Life Data Research Articles

Remaining Useful Life Prediction of Pressure Regulating Shutoff Valve based on Feature Fusion and Bidirectional Gated Recurrent Unit

Abstract In the bleed air system of the ARJ21 aircraft, pressure regulating shutoff valve (PRSOV) failures are common, and their failures can lead to disasters and economic losses. Accordingly, prediction of the degradation performance of PRSOV is crucial. This work proposes a life prediction method based on principal component analysis (PCA) and bidirectional gated recurrent units (BiGRU) to achieve accurate prediction. After obtaining pressure data throughout the entire life of PRSOV, considering that the pressure required for PRSOV during the takeoff and climb phases is the most critical, data from this phase are selected for focused monitoring. Classical statistical feature extraction methods are used to extract features from the raw pressure data during the takeoff and climb phases. An empirical feature extraction method with low-pressure weighting is also proposed based on engineering practical experience. Feature fusion is performed using PCA based on these two types of features. Finally, BiGRU is utilized to model the fused degradation feature indicators and estimate the remaining service life of PRSOV. The results of the analysis of the full life data of PRSOV in ARJ21 aircraft indicated that the proposed method can effectively predict its remaining service life. The proposed method demonstrated higher prediction accuracy compared with the related prediction methods.

CONCEPÇÕES ACERCA DO ENSINO-APRENDIZAGEM DE PROBABILIDADE E ESTATÍSTICA

This article discusses conceptions regarding the teaching and learning of Probability and Statistics, emphasizing their relevance in shaping critical citizens capable of interpreting data in everyday life. It highlights that the teaching of these topics, included in the Brazilian curriculum through the National Curricular Parameters in 1997, still faces significant challenges, such as difficulties in understanding Mathematics as a language and a limited focus on exam preparation. This work aims to promote statistical literacy through pedagogical activities that involve reading, interpreting, and critically analyzing data in various formats. Similarly, solving realworld problems is recommended to stimulate creative strategies and reflections on the impacts of data-driven decisions. Moreover, the teacher’s role is central, requiring the integration of reading, writing, and argumentation practices in teaching. It is concluded that the critical and contextualized integration of Probability and Statistics into basic education is essential to prepare students to autonomously engage with statistical language and its social implications.

Alexithymia and estimated 10-year cardiovascular disease risk in healthy adults: a community-based cross-sectional study.

This cohort study aimed to explore whether and to what extent alexithymia would be associated with cardiovascular disease (CVD) risk over an estimated 10-year period, over and above established clinical cofactors (i.e., depressive symptoms, quality of life, sociodemographic, anthropometric, lifestyle, and biological data), in a low-risk population of blood donors. A sample of 1,021 adult Italian blood donors (age 46.9 ± 8.39; 61.2% men) was consecutively recruited. The 10-year-CVD risk score was estimated using the CUORE risk score (CRS). Sociodemographic, lifestyle, anthropometric, biological, and psychological (i.e., quality of life, depressive symptoms, and alexithymia) CVD risk data were assessed using validated self-report measures or clinical records. As expected, most participants (78.5%) had a low CVD risk (CRS < 3%) and an overall low-risk profile for all the parameters. Compared with subjects at low risk of CVD (n = 911, 78.5%), those with high risk (i.e., rated ≥3 on CUORE risk assessment; n = 250, 21.5%) reported higher levels of alexithymia (p < 0.001). Subjects with higher alexithymia (n = 236, 23.1%) reported higher levels of psychosocial impairment, depressive symptoms, and biological risk variables for CVD. Alexithymia was significantly associated with 10-year CVD risk (OR = 1.02, 95% CI = 1.01-1.04, p = 0.009), even after adjusting for key sociodemographic and clinical risk variables. Although limited by the cross-sectional design, this study is the first to show that alexithymia leads to a higher risk for 10-year CVD estimate in healthy subjects with low-risk profile, regardless of known biomarkers and traditional CVD risk factors.

Improving Fatigue Life Prediction of Natural Rubber Using a Physics‐Informed Neural Network Model

ABSTRACTTraditional physical models and purely data‐driven approaches often struggle with small sample sizes and the complex effects of strain ratios. To overcome these challenges, this study integrates physical principles with machine learning techniques to improve fatigue life predictions for natural rubber (NR). A uniaxial fatigue test on NR was performed, generating data to construct a physical model. A physics‐informed neural network (PINN) model was subsequently developed, utilizing the fatigue life predicted by the physical model, along with engineering strain amplitude and strain ratio as input variables, whereas the experimentally observed fatigue life served as the output variable. The accuracy of the physical model, a data‐driven model, and the proposed PINN model was evaluated by comparing their predictions against measured fatigue life data. The findings demonstrate that the PINN model significantly enhances prediction accuracy, with its fatigue life estimates consistently falling within 1.5 times the measured values.

Three-way conceptual knowledge updating in incomplete contexts

We usually encounter incomplete data in daily life due to the uncertainty of data and limitation of data acquisition technology. In formal concept analysis, the incomplete formal context is used to reflect uncertain relation between objects and attributes caused by missing data. The conceptual knowledge of the incomplete formal context is presented by a kind of three-way concept called partially-known formal concept. As time passes and technology matures, some initially missing data becomes obtainable, the incomplete formal context is updated accordingly, and the corresponding concepts change as well. However, obtaining partially-known concepts from the updated context based on definition is time-consuming and does not fully utilize the conceptual knowledge implicit in the original context. In order to make full use of existing conceptual knowledge and acquire new concepts quickly and efficiently, we discuss how to obtain new partially-known formal concepts by updating original partially-known formal concepts, and design corresponding concept updating algorithms. Finally, through data experiments, we validate that our proposed concept update algorithm can significantly improve the efficiency of concept acquisition, especially when the updating rate is small.

Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations.

The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this article, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants involved, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations.

Detection of Stroke Disease Using Machine Learning

With the increasing number of fatalities due to heart strokes, it is crucial to develop an accurate and efficient system to predict heart stroke risks. This project aims to build a machine learning model that can precisely anticipate the likelihood of heart strokes, providing healthcare professionals and patients with critical insights for early intervention. Utilizing the Random Forest algorithm, which demonstrated a high accuracy of 99.17% on the Kaggle dataset, the system effectively predicts stroke risks. One of the key advancements of this project is its integration with smartwatch technology, allowing the system to gather real-time health metrics, such as heart rate, blood pressure, and oxygen levels, directly from the user’s smartwatch. By analyzing this live data, the model continuously monitors the user’s condition and, if it detects abnormal patterns associated with stroke risk, it instantly triggers an alert notification to the user and medical professionals. This real-time capability enhances the system's preventive power, helping to significantly reduce the time to intervention. Future improvements of this system could involve scaling the platform for use in mobile and web applications, which would provide a more user-friendly interface and allow healthcare providers to monitor multiple patients more effectively. By expanding the dataset to include more diverse health parameters, the model’s accuracy and reliability in predicting heart strokes could also be further optimized.

Association of gestational hypertension and health-related quality of life

Objectives: The aim of this study was to find out the association between pregnancy-induced hypertension and the quality of life of the study participants. The study also aimed to detect an association between quality of life and certain demographic data for pregnant women with elevated blood pressure during different gestational periods. Methods: A descriptive correlational study design was used. A non-probability (purposive) sample of 70 pregnant women was selected. The study tool used was a questionnaire, and interviews were used as a data collection method. Data were analyzed using SPSS version 25. Results: Most of the respondents were aged between 26 and 35 years, and the majority of the sample reported that they were satisfied with their income to some extent. The study showed a negative significant correlation between quality of life and some variables. Conclusion: Quality of life decreased among mothers with a history of abortion, gestational diabetes mellitus, oligohydramnios, and elevated blood pressure. All pregnant mothers who suffered from gestational hypertension had fair quality of life.

True Cost Accounting Using Life Cycle Assessment Methods and Data: A Case Study Comparing Palm, Rapeseed, and Coconut Oils for Sustainability and Nutrition

True Cost Accounting (TCA) aims to assess positive and negative externalities in agri-food value chains by focusing on four capitals: produced, natural, human, and social. While produced capital is easily measured through market values, obtaining relevant data for measuring natural, human, and social capitals can be challenging. To address this challenge, this paper presents a case study around the production in Asia of three globally important edible plant oils: palm, rapeseed, and coconut. Results show that readily available methods and data from different types of Life Cycle Assessments (LCAs) can be integrated into the framework of TCA to quantify and monetize positive and negative externalities associated with the production and consumption of these oils and to analyze the tradeoffs involved in choosing among them. However, the use of LCA methods and data also entails significant assumptions and limitations that must be considered when evaluating their use in TCA. Specific results show that the environmental impacts of production are small in comparison to the potential nutritional health benefits of consuming one oil over another. This study provides a roadmap for conducting similar analyses, allowing for a greater understanding of often overlooked sustainability and nutritional issues of food items.

Results of an Australian trial of an automated insulin delivery (AID) system and other studies support likely benefit of AID use for many Australian adults with type 1 diabetes.

Less than 20% of Australians with type 1 diabetes (T1D) meet recommended glucose targets. Technology use is associated with better glycaemia, with the most advanced being automated insulin delivery (AID) systems, which are now recommended as gold-standard T1D care. Our Australian AID trial shows a wide spectrum of adults with T1D can achieve recommended targets. Other studies, including lived experience data, are supportive. Insulin pumps are not subsidised for most Australian adults with T1D. We advocate change.

A simple MiMIC based approach for tagging endogenous genes to visualise live transcription in Drosophila.

Live imaging of transcription in the Drosophila embryo using the MS2 or PP7 systems is transforming our understanding of transcriptional regulation. However, insertion of MS2/PP7 stem loops into endogenous genes requires laborious CRISPR genome editing. Here we exploit the previously described Minos-mediated integration cassette (MiMIC) transposon system in Drosophila to establish a method for simply and rapidly inserting MS2/PP7 cassettes into any of the thousands of genes carrying a MiMIC insertion. In addition to generating a variety of stem loop donor fly stocks, we have made new stocks expressing the complementary coat proteins fused to different fluorescent proteins. We show the utility of this MiMIC-based approach by MS2/PP7 tagging and live imaging transcription of endogenous genes and the long non-coding RNA, roX1, in the embryo. We also present live transcription data from larval brains, the wing disc and ovary, thereby extending the tissues that can be studied using the MS2/PP7 system. Overall, this first high throughput method for tagging mRNAs in Drosophila will facilitate the study of transcription dynamics of thousands of endogenous genes in a range of Drosophila tissues.

Fluorescence based live cell imaging identifies exon 14 skipped hepatocyte growth factor receptor (MET) degraders.

Despite ongoing efforts to employ structure-based methods to discover targeted protein degraders (TPD), the prevailing strategy continues to be the synthesis of a focused set of heterobifunctional compounds and screen them for target protein degradation. Here we used a fluorescence based live cell imaging screen to identify degraders that target exon 14 skipped hepatocyte growth factor receptor (MET). MET is a known oncogenic driver. MET exon 14 skipping mutations (METex14Δ) are found in lung cancers and result in the loss of a degron that is required for E3-ligase recognition and subsequent ubiquitination, prolonging the half-life and oncogenicity of MET. Since proteolysis targeting chimeras (PROTACs) are heterobifunctional molecules that promote target degradation by the proteosome, we sought to restore degradation of MET lost with METex14Δ using a MET-targeting PROTAC. We generated a library of sixty PROTACs of which 37 used the MET inhibitor capmatinib as the protein of interest targeting ligand. We screened this PROTAC library for targeted degradation of METex14Δ-GFP using live cell imaging. We benchmarked out MET-targeting PROTACs to that of a previously reported MET-targeting PROTAC, SJF8240. Curve fitting live cell imaging data affords determination of time required to degrade 50% of the target protein (DT50), which was used in determining structure activity relationships. A promising candidate, 48-284, identified from the screen, exhibited classic PROTAC characteristics, was > 15-fold more potent than SJF8240, had fewer off targets compared to SJF8240, and degraded MET in multiple cell lines.

Health Analysis and Recommendation Based on Food

Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. In this project, we profoundly analyzed the relationship between nutritional ingredients and diseases by using data mining methods. First, more than n number of diseases was obtained, and we collected the recommended food and taboo food for each disease. The experiments on real life data show that our method based on data mining improves the performance compared with the traditional statistical approach. We can assist doctors and disease researchers to find out positive nutritional ingredients that are conducive to the rehabilitation of the diseases as accurately as possible. At present, some data is not available, because they are still in the medical verification. The dataset uploaded will be preprocessed, Feature Extraction, noisy data will be removed, and classification of dataset will take places using random forest algorithm based on this analysis the diseases prediction takes places for the food intake by the individual.

Loop duodenal switch confers more complications with little gain of weight loss compared to Roux-en-Y gastric bypass.

There are several surgical options each with their potential for complications, differences in length of procedure, and in meaningful outcomes. This study aims to explore those outcomes after Roux-en-Y Gastric Bypass (RYGB) and Loop Duodenal Switch (LDS). The purpose of this project is to offer a comparative analysis of RYGB and LDS at the NorthShore University Health System for up to 4years postoperatively. A retrospective review of a prospectively maintained database was queried for all patients who underwent RYGB and LDS from 2019 to 2023. Demographic, preoperative, post-operative data, and Quality of Life (QOL) data were included. χ2 and Kruskal-Wallis tests were used for comparison. Statistical significance was set at p < 0.05. Patient database included 238 patients who underwent RYGB, and 54 who underwent LDS. Initial BMI was significantly higher in LDS compared to RYGB (56.9 ± 8.0; 46.5 ± 7.3; p < 0.01). There were no statistically significant differences between reported comorbidities. There were no differences in intraoperative complications between the two groups, however postoperative complications were significantly higher in the LDS population (16.7%, 7.1%; p < 0.01). Percent total body weight loss (%TBWL) was significantly different at 2years post operatively with LDS having more %TBWL than RYGB (LDS: N = 10, %TBWL = 44.7 ± 14.1%; RYGB: N = 47) There were no statistically significant differences at any other postoperative time point. Subgroup analysis was completed in patients with initial BMI 50. There were no significant differences at any postoperative time point. QOL data showed no significant difference between both procedures at all postoperative timepoints. Patients who undergo LDS are more likely to experience postoperative complications compared to RYGB with no added benefit in weight loss or comorbidity resolution up to 3years post operatively.

Trends of extreme hyperbilirubinemia related infant mortality in select European countries (1990-2019).

Limited data exist on population-level mortality outcomes related to extreme neonatal hyperbilirubinemia (EHB) and this study examines trends in annual infant mortality rate (IMR) attributed to hemolytic and perinatal jaundice among Germany, France, Italy, Portugal, Greece and Spain from 1990 to 2019. Data on annual incident cases and disability-adjusted life years were collected from the 2019 Global Burden of Disease study. Live birth cohort data were sourced from UN World Population Prospects. We quantified temporal trends, with relative percent changes. Average annual percent changes (AAPCs) were evaluated using the Joinpoint Regression Program. EHB-related infant mortality decreased from 21.4 (95%CI: 16.1, 27.1) in 1990 to 4.2 (95%CI: 1.9, 7.6) per million live births in 2019. Germany demonstrated lowest AAPC of -3.2% (95% CI: -3.8, -2.5), while Portugal had the highest AAPC of -8.6% (95% CI -11.9, -5.1) in reducing infant mortality due to EHB. There were distinct divergences in the trajectories of declining EHB mortality among the studied countries. This study highlights a significant decline in infant mortality due to extreme hyperbilirubinemia, emphasizing the need for national surveillance and tailored guidelines to prevent bilirubin induced neurological damage. This cross-sectional analysis revealed a marked decline in infant mortality rates attributed to extreme hyperbilirubinemia across the selected European countries. The rates of decline varied significantly between countries, demonstrating notable heterogeneity in mortality trends when stratified by age at death. Implementing data-driven surveillance systems can optimize the alignment of equitable healthcare services, strengthen accountability measures, and identify critical operational inefficiencies. In the European Union, country-specific hyperbilirubinemia guidelines should be reinforced to ensure effective screening and post-discharge follow-up protocols that are tailored to risk burden and available healthcare resources.

An ordered approach on geometric vitality function for risk measurement and its application in stock market analysis

In reliability analysis, the residual life of a component or system plays a key role in explaining a component's present condition as well as how long it will continue to function as intended under specific circumstances, thereby preventing premature failure and saving money. Based on this, Nair and Rajesh [Geometric vitality function and its applications to reliability. IAPQR Trans. 2000;25(1):1–8] proposed a new measure called the geometric vitality function that explains the system's failure pattern. In the present study, we have introduced the geometric vitality function in the context of order statistics by means of the relevation transform and also analysed the monotone and ordering properties of the proposed measure. The application of the proposed measure in the stock markets are explored in the present study. Finally, to estimate the proposed measure using a real life data set, a simple estimator has been introduced as well.

Exploratory Cost-Utility Analysis of a 37-Gene Panel Versus Usual Care to Guide Therapy for Patients with Intermediate-Risk Myeloid Malignancies.

Genomic risk stratification methods for myeloid malignancies have moved beyond conventional karyotyping and single gene approaches to better define disease behaviour. Next-generation sequencing has been established as the new standard-of-care tool to accurately define prognosis at diagnosis and guide therapy decisions. We aimed to determine the economic value of a 37-gene panel test for informing subsequent care for patients with intermediate-risk myeloid malignancies. We performed an exploratory cost-utility analysis of a 37-gene panel test to inform stem cell transplantation therapy in patients with myeloid malignancies in Queensland, Australia. Clinician surveys provided data on management choice with and without genomics information while both published and individual-level data were used for healthcare costs, quality of life, relapse rates and survival data. We used a decision-analytic cohort model with Markov chains and 5000 simulations to derive the incremental cost per quality-adjusted life year (QALY) gained. Scenario, one-way and probabilistic sensitivity analyses were undertaken to test input variation on the stability of the main findings. Over 10 years, the model predicted mean costs of AU$125,561 for the panel testing strategy and AU$117,045 for usual care, indicating an incremental cost of AU$8516 for panel testing. The corresponding mean QALYs were 4.52 for panel testing and 4.46 for usual care, producing a cost of AU$153,854 per QALY gained. In the Australian system, the likelihood that panel testing would be cost effective was <1 % and would have a more favourable cost-effective profile at a willingness-to-pay of AU$140,000 per QALY gained. Driven by small gains in survival and relapse rates following therapies, genomic panel sequencing for myeloid malignancies in people with intermediate-risk disease is unlikely to be cost effective in Australia.

Role of intravenous vitamin C on outcomes in hospitalized patients with moderate or severe COVID-19: a real life data of Turkish patients.

We aimed to examine the efficacy of intravenous vitamin C (IV-VC) in the treatment of hospitalized patients with moderate or severe COVID-19. We conducted a single-center and retrospective study including patients with COVID-19 diagnosis who were hospitalized. Patients were categorized into three groups as those who received low-dose (LDVC group, 2g/day, n = 183) or high-dose IV-VC (HDVC group, 25g/day, n = 41) and who did not receive IV-VC (control group, n = 46). 270 patients aged 19-97years were enrolled. The median length of stay (LOS) was significantly high (9days) in patients treated with high-dose VC when compared to patients treated with low-dose VC and control patients (6 vs 5days, respectively). Need for intensive care unit (ICU) transfer was found to be significantly low in patients treated with low-dose VC (25.7%); contrarily, control patients had significantly higher rates of ICU transfer (67.4%), when compared to patients treated with high-dose VC (39%). Mortality of the LDVC group was significantly lower than that of the HDVC group (11.5 vs 29.3%). However, mortality rates were similar between the control and HDVC groups (21.7 vs 29.3%). According to the multivariate stepwise logistic regression mortality analysis, percent of change (∆%)-BUN was the most significant variable (p < 0.001), the second significant variable was ∆%-AST (p = 0.002), the third significant variable was respiratory distress (p = 0.002), and the fourth significant variable was the IV-VC groups (p = 0.017). The mortality risk of those in the LDVC group was 10.2 times low compared to the control group. Similarly, the risk of mortality in the HDVC group was 6.5 times lower than that of the control group. Especially low and continious doses of IV-VC suggest fewer days of in-hospital LOS and survival benefit in hospitalized patients with moderate and severe COVID-19. Logistic regression analysis revealed that high-dose VC supplementation also had a mortality-reducing effect.

Real-life data of hepatitis C treatment with direct acting antiviral therapy in persons injecting drugs or on opioid substitution therapy.

HCV treatment has been revolutionized by introduction of direct-acting antiviral therapy (DAA). Short treatment duration of eight to twelve weeks combined with significantly improved tolerability opened the opportunity to reach out to difficult-to-treat populations. Here, we retrospectively analyzed real life data on HCV treatment adherence and outcome in people who inject drugs (PWID) or on opioid substitution therapy (OST). All PWID or on OST receiving DAA therapy between 3/2021-11/2022 at an infectious disease clinic in Bonn were retrospectively analyzed. Patients received either 8weeks glecaprevir/pibrentasvir or 12weeks sofosbuvir/velpatasvir (+ ribavirin in genotype 3 cirrhotic patients). Sustained virological response (SVR) was measured 4 and 12weeks after HCV therapy. In our cohort 47 patients (68%) received treatment with glecaprevir/pibrentasvir and 22 patients (32%) sofosbuvir/velpatasvir. All 47 (100%) patients started on glecaprevir/pibrentasvir received prescriptions for the full length of therapy, while patients on sofosbuvir/velpatasvir completed 12weeks therapy in 86% and 8weeks in 14% (p = 0.029). Of 69 patients 74% were found to achieve SVR. In 20% no information is available as they were lost to follow-up. Re-infection was documented in 3 patients and one relapse in a gt3 patient with cirrhosis. High adherence and response rates to HCV treatment were found following DAA based therapy in PWID supporting the call to include difficult-to-treat populations into HCV treatment efforts on the way to HCV elimination. Treatment of OST and HCV at one institution supporting patients by a multidisciplinary team may further facilitate adherence to follow up visits enabling documentation of treatment outcomes more easily.

Shadow Testing in Autonomous Vehicles : A Novel Approach to Validating Full Self-Driving AI Systems

As autonomous vehicles progress towards widespread deployment, ensuring the reliability and safety of Full Self-Driving (FSD) AI systems remains a critical challenge. This article presents a comprehensive analysis of shadow testing, an innovative approach to validating new FSD AI models in real-world conditions without compromising user safety. We examine the methodology of deploying new AI models in "shadow mode," where they process live sensory data alongside the operational system but do not control the vehicle. This approach enables the collection of valuable performance metrics and the identification of edge cases while mitigating risks associated with direct deployment. Our study demonstrates how shadow testing facilitates a continuous feedback loop for iterative improvement, enhancing model reliability through data-driven refinement. We further explore the challenges of implementing shadow testing at scale, including data processing requirements and ethical considerations. Our findings suggest that shadow testing not only accelerates the development of robust FSD systems but also plays a crucial role in building public trust in autonomous vehicle technology. This article contributes to the growing body of knowledge on AI safety validation techniques and provides insights for standardizing testing protocols in the autonomous vehicle industry.