Subset Of Data Research Articles

Translators in the platform economy: a decent work perspective

ABSTRACT In the wake of the platform economy’s transformative influence on translation work, this study aims to address a critical concern: the alignment of translation workers’ labour conditions with the principles of decent work. Through a quantitative analysis of a subset of questionnaire data collected from translators in Turkey engaging with various digital labour platforms, the findings suggest substantial disparities in meeting the six fundamental conditions of decent work, as defined by the International Labour Organization (ILO). These include insufficient earnings, excessive and asocial working hours, difficulties in achieving work-life balance, absence of a safe and healthy work environment, limited social security access, and a deficiency in social dialogue, representation, and workplace democracy. The identified issues align with the findings of prior studies, which warn that the techno-political developments in the translation industry, coupled with the dominance of capitalist business structures, may introduce new challenges and constraints to translation work and its workers, ultimately leading to exploitative and unsustainable working conditions.

Real world performance of the 21st Century Cures Act population-level application programming interface.

To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12000 resources/min. The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Exploring Family Adjustment Among Parents of a Child With a Disability Attending Relationship Education

Objective: The current exploratory study examined dyadic changes in family adjustment (i.e., parental stress, positive coping, family-based support, social support) reported by parents of a child with a disability after 12-hours of relationship education (RE). Background: Parents of a child with a disability encounter social barriers that contribute to parental stress and inhibit family well-being. RE reduced psychological and relationship distress in community samples—yet, ability status and the influence on a family's presenting needs/resources are overlooked in previous research. Methods: We extracted a subset of data to examine pre- and post-reports of family adjustment among couples parenting a child with a disability who completed the PREP curriculum. We used structural equation modeling to examine an actor–partner interdependence model for the dyadic association of mean-centered baseline subscale measures of family adjustment and residual change scores for men and women in a relationship post-RE intervention. Results: Actor effects were significant and predicted the amount of residual change for men and women. Significant partner effects existed for male social support and male and female family-based support. Conclusion: Results expand our understanding of RE effectiveness with an understudied subset of parents, those raising a child with a disability. Parents reported improvements in family adjustment and partners influenced one another in terms of family adjustment changes. Implications: Supportive parent programming, such as RE, may be an important consideration for families that include a child with a disability to address the social barriers that tax existing parent and family resources.

Efficient quality assurance and quality control for passive acoustic monitoring data: reducing and documenting false-positive and false-negative errors

ABSTRACT Autonomous Recording Units (ARUs) are widely used to survey for a variety of taxa. This survey method allows for high spatial and temporal coverage but will typically include identification errors that can bias estimates of occupancy. In some instances, verifying all individual detections is prohibitive. To direct verification effort, we developed a model to estimate the probability that transcribers would agree on an identification. Agreement probability was positively influenced by transcriber skill, identification confidence, species commonness and some song types. In contrast, agreement probability was lower when an acoustic signal was classified as a trill. We evaluated our model on independent data where all species detections were verified, and verification effort (time) was quantified. Our model performed well at predicting transcriber agreement on independent data (AUC = 0.71). We applied the model to randomised subsets of the independent data to compare the cost benefit of three approaches to verification under varying effort. We show how modelling probability of transcriber agreement can be used to more efficiently direct verification of species acoustic tags. Our approach could be adapted elsewhere to quantify and reduce species misidentifications in unverified passive acoustic monitoring data for either manual processing or detections from automated classifiers.

Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Battery energy storage systems (BESS) play a pivotal role in energy management, and the precise estimation of battery capacity is crucial for optimizing their performance and ensuring reliable power supply. Deep learning methodologies applied to battery capacity estimation have exhibited exemplary performance. However, deep learning methods necessitate supervised training with a significant volume of labeled data, presenting challenges for data collection in industrial scenarios. Moreover, a diverse range of battery types in industrial settings makes it difficult to develop capacity estimation models for different types of batteries from scratch. To address these issues, a semi-supervised adversarial deep learning (SADL) method is proposed for lithium-ion battery capacity estimation. Initially, a subset of labeled lithium-ion battery data, coupled with a subset of unlabeled data, is collected. Voltage and current data are then transformed into capacity increment features. Subsequently, an adversarial training strategy is employed, subjecting labeled and unlabeled data to adversarial training to enhance the performance of SADL. Finally, the effectiveness of the SADL method in estimating the capacity of other lithium-ion batteries is analysed. Experimental results demonstrate that the SADL method accurately estimates the capacity of various battery types, showcasing an RMSE error of approximately 2%, surpassing the performance of other methods. The proposed SADL method emerges as a promising solution for the precise estimation of lithium-ion battery capacity in BESS.

Development and validation of a hybrid model for prediction of viable cell density, titer and cumulative glucose consumption in a mammalian cell culture system

Among the many approaches to build predictive models for bioprocesses, hybrid models have gained increased attention due to their ability to combine data driven approaches with process knowledge. This study develops and compares hybrid models that predict the performance of a mammalian CHO cell culture producing a therapeutic product. Three machine learning algorithms, MLP, Random Forest and XGBoost regressors are compared to understand the effect of algorithm choice on the rates predicted and overall performance of the hybrid model. When combined in series with mechanistic equations, all three algorithms could predict next day Viable Cell Density, titer and the cumulative amount of glucose consumed with low error, although decision tree-based algorithm required less computational time for training. The models performed well using a dataset for a new product, without further model training. Retraining the model on a subset of data from the new product improved the prediction accuracy of VCD and titer. The potential for application and further model developments are also outlined.

IClassifier: A digital research tool for corpus-based classifier networks in complex writing systems

This article presents the method applied by the iClassifier (©Goldwasser/Harel/Nikolaev) digital research tool for the study of the linguistic phenomenon of classifiers. The tool was created in 2019 with the objective of curating corpus-based and data-driven documentation of classifier systems. The record of classifiers comprises millions of tokens worth of “big data” analysis. By tagging classifiers in various corpora, a topography of categories emerges, visualized as complex, multilayered networks. This article offers an overview of how classifier-based networks are created and how network analysis methods can be applied to analyze knowledge organization. We present the data structure and annotation scheme of the iClassifier research tool, demonstrating how one can plot classifier networks and generate reports of lemma and classifier repertoires in each corpus. The iClassifier tool provides quantitative reports, including classifier frequency, variation and co-occurrence statistics. Each data subset, such as a certain part of speech, timespan, geographical location or textual genre, can be queried and visualized. The tool is meant to allow browsing between a macro-overview of all categories in a corpus and zooming in into micro-analysis of the individual categories and lemmas that built up a corpus. Each classifier is seen as a category head, and the categories are drawn in their multilayered and multidimensional relationships. The potency of this tool is in documenting the phenomenon in large corpora of texts and expanding our knowledge about the rules and functions of classifier systems, leading us to a more refined mind-mapping of ancient cultures. Currently, very little systematic analysis has been done on this ancient record of emic information.

A Feature Selection Method Based on Graph Theory for Cancer Classification.

Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification. In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data. In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods. In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.

Detecting ringed seal vocalizations in multiple environments using deep learning

Deep learning methods have recently been successfully applied to create a variety of automated acoustic detectors in the field of marine bioacoustics. Automated detectors are essential for analyzing large volumes of passive acoustic monitoring (PAM) data since manual analysis is prohibitively time-consuming and costly. PAM is the primary method for obtaining data on species which are endemic to remote regions, such as the Canadian Arctic. Arctic ringed seals are listed as a Species of Special Concern in Canada due to a loss of critical habitat caused by the effects of climate change. Here, ResNet, a convolutional neural network architecture, is trained on thousands of examples of ringed seal vocalizations recorded at various locations within the Canadian Arctic to create the first practical automated ringed seal detector. The network achieves a precision of 0.89, recall of 0.80, and F1 score of 0.85 when tested on 215 five-minute recordings from sites included in the training process. To improve the generalizability of the detector for new locations, fine-tuning is performed using a small subset of annotated data from new sites. The detector will be available as an open-source tool for researchers to use as the basis for further development of new automated detectors.

Characterizing Heart Rate Response During Upper Extremity Repetitive Task Practice in Chronic Stroke.

Although the cardiopulmonary benefits of aerobic exercise poststroke are well-established, typical stroke rehabilitation does not elicit an aerobic response. To characterize heart rate response during upper extremity repetitive task practice (RTP) and determine factors that predict a higher aerobic intensity during RTP. Secondary analysis of a subset of data from a randomized clinical trial. Research laboratory in a large academic medical center. Patients with chronic stroke (N = 19). Participants received 90 min of RTP for 24 sessions across 8 wk. Aerobic intensity as measured by heart rate reserve (HRR) during RTP. A total of 2,968 tasks were included in the analysis. Of the tasks performed, approximately 79.5% elicited a very light aerobic response (<30% HRR), 10.2% elicited a light aerobic response (30%-39% HRR), and 10.3% elicited a moderate to vigorous intensity aerobic response (≥40% HRR). Of the tasks that elicited a moderate to vigorous intensity aerobic response, 54.1% were performed in standing, 79.7% were gross motor in nature, and 27.9% had targets at or above shoulder height. Standing position, targets at or above shoulder height, and gross motor tasks predicted higher HRR (all ps < .001). To maximize aerobic intensity during poststroke RTP, therapists should include gross motor tasks trained in standing with targets at or above shoulder height. Plain-Language Summary: The study characterizes heart rate response in stroke rehabilitation and identifies factors that predict a higher aerobic intensity during upper extremity repetitive task practice. Certain task characteristics were more likely to produce an aerobic response, including gross motor, targets at or above the shoulder, and a standing position. Occupational therapists should include gross motor tasks trained in standing with targets at or above shoulder height to maximize aerobic intensity during poststroke repetitive task practice. Monitoring heart rate may improve awareness of aerobic response to training.

Ensembles of quantum classifiers

In the current era, known as Noisy Intermediate-Scale Quantum (NISQ), encoding large amounts of data in the quantum devices is challenging and the impact of noise significantly affects the quality of the obtained results. A viable approach for the execution of quantum classification algorithms is the introduction of a well-known machine learning paradigm, namely, the ensemble methods. Indeed, the ensembles combine multiple internal classifiers, which are characterized by compact sizes due to the smaller data subsets used for training, to achieve more accurate and robust prediction performance. In this way, it is possible to reduce the qubit requirements with respect to a single larger classifier while achieving comparable or improved performance. In this work, we present an implementation and an extensive empirical evaluation of ensembles of quantum instance-based classifiers for binary classification, with the purpose of providing insights into their effectiveness, limitations, and potential for enhancing the performance of basic quantum models. In particular, three classical ensemble methods and three quantum instance-based classifiers have been taken into account here. Hence, the scheme that has been implemented (in Python) has a hybrid nature. The results (obtained on real-world datasets) have shown an accuracy advantage for the ensemble techniques with respect to the single quantum classifiers, and also an improvement in robustness. In fact, ensembles have proven effective not only in mitigating unsuitable data normalizations but also in reducing the impact of noise on quantum classifiers, enhancing their stability.

Advanced exploitation of unmerged reflection data during processing and refinement with autoPROC and BUSTER.

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) - F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

Surgical safety checklist compliance process as a moral hazard: An institutional ethnography.

Charting is an essential component of professional nursing practice and is arguably a key element of patient safety in surgery: without proper, objective, and timely documentation, both benign and tragical errors can occur. From surgery on wrong patients to wrong limbs, to the omission of antibiotics administration, many harms can happen in the operating room. Documentation has thus served as a safeguard for patient safety, professional responsibility, and professional accountability. In this context, we were puzzled by the practices we observed with respect to charting compliance with the surgical safety checklist (SSC) during a study of surgical teams in a large, urban teaching hospital in Canada (pseudonym 'C&C'). This article leverages institutional ethnography and a subset of data from a larger study to describe and explain the social organisation of the system that monitored surgical safety compliance at C&C from the standpoint of operating room nurses. This data included fieldnotes from observations of 51 surgical cases, on-the-spot interviews with nurses, formal interviews with individuals who were involved in the design and implementation of the SSC, and open-ended questions from two rounds of survey of OR teams. We found that the compliance form and not the SSC itself formed the basis for reporting. To meet hospital accuracy in charting goals and legislated compliance documentation reporting requirements nurses 'pre-charted' compliance with the surgical checklist. The adoption of this workaround technically violated nursing charting principles and put them in ethically untenable positions. Documenting compliance of the SSC constituted a moral hazard, constrained nurses' autonomy and moral agency, and obscured poor checklist adherence. The findings highlight how local and extra local texts, technologies and relations create ethical issues, raise questions about the effectiveness of resulting data for decision-making and contribute to ongoing conversations about nursing workarounds.

A distance-based kernel for classification via Support Vector Machines.

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.

Iterative Ensemble Learning over High Dimensional Data for Sentiment Analysis

For sentiment analysis in particular, the problem of processing and analyzing high-dimensional data becomes more prominent in recent past. This is where the IEL-HDDSA model, which aims to increase accuracy and performance in complex, high-dimensional data streams sentiment analysis comes into play. Iterative approach in ensemble learning; a contribution to the field. It integrates preprocessing techniques such as tokenization, stop word removal, lemmatization and the collection of sentiment-related features. Then the training corpus is divided by label, and features with high mutual information are selected. Highly replicated points of data for model training can also be identified at this point. First a Naive Bayes model is trained, then later it's placed in an ensemble as part of bagging. Its major advantage over earlier methods is that IEL-HDDSA can iteratively train on selected subsets of data until the performance in sentiment analysis for high-dimensional objects reaches an optimum level. A 10-fold cross validation method was used to rigorously evaluate the performance of this model, which showed consistently high levels of operation with almost no variation across different measures. IEL-HDDSA's precision ranged from 0.9359 to 0.9492, and its specificity was between 0. Its accuracy differed from 0.93 to around 0.95, and its F1-measure fluctuated between the values of about 0.94 and above; so here too balance was well maintained in a manner that satisfied both precision and recall requirements equally. The false alarming rate fell from 0.056 to 0.1, a fairly low ratio of incorrect positive classifications; Moreover, MCC quantities ranged from 0.8668 to 0. These results testify to the IEL-HDDSA model's stable effectiveness and high reproducibility in sentiment analysis applications, especially for massive data flows.

Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization

The immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task, often requiring repetitive numerical experiments. As a result, significant attention is currently being devoted to developing methods aimed at tailoring hyperparameters for specific CNN models and classification tasks. While existing optimization methods often yield favorable image classification results, they do not provide guidance on which hyperparameters are worth optimizing, the appropriate value ranges for those hyperparameters, or whether it is reasonable to use a subset of training data for the optimization process. This work is focused on the optimization of hyperparameters during transfer learning, with the goal of investigating how different optimization methods and hyperparameter selections impact the performance of fine-tuned models. In our experiments, we assessed the importance of various hyperparameters and identified the ranges within which optimal CNN training can be achieved. Additionally, we compared four hyperparameter optimization methods—grid search, random search, Bayesian optimization, and the Asynchronous Successive Halving Algorithm (ASHA). We also explored the feasibility of fine-tuning hyperparameters using a subset of the training data. By optimizing the hyperparameters, we observed an improvement in CNN classification accuracy of up to 6%. Furthermore, we found that achieving a balance in class distribution within the subset of data used for parameter optimization is crucial in establishing the optimal set of hyperparameters for CNN training. The results we obtained demonstrate that hyperparameter optimization is highly dependent on the specific task and dataset at hand.

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy

Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.

TH-stopping in Philadelphia Puerto Rican English

Abstract Analyzing data from the Puerto Rican English in Philadelphia (PREP) corpus, we investigate participation in TH-stopping, a socially stigmatized yet stable variable documented in Philadelphia. While previous studies have been impressionistic and have considered voiced and voiceless tokens to pattern together, this work validates novel, acoustically based stopping indices: mean harmonics-to-noise ratio for voiced tokens and skewness for voiceless tokens. We apply these indices to the corpus data and analyze stopping under a Bayesian framework, and we compare results from a model built from impressionistic coding of a subset of the same data. We find convergent evidence that TH-stopping is a stable variable in the Puerto Rican English data as well. Findings are compared with those of existing studies, noting future directions for research on the variable and underscoring the importance of establishing demographically representative baselines for linguistic research in diverse urban centers.

Relationships between physical fitness characteristics, technical skill attributes, and sports injury in female Australian football players.

To explore the relationships between physical fitness and i) technical skills and ii) time-loss from Australian football injury in female players across the talent and participation pathways. This study uses a subset of data from two cross-sectional and one prospective cohort studies. A total of 223 female Australian football players across five competition levels (elite/non-elite senior, high-level junior, and non-elite junior (14-17 years)/(10-13 years)) were included in this study. Comprehensive physical fitness assessments and modified Australian football kicking and handballing tests were conducted in the 2018/19 pre-seasons. During the respective competitive in-season, time-loss injuries were recorded by team personnel. Stepwise multiple linear regressions were performed to determine the relationship between physical fitness and kicking and handballing scores. Cox proportional regressions were conducted to identify physical fitness factors associated with injury. Increased running vertical jump height, greater hip abduction strength, and faster timed 6 m hop speed demonstrated a relationship with kicking accuracy when adjusted for years of Australian football playing experience (adjusted R2 = 0.522, p < 0.001). Faster agility time and increased lean mass were associated with better handballing accuracy (adjusted R2 = 0.221, p < 0.001). Multivariate Cox regression revealed an increased risk for sustaining a time-loss injury in less agile players (adjusted HR 2.41, 95% CI 1.23, 4.73, p = 0.010). However, this relationship no longer remained when adjusted for age and years of Australian football experience (adjusted HR 1.68, 95% CI 0.81, 3.50, p = 0.166). Physical fitness may be a significant factor contributing to development of Australian football technical skills in female players. However, its role is unclear in protecting against injury risk in this athlete population. Further research is needed to explore the multifactorial and complex phenomenon of talent development and injury risk reduction in female Australian football players.

An update of skin permeability data based on a systematic review of recent research

The cutaneous absorption parameters of xenobiotics are crucial for the development of drugs and cosmetics, as well as for assessing environmental and occupational chemical risks. Despite the great variability in the design of experimental conditions due to uncertain international guidelines, datasets like HuskinDB have been created to report skin absorption endpoints. This review updates available skin permeability data by rigorously compiling research published between 2012 and 2021. Inclusion and exclusion criteria have been selected to build the most harmonized and reusable dataset possible. The Generative Topographic Mapping method was applied to the present dataset and compared to HuskinDB to monitor the progress in skin permeability research and locate chemotypes of particular concern. The open-source dataset (SkinPiX) includes steady-state flux, maximum flux, lag time and permeability coefficient results for the substances tested, as well as relevant information on experimental parameters that can impact the data. It can be used to extract subsets of data for comparisons and to build predictive models.