five different strategic categories to achieve that target were identified within this process. These strategic categories are; opportunity to break with the past, workplace redesign, technology, digital strategy, and remote working and new sense of workplace.
 Conclusion: COVID-19 exposed many weaknesses and issues that have been present in business for a long time, in particular how to carry out daily activities from physical to virtual interactions perspective. In the new way of operating, companies are able to identify which roles need to be carried out face to face or which of them do not, and to what extent.
 Recommendations: The study recommend human resources managers to position themselves as HR who are designed for speed, new ways of working, digital first, teams, adaptable organizational strategies, and changing business requirements. As organizations rethink work, it is important they communicate how and why they are redeploying workers and identify how this supports new business priorities. This includes providing context and rationale for changes and clear communication on new workforce policies. The study also recommend HR leaders to reassess and explain compensation and promotion plans for the short term while managing expectations through the recovery process and toward sustainable operations.

Change Management in Medical Contexts, especially in Medical Education: A Systematized Review.

Introduction: Change is a multifaceted and challenging process. Due to the growing and increasing volume of technologies and organizational processes, there is a need to adapt to these changes because adaptation to changes is essential for the organization survival. The purpose of this study was to investigate change management in medical education in order to identify and categorize the strategies, barriers, and other important issues related to change management.Methods: A systematized review of the related studies was carried out according to the Khan et al.’s guideline. Five bibliographic databases and search engines including Cochrane Library, Eric, PubMed, SCOPUS, and Web of Sciences were searched. The following keywords were used with a period constraint of 2017 to March 2021 to search various online data sources: change management and medical issues. Advanced search options and Boolean operator (AND) were also used to find out more relevant records.Results: Overall, 498 records were identified. After removing duplicate records and those with irrelevant titles, abstracts, or full texts, we selected 40 articles for data extraction. The Kotter model is frequently used to manage change. Also, consideration of resistance to change and having a plan for it have been important elements of change management.Conclusion: In most cases, resistance to change was observed, and several ways for resolution merged. Resistance to change and coping strategies are considered as one of the most important factors that must be considered in change situations. Awareness of change management principles and utilization of available models can pave the way for management of the change.

Inter Communal Conflicts (2017-2018) and the Protection of IDPs in Ethiopia: The Need for Specific Legal and Institutional Regime

The increasing number of Internally Displaced Persons (IDPs), and the gap in the legal and institutional framework for the protection scheme, remains a concern in Ethiopia. It is well established that inter-communal conflict forced thousands of people to flee their homes. This article assesses the adequacy of the national framework for the protection of IDPs. Specifically, it investigates whether the national response for disasters accommodates the needs of inter-communal conflict induced IDPs. In this context, the term IDPs is defined as provided in the UN Guiding principles on Internal Displacement. In order to determine how the national response works for plight of IDPs, questionnaires were distributed to the concerned government authorities, and the joint reports of government and international organizations was reviewed from online data sources. Results showed that there was no comprehensive legal and institutional framework and coordination system to address the protection and assistance needs of inter-communal conflict induced IDPs in Ethiopia in the year 2017-2018. These results suggest that the needs of IDPs could be addressed more effectively, with a national action plan or policy framework, and institutional scheme for IDPs.

The Role of Aerobic Exercise to Prevent Coronary Artery Disease

Abstract: Background: Coronary Artery Disease (CAD) is a disorder of heart function caused by blockage or narrowing of the coronary arteries so that there is an insufficiency of blood and oxygen flow to the myocardium and becomes the leading cause of death in the world. CAD is one of the causes of the decline in the quality of life of a human being. Methods: This study used a literature review method using 11 scientific articles as online data sources which were then further analyzed. Result and Discussion: Exercise is a recommended therapy as half of the pharmacotherapy. Moderate intensity exercise of 150 minutes per week and vigorous-intensity of more than 75 minutes per week greatly help CR also provide significant benefits when started within 15 days of cardiovascular symptoms appearing. Conclussion: CAD causes the most deaths in the world. From various scientific articles that have been analyzed, exercise, especially aerobic exercise, has been shown to have a good impact on people with CAD that affect lipid metabolism, heart remodeling, insulin resistance, and endothelial function.

The role of physical exercise as a prevention towards dementia

Background: Indonesia as a country with an aging population structure is highly potential to develop an increase in dementia cases. Dementia is known as a syndrome of decreased cognitive function that can affect emotions, behavior, and daily activities, thus affecting the patient's quality of life. The management of dementia remains limited until this day, so it is necessary to prevent dementia by improving lifestyle, one of which is through exercise and physical activity. Methods: This study used a literature review method using 18 scientific articles as online data sources which were then further analyzed. Results and Discussion: Physical activity and exercise are important non-pharmacological interventions in reducing the risk of dementia. The risk of dementia is related to the intensity of the activity - the higher the intensity, the greater the role it has in reducing risk of dementia. Activities of moderate to vigorous intensity can improve cognitive function, induced by neurogenesis and neurotrophic factors expression that prevent brain atrophy. Aerobic exercise is predominantly done, supported by a combination of resistance training or other forms of exercise. Conclusion: Exercise or physical activity plays a role in the prevention of dementia through the mechanism of neurogenesis and preventing brain mass reduction. The delay in the progression of neurocognitive disorders by doing exercise or physical activity is influenced by the type and intensity of exercise, combined with lifestyle improvements such as nutritional diet and cognitive exercise. This intervention needs to be recommended for elderly adults to prevent dementia and other neurocognitive diseases.