Correction Effort Research Articles

Predicting human effort needed to correct auto-segmentations.

Medical image auto-segmentation techniques are basic and critical for numerous image-based analysis applications that play an important role in developing advanced and personalized medicine. Compared with manual segmentations, auto-segmentations are expected to contribute to a more efficient clinical routine and workflow by requiring fewer human interventions or revisions to auto-segmentations. However, current auto-segmentation methods are usually developed with the help of some popular segmentation metrics that do not directly consider human correction behavior. Dice Coefficient (DC) focuses on the truly-segmented areas, while Hausdorff Distance (HD) only measures the maximal distance between the auto-segmentation boundary with the ground truth boundary. Boundary length-based metrics such as surface DC (surDC) and Added Path Length (APL) try to distinguish truly-predicted boundary pixels and wrong ones. It is uncertain if these metrics can reliably indicate the required manual mending effort for application in segmentation research. Therefore, in this paper, the potential use of the above four metrics, as well as a novel metric called Mendability Index (MI), to predict the human correction effort is studied with linear and support vector regression models. 265 3D computed tomography (CT) samples for 3 objects of interest from 3 institutions with corresponding auto-segmentations and ground truth segmentations are utilized to train and test the prediction models. The five-fold cross-validation experiments demonstrate that meaningful human effort prediction can be achieved using segmentation metrics with varying prediction errors for different objects. The improved variant of MI, called MIhd, generally shows the best prediction performance, suggesting its potential to indicate reliably the clinical value of auto-segmentations.

A Clustering-Based Optimization Approach for Hospital Miscoding Correction

This paper addresses the problem of correcting medical coding errors with respect to some coding recommendations. The problem consists in clustering medical codings and determining for each cluster the set of features to correct in order to maximize the financial benefits subject to coding correction effort constraints. For this purpose, we model the coding recommendation as a disjunction of hypercubes and introduce the concept of correction sets. A mixed integer linear programming model is then proposed to assign medical codes to correction sets in order to maximize the financial benefits. The miscoding is then explained by characterizing optimal clusters with association rules and coding error distribution. A case study on patient stays associated with malnutrition-related ICD codes is presented, and the performance of the proposed methodology is assessed in regard to the current coding staff practice. A significant increase in health services reimbursement is achieved with a limited number of subjects’ features reviewed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Medical miscoding has a significant negative impact on hospitals with a financial loss for under coding and a penalty for over coding. Whether a medical review is necessary for all descriptive features of a miscoded subject? Is it possible to reduce unnecessary medical reviews without compromising the goal of increasing hospital financial benefits? This article attempts to answer these questions with a data-driven optimization approach to determine a limited number of miscoding clusters and the set of features to review for each in order to best balance the financial benefits and the medical review workload. The application to a real-life case study leads to a significant increase in hospital fiscal revenue of nearly 6,992,489.69, while reviewing only a small number of descriptive features (5293 out of 22056 features, or 24% of features). Causes are also provided for each discovered coding error subtype to ameliorate medical coders’ coding practices. Furthermore, the proposed approach allows the decision-maker to balance the cost-benefit and the requirement of public health institutions (i.e., miscoding rate).

Is biomedical research self-correcting? Modelling insights on the persistence of spurious science.

The reality that volumes of published biomedical research are not reproducible is an increasingly recognized problem. Spurious results reduce trustworthiness of reported science, increasing research waste. While science should be self-correcting from a philosophical perspective, that in insolation yields no information on efforts required to nullify suspect findings or factors shaping how quickly science may be corrected. There is also a paucity of information on how perverse incentives in the publishing ecosystem favouring novel positive findings over null results shape the ability of published science to self-correct. Knowledge of factors shaping self-correction of science remain obscure, limiting our ability to mitigate harms. This modelling study introduces a simple model to capture dynamics of the publication ecosystem, exploring factors influencing research waste, trustworthiness, corrective effort and time to correction. Results from this work indicate that research waste and corrective effort are highly dependent on field-specific false positive rates and time delays to corrective results to spurious findings are propagated. The model also suggests conditions under which biomedical science is self-correcting and those under which publication of correctives alone cannot stem propagation of untrustworthy results. Finally, this work models a variety of potential mitigation strategies, including researcher- and publisher-driven interventions.

A Simulation-based Approach for Quantifying the Impact of Interactive Label Correction for Machine Learning.

Recent years have witnessed growing interest in understanding the sensitivity of machine learning to training data characteristics. While researchers have claimed the benefits of activities such as a human-in-the-loop approach of interactive label correction for improving model performance, there have been limited studies to quantitatively probe the relationship between the cost of label correction and the associated benefit in model performance. We employ a simulation-based approach to explore the efficacy of label correction under diverse task conditions, namely different datasets, noise properties, and machine learning algorithms. We measure the impact of label correction on model performance under the best-case scenario assumption: perfect correction (perfect human and visual systems), serving as an upper-bound estimation of the benefits derived from visual interactive label correction. The simulation results reveal a trade-off between the label correction effort expended and model performance improvement. Notably, task conditions play a crucial role in shaping the trade-off. Based on the simulation results, we develop a set of recommendations to help practitioners determine conditions under which interactive label correction is an effective mechanism for improving model performance.

Abuse of Village Treasury Land Use in the Yogyakarta Special Region since the Enactment of Law Number 13 of 2012 Concerning Specialties of the Yogyakarta Special Region

Village land is Land owned by the village government as a source of village income and should be used for the prosperity of the village community, but in reality, village treasury land is currently being misused and even sold for housing even though this is prohibited. This research aims to determine the modes of misuse of village treasury land and what efforts are needed to overcome the misuse of village treasury land in Sleman Regency, Special Region of Yogyakarta. This research uses empirical and normative types of legal research. The data sources used are primary data and secondary data. The results of this research are that four modes of abuse of village treasury land occur in the Special Region of Yogyakarta, namely inappropriate land use permits, buying and selling of village treasury land, village treasury land being used as housing, and village officials corrupting the rental proceeds of village treasury land. Efforts to overcome the misuse of village treasury land can be made through preventive and corrective efforts.

Imperfect Debugging-Based Generalized Exponential Testing Effort Estimation for Software Fault Detection and Removal

In the current technological era, the reliability growth model depicts the failure in software that is employed for estimating the reliability of software. Conventional software reliability growth model (SRGMs) usually presume that faults in software are immediately corrected once a software fault is found and no new faults are introduced. This consideration can be impractical. However, introduction of several new faults is possible throughout debugging, which is called imperfect debugging. During such a process of debugging and introduction of new faults, consumption of testing effort also plays an important role. Hence, in this paper, two models are proposed that incorporate testing effort along with imperfect debugging during fault correction. Additionally, the combined testing effort is the addition of two efforts, specifically detection effort as well as correction effort. The formulated models are validated for real data set and contrasted with additional famous SRGMs. These inferences indicate that Model 2 performs best in terms of fitting and prediction capability.

To Correct or Not to Correct: Are Investors Able to Discern Fake Financial News?

This study attempts to understand how to reduce the continued influence of misleading financial news and examine the effectiveness of corrective efforts such as ex-ante disclosure of compensation of a promoter/writer and the immediacy of ex-post correction. In the 2 (disclosure vs. no disclosure) × 2 (immediate correction vs. delayed correction) experiment, student participants received a (fake) news article including or excluding the author’s affiliation and compensation scheme and were then provided a correction invalidating its content. The correction was provided either immediately or with a delay. The results showed that although participants were susceptible to the influence of positive yet misleading news, providing warnings about potential economic conflicts of interest seemed to temper this enthusiasm among investors. Participants exhibited greater receptiveness to explicit corrections when the initial news article included a compensation disclosure, and when they were not immediately prompted to process the correction. Our findings imply that delaying corrections may offer distinct advantages, as it provides investors with the opportunity to assimilate the compensation disclosure information into their investment decisions over time. Additionally, our results indicate that a dual approach involving conflict of interest disclosure and subsequent correction can be an effective long-term strategy in mitigating investors’ vulnerability to misinformation.

Openness to change among COVID misinformation endorsers: Associations with social demographic characteristics and information source usage

RationaleMisinformation is a major concern for public health, with its presence and impact strongly felt in the COVID-19 pandemic. Misinformation correction has drawn strong research interest. In contrast, relatively little attention has been given to the likelihood of favorable behavioral change post correction (i.e., openness to change). ObjectiveThis study seeks to identify the characteristics of COVID misinformation endorsers who are open to adopt more self-protective behaviors after misinformation correction. MethodsCOVID misinformation endorsers (N = 1991) in a large, nationally representative survey (fielded in October and November 2021) were asked how likely they would adopt self-protective behaviors if a source they trusted debunked their prior misperceptions. Multiple linear regression estimated the relationships between openness to change and socio-demographics, health-related factors, and health information source usage patterns. ResultsLess than half of the misinformation endorsers in the sample (41.3%) indicated any openness to change. Openness to change was positively associated with minority status and negatively associated with leaning Republican in political affiliation, higher income, being currently unvaccinated or unsure about vaccination status, better health, and a greater number of misinformation items endorsed. Past-month use of government, community, and personal sources for health information positively predicted openness. The use of online media was negatively associated with openness. ConclusionsOpenness to change is not guaranteed after misinformation correction. Targeted interventions based on openness to change are needed to enhance the public health impact of misinformation correction efforts.

Supply and Demand for Radiation Oncologists in Canada: Workforce Planning Projections From 2020 to 2040

The number of Canadians diagnosed with cancer, and subsequent demand for radiation therapy, are expected to increase over time. This study aimed to update our needs-based workforce planning model to ensure appropriate staffing levels in the future. The supply of radiation oncologists, by age group, sex, and full-time equivalent status, was projected from 2020 to 2040 using a recursive-aging, input-output model developed with seeding parameters derived from national sources. The demand for radiation oncologists until 2040 was estimated using referral patterns for radiation therapy and consultation workload metrics applied to projected annual cancer incident cases to calculate required full-time equivalent positions. Baseline model parameters were also applied to the 2005-2019 workforce and incident case data to evaluate preprojection supply and demand trends. Preprojection trends for 2005 to 2019 revealed accelerated staffing growth that transitioned from a workforce shortage to a surplus state in 2014 followed by substantial growth slowdown in 2016. The model predicts a transient surplus of radiation oncologists until 2026 followed by a projected deficit in subsequent years. Sensitivity analyses using the plausible range for each parameter continued to favor an undersupply, suggesting a trainee shortage unable to meet workforce expansion needs. Considering possible future declining trends in radiotherapy utilization and workload, calculations to inform corrective efforts in resident numbers resulted in 25 entry positions per year, up from 21 per year currently. Geographic distribution of trainees, relative to workforce and cancer incidence distributions, could be improved with more residency positions in Canadian regions outside Ontario. Demand for radiation therapy and radiation oncologists in Canada are expected to grow more quickly than future expansion in staffing levels. Our workforce planning model provides evidence for more trainee requirements to inform stakeholders of possible corrective actions to training programs and recruitment. Further research is needed to explore additional strategies to expand capacity and high-quality delivery of radiation therapy to meet the foreseeable increase in Canadian patients with cancer.

Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation.

The diagnosis and prognosis of breast cancer relies on histopathology image analysis. In this context, proliferation markers, especially Ki67, are increasingly important. The diagnosis using these markers is based on the quantification of proliferation, which implies the counting of Ki67 positive and negative tumoral cells in epithelial regions, thus excluding stromal cells. However, stromal cells are often very difficult to distinguish from negative tumoral cells in Ki67 images and often lead to errors when automatic analysis is used. We study the use of automatic semantic segmentation based on convolutional neural networks (CNNs) to separate stromal and epithelial areas on Ki67 stained images. CNNs need to be accurately trained with extensive databases with associated ground truth. As such databases are not publicly available, we propose a method to produce them with minimal manual labelling effort. Inspired by the procedure used by pathologists, we have produced the database relying on knowledge transfer from cytokeratin-19 images to Ki67 using an image-to-image (I2I) translation network. The automatically produced stroma masks are manually corrected and used to train a CNN that predicts very accurate stroma masks for unseen Ki67 images. An -score value of 0.87 is achieved. Examples of effect on the KI67 score show the importance of the stroma segmentation. An I2I translation method has proved very useful for building ground-truth labeling in a task where manual labeling is unfeasible. With reduced correction effort, a dataset can be built to train neural networks for the difficult problem of separating epithelial regions from stroma in stained images where separation is very hard without additional information.

Urgensi Indonesia Untuk Meratifikasi Konvensi Tentang Perlindungan Terhadap Penghilangan Orang Secara Paksa

Introduction: The Special Committee of the House of Representatives on the Handling of Discussions on the Results of the Investigation into the Enforced Disappearance of Persons for the Period 1997-1998, has issued four points of recommendation to the President. One of the four points is to urge the government to immediately ratify the convention on protection against forced disappearances of persons.Purposes of the Research: The purpose of this research was to determine the regulation of international law against forced disappearances of persons, and to know the urgency of Indonesia to ratify the convention on the protection against forced disappearances of persons. Methods of the Research: The research methods used are normative juridical research methods, analytical descriptive research types, sources of legal materials, namely primary legal materials, secondary legal materials and tertiary legal materials. Technical collection of legal materials through library research and processing techniques for legal materials using qualitative analysis.Results of the Research: The result of the study is that international convention for the protection of all persons from enforced disappearance is an international instrument that regulates the obligations and responsibilities of states in providing protection to all persons from enforced disappearances. Indonesia has not yet ratified the convention of the protection of all persons from enforced disappearances. This Convention is very urgent to be ratified by Indonesia because it is one of the foundations of international human rights law that can provide protection, as well as a preventive and corrective effort of the state in ensuring the protection of all people from enforced disappearances. In addition, it is also to encourage cases of enforced disappearances in Indonesia in the past to be resolved and not repeated in the future.

Correcting climate change misinformation on social media: Reciprocal relationships between correcting others, anger, and environmental activism

Although correcting others on social media is a frequently used strategy to combat the spreading of misinformation, we lack knowledge on the drivers of such corrective efforts. We theorize that climate change-related anger and political environmental activism can explain why people chose to correct others on social media. We further assume reciprocal relationships in which the act of correcting others predicts anger and political environmental activism. By doing so, we extend the concept of expression effects of correcting others from a cognitive level that predicts activism to an affective level (i.e., predicting anger). Structural equation modeling using data from a two-wave panel survey (N = 549) showed reciprocal relations between political environmental activism and correcting perceived climate change misinformation. Correction was further positively related to climate change-related anger, which, in turn, was positively related to political environmental activism. The data showed neither support for climate change-related anger predicting correction nor for political environmental activism predicting anger. This paper discusses the proposed extension of expression effects to an affective level in the context of misinformation research.

IMPACT OF GLOBAL WARMING ON AQUACULTURE IN NORWAY

While aquaculture is predicted to play a significant role in addressing future global food demands, climate change is having a complex impact on aquaculture output. Climate change will have an effect on output levels, growth, feeding efficiency, and consequently farm productivity and profitability. The most significant challenges facing aquaculture are current and future climate change, which affects the viability of fish farming in Norway and around the world. Global warming and climate change have resulted in a decline in lake waters, an increase in sea level, changes in streams, and changes in precipitation models, all of which have begun to have a negative influence on all aquatic animals. According to the findings of this study, the yearly maximum temperature was 21.1 °C in July and the lowest was -2.8 °C in January, with the minimum temperature ranging from 13.5 °C in July to -7.5 °C in January. The highest average temperature was 17.4 °C in July, and the lowest was -5.1 °C in January. In terms of precipitation, the greatest was 118 mm in August, while the lowest was 56 mm in March. In terms of rainy days, the highest total was 11 mm in August, while the lowest total was 6 mm in March. In November, the humidity reached 90%, whereas in June, it was 66%. On bright days, the highest daylight hours were recorded in June at 12.1 hours, and the lowest hours were recorded in December at 2.5 hours. In this research, we explicitly investigated how these climate components may affect aquaculture in Norway. As a result, because it affects the stability of fish farming, this significant environmental issue must be addressed. It is critical to shed light on and thoroughly analyse the elements related to climate change in order to avoid the damages that result from them, as well as to identify strategies to adapt to these conditions and limit their effects on production and productivity. Corrective efforts should be implemented to reduce climate change and its consequences on fish output.

Updated frequency-dependent directivity indices for large solid rocket motors

For many years, empirical models for rocket noise radiation relied on the directivity indices published in NASA SP-8072 (K. Eldred, 1971). Because these indices had known issues, NASA led a campaign to update the indices using a Space Shuttle reusable solid rocket motor (RSRM). The RSRM measurements, involving a polar arc at a radius of about 80 nozzle exit diameters, resulted in updated directivity indices (Haynes and Kenny, AIAA 2009–3160). However, because the arc origin was placed at the nozzle exit plane, James et al. [Proc. Mtgs. Acoust., 18, 040008 (2012)] corrected the low-frequency indices using an estimated dominant source position for each frequency and assumed spherical spreading. This paper revisits that correction effort by using near-field vector intensity measurements from a similar, but smaller diameter and lower thrust, GEM-60 motor to determine the frequency-dependent origin to adjust the apparent angles and distances of the measurements. Additionally, an effort is made to account for RSRM plume impingement downstream that likely resulted in lower high-frequency levels than would have been measured if the plume had been entirely free. This analysis results in updated, frequency-dependent directivity indices for a large solid rocket motor. Their applicability to other rockets will be discussed.

Demand response for frequency regulation with neural network load controller under high intermittency photovoltaic systems

The rate of change of frequency (ROCOF) has become a key parameter to be monitored under high penetration of renewable energy. Any significant ROCOF should be mitigated immediately to avoid any shutdown of power plants and hence power interruptions to the customers. However, ROCOF is found to be significant and often in a region near to equatorial line because the power output of PV is highly intermittent due to a large number of passing clouds. Demand response (DR) is one of the most cost-effective means for mitigating any high ROCOF through controlling heating elements, refrigerators, and air-conditioners. However, most of the DR controllers use pre-determined conditions, also known as the condition-based approach, to switch the loads for reducing frequency changes. This approach may not be effective enough to respond to a new load condition. Furthermore, many DR controllers, including those accompanied by machine learning, are mainly developed to mitigate frequency changes with little focus on the mitigation of high ROCOF. Hence, an artificial neural network (ANN)-based controller is proposed in this article for frequency regulation with auto corrective efforts on high ROCOF under high intermittency of PV systems. The proposed controller is designed to manage several controllable loads on a grid emulator with PV systems at Universiti Tunku Abdul Rahman, Malaysia. It is shown that the proposed controller can reduce the frequency deviation by 23% and improve ROCOF by 19.7% as compared to that without any controller.

Healthcare-Associated Infection in Surgery Department: A Big Challenge for African Surgical Teams.

Healthcare-associated infections (HAIs) are increasing in health facilities in Mali, due to health disparities and growing costs. Twenty to fifty percent of HAIs in the surgery department can be prevented with appropriate measures. This study aimed to determine the burden of HAI and its risk factors. This was a prospective cohort study from January to June 2021 at the CHU Gabriel TOURE, Bamako, Mali. The sample size was determined based on the CDC Atlanta criteria, used to confirm HAI in surgical settings. Demographic, clinical, and biological parameters were determined. For the confirmed case of infection, the incriminated bacteria were isolated and tests were performed for the choice of drugs. Of the total 1001 patients included in this study, 195 patients (19.48%) have HAIs. The types of infections were as follows: 70 cases of surgical site infections, 54 infections on burns victims, 40 urinary tract infections, and 31 cases of bacteraemia. Germs such as Escherichia Coli, Klebsiella pneumoniae, and Acinetobacter were often isolated. We found increasing hospital stays as well as some postoperative mortality related to infections. At the end of this study, corrective efforts were implemented to prevent HAI. Among them are improvements in sterilisation techniques as far as surgical materials were concerned. In addition to a surgical checklist, locally used drapes were replaced with single-use surgical supplies. Advanced training of the surgical team on things such as bladder catheterisation was also conducted in the department. It is important to put in place a committee, to prevent nosocomial infection in our hospital. The selected committee will be responsible for planning and implementing diverse strategies to prevent infections. The prevention of HAIs will reduce health costs and improve the quality of surgical care.

Aerides multiflora Roxb.: An Important Ornamental and Medicinal Orchid

Aerides multiflora Roxb. belonging to the family Orchidaceae is known for its multi-utility. It is being used in the preparation and for cuts and wounds traditionally and traded for its fragrance and as a cut flower. Among the isolates studied, emerged as the most potent inhibitor, suggesting that it be a candidate structure for inhibitor drug development in the management of type 2 DM. As a result, an attempt has been undertaken to analyze the species therapeutic potential in both traditional and modern medicine systems. The high demand, this species has attracted a lot of attention for conservation and domestication. It is subjected to indiscriminate and unsustainable harvesting, habitat degradation, road construction, and other anthropogenic activity in the wild, resulting in decreased natural populations. This review throws light on the relevant information about the properties of the high-value medicinal orchid and immediate corrective efforts required for the conservation of this medicinal orchid in its natural habitat and promotion of cultivation and GACP to assure a long-term mode of utilization.

Is China’s Fishing Capacity Management Sufficient? Quantitative Assessment of China’s Efforts toward Fishing Capacity Management and Proposals for Improvement

As the country with the world’s largest fishing capacity, China suffers from the depletion of living marine resources, mainly caused by overexploitation, the effects of which also distinctly influence global sustainable utilization of marine resources and maritime order. The country has implemented a series of measures to control its national fishing capacity, notably since the late 1980s, and strongly expressed its responsibility and commitment to sustainable resource utilization in the 13th Five-Year Plan (2016–2020). This study quantitatively assesses the effect of China’s fishing capacity management efforts by examining changes in the fishing capacity of marine capture fisheries by region from 1979 to 2019 in Chinese waters, based on window data envelopment analysis (DEA), and further proposes directions for policy adjustment. The results suggest that China’s coastal and offshore fisheries are still subject to overcapacity of approximately 20%, despite major corrective efforts invested in past decades. This implies that China needs to further improve its fishing capacity management and set clear objectives, as determined by the availability of resources and regional features. Global fisheries management’s focus is shifting from input to output control, but overcapacity still threatens the sustainable use of fisheries resources in China and poses obstacles to other systems.

Karakteristik siswa dalam menyelesaikan masalah matematika menggunakan model pembelajaran polya

This research is motivated by the problem solving ability of elementary school mathematics stories which still require proper and correct efforts. Especially in grade IV, appropriate and appropriate learning models are needed to improve students' problem-solving skills on math story problems. One of the learning models whose core is problem solving learning is the Polya learning model which is expected to be able to make students able to solve math story problems. The purpose of this study was to determine the effect of the Polya learning model on the ability to solve problems in mathematics story problems in fourth grade elementary school students in Surabaya. This research uses qualitative research. The method that will be used in this quantitative research is a case study using tests and observations to collect data. Based on data collection using tests and observations that show the ability to solve math story problems, fourth grade elementary school students in Surabaya can be trained through the Polya learning model.

Ethnogenesis and Stranger-Kings in Old Scandinavian Literature

Ethnogenesis and Stranger-Kings in Old Scandinavian Literature