Rate Of Change Research Articles

Prediction of visual field progression with serial optic disc photographs using deep learning

AimWe tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time...

Nitrogen fertilization affected microbial carbon use efficiency and microbial resource limitations via root exudates

Root exudation and its mediated nutrient cycling process driven by nitrogen (N) fertilizer can stimulate the plant availability of various soil nutrients, which is essential for microbial nutrient acquisition. However, the response of soil microbial resource limitations to long-term N fertilizer application rates in greenhouse vegetable systems has rarely been investigated. Therefore, we selected a 15-year greenhouse vegetable system, and investigated how N fertilizer application amount impacts on root carbon and nitrogen exudation rates, microbial resource limitations and microbial carbon use efficiency (CUEST). Four N treatments were determined: high (N3), medium (N2), low (N1), and a control without N fertilization (N0). Compared to the control (N0), the results showed that the root C exudation rates decreased significantly by 42.9 %, 57.3 % and 33.6 %, and the root N exudation rates decreased significantly by 29.7 %, 42.6 %, and 24.1 % under N1, N2, and N3 treatments, respectively. Interactions between fertilizer and plant roots altered microbial C, N, P limitations and CUEST; Microbial C and N/P limitations were positively correlated with root C and N exudation rates, negatively correlated with microbial CUEST. Random Forest analysis revealed that the root C and N exudation rates were key factors for soil microbial resource limitations and microbial CUEST. Through the structural equation model (SEM) analysis, soil NH4+ content had significant direct effects on the root exudation rates after long-term N fertilizer application. An increase in root exudation rates led to enhanced microbial resource limitations in the rhizosphere soils, potentially due to increased competition. This enhancement may reduce microbial carbon use efficiency (CUE), that is, microbial C turnover, thereby reducing soil C sequestration. Overall, this study highlights the critical role of root exudation rates in microbial resource limitations and CUE changes in plant-soil systems, and further improves our understanding of plant-microbial interactions.

Does change in trauma-related shame predict change in PTSD symptomatology?

Trauma-related shame (TR-shame) is associated with Posttraumatic Stress Disorder (PTSD) symptoms. However, research findings are inconsistent regarding the role of TR-shame in PTSD treatment. The purpose of this study was to examine if change in TR-shame predicted change in PTSD symptoms in treatment. A sample of 462 adults who received PTSD treatment at a Partial Hospitalization Program completed questionnaires measuring TR-shame (i.e., Trauma-Related Shame Inventory; TRSI) and PTSD symptoms (PTSD Checklist for DSM-5; PCL-5). Latent growth curve models were estimated to examine if the rate of change in TRSI predicted the rate of change in PCL-5 by using structural equation modeling. Furthermore, a latent regression model was estimated to predict the intercept and slope of the PCL-5. Model fits for the PCL-5 and the TRSI linear models were acceptable and both linear slopes were significant. On average, PCL-5 scores reduced 22.18 points from admission to discharge, while TRSI scores reduced 21.9 points from admission to discharge. The results of latent curve regression model suggested that the TRSI linear slope and intercept predicted the PCL-5 linear slope and intercept, respectively. Additionally, post-hoc analyses suggested that the variance in PCL-5 factors at discharge that were explained by TRSI intercept and linear slope ranged from 18.6% to 34.9%. The results of this study indicated that the rate of change in TR-shame predicted the rate of change in PTSD symptoms. Given the negative impact of TR-shame on PTSD symptoms, TR-shame should be a target in treatment for PTSD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Applying behavioral economics to understand changes in alcohol outcomes during the transition to adulthood: Longitudinal relations and differences by sex and race.

Population drinking trends show clear developmental periodicity, with steep increases in harmful alcohol use from ages 18 to 22 followed by a gradual decline across the 20s, albeit with persistent problematic use in a subgroup of individuals. Cross-sectional studies implicate behavioral economic indicators of alcohol overvaluation (high alcohol demand) and lack of alternative substance-free reinforcers (high proportionate alcohol-related reinforcement) as potential predictors of change during this developmental window, but longitudinal evidence is sparse. Using a sample of emerging adults (N = 497, Mage = 22.61 years, 62% female, 48.69% White, 40.44% Black), this study examined prospective, bidirectional relations between both past-week heavy drinking days (HDD) and alcohol problems and proportionate alcohol-related reinforcement (reinforcement ratio), alcohol demand intensity (consumption at zero price), alcohol demand Omax (maximum expenditure), and change in demand elasticity (rate of change in consumption across escalating price) over five assessments (every 4 months) using random intercept cross-lagged panel models. Alcohol problems and HDD decreased across assessments. Significant between-person effects indicated that each behavioral economic variable was associated with increased drinking risk. Change in reinforcement ratio was positively associated with decreases in alcohol problems. Multigroup invariance modeling revealed distinct risk pathways in that change in demand intensity and Omax predicted change in alcohol problems for male participants and change in intensity predicted change in alcohol problems for non-White participants. The study provides consistent support for proportionate alcohol-related reinforcement and mixed support for demand as within-person predictors of reductions in drinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Erosive hand osteoarthritis and sarcopenia: data from Osteoarthritis Initiative cohort

ObjectivesThere is no evidence linking specific osteoarthritis (OA) types, such as erosive hand OA (EHOA), with distant generalised changes in muscle composition (sarcopenia), which can potentially be modified. This study...

A Noncontact Method for Simultaneous Measurement of di/dt and dv/dt

This letter proposes a non-contact method for simultaneously measuring the current and voltage change rates by Rogowski coils. The dv/dt measurement capability is achieved based on electric field coupling, which is incorporated into the state-of-the-art Rogowski coil based current sensing solutions. The application of the proposed method for power semiconductor devices is demonstrated by a case study of an IGBT switching circuit. The Rogowski coils are integrated with a Printed Circuit Board (PCB) and fit with the IGBT module terminals. The comparable results from high-performance commercial instruments verify the di/dt and dv/dt measurement accuracy of the presented compact and cost-effective solution.

Incidence trends of kidney cancer in China from 1990 to 2019: A joinpoint and age-period-cohort analysis.

Occupational carcinogens, smoking, and obesity are believed to be the main causing agents of kidney cancer. China is undergoing rapid industrialization, and hence the people's lifestyles have witnessed tremendous changes. However, the trend of kidney cancer incidence during the late 20th and early 21st centuries remains unexplored in China. Data from the Global Burden of Diseases (GBD; 2019) was retrieved for the incidence of kidney cancer from 1990 to 2019. The rates of disease average annual percentage changes (AAPC) were assessed using joinpoint regression analysis. Age-period-cohort (APC) model was used to assess age, period, and cohort effects on the incidence of the disease simultaneously. An increase in age-standardized incidence rates (ASIR) of kidney cancer was observed from 1990 to 2019 in total residents (1.33 - 4.24), men (1.56 - 6.15), and women (1.11 - 2.31) per 100,000 population suggesting a more obvious increase in males than in females. A consistent increase in age effects was observed in all age groups, especially in males. On the other hand, the 70 - 74 age group in females showed greater age effects. In addition, the period effects analysis showed that the incidence of kidney cancer increased with time. Moreover, the analysis of cohort effects showed a decrease in the disease in birth cohorts, especially before 1940. The incidence of kidney cancer is increasing rapidly in China. The kidney cancer burden will rise in the next decades due to population aging, environmental pollution, occupation, food safety, and so on. Results of this study suggest that more etiological studies should be performed to identify the driving factors for kidney cancer trends, and appropriate preventive measures should be implemented for the age-, period-, and cohort-related factors in the population.

Drought Responses in Three Apple Cultivars Using an Autonomous Sensor-based Irrigation System

Irrigation decision support systems evolving in the domestic temperate tree fruit production industry incorporate measures of soil moisture status, which diverges from classic physiological indicators of edaphic stress. This study used an autonomous sensor-based irrigation system to impose a water deficit (soil matric potential targets of –25, –40, –60, and –80 kPa) on ‘Autumn Gala’, ‘CrimsonCrisp’, and ‘Golden Delicious’ apple (Malus domestica) cultivars grafted to ‘Budagovsky 9’ rootstock in the greenhouse (n = 60). It was hypothesized that relationships between physiological plant function, assessed via infrared gas exchange and chlorophyll fluorescence, and the soil matric potential may be used to advance emerging irrigation decision support systems. Complications arising from defoliation by day 11 at –60 and –80 kPa indicate the generation of substrate-specific soil–water relationships in research applications of autonomous sensor-based irrigation systems. ‘Autumn Gala’ carbon assimilation rates at –80 kPa declined from day 0 to day 8 (9.93 and 5.86 μmol⋅m–2⋅s–1 carbon dioxide), whereas the transpiration rate was maintained, potentially reducing observed defoliation as other cultivars increased transpiration to maintain carbon assimilation. Correlation matrices revealed Pearson’s r ≤ |0.43| for all physiological metrics considered with soil matric potential. Nevertheless, exploratory regression analysis on predawn leaf water potential, carbon assimilation, transpiration, stomatal conductance, and nonphotochemical quenching exposed speculatively useful data and data shapes that warrant additional study. Nonlinear piecewise regression suggested soil matric potential may useful as a predictor for the rate of change in predawn leaf water potential upon exposure to a water deficit. The critical point bridging the linear spans, –30.6 kPa, could be useful for incorporating in emerging irrigation decision support systems.

An ANN-Based Output-Error-Driven Incremental Model Predictive Control for Buck Converter Against Parameter Variations

DC/DC buck converters have been widely applied in the power processing of distributed energy resources (DERs) penetrated DC microgrid. Model predictive control (MPC) provides optimal control for the buck converter, but it often requires multiple sensors or complex observers to realize zero-steady-state-error control against parameter variations. An output-error-driven incremental MPC (OEDIMPC) for buck converter is proposed in this paper. Different from the traditional concept of calculating the optimal duty cycle, the proposed MPC calculates the optimal rate of change of the duty cycle by using a reduced-state output-error-driven prediction model. As a result, the OEDIMPC achieves zero-steady-state-error control against parameter variations, including load value, input voltage, output inductor, and output capacitor, with significantly improved dynamic performance and minimal sensor and observer requirements Besides, small- and large-signal analyzes are used to study the stability boundary of the OEDIMPC under parameter variations, and a shallow layer artificial neural network (ANN) is utilized to realize the OEDIMPC on the hardware. The proposed technique has been evaluated experimentally on a buck converter with resistor load and constant power load (CPL), confirming its effectiveness.

Network Pharmacology and Intestinal Microbiota Analysis Revealing the Mechanism of Punicalagin Improving Bacterial Enteritis.

The Chinese medicine punicalagin (Pun), the most important active ingredient in pomegranate peel, has significant bacteriostatic and anti-inflammatory properties. The potential mechanisms of Pun for bacterial enteritis, however, are unknown. The goal of our research is to investigate the mechanism of Pun in the treatment of bacterial enteritis using computer-aided drug technology, as well as to investigate the intervention effect of Pun on mice with bacterial enteritis using intestinal flora sequencing. The targets of Pun and Bacterial enteritis were obtained by using the specific database, and cross-targets were screened among these targets, followed by PPI and enrichment analysis of the targets. Furthermore, the degree of binding between Pun and key targets was predicted through molecular docking. After successfully establishing the bacterial enteritis model in vivo, mice were randomly assigned to groups. They were treated for 7 days, the symptoms were observed daily, and the daily DAI and body weight change rate were calculated. Following administration, the intestinal tissue was removed, and the contents were separated. The tight junction protein expression was detected in the small intestine by the immunohistochemical method; ELISA and Western Blot (WB) were performed to detect the expressions of tumor necrosis factor-α (TNF-α) and interleukin- 6 (IL-6) in the serum and intestinal wall of mice. The 16S rRNA sequence was used to determine the composition and diversity of the intestinal flora of mice. In total, 130 intersection targets of Pun and disease were screened by network pharmacology. The enrichment analysis showed cross genes were closely related and enriched in the cancer regulation and the TNF signal pathway. The active components of Pun could specifically bind to the core targets TNF, IL-6, etc., determined from molecular docking results. In vivo experiment results showed that the symptoms in the PUN group mice were alleviated, and the expression levels of TNF-α and IL-6 were significantly reduced. A Pun can cause substantial changes in the intestinal flora of mice in terms of structure and function. Pun plays a multi-target role in alleviating bacterial enteritis by regulating intestinal flora.

Modelling hydrologic processes of a karst region in Southern China using coupled hydrological models

ABSTRACT In the karst area, the epikarst increases the infiltration of rainfall and the depressions and conduits make water exchange frequently between surface rivers and karst aquifers. To improve simulation accuracy, we proposed the Soil and Water Assessment Tool-Genetic Algorithm-Back Propagation (SWAT-GA-BP) model. Also, the SWAT-tank model was created to elaborate the runoff processes in different karst systems. Furthermore, we designed three scenarios to study the influence of land use changes on runoff. The Zuo River Basin was selected as a case study. Compared to SWAT, the determination coefficient of the SWAT-GA-BP increased by 4.8% and the Nash-Sutcliffe efficiency increased by 4.9%; the rapid flow accounted for more than 50% of the total runoff, and the slow flow for less than 10%; the contribution of the land use changes was 10.80% from 2008 to 2012 and 5.57% from 2013 to 2017. Therefore, the effect of land use change on runoff should not be ignored.

Blood Kinetics of Lipophilic and Proteinophilic Pollutants during Two Types of Long-Term Fast in King Penguins.

In vertebrates, fasting is an intricate physiological process associated with strong metabolic changes, yet its effect on pollutant residue variation is poorly understood. Here, we quantified long-term changes in plasma concentrations of 20 organochlorine and 16 perfluoroalkyl pollutants in king penguins Aptenodytes patagonicus during the breeding and molting fasts, which are marked by low and high levels of protein catabolism, respectively, and by strong lipid use. The profile of measured pollutants in plasma was dominated by perfluorooctanesulfonic acid (PFOS, initial relative contribution of 60%). Initial total pollutant concentrations were similar in molting (3.3-5.7 ng g-1 ww) and breeding penguins (range of 4.2-7.3 ng g-1 wet weight, ww). Long-term fasting (25 days) for molting and breeding led, respectively, to a 1.8- and 2.2-fold increase in total plasma pollutant concentrations, although the rate and direction of change were compound-specific. Hexachlorbenzene (HCB) and PFOS concentrations increased in plasma (net mobilization) during both types of fasting, likely due to lipid use. Plasma perfluoroundecanoate (PFUnDA) and perfluorotridecanoate (PFTrDA) concentrations increased in breeders (net mobilization) but decreased in molting individuals (net excretion), suggesting a significant incorporation of these pollutants into feathers. This study is a key contribution to our understanding of pollutant variation in blood during long-term fasting in wildlife.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Forecasting some climate parameters of Türkiye using the SSP3-7.0 scenario for the years 2040–2059

This study employs the Coupled Model Inter-comparison Projects (CMIPs) and the Sixth phase of CMIPs (CMIP6) to unravel the multifaceted impacts of global climate change on climate of Türkiye. The CMIP6 data, fundamental to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, forms the basis for projecting future climate scenarios, specifically under the medium-high reference scenario SSP3-7. Utilizing a suite of global climate models, including the innovative Multi-Model Ensemble (MME) approach, this study combines predictions to enhance the precision climate projections of Türkiye. Historical data spanning from 1951 to 2020 were subjected to rigorous statistical analysis, including descriptive statistics and regression analysis. The findings reveal an unequivocal upward trajectory in Türkiye’s annual mean temperature, with an accelerated pace in recent decades. Despite a lack of a significant long-term trend in annual precipitation from 1951 to 2020, the rate of change in precipitation is accelerating, indicating potential future challenges. Projections for 2040-2059 under the SSP3-7.0 scenario indicate a non-uniform increase in mean temperature across Türkiye, with the southern and western regions facing the most significant impact. This warming trend poses imminent threats to agriculture, altering crop yields and increasing the risk of heat stress for livestock. Additionally, the projected decrease in precipitation, alongside a surge in hot days and tropical nights, underscores the urgency for adaptive measures. As Türkiye navigates the complex terrain of climate change, this study provides valuable insights, emphasizing the significance of robust climate modeling for informed decision-making. The results underscore the imminent challenges Türkiye faces and emphasize the critical importance of proactive climate action on both national and global fronts.

Mathematical model for characterization of temperature-responsive polymers: A study on the rheological behavior of gelatin and poly(N-isopropylacrylamide)

This study introduces a mathematical model for detailed characterization of temperature-responsive polymers, focusing on critical temperature, viscosity, storage modulus, their rate of change in the transition region, flow behavior, and potential hysteresis. Applied to gelatin (GEL) and poly(N-isopropylacrylamide) (pNIPAm), the model analyzes their rheological responses across various temperatures. It was observed that gel's critical temperature is slightly above its polymer's lower critical solution temperature, and vice versa for upper critical solution temperature polymers. The critical temperature for GEL was 28 °C, as opposed to 37 °C for pNIPAm. While concentration changes affected these values, the contribution of tetramethylethylenediamine (typically used in combination with various initiators to start the polymerization reaction) was found to be insignificant. GEL exhibited significant hysteresis, unlike pNIPAm's rapid transition. Their storage modulus and viscosity showed a linear correlation with concentration, with deviations at high pNIPAm concentrations suggesting the influence of polymer interactions and crosslinking. These findings offer valuable insights for applications requiring temperature sensitivity.

A novel approach to model cumulative stress: Area under the s-factor curve

ObjectiveUsing a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes. MethodWe used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes. ResultsThe prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change. ConclusionsLike endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.

A quantitative genetic model of background selection in humans.

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.

Telehealth during COVID-19 restrictions in patients with cardiovascular disease: impact on medication prescriptions and patient satisfaction.

Telehealth plays an integral part in healthcare delivery. The impact of telehealth and the COVID-19 pandemic on medication prescribing and patient satisfaction with telehealth in cardiology clinics remains unknown. A retrospective study of cardiology clinic patients at an Australian tertiary hospital was conducted; 630 patients seen before the COVID-19 pandemic (0.6% telehealth) and 678 during the pandemic (91.2% telehealth) were included. Medication changes, new prescriptions and time to obtaining prescriptions after clinic were compared. To evaluate patients' experiences, cardiology clinic patients reviewed during the pandemic were prospectively invited to participate in an electronic survey sent to their mobile phones. The overall rates of medication changes made in the clinic between the prepandemic and the pandemic periods did not differ significantly (26.9% vs 25.8%). Compared with prepandemic, new cardiac medication prescriptions during clinic were significantly less (9.3% vs 2.5%; P < 0.0001) and recommendations to general practitioners (GP) to initiate cardiac medications were significantly more (2.6% vs 9.1%; P < 0.0001). Time to obtaining new prescriptions was significantly longer in the pandemic cohort (median 0 days (range: 0-32) vs 10.5 days (range: 0-231); P < 0.0001). Two hundred forty-three (32.7%) patients participated in the survey; 50% reported that telehealth was at least as good as face-to-face consultations. Most patients (61.5%) were satisfied with telehealth and most (62.9%) wished to see telehealth continued postpandemic. Telehealth during the COVID-19 pandemic was associated with greater reliance on GPto prescribe cardiac medications and delays in obtaining prescriptions among cardiology clinic patients. Although most patients were satisfied with telehealth services, nearly half of the cardiac patients expressed preference towards traditional face-to-face consultations.

The Distribution and Evolution of Groundwater Level Depths and Groundwater Sustainability in the Hexi Corridor over the Last Five Years

Groundwater overexploitation for agricultural irrigation is prone to lead to numerous ecological concerns. This study delved into the present distribution and recent trend of groundwater levels in the plain areas of the Hexi Corridor in Northwest China according to the groundwater level depth (GWD) data from 264 monitoring wells in the Shiyang River Basin (SYB) and 107 in the Shule River Basin (SLB), recorded annually in April from 2019 to 2023. The key findings include the following: (1) Over the five-year span, the SYB’s GWD experienced change rates (CRs) ranging from −12.17 to 9.11 m/a (average: −0.13 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 50% and 50%, respectively. By contrast, the SLB’s GWD exhibited CRs ranging from −1.87 to 2.06 m/a (average: 0.01 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 52% and 48%, respectively; (2) the Wuwei (CR = 0.09 m/a) and Changning (0.58 m/a) basins in the SYB and the Yumen (0.06 m/a), Guazhou (0.05 m/a), and Huahai (0.03 m/a) basins in the SLB, witnessed rising groundwater levels. In contrast, the Minqin Basin (0.09 m/a) in the SYB and the southern Dunhuang Basin (0.04 m/a) in the SLB witnessed declines in the groundwater levels; (3) The groundwater sustainability assessment showed that the groundwater is still extremely unsustainable. This study’s insights are instrumental in targeted treatment, as well as the preparation and adjustment of sustainable groundwater protection strategies.

Research on vacuum glass insulation performance prediction based on unsteady state multivariate data screening and multi-model fusion self-optimization

A self-optimization regression method based on multivariate data screening and multi-model fusion is proposed for the regression prediction of vacuum glass insulation performance. Firstly, we analyzed the positive correlation between the temperature change rate and heat transfer coefficient. Secondly, we combined the technique of multi-distribution overall diffusion to generate virtual sample data with multiple variables corresponding to the small neighborhood of the target variable, addressing the issue of insufficient sample size. We conducted quantity and threshold screening on the generated data to enhance the effectiveness of the virtual sample data. The screened data and original training set data are integrated to form new training samples. Then, the limited-memory Broyden Fletcher Goldfarb Shanno with boundary constraints (L-BFGS-B) algorithm is introduced to optimize the parameters of multiple models to improve the accuracy of model regression prediction. Finally, through the self-searching optimization framework structure, the virtual sample data screening and optimization of various model parameters are updated. Eventually, the optimal model for predicting the heat transfer coefficient of vacuum glass insulation performance is determined from multiple models. In order to validate the effectiveness of self-optimization regression method, we conducted insulation performance heat transfer coefficient predictions under 30 different ways for vacuum glass. Experimental results demonstrate that the self-optimization regression method based on multivariate data screening and multi-model fusion can obtain the most effective regression prediction model, achieving accurate, rapid and intelligent prediction of vacuum glass insulation performance.