Trends In Data Research Articles

Health facility and workforce factors influencing utilization of reproductive health services in selected South Sudan public health facilities

Background: Despite representing only one-tenth of the global population and 20% of worldwide births, nearly half of all maternal deaths occur in Africa. RH issues account for up to 18% of the global disease burden and 32% for women of reproductive age. Access to essential RH interventions, including family planning, is limited in Sub-Saharan Africa, characterized by low contraceptive use (13%) and a high total fertility rate (5.5 children per woman). Methods: This study used a mixed-method research design, integrating both quantitative and qualitative approaches to provide a comprehensive understanding of the factors influencing RH service utilization in South Sudan. This design allowed for in-depth exploration and quantitative analysis of data patterns and trends. Results: The study identified significant barriers to RHS utilization among women in South Sudan. Although many women were aware of the nearest RH clinic, 42.3% were not. Accessibility was a major issue, with 45.8% of women living more than 2 km from the nearest clinic, and the average distance being 2.2 km. Statistically significant relationships were found between health facility characteristics and RHS use. Interviews indicated that higher education levels among healthcare providers improved their skills, critical thinking, and ability to deliver comprehensive RH care, positively influencing service utilization. Conclusions: Key determinants of RHS utilization included awareness of services, proximity to facilities, and availability of contraceptives. The health workforce’s education, staffing levels, workload management, and fair compensation were also critical. To improve RHS utilization, the study recommends community outreach to raise awareness, expand facilities in remote areas, and offer diverse contraceptive options. Future research should address limitations like sampling biases and regional focus, explore broader geographical areas, longitudinal trends, and the impact of specific interventions, and consider technology’s role in enhancing access.

OnsitNet: A memory-capable online time series forecasting model incorporating a self-attention mechanism

Traditional time series (TS) forecasting models are based on fixed, static datasets and lack scalability when faced with the continuous influx of data in real-world scenarios. Real-time online learning of data streams is crucial for improving forecasting efficiency. However, few studies focus on online TS forecasting, and existing approaches have several limitations. Most current online TS forecasting models merely train on data streams and are ineffective in handling concept drift scenarios. Furthermore, they often fail to adequately consider dependencies between variables and do not leverage the robust modeling capabilities of offline models. Therefore, we propose an innovative online learning method called OnsitNet. It consists of multiple learning modules that progressively expand the receptive field of convolutional kernels within the learning modules using an exponentially growing dilation factor, aiding in the capture of multi-scale data features. Within the learning modules, we propose an online learning strategy focusing on memorizing concept drift scenarios, with a fast learner, memorizer, and Pearson trigger. The Pearson trigger activates dynamic interaction between the fast learner and memorizer by detecting new data patterns, facilitating online rapid learning of data streams. To capture the dependencies between variables, we propose a new model, SITransformer, which is a streamlined version of the offline model ITransformer. Unlike the traditional Transformer, it reverses the roles of the feed-forward network and the attention mechanism. This inverted architecture is more effective at learning the correlations between variables. Experimental results on five real-world datasets show OnsitNet achieves lower online prediction errors, enabling timely and effective forecasting of future trends in TS data.

Bitcoin Return Prediction: Relevant Factors Determining Return

Cryptocurrencies with Bitcoin being the most well-known, are volatile in price. Being able to predict or speculate price movements in Cryptocurrencies would then be meaningful for researchers and lucrative for investors. This paper hypothesized relevant factors that may determine return of Bitcoin, namely Bitcoin price data, google trend and precious metal price data, and collects data for the past 6 years. After regrouping and processing the data, a regression model is used to test the strengths of predictability of each possible factor. The results reveal that Bitcoin price data is a valid predictor of daily Bitcoin return, google trend or search volume is valid only for weekly return predictions, and precious metal prices do not have a significant relationship with Bitcoin return.

Research on the development relationship between safety production indicators and economic and social indicators in China

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research.

Advancements in Machine Learning Algorithms for Predictive Analytics in Healthcare

Recent improvements in machine learning (ML) algorithms have changed predictive analytics in healthcare in a way that has never been seen before. This means that there are now more ways than ever to improve patient results and business efficiency. This summary looks at important changes and what they mean. A lot of healthcare data, like electronic health records (EHRs), medical images, genetics, and personal sensor data, is being used more and more with machine learning methods like guided learning, unsupervised learning, and deep learning. These methods make it possible to use predictive models to diagnose diseases, give patients specific treatment suggestions, and keep track of their care. For sorting things into groups, supervised learning techniques like support vector machines (SVM) and random forests have been used to correctly spot diseases based on complicated data trends. For example, SVMs have been useful for telling the difference between different types of cancer from genetic data, which helps with focused treatments. On the other hand, unsupervised learning algorithms like grouping algorithms help find groups of patients who share similar traits, which makes personalized medicine possible. Deep learning, has been very successful in medical picture analysis, being more accurate than humans at tasks like finding tumors in x-rays and lab slides. Its ability to instantly learn traits from raw data has made diagnosis easier and more accurate. ML algorithms also help healthcare operations run more smoothly by using prediction analytics to help hospitals handle their resources better, make the best use of their staff, predict which patients will need to be admitted, and lower the number of times they have to be readmitted. These predictive models use a variety of data sources to guess how patients will do and how they will use healthcare resources, which helps people make smart decisions and make the best use of their resources.

EPMITS: An efficient prediction method incorporating trends and shapes features for chemical process variables

With the transformation of industrial production digitization and automation, process monitoring has been an indispensable technical method to realize the safe and efficient production of chemical process. Accurate prediction of process variables in chemical process can indicate the possible system change to reduce the probability of abnormal conditions. Current popular deep learning prediction methods trained with MSE or its variants may exhibit limitations in extracting shape features of chemical process data. In this paper, we proposed an efficient prediction method incorporating trends and shapes features (EPMITS) for chemical process variables. Specifically, we introduced a novel differentiable loss function Efficient Shape Error (ESE) to quantify shape differences between two time series of equal length in chemical process data. Then we trained deep learning models with MSE and ESE as loss function by two steps in training stage, to effectively acquire both trend and shape features of chemical process data. The proposed method was evaluated by the Tennessee Eastman process datasets and a real fluid catalytic cracking dataset from a petrochemical company. The results indicate that EPMITS models exhibit high prediction accuracy and short model training time across various time scales. These findings demonstrate the considerable feasibility and significant potential of EPMITS for future fault prognosis applications.

Blended learning dalam pembelajaran matematika: A systematic literature network analysis

As technology continued to advance, the educational landscape evolved, leading to the development of learning management systems, online platforms, and multimedia resources. Blended learning has since become a cornerstone in educational strategies. The purpose of this study is to describe the research trend data on Blended learning . The Systematic Literature Network Analysis (SLNA) method was employed for the investigation. This study's data is made up of non-time-limited research articles on Blended learning. The articles were retrieved from the Scopus database. The data was evaluated using a bibliometric and network analysis technique with programs R and VOSviewer. The findings indicate that the number of studies and research citations fluctuate. Keyword cluster analysis indicates that the predominant focus of research lies in Blended learning. Simultaneously, the terms 'personnel training' and 'academic performance' exhibit interconnections with inquiries into Blended learning . Subsequent scholarly investigations ought to broaden and elaborate upon facets of Blended learning conceptualization, assessment methodologies, pedagogical instruction, and organizational backing, to furnish a more exhaustive and all-encompassing research outlook.

Are They Prepared? Comparing Intern Milestone Performance of Accelerated 3-Year and 4-Year Medical Graduates.

Accelerated 3-year programs (A3YPs) at medical schools were developed to address student debt and mitigate workforce shortage issues. This study investigated whether medical school length (3 vs 4 years) was associated with early residency performance. The primary research question was as follows: Are the Accreditation Council for Graduate Medical Education Milestones (MS) attained by A3YP graduates comparable to graduates of traditional 4-year programs (T4YPs) at 6 and 12 months into internship? The MS data from students entering U.S. medical schools in 2021 and 2022 from the 6 largest specialties were used: emergency medicine, family medicine, internal medicine, general surgery, psychiatry, and pediatrics. Three-year and 4-year graduates were matched for analysis (2,899 matched learners: 182 in A3YPs and 2,717 in T4YPs). The study used a noninferiority study design to examine data trends between the study cohort (A3YP) and control cohort (T4YP). To account for medical school and residency program effects, the authors used cross-classified random-effects regression to account for clustering and estimate group differences. The mean Harmonized MS ratings for the midyear and end-year reporting periods showed no significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = 0.01 [0.02], P = .77). Mean MS ratings across internal medicine MS for the midyear and end-year reporting periods showed no significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = -0.03 [0.03], P = .31). Similarly, for family medicine, there were no statistically significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = 0.01 [0.02], P = .96). For the specialties studied, there were no significant differences in MS performance between 3-year and 4-year graduates at 6 and 12 months into internship. These results support comparable efficacy of A3YPs in preparing medical students for residency.

Neonatal Adverse Outcomes among Hospital Livebirths in Canada: A National Retrospective Study

Introduction: In Canada, newborn morbidity far surpasses mortality. The neonatal adverse outcome indicator (NAOI) summarizes neonatal morbidity, but Canadian trend data are lacking. Methods: This Canada-wide retrospective cross-sectional study included hospital livebirths between 24 and 42 weeks’ gestation, from 2013 to 2022. Data were obtained from the Canadian Institute of Health Information’s Discharge Database, excluding Quebec. The NAOI included 15 newborn complications (e.g., birth trauma, intraventricular hemorrhage, or respiratory failure) and seven interventions (e.g., resuscitation by intubation and/or chest compressions), adapted from Australia’s NAOI. Rates of NAOI were calculated by gestational age. Unadjusted rate ratios (RR) and 95% confidence interval (CI) were calculated for neonatal mortality, neonatal intensive care unit (NICU) admission, and extended hospital stay, each in relation to the number of NAOI components present (0, 1, 2, 3, 4, or ≥5). Results: Among 2,821,671 newborns, the NAOI rate was 7.6%. NAOI increased from 7.3% in 2013 to 8.0% in 2022 (p < 0.01). NAOI prevalence was highest in the most preterm infants. Compared to no NAOI, RRs (95% CI) for mortality were 8.5 (7.6–9.5) with 1, 118.1 (108.4–128.4) with 3, and 395.3 (367.2–425.0) with ≥5 NAOI components. Respective RRs for NICU admission were 6.7 (6.6–6.7), 11.2 (10.9–11.3), and 11.9 (11.6–12.2), and RR for extended hospital stay were 6.6 (6.4–6.7), 12.2 (11.7–12.7), and 26.4 (25.2–27.5). International comparison suggested that Canada had a higher prevalence of NAOI. Conclusion: The Canadian NAOI captures neonatal morbidity using hospitalization data and is associated with neonatal mortality, NICU admission, and extended hospital stay. Newborn morbidity may be on the rise in recent years.

The freedom space - a new set of commercially available molecules for hit discovery.

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make-on-demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit-to-lead campaigns.

A new approach to determine abnormality of radioactive discharges from pressurized water reactors and to derive abnormality indicators correlated with a specific causal event

Abstract A novel methodology to analyze the trend of radioactive effluent data from nuclear power plants (NPPs) has been proposed to estimate the possibility of abnormal releases of radioactive materials, and to identify sensitive radionuclides with high correlation to specific events and abnormal releases. Actual discharge data including abnormal releases for 21 years (1,610 reactor-years) from 62 US and 22 Korean pressurized water reactors were collected. A release correlation factor (RCF) was newly proposed as a quantitative indicator of the abnormality of radioactive discharges based on annual discharge data from each NPP. The probability of correct estimation of abnormality using the RCF for a test case ranges 27–36 %, and such a modest estimation power was ascribed to the relatively low contribution (i.e., about 12 %) of a specific causal event to the annual radioactivity release. Through this, the feasibility of the RCF for estimating the abnormality of radioactive discharges was demonstrated. The RCF was derived for three types of causal events, and subsequently the sensitive radionuclides to each specific event were derived as 88Kr, 137Xe, and 138Xe for leakage from gas decay tank, 3H for leakage from steam generator’s power operated relief valve, and 85Kr, 88Kr, 133Xe, 133mXe, 3H and 14C for leakage from defected spent fuel during handling. The new approach proposed in this study is to be used to suppose the significant unreported abnormality in radioactive effluent data and the potential causal event, which may contribute to improving the safety and performance of radioactive effluent control as necessary.

Knowledge domain and emerging trends in the rupture risk of intracranial aneurysms research from 2004 to 2023.

Intracranial aneurysms (IAs) pose significant health risks, attributable to their potential for sudden rupture, which can result in severe outcomes such as stroke and death. Despite extensive research, the variability of aneurysm behavior, with some remaining stable for years while others rupture unexpectedly, remains poorly understood. To employ bibliometric analysis to map the research landscape concerning risk factors associated with IAs rupture. A systematic literature review of publications from 2004 to 2023 was conducted, analyzing 3804 documents from the Web of Science Core Collection database, with a focus on full-text articles and reviews in English. The analysis encompassed citation and co-citation networks, keyword bursts, and temporal trends to delineate the evolution of research themes and collaboration patterns. Advanced software tools, CiteSpace and VOSviewer, were utilized for comprehensive data visualization and trend analysis. Analysis uncovered a total of 3804 publications on IA rupture risk factors between 2006 and 2023. Research interest surged after 2013, peaking in 2023. The United States led with 28.97% of publications, garnering 37706 citations. Notable United States-China collaborations were observed. Capital Medical University produced 184 publications, while Utrecht University boasted a citation average of 69.62 per publication. "World Neurosurgery" published the most papers, contrasting with "Stroke", the most cited journal. The PHASES score from "Lancet Neurology" emerged as a vital rupture risk prediction tool. Early research favored endovascular therapy, transitioning to magnetic resonance imaging and flow diverters. "Subarachnoid hemorrhage" stood out as a recurrent keyword. This study assesses global IA research trends and highlights crucial gaps, guiding future investigations to improve preventive and therapeutic approaches.

Multifractal characterization of meteorological to agricultural drought propagation over India

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

Additive inclusion in plastic life cycle assessments, part II: Review of additive inventory data trends and availability

AbstractPlastic additives are as essential as polymers to the production and performance of plastic materials. Additive content can vary in composition and functionality depending on the product, producer, application, and production method. Such variation may be a barrier to achieving high‐quality recycling and planning for plastic circular economy futures. Yet, as found in Part I, although there is increasing awareness of the importance of additives in plastics, they are often poorly disclosed or only briefly discussed in life cycle assessments (LCAs). In part II, we focus on the inclusion of additives in plastic processes in the database most used in plastic LCAs to date (Ecoinvent) and find that additives have historically been omitted from plastic granulate data and in production processes in the evaluated database. Thus, many practitioners will need to separately include additives in their models of plastic life cycles. To support practitioners in this endeavor, we then assess the availability of the 13,587 additives identified in the recent UN Chemicals in Plastics Report across the three major LCI databases (CarbonMinds, Ecoinvent, and LCA for Experts [GaBi]). We find that databases currently cover only 1,209 of these additives. Moreover, we assert that transparency regarding additive inclusion in plastics datasets, availability of additive datasets, and additive data completeness are major barriers to additive inclusion in plastic LCAs. Thus, we recommend focusing on the development of additive datasets, and we provide a tool for the identification of additive dataset availability and data gaps to improve the quality of plastic LCAs.

A Comparative Study of Detrending Methods on Crop Yield Time Series for Drought Studies

Detrending is a statistical technique that removes systematic variations or trends from time series data, allowing analysts to focus on the underlying patterns or fluctuations. While multiple detrending approaches have been applied but rarely discussed their consistency of outcomes and effectiveness in accurately capturing better yield trends. The validation of drought occurrences has proven to be a challenging task due to the non-stationary characteristics of time series data related to crop yield. This research utilizes time series of cotton yield data from the Marathwada region covering the period from 1998 to 2021. Three traditional trend models, including simple linear regression, second-order polynomial regression and central moving average were applied. Additionally, two machine learning models (random forest and support vector regression) were tested with a novel approach. Moreover, two decomposition models (additive and multiplicative) were used to remove non-linear trends in crop yield time series data. The performance of the chosen models was evaluated based on metrics such as root mean square error, mean absolute error, Nash-Sutcliffe efficiency, and index of agreement. The results suggest that the most effective detrending approach involves combining a random forest machine learning model with an additive decomposition model.

Enhancing our heritage—40 years of JOSA A and JOSA B in the ever-changing world of optical science and technology: editorial

JOSA A and JOSA B are archival journals that have preserved the heritage and served the community in the field of optics for the past 40 years. In line with the community’s expansion and evolution over this time, the journals have regularly reinvented themselves while also keeping an eye on the future. This editorial celebrates the journals’ ruby anniversary and introduces readers to some recent statistical data points and trends.

Enhancing our heritage—40 years of JOSA A and JOSA B in the ever-changing world of optical science and technology: editorial

JOSA A and JOSA B are archival journals that have preserved the heritage and served the community in the field of optics for the past 40 years. In line with the community’s expansion and evolution over this time, the journals have regularly reinvented themselves while also keeping an eye on the future. This editorial celebrates the journals’ ruby anniversary and introduces readers to some recent statistical data points and trends.

PMFORECAST: leveraging temporal LSTM todeliver in situairqualitypredictions.

The physical and chemical properties of atmospheric aerosol particles are crucial in influencing global climate and ecosystem processes. Given the numerous studies highlighting adverse health effects from exposure to aerosol particulates, particularly Particulate Matter (PM), effective air quality management strategies are under consideration (Annesi-Maesano et al. Eur Respir Soc 29(3):428-431. 2007). Herein, we introduce a predictive model-PMFORECAST-employing a self-adaptive long short-term memory (LSTM) architecture to predict PM values in the real atmosphere. Specifically, we explore adopting a LSTM model to better benefit from temporal dimensions. PMFORECAST is strategically designed with four key phases: preprocessing, temporal attention, prediction horizon, and LSTM layers. By leveraging LSTM's significant predictive ability in time-series data, the inclusion of temporal attention enhances the model's specificity. Temporal dynamics modeling entails generating insights over time, utilizing temporal attention to extract essential characteristics from historical air pollutant concentrations, with the flexibility to adjust the historical data according to the forecasting period. To assess PMFORECAST, we consider measurements collected from the QAMELEO network, a sparse network of air-quality micro-stations deployed in Dijon, France. The self-adaptive capabilities of PMFORECAST allow the model to be dynamically updated, evaluating its performance and continuously tuning hyper-parameters based on the latest data trends. Our empirical evaluation reports that PMFORECAST outperforms the state of the art, achieving notable accuracy in both short-term and long-term predictions. The PMFORECAST deployment at scale can serve as a valuable tool for proactive decision-making and targeted interventions to mitigate the health risks associated with air pollution.

Next Generation Heterogeneous 3D IC Chip Packaging

The demand for a faster and more efficient IC has been steadily increasing more than ever to accommodate the trend of Artificial Intelligence, Industry 4.0, Machine Learning, and Big Data. Traditionally, the semiconductor industry has kept up with demand of faster and more efficient IC by shrinking the size of transistor. As we approach a 1 nm transistor the technological challenge will be challenging to overcome as quantum tunneling becomes more pronounced and would decrease the reliability of the chip. To overcome this technological challenge, an advance packaging of 3D IC chip technology has been developed and delivered to market such as CoWoS (Chip-on-Wafer-on-Substrate) and InFO (Integrated Fan-Out). However, the aforementioned technology have their drawbacks with CoWoS uses interposer and TSV (through-silicon substrate via) which make it costly, time consuming, and wastes a lot of energy and InFO is limited in the number of layers it can accommodate for chiplets. Both of those technology are currently limited to 2 or 3 layer of IC and difficult to be modularize. In this study, we aim to develop a heterogeneous 3D IC package that can overcome the aforementioned problem which is modular, cost-effective, able to integrate chip from different manufacturers, and capable to stack more than 3 layers of ICs. This study has successfully completed the first stage of development by employing an advanced package consisting of Epoxy substrate with 200Å Ti/500Å Pt/1000Å Au metal interconnects, previously fabricated as a base for 3D stacking. Keywords : 3D IC Package, Advance Packaging, Modulable IC Packaging, Heterogeneous Integration Figure 1

Unexpected judgments: the role of gender identity and provocation on blame and affect in a mock jury paradigm

IntroductionPanic defenses are a form of legal defense positing that a defendant is less culpable for their crime because of an extreme emotional reaction. However, limited research has examined the use of panic defenses when the victim is a transgender individual.MethodsIn two studies (Total N = 995) previously existing scales and vignettes were used to evaluate perceptions of panic defenses when the victim was a transgender woman, transgender man, or a gay man. Study 1 (N = 557) used a 2 (sexual provocation or non-sexual provocation) x 3 (transgender woman, transgender man, or gay man victim) design to evaluate perceptions of victim blame, negative affect, and perception that the crime was a hate crime.ResultsContrary to demographic data trends, Study 1 found that transgender women were rated more positively as victims, were blamed less than transgender men or gay men, and that the crime was more likely to be labeled as a hate crime. Study 2 (N = 438) was a replication of Study 1. While the results were largely non-significant, the trend in means was in the same direction as the findings of Study 1.DiscussionWe argue that these findings, despite not being in line with broader data on the topic, are important evidence that anti-transgender research, particularly in mock jury settings, may need to reimagine existing designs and vignettes to understand rates of violence and discrimination toward transgender communities.