Joint Effects Research Articles

The independent and joint association between physical activity, sleep duration and daily sitting time with bone mineral density: A real world study from NHANES 2007–2018.

PurposeTo assess the independent and joint effect of physical activity, sleep duration, and daily sitting time on bone mineral density (BMD), based on National Health and Nutrition Examination Survey (NHANES) 2007–2018. DesignCross-sectional design. MethodsThe primary outcome was risk of low BMD. All associations between lifestyle factors and the prevalence of low BMD were based on logistic regression, and dose-response relationships were further explored by restricted cubic spline (RCS). Finally, multiplicative and additive interaction was examined by P interaction and relative excess risk due to interaction (RERI). Results10,346 individuals (N normal BMD = 6353; N Low BMD = 3993) were analyzed. Multivariate logistic regression indicated low intensity physical activity (odds ratio [OR] 0.84; 95 % confidence interval [95%CI] 0.78–0.90) and high intensity physical activity (0.67, 0.56–0.78) had protective impact on risk of low BMD, whereas short sleep (1.41, 1.20–1.64), long sleep (1.36, 1.03–1.79) and prolonged daily sitting (1.58, 1.32–1.88) had harmful effect. RCS revealed dose-response associations between physical activity (J-shaped), sleep duration (U-shaped), daily sitting time (positive-associated) and risk of low BMD. Multiplicative interaction between sleep duration and physical activity was observed (P interaction = 0.003), while not between daily sitting time and physical activity (P interaction = 0.600). Notably, negative additive interactions indicated that physical activity mitigated the increased risk of low BMD associated with irregular sleep patterns and prolonged sedentary behavior. ConclusionIncreasing physical activity was presented as a modulating factor, potentially altering the relationship between independent variables that have deleterious effects on BMD like sleep duration and sedentary behavior. The study underscores the importance of lifestyle modifications in the prevention of early onset low BMD.

Balancing act: Exploring the interplay of production pressure and innovation/flexibility climates on employee well‐being

This study explored the separate and joint effects of innovation/flexibility and pressure to produce climates on employee exhaustion, and ultimately, turnover intentions. Survey data collected from 335 full‐time employees across various sectors in Türkiye were analysed using a latent moderated structural equations approach. The findings revealed that, while innovation climates were inversely related to exhaustion and turnover intentions, production pressure climate had the opposite effect, which provided support for the job demands‐resources (JD‐R) model. Importantly, the study uncovered a significant interactive effect, indicating that the positive impact of production pressure on exhaustion was amplified under increased innovation demands. This finding provided support for the conservation of resources (COR) theory, suggesting that heavy and conflicting job demands depleted employees' psychological resources by impeding the deployment of defensive strategies. The findings contribute to innovation research by revealing that individual‐level competing demands of balancing exploration and exploitation through contextual ambidexterity may pose challenges to employees' psychological well‐being.

Parasite Abundance‐Occupancy Relationships Across Biogeographic Regions: Joint Effects of Niche Breadth, Host Availability and Climate

ABSTRACTAimChanging biodiversity and environmental conditions may allow multi‐host pathogens to spread among host species and affect prevalence. There are several widely acknowledged theories about mechanisms that may influence variation in pathogen prevalence, including the controversially debated dilution effect and abundance‐occupancy relationship hypotheses. Here, we explore such abundance‐occupancy relationships for unique lineages of three vector‐borne avian blood parasite genera (the avian malaria parasite Plasmodium and the related haemosporidian parasites Parahaemoproteus and Leucocytozoon) across biogeographical regions.LocationNearctic‐Neotropical and Palearctic‐Afrotropical regions.MethodsWe compiled a cross‐continental dataset of 17,116 bird individuals surveyed from 46 bird assemblages across the Nearctic‐Neotropical and Palearctic‐Afrotropical regions and explored relationships between local parasite lineage prevalence and host assemblage metrics in a Bayesian random regression framework.ResultsMost lineages from these three genera infected ≥ 5 host species and exhibited clear phylogenetic or functional host specificity. Lineage prevalence from all three genera increased with host range, but also with higher degrees of specialisation to phylogenetically or functionally related host species. Local avian community features were also found to be important drivers of prevalence. For example, bird species richness was positively correlated with lineage prevalence for Plasmodium and Leucocytozoon, whereas higher relative abundances of the main host species were associated with lower prevalence for Plasmodium and Parahaemoproteus but higher prevalence for Leucocytozoon.ConclusionsOur results broadly support several of the leading hypotheses about mechanisms that influence pathogen prevalence, including the niche breadth hypothesis in that higher avian host species diversity and broader host range amplify prevalence through increasing ecological opportunities and the trade‐off hypotheses in that specialisation among subsets of available host species may increase prevalence. Furthermore, the three studied avian haemosporidian genera exhibited different abundance‐occupancy relationships across the major global climate gradients and in relation to host availability, emphasising that these relationships do not strictly follow common rules for vector‐borne parasites with different life histories.

Drinking motives and alcohol sensitivity mediate multi-dimensional genetic influences on alcohol use behaviors.

Genetic influences account for a substantial proportion of individual differences in alcohol use behaviors (AUBs). However, multiple distinct sets of genes are linked to different AUBs, which may explain their dramatic variability in risk factors and manifestations. In this study, we explore whether intermediate neurobiological traits and alcohol-related cognitions mediate the relationship between polygenic scores (PGS) and multiple AUBs, with the aim to better understand processes captured by different genetic profiles. Using results from prior genome-wide association studies, we derived PGS for 6 AUBs in participants from Spit for Science, a longitudinal study of college students in the U.S. (n=4,549). Self-report measures included personality traits, alcohol expectancies, drinking motivations, and alcohol sensitivity measures as well as drinking frequency, drinking quantity, alcohol use disorder (AUD) symptoms, and maximum drinks in 24 hours. Using linear regression and multiple mediation models, we investigated the direct and indirect effects of PGS on AUBs. In univariable regression results, PGSs indexing broad AUB dimensions such as drinks per week (DPW) and AUD predicted higher levels of sensation-seeking and multiple drinking motives, while BeerPref PGSs (indexing a variable pattern of alcohol problems associated with a preference for beer) predicted higher negative urgency and lower alcohol sensitivity. Mediational models indicated strong direct and indirect effects of DPW PGSs on multiple AUBs via social/enhancement drinking motives and alcohol sensitivity, indirect effects of AUD PGSs on AUD symptoms via coping motives, and indirect effects of BeerPref PGS on all AUBs via the joint effect of mediators including alcohol sensitivity. These findings provide initial evidence that the genetic influences on different AUBs are associated with and partially mediated by intermediate neurobiological and cognitive factors, which may be more amenable to intervention. Greater focus on drinking motives and alcohol sensitivity is warranted in genetic research, as well as attention to the heterogeneous pathways linking genes to alcohol use outcomes.

Elevated blood glucose levels are associated with the progression of brain hypometabolism, and HDL-C and APOE4 add to this association.

Brain glucose hypometabolism has consistently been found in neurodegenerative disorders, including Alzheimer's disease (AD). High blood glucose and HDL cholesterol (HDL-C) levels have also been linked to neurodegeneration and AD. However, there is limited understanding of the relationships between dementia-related risk factors in the brain and blood. A linear mixed model was used to examine the relationship between blood glucose and HDL-C levels and the progression of brain hypometabolism, adjusting for APOE4 and other clinical covariates. The hypometabolic convergence index (HCI) was measured by fluorodeoxyglucose-18 (FDG) positron emission tomography (PET) in participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Data visualizations were generated to understand the joint effects of plasma glucose, HDL-C, and APOE4 on HCI. There were 336 individuals (781 observations), of whom 22.62% had AD. The majority were male (63.98%) and of white race, and 48.51% were carriers of APOE4. Over time, high blood glucose level was associated with the progression of brain glucose hypometabolism (β=0.33, 95% CI: 0.02, 0.64, p<0.05). A high plasma HDL-C level (β=1.22, 95% CI: 0.09, 2.35, p<0.05), more study visits (β=1.67, 95% CI: 1.37, 1.98, p<0.001), and being an APOE4 allele carrier (β=1.29, 95% CI: 0.15, 2.42, p<0.05) were also significant predictors of brain hypometabolism progression. APOE4 carrier status and number of visits account for the largest proportion of the variance from the fixed effects model. Random effects due to participant characteristics and fixed effects together accounted for 95.2% of the model variance. Subgroup analysis revealed that these effects were observed only in those without AD. High plasma glucose levels facilitated the progression of brain hypometabolism. The effect was more prominent in the APOE4 double-carriers with elevated HDL-C. Elevated blood glucose may reflect systemic insulin resistance, which could impair brain glucose uptake, resulting in brain hypometabolism. Controlling blood glucose and HDL-C levels in APOE4 carriers may improve brain metabolism, potentially delaying the onset of dementia.

Abstract B139: Redlining bias and progression of chronic health conditions in survivors of childhood cancer: A report from the St. Jude lifetime cohort study

Abstract Background/Aims: Racial health disparities have been linked to racial and residential segregation. Redlining bias is a special form of racial and residential segregation involving the systematic denial of mortgages, insurance, and other financial services to racial/ethnic minorities. This study examined associations between redlining bias and the progression of chronic health conditions (CHCs) in adult survivors of childhood cancer. Methods: We included 1,670 adult survivors of childhood cancer from the St. Jude Lifetime Cohort Study who had undergone two or more clinical assessments. Over 200 individual CHCs were graded using a modified CTCAE version and aggregated to determine the global CHC burden. CHC progression from baseline to follow-up evaluation was classified as either worsening over time (from Grade 2 to 3-5 or from Grade 3 to 4-5) or new-onset (from Grades 0-1 to 2-5). Survivors' home addresses at baseline (2007-2020) were linked to contemporary redlining bias data (2010-2017) created by Beyer et al. (J Clin Oncol, 2021). Redlining bias was defined as the odds ratio of a mortgage application denial for properties in a local area versus all metropolitan statistical areas and further classified as high (≥2.0), moderate (1.0-1.9), low (0.5-0.9), or least bias (&amp;lt;0.5). Multivariable logistic regression tested associations of risk for worsening/new-onset CHCs (vs. no progression) with redlining bias (high vs. moderate/low/least bias), adjusting for age, sex, treatment factors, personal socioeconomic status (SES; education and health insurance), and lifestyle factors. Joint effects of redlining bias and personal SES on CHC progression were also evaluated. Results: Survivors’ mean±SD age at baseline was 31.7±8.3 years; 17.5% lived in high and 19.5% in moderate redlining neighborhoods. The bivariate analysis found that living in a neighborhood classified as having high redlining bias (vs. moderate/low/least bias) increased the risk of progression of the global burden of CHCs (OR=1.32, 95% CI: 1.02-1.70). Multivariable analysis found significant associations of redlining bias with the risk of worsening/new-onset of individual CHCs such as cardiomyopathy (OR=1.63, 95% CI: 1.07-2.48), peripheral motor neuropathy (OR=1.56, 95% CI: 1.00-2.44), asthma (OR=1.85, 95% CI: 1.04-3.32), and restrictive pulmonary deficit (OR=1.79, 95% CI: 1.05-3.04). Considering joint effects, survivors enrolled in public insurance programs and living in a high redlining area had a higher risk of progression of the global burden of CHCs (OR=1.73, 95% CI: 1.05-2.87) and of worsening/new-onset in peripheral motor neuropathy (OR=3.18, 95% CI: 1.57-6.42) and restrictive pulmonary deficit (OR=5.29, 95% CI: 2.46-11.37) CHCs. Conclusions: The significant associations of redlining bias with the progression of CHCs among adult survivors of childhood cancer suggest an urgent need for interventions (typically social or healthcare policies) targeting neighborhoods with high discriminatory exposures to prevent CHC burden in this population. Citation Format: Jaesung Choi, Madeline R. Horan, Nickhill Bhakta, Deo Kumar Srivastava, Kirsten K. Ness, Gregory T. Armstrong, Melissa M. Hudson, I-Chan Huang. Redlining bias and progression of chronic health conditions in survivors of childhood cancer: A report from the St. Jude lifetime cohort study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B139.

Soil bacteria and fungi on tropical mountainsides: Joint effects of weathering, climate, and chemical factors

Contemporary environmental factors such as temperature and pH are generally identified as primary influences on microbial diversity, while the role of geological processes remain understudied. Here, we investigated the diversity and community composition of bacteria and fungi along an elevational gradient from703 to 4514 m on Mt. Kilimanjaro, East Africa. We further examined the effects of contemporary environment and geological processes such as weathering on microbial communities and diversities. For community composition, bacteria and fungi showed clear differentiation along elevations and their community dissimilarities increased with elevational distance indicating elevational distance-decay relationships. Multiple variables such as weathering, climate and chemical factors were significantly associated with microbial communities and showed greater effects on bacterial than fungal communities. Specifically, soil pH mainly shaped bacterial communities, while mean annual temperature for fungi, followed by other variables such as weathering processes. For Shannon diversity, bacteria and fungi showed significant hump-shaped elevational patterns with the peak values at 1857 and 1436 m, respectively. Shannon diversity was mainly affected by soil weathering accounting for 8.9% of the total variance for bacteria, while jointly by weathering and climate accounted for 14.3% of fungi. For the community uniqueness, represented by local contribution to beta diversity (LCBD), there were U-shaped patterns for both taxonomic groups. LCBD was mainly explained by the joint effects of chemical and climate variables which accounted for 51.1% and 33.4% for bacteria and fungi, respectively. Our results highlight the effects of soil weathering processes on diversity and community composition for bacteria and fungi. Thus, the integration of weathering with contemporary environments could provide new insights into microbial elevational diversity patterns.

Idiosyncratic covariates of unemployment duration in Ghana: The joint effect of migration and education

Idiosyncratic covariates of unemployment duration in Ghana: The joint effect of migration and education

No deceleration signs in the permafrost ground subsidence four years after the 2019 fire in Northwest Territories, Canada

Abstract The circum-arctic permafrost environment is often disturbed by wildfires but could also show resilience to these disturbances. However, the increased frequency and extent of wildfires, coupled with unprecedented hot weather, have introduced greater uncertainties in the post-fire permafrost dynamics. We need to address emerging questions, e.g., How will permafrost respond to the joint effect of hot anomalies and wildfires? To what extent will post-wildfire deformation evolve? How will permafrost resilience to wildfires vary? Utilizing Interferometric Synthetic Aperture Radar (InSAR) time series analysis, we investigated the post-wildfire ground deformation around a 2019 fire scar in the lower Mackenzie Valley, Northwest Territories, Canada, where dramatic heat anomalies and severe wildfires have been recorded in recent years. The resilience of permafrost to wildfires appears to be weakened by the continuous and rapid warming after the fire, as evidenced by the year-on-year acceleration in subsidence rates. Such acceleration was never reported by previous findings that typically observed deceleration in subsidence rates four to five years after wildfires. The deformation along the line of sight (LOS) of the satellite demonstrates significant permafrost degradation induced by wildfires and exacerbated by climate warming, and the cumulative subsidence was detected up to 25 cm in the LOS direction in the upland areas and up to 10 cm in the lowland areas four years after the fire. The difference in deformation magnitude could be attributed to local factors, including ground ice, topography, and vegetation. Our study highlights the increasingly severe threat to circum-arctic permafrost due to the combined effects of wildfires and extreme heat anomalies.

Optimizing tool offset in static shoulder friction stir welding of Al6061 to Ti6Al4V for enhanced joint integrity

The precise control of tool offset in static shoulder friction stir welding (SSFSW) is vital for enhancing the mechanical properties and overall effectiveness of dissimilar metal joints. The present study investigates the impact of tool offset on the development of microstructural features, intermetallic compound formation, texture analysis, and mechanical characteristics of SSFSWed Al6061 to Ti6Al4V joints. The results demonstrate that a 2.5 mm tool offset results in an average grain size of 2.65 μm, compared to larger grains of about 4.76 μm and 7.31 μm observed at 2.0 mm and 1.5 mm offsets, respectively. The percentage of low-angle grain boundaries also varies, reaching up to 42.2% at a 2.5 mm offset compared to 48.6% at 2.0 mm and 64.1% at 1.5 mm, indicating enhanced homogeneity in the microstructural properties. Moreover, the ultimate tensile strength at the 2.5 mm tool offset reaches 279 MPa, which is 5.7% higher than at the 2.0 mm offset and 13.4% higher than at the 1.5 mm offset. This improvement is attributed to more uniform distribution of Ti particles over the stir zone. Texture analysis further supports these findings, with the development of alpha fibre at 2.5 mm offset enhancing ductility, thereby underscoring the critical role of tool offset in achieving sound dissimilar metal weldments.

An analysis of the Impact of Political Regimes on the Economy: Understanding the Corruption-Growth Relationship

Corruption and economic growth are co- related. No theoretical evidence in the literature provides clear evidence of their relationship. Some researchers believe corruption increases economic growth while others say it negatively affects the economy. This paper aims to analyse the joint effect of corruption and the type of political government (i.e., democratic or autocratic) on economic growth to provide some clarity. Panel data (2000-2020) analysis of Pakistan and Saudi Arabia is used in this research paper to examine the relationship between corruption and the type of political government. GDP per capita is used as a dependent variable and economic factors are used as independent variables. A standard regression estimation approach is used. The relationship is estimated using econometric and statistical panel estimation techniques: FE, Two-stage least square and Dynamic-Panel-System GMM method. The data collected is from ICRG and WDI. The results show that corruption has a positive effect in autocratic systems than in democratic. Thus, it proves that the type of political government, corruption and economic growth are related.

A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.

Generation of strong mechanical squeezing through the joint effect of two-tone driving and parametric pumping

We propose an innovative scheme to efficiently prepare strong mechanical squeezing by utilizing the synergistic mechanism of two-tone driving and parametric pumping in an optomechanical system. By reasonably choosing the system parameters, the proposal highlights the following prominent advantages: the squeezing effect of the cavity field induced by the optical parametric amplifier can be transferred to the mechanical oscillator, which has been squeezed by the two-tone driving, and the degree of squeezing of the mechanical oscillator will surpass that obtained by any single mechanism; the joint mechanism can enhance the degree of squeezing significantly and break the 3 dB mechanical squeezing limit, which is particularly evident in range where the red/blue-detuned ratio is sub-optimal; the mechanical squeezing achieved through this distinctive joint mechanism exhibits notable robustness against both thermal noise and decay of mechanical oscillator. Our project offers a versatile and efficient approach for generating strong mechanical squeezing across a wide range of conditions.

Family history and genetic risk score combined to guide cancer risk stratification: A prospective cohort study.

Family history (FH) of cancer and polygenic risk scores (PRS) are pivotal for cancer risk assessment, yet their combined impact remains unclear. Participants in the UK Biobank (UKB) were recruited between 2006 and 2010, with complete follow-up data updated until February 2020 for Scotland and January 2021 for England and Wales. Using UKB data (N = 442,399), we constructed PRS and incidence-weighted overall cancer PRS (CPRS). FH was assessed through self-reported standardized questions. Among 202,801 men (34.6% with FH) and 239,598 women (42.0% with FH), Cox regression was used to examine the associations between FH, PRS, and cancer risk. We found a significant dose-response relationship between FH of cancer and corresponding cancer risk (Ptrend < .05), with over 10 significant pairs of cross-cancer effects of FH. FH and PRS are positively correlated and independent. Joint effects of FH of cancer (multiple cancers) and PRS (CPRS) on corresponding cancer risk were observed: for instance, compared with participants with no FH of cancer and low PRS, men with FH of cancer and high PRS had the highest risk of colorectal cancer (hazard ratio [HR]: 3.69, 95% confidence interval [CI]: 3.01-4.52). Additive interactions were observed in prostate and overall cancer risk for men and breast cancer for women, with the most significant result being a relative excess risk of interaction (RERI) of 2.98, accounting for ~34% of the prostate cancer risk. In conclusion, FH and PRS collectively contribute to cancer risk, supporting their combined application in personalized risk assessment and early intervention strategies.

Number and dissimilarity of global change factors influences soil properties and functions

Soil biota and functions are impacted by various anthropogenic stressors, including climate change, chemical pollution or microplastics. These stressors do not occur in isolation, and soil properties and functions appear to be directionally driven by the number of global change factors acting simultaneously. Building on this insight, we here hypothesize that co-acting factors with more diverse effect mechanisms, or higher dissimilarity, have greater impacts on soil properties and functions. We created a factor pool of 12 factors and calculated dissimilarity indices of randomly-chosen co-acting factors based on the measured responses of soil properties and functions to the single factors. Results show that not only was the number of factors important, but factor dissimilarity was also key for predicting factor joint effects. By analyzing deviations of soil properties and functions from three null model predictions, we demonstrate that higher factor dissimilarity and a larger number of factors could drive larger deviations from null models and trigger more frequent occurrence of synergistic factor net interactions on soil functions (decomposition rate, cellulase, and β-glucosidase activity), which provides mechanistic insights for understanding high-dimensional effects of factors. Our work highlights the importance of considering factor similarity in future research on interacting factors.

Joint effects of ill-health, health shocks and social protection on the intensive margin of labour supply: evidence from Malawi

BackgroundThere is sparse evidence on the joint effects of ill-health, health shocks and social protection on the intensive margin of labour supply, particularly in developing countries. We interact ill-health and health shocks with access to social protection and estimate their joint effects on weekly hours of work.MethodsWe employ a zero-inflated Poisson model to assess joint effects of ill-health, health shocks and social protection on weekly hours of work exploiting pooled repeated cross-sectional data from Malawi.ResultsWe find that overall, individuals who suffered from ill-health or a health shock, including an illness/injury, a hospital admission or a chronic illness and benefited from social protection, reduced their weekly hours of work.ConclusionsThe study provides novel empirical evidence on the potential joint effects of ill-health, health shocks and social protection on the intensive margin of labour supply, shedding light on the role social protection can play in developing countries.

Experiment and prediction of the strain-range dependent ultra-low-cycle fatigue based on micromechanical cyclic void growth model

Under severe seismic action, the local stress concentration region in steel structures is prone to the ultra-low-cycle fatigue (ULCF) under high triaxiality and irregular cyclic deformation with intensively varying strain amplitudes. It emphasizes the necessity of the jointed effects of the stress triaxiality and strain range incorporated in the ULCF life prediction. Given the limited relevant studies in terms of the underlying micro-mechanisms, this paper focuses on experimental and modeling studies of the combined effects of stress triaxiality and strain range on the ULCF failure, based on Gurson-Tvergaard-Needleman (GTN) microvoid evolution theories. Monotonic tensile tests on three types of notched round bars suggest that fracture strain decreases with increasing triaxiality. Cyclic tests on two types of notched bars under various strain ranges confirm the combined effects of stress triaxiality and strain range. Given a specific triaxiality, the ULCF damage accumulation rate increases linearly as the tensile plastic strain range ramps up. For the same tensile plastic strain range, higher triaxiality results in a greater damage accumulation rate. In the modeling studies, the consistency of the GTN and uncoupled damage models has been proven in terms of ULCF failure of structural steel. Based on GTN theories, a new micromechanical cyclic void growth model (GCVGM) is proposed, considering the detailed processes of void nucleation, growth, and coalescence under monotonic and cyclic loadings. Moreover, the joint effects of stress triaxiality and strain range can be explicitly described within a unified framework, without additional empirical assumptions. Based on experimental and simulation results, the proposed GCVGM shows significant advantages over conventional models in accurately predicting ULCF failure under various loading protocols, with low average errors of 2.73 % and −0.91 % for two types of notched bar specimens, respectively.

Joint effects of tobacco smoke exposure and heavy metals on serum sex hormones in adult males.

This study aimed to explore the associations of tobacco smoke exposure (TSE) and heavy metal exposure on sex hormones and the joint effects between them in adult males. The study used data of 2244 adult males from the National Health and Nutrition Examination Survey (NHANES, 2013-2016). Weighted linear regression models were used to calculate their beta (β) coefficients and corresponding confidence interval (95% CI), which assessed the joint effects of TSE and heavy metals on sex hormones. Sex hormone-binding globulin (SHBG) showed a positive association with increased per standard deviation (SD) for cotinine (β=0.024 [0.004, 0.043]; P<0.001), lead (β=0.021 [0.002, 0.039]; P=0.028), and cadmium (β=0.034 [0.015, 0.053]; P<0.001). Manganese was positively associated with estradiol (E2) (β=0.025 [0.009, 0.042]; P=0.002). The subjects with higher cadmium levels were more likely to have higher total testosterone (TT) (β=0.042 [0.023, 0.062]; P<0.001). TSE and lead exerted synergistic effects on TT (p for interaction = 0.015) and E2 (p for interaction = 0.009), as also did TSE and cadmium on SHBG (p for interaction = 0.037). Compared with the reference group, TSE participants who were exposed to high concentrations of lead, cadmium, mercury, and manganese had significantly elevated TT levels, but these high levels presented no significant association with E2 levels. A significantly higher level of SHBG among TSE participants was detected in high concentrations for lead, cadmium, and mercury. TSE exacerbated sex hormone imbalances when combined with high levels of metal exposure. Smoking cessation is crucial, especially in the case of high levels of occupational exposure to heavy metals.

Подавление пульсаций выходного напряжения высоковольтного источника питания блоков детектирования ионизирующего излучения

Operation and results of tests of operating model of high-voltage power supply of ionizing radiation detection units with active suppression of output voltage pulsations are described. The joint effect of the smoothing RC-filter and the circuit of active pulsation suppression on the value of their amplitude was experimentally investigated. The device was tested at an output voltage of 1 kV and a load current of 1 mA. The minimum peak-to-peak ripple amplitude achieved does not exceed 1mV.

KPI-oriented process monitoring based on causal-weighted partial least squares

With the increasing demand for product quality, Key performance indicator (KPI)-oriented process monitoring plays an important role in modern industrial. Partial least squares (PLS)-based methods are widely adopted for KPI-oriented process monitoring, in which the process variable space is decomposed into a principal component subspace that has a strong correlation with KPIs and a residual subspace unrelated to KPIs, and the two spaces are monitored separately. However, if a KPI-unrelated fault occurs in a variable that has no causal relation with any KPI, but has a spurious correlation with some KPIs because of the existence of confounders, PLS based methods may mis-regard the KPI-unrelated fault as a KPI-related fault. This occurs because as a correlation analysis-based method, PLS cannot discriminate whether a variable is a real cause of KPIs or has a spurious correlation with KPIs. Motivated by this, this paper proposes two causal-weighted PLS methods for KPI-oriented process monitoring by combining the LiNGAM-based causal discovery with PLS, which first calculate causal weights based on the modified LiNGAM algorithm and bootstrap strategy, and then employ the causal weights to reweight the weight vectors of PLS to enhance the influence of causal variables in the KPI-related principal subspace and reduce the influence of spurious correlations. Case studies based on a simulated dataset, Tennessee Eastman Process dataset and field data from finishing rolling mill process show that the proposed methods can significantly reduce the false alarm rate for KPI-unrelated faults (i.e., the probability that a KPI-unrelated fault sample is mis-regarded as a KPI-related fault sample) caused by spurious correlation without significantly compromising the fault detection rate of KPI-related faults.