Negative Contribution Research Articles

Anthropogenic-induced ecological risks on marine ecosystems indicated by characterizing emerging pollutants in Pearl River Estuary, China

Anthropogenic Contaminants of Emerging Concern (CECs) in marine environments have raised significant concerns. Yet, analyses detailing their origins, fate, and environmental effects are limited. This study employs an integrated non-target screening methodology to elucidate CECs existence across 46 sampling sites in the Pearl River Estuary (PRE) of the South China Sea. Assisted by advanced liquid chromatography-high resolution mass spectrometry, we discovered 208 chemicals in six usage categories, with pesticides (33 %) and pharmaceuticals (29 %) predominating. Several CECs drew attention for their consistent detections, profound abundance, and significant ecotoxicities. The wide detection of them at offshore sites further implies that anthropogenic activities may contribute to large-scale contamination. Meanwhile, distinct distribution patterns of CECs across PRE are evident in semi-quantitative results, indicating regional anthropogenic influences. Identified transformation products may establish a novel and non-negligible negative contribution to ecology through elevated environmental toxicities, exemplified by HMMM and atrazine. Based on the ecological risks, we compiled a prioritized list of 21 CECs warranting intensified scrutiny. Our findings indicate the introduction of various CECs into the South China Sea via PRE, emphasizing the urgent necessity for ongoing surveillance of discharged CECs at estuary areas and assessment of their marine ecological consequences.

Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China

A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.

Summer heatwaves in China during 1961–2021: The impact of humidity

In additon to temperature, heatwaves are also closely related to humidity. In this study, daily wet-bulb temperature was used to define heatwaves (WBHWs) with a unified threshold for more than 2000 meteorological stations throughout China. The climatic averages of summer heatwaves and changes from 1961 to 2021 were investigated. WBHWs primarily occur in three key subregions, namely North China, South China, and Yangtze River valley (YRV). Humidity makes heatwaves in South China more severe, with an average of 25 days per summer. Over the past decades, the frequency and intensity of WBHWs in China considerably increased accompanied by decreasing relative humidity (RH) and increasing temperature. South China experiences the strongest increase in WBHWs, with change rate fivefold higher than the national average. The occurrence of WBHWs in YRV is dominated by temperature, whereas in North and South China it is controlled by RH. The long-term increases in WBHWs for China and its three key subregions are primarily caused by temperature changes, but RH affects WBHWs at interdecadal time scales and generally exerts a negative contribution. The average contribution rates of temperature and RH to the increase in WBHWs in China for the period 1991–2021 are 2.3 days and − 0.6 days. Changes in WBHWs in North China are almost completely controlled by temperature, since the opposite phases of specific humidity and temperature result in a negligible effect of RH. Temperature and RH in other two subregions have substantial positive and negative effects, respectively. The negative effect of RH in South China is mainly attributed to the indirect effect of temperature, while that in YRV, particularly in the lower reaches, is a combination of specific humidity and temperature.

Anhydrous Aluminum Iodate: Strong Second Harmonic Generation Effect Contributed by Unbonded and Antibonding Orbitals.

Exploring materials that balance the second harmonic generation (SHG) effect and laser-induced damage threshold (LIDT) is the frontier of nonlinear optical (NLO) crystal research at present. In this work, the NLO property of anhydrous aluminum iodate is extensively explored and discussed first. It exhibits a strong SHG intensity of 18.3 × KH2PO4 (KDP) and a high-powder LIDT of 1.4 × KDP at 1064 nm. Combining experimental and theoretical studies at the atomic level and electronic levels, it is found that the cations in the structure are replaced by cations with small radius and high valence, enabling the production of materials with large SHG responses. Unbonded and antibonding orbitals play a crucial positive role in the SHG response of the structure, whereas bonding orbitals produce a large negative contribution. This provides a scarce example of materials in which bonding orbitals make significant negative contributions.

Undergraduate students’ learning experiences and strategies in online learning during the pandemic: An Indonesian perspective

Transitions in learning implementation have occurred at various levels of education during the COVID-19 pandemic. Learning that previously took place in conventional, face-to-face formats has been adjusted to fully online learning. Not all educational institutions are equally prepared for a pandemic, necessitating the study of student learning experiences and the strategies they employ in online classes during the pandemic. This study focuses on the positive and negative contributions of Virtual Learning Environment (VLE) features in student learning experiences and strategies during the pandemic. This study used a survey method consisting of closed- and open-ended questions. Data analysis using descriptive statistics and qualitative data analysis was employed to obtain several themes. Approximately 1,400 students from 23 higher education institutions across several provinces in Indonesia participated in this research. The results show both positive and negative contributions of VLE features. Among the VLE features that positively contribute to learning during the pandemic are video materials and downloadable documents. More than half of the respondents claimed there were no negative features of the VLE. Findings also revealed the most frequently reported negative aspect is poor internet performance. In addition, seven themes related to student learning strategies emerged including: considering the availability of infrastructure, monitoring the class schedule and calendar (or announcements), collaborating with peers, participating in the university’s learning management system, applying notetaking strategies and other specific strategies, searching for additional learning resources, and no specific strategy applied while learning during the pandemic.

Prediction of atrazine degradation in soil based on XGBoost model.

We established the optimal model by using the automatic machine learning method to predict the degradation efficiency of herbicide atrazine in soil, which could be used to assess the residual risk of atrazine in soil. We collected 494 pairs of data from 49 published articles, and selected seven factors as input features, including soil pH, organic matter content, saturated hydraulic conductivity, soil moisture, initial concentration of atrazine, incubation time, and inoculation dose. Using the first-order reaction rate constant of atrazine in soil as the output feature, we established six models to predict the degradation efficiency of atrazine in soil, and conducted comprehensive analysis of model performance through linear regression and related evaluation indicators. The results showed that the XGBoost model had the best performance in predicting the first-order reaction rate constant (k). Based on the prediction model, the feature importance ranking of each factor was in an order of soil moisture > incubation time > pH > organic matter > initial concentration of atrazine > saturated hydraulic conductivity > inoculation dose. We used SHAP to explain the potential relationship between each feature and the degradation ability of atrazine in soil, as well as the relative contribution of each feature. Results of SHAP showed that time had a negative contribution and saturated hydraulic conductivity had a positive contribution. High values of soil moisture, initial concentration of atrazine, pH, inoculation dose and organic matter content were generally distributed on both sides of SHAP=0, indicating their complex contributions to the degradation of atrazine in soil. The XGBoost model method combined with the SHAP method had high accuracy in predicting the performance and interpretability of the k model. By using machine learning method to fully explore the value of historical experimental data and predict the degradation efficiency of atrazine using environmental parameters, it is of great significance to set the threshold for atrazine application, reduce the residual and diffusion risks of atrazine in soil, and ensure the safety of soil environment.

Small Disturbance Stability Analysis of Onshore Wind Power All-DC Power Generation System Based on Impedance Method

The Onshore Wind Power All-DC Generation System (OWDCG) is designed to integrate with renewable energy sources by modifying the grid structure. This adaptation supports the grid infrastructure and addresses the challenges of large-scale wind power AC collection and harmonic resonance during transmission. Crucially, small disturbance stability parameters are essential for ensuring the system’s stable operation. Unlike conventional power systems, the OWDCG exhibits strong coupling between subsystems, accentuating the small disturbance stability issue due to the dynamic nature of its converter control system. The impedance method facilitates the decomposition of such systems into subsystems, offering insights into the destabilization mechanism through the lens of negative impedance contribution. This approach is conducive to conducting small disturbance stabilization analyses. To tackle this issue, the initial step involves deriving the input and output equivalent impedance models of the subsystem, considering the topological structure, control features, and operational dynamics of the OWDCG. Subsequently, the impact of circuit and control parameters on the system’s impedance characteristics and small-disturbance stability is examined through Bode diagrams and Nyquist curves. This analysis identifies critical parameters for small-disturbance stability, guiding the stable operation and parameter optimization of the OWDCG. The analysis highlights that the main control strategies for stability are the Modular Multilevel Converter (MMC) DC voltage control and the inner-loop current control gain. Validation of the theoretical findings is achieved through simulation results using PSCAD/EMTDC.

Explainable machine learning for predicting stomatal conductance across multiple plant functional types

Stomatal conductance (gs) is a key leaf-level function controlling water, carbon, and energy exchange between vegetation and the surrounding environment. Conventionally, semi-empirical models have been used to model gs, but these models require re-parameterization as ecosystems undergo phenological changes over the growing season. In contrast, machine learning (ML) models offer a potential path to overcome this problem but are less interpretable than process-based models. This study explores ML as an approach to develop flexible and robust models of gs for a range of plant functional types (PFTs), including C3 crops, C3 grasses, shrubs, and tree species across different continents. An explainable machine-learning approach (eXML) was used here to provide novel interpretations and insights into the ML model formulations and relative predictor importance. We contrast the performance of three ML architectures: extreme gradient boosting, random forests, and neural networks. Models were developed and examined using many combinations of environmental and physiological predictors. The results demonstrated that ML models significantly outperform conventional semi-empirical models in predicting gs responses to the environment, while not requiring re-parameterization as is required in the semi-empirical paradigm. Particular focus is placed on models formulated around predictor sets that are: (a) relevant to gs estimation in modern terrestrial biophysical simulation models, and (b) composed of variables describing environmental and physiological drivers that can be remotely sensed non-invasively. “Generalized” models developed using data from all four PFTs demonstrated strong predictive performance using only three predictor variables, capturing 63–80 % of the variability in stomatal conductance across all ML architectures. Four predictor variables resulted in models capturing 79–83 % of gs variability, and models developed using all five predictor variables examined here were able to capture as much as 87 % of gs variability across all PFTs. Uncertainty in gs predictions was quantified using quantile regression. Shapley additive explanations was applied to unravel instance-based positive and negative contributions of environmental and physiological predictors to gs modeling, while illustrating that the models are consistent with the underlying ecophysiology. This work demonstrates the power of ML to introduce a new paradigm in the simulation of highly dynamic ecophysiological processes critical to environmental prediction.

Study on the calculation method of the maximum number of bonded steel plates at the bottom of reinforced concrete beams

Purpose When the reinforced concrete beams are reinforced by bonding steel plates to the bottom, excessive use of steel plates will make the reinforced concrete beams become super-reinforced beams, and there are security risks in the actual use of super-reinforced beams. In order to avoid the occurrence of this situation, the purpose of this paper is to study the calculation method of the maximum number of bonded steel plates to reinforce reinforced concrete beams.Design/methodology/approach First of all, when establishing the limit failure state of the reinforced member, this paper comprehensively considers the role of the tensile steel bar and steel plate and takes the load effect before reinforcement as the negative contribution of the maximum number of bonded steel plates that can be used for reinforcement. Through the definition of the equivalent tensile strength, equivalent elastic modulus and equivalent yield strain of the tensile steel bar and steel plate, a method to determine the relative limit compression zone height of the reinforced member is obtained. Second, based on the maximum ratio of (reinforcement + steel plate), the relative limit compression zone height and the equivalent tensile strength of the tensile steel bar and steel plate of the reinforced member, the calculation method of the maximum number of bonded steel plates is derived. Then, the static load test of the test beam is carried out and the corresponding numerical model is established, and the reliability of the numerical model is verified by comparison. Finally, the accuracy of the calculation method of the maximum number of bonded steel plates is proved by the numerical model.Findings The numerical simulation results show that when the steel plate width is 800 mm and the thickness is 1–4 mm, the reinforced concrete beam has a delayed yield platform when it reaches the limit state, and the failure mode conforms to the basic stress characteristics of the balanced-reinforced beam. When the steel plate thickness is 5–8 mm, the sudden failure occurs without obvious warning when the reinforced concrete beam reaches the limit state. The failure mode conforms to the basic mechanical characteristics of the super-reinforced beam failure, and the bending moment of the beam failure depends only on the compressive strength of the concrete. The results of the calculation and analysis show that the maximum number of bonded steel plates for reinforced concrete beams in this experiment is 3,487 mm2. When the width of the steel plate is 800 mm, the maximum thickness of the steel plate can be 4.36 mm. That is, when the thickness of the steel plate, the reinforced concrete beam is still the balanced-reinforced beam. When the thickness of the steel plate, the reinforced concrete beam will become a super-reinforced beam after reinforcement. The calculation results are in good agreement with the numerical simulation results, which proves the accuracy of the calculation method.Originality/value This paper presents a method for calculating the maximum number of steel plates attached to the bottom of reinforced concrete beams. First, based on the experimental research, the failure mode of reinforced concrete beams with different number of steel plates is simulated by the numerical model, and then the result of the calculation method is compared with the result of the numerical simulation to ensure the accuracy of the calculation method of the maximum number of bonded steel plates. And the study does not require a large number of experimental samples, which has a certain economy. The research result can be used to control the number of steel plates in similar reinforcement designs.

Factors Influencing Economic Growth in Nigeria: A Growth Accounting Framework Analysis

This research examines the drivers of economic expansion in Nigeria from 1960 to 2017, employing the growth accounting methodology based on the classical neoclassical production function. The focus is on assessing the contributions of capital, labor, and total factor productivity (TFP) to Nigeria's economic growth. Furthermore, to determine the interrelations among capital, labor, TFP, and economic growth, correlation coefficients among these variables were calculated. The correlation analysis indicated positive associations between the growths of capital, labor, TFP, and economic growth. Moreover, findings from the growth accounting analysis highlighted capital as the primary contributor to economic growth in Nigeria throughout 1961-2017. In specific sub-periods, capital remained the dominant growth driver in the initial phase (1961-1980), while labor took precedence in the 1981-2000 period, with capital following and TFP growth showing a decline, as evidenced by its negative contribution. Nonetheless, TFP emerged as the critical factor driving economic growth from 2001-2017. Consequently, the study suggests the implementation, maintenance, and enhancement of policies that promote physical capital, human capital, and technological advancements through both domestic and foreign investments, recognizing their essential roles in fostering economic growth and development.

Modulations of Madden–Julian Oscillation and Quasi-Biweekly Oscillation on Early Summer Tropical Cyclone Genesis over the Bay of Bengal and South China Sea

Abstract The Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are prominent components of the intraseasonal oscillations over the tropical Indo-Pacific Ocean. This study examines the tropical cyclone (TC) genesis over the Bay of Bengal (BOB) and the South China Sea (SCS) on an intraseasonal scale in May–June during 1979–2021. Results show that the convection associated with the two types of intraseasonal oscillations simultaneously modulates TC genesis in both ocean basins. As the MJO/QBWO convection propagated, TCs form alternately over the two basins, with a significant increase (decrease) in TC genesis frequency in the convective (nonconvective) MJO/QBWO phase. Based on the anomalous genesis potential index associated with the MJO/QBWO, an assessment of the influence of various factors on TC genesis is further assessed. Middle-level relative humidity and lower-level relative vorticity play key roles in the MJO/QBWO modulation on TC genesis. The MJO primarily enhances large-scale cross-equatorial moisture transport, resulting in significant moisture convergence, while the QBWO generally strengthens the monsoon trough and induces the retreat of the western North Pacific subtropical high, increasing the regional lower-level relative vorticity. The potential intensity and vertical wind shear make small or negative contributions. This study provides insights into the neighboring basin TC relationship at intraseasonal scales, which has a potential to improve the short-term prediction of regional TC activity. Significance Statement The Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are two types of intraseasonal tropical atmospheric oscillations. The development of tropical cyclones (TCs) is often accompanied by intraseasonal convection. This study highlights the distinct roles of MJO and QBWO in TC genesis over the South Asian marginal seas (e.g., Bay of Bengal and South China Sea). The QBWO can co-regulate TC genesis along with the background of the MJO, where the large-scale MJO mainly provides moisture, while the small-scale QBWO mainly contributes to vorticity. These findings provide useful information for subseasonal TCs forecasting. There are many developing countries along the South Asian marginal seacoast; therefore, further research on regional TC climate would help effectively reduce casualties and property damage.

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.

Machine learning – Driven surface grafting of thin-film composite reverse osmosis (TFC-RO) membrane

Modifying reverse-osmosis (RO) membrane performance is challenging and time-consuming due to the complex interplay of various factors that influence the membrane's performance. To address this challenge, we have explored the potential of using machine-learning (ML) to graft the polyamide (PA) surface of an RO membrane to increase water permeability and overcome the limitations of the permeability/selectivity tradeoff. We identified moieties with positive and negative contributions toward water permeability by applying Shapley-Additive-exPlanations (SHAP) analysis to our model as an explainable artificial intelligence (XAI) method. We attempted to improve the subunits of the PA's structure with positive Shapley values and graft the polyamide RO membrane layer of a commercial membrane, Dupont XLE, resulting in a substantial increase in water permeability. The membranes were characterized using FTIR, EDS, and SEM analysis, and their performance was evaluated for water permeance and NaCl rejection using a dead-end stirred cell. The modified membrane exhibited a significant improvement in the commercial membrane's water flux, increasing from 2.45 LMH to 4.9 LMH. Our results demonstrate the potential of using ML to replace traditional trial-and-error methods for modifying PA-RO membrane polyamide layers and advancing the development of higher efficient and sustainable RO membranes for water treatment and purification applications.

A cosmic glitch in gravity

We investigate a model that modifies general relativity on cosmological scales, specifically by having a `glitch' in the gravitational constant between the cosmological (super-horizon) and Newtonian (sub-horizon) regimes, as motivated e.g. in the Hořava-Lifshitz proposal or in the Einstein-aether framework. This gives a single-parameter extension to the standard ΛCDM model, which is equivalent to adding a dark energy component, but where the energy density of this component can have either sign. Fitting to data from the Planck satellite, we find that negative contributions are, in fact, preferred. Additionally, we find that roughly one percent weaker superhorizon gravity can somewhat ease the Hubble and clustering tensions in a range of cosmological observations, although at the expense of spoiling fits to the baryonic acoustic oscillation scale in galaxy surveys. Therefore, the extra parametric freedom offered by our model deserves further exploration, and we discuss how future observations may elucidate this potential cosmic glitch in gravity, through a four-fold reduction in statistical uncertainties.

Cartographic evaluation of the risk of natural elements' deficiency in the soil cover provoking spatial variation of the endemic morbidity level (on example of thyroid morbidity among population of the Central Federal District, Russia).

The main goal of the study is to evaluate the contribution of the natural geochemical risk in the central part of the Eastern European Plain to the spatial distribution of human diseases provoked by the deficiency of biologically significant microelements (Co, Cu, and I) in the environment. The Central Federal District (CFD) of Russia, located in the Eastern European Plain is characterized by a deficiency of Co, Cu, and I in the environment (soils, local food). To access the risk of thyroid diseases associated with Co, Cu, and I content in soils of the CFD based on published data of trace elements concentrations and digital soil map we create maps of the elements variation in soil cover allowing to estimate their mean concentration in the regions. The obtained cartographic estimates are comparable with the previously published assessments and averaged study results at the regional level. Comparison with medical data on thyroid disease morbidity from 2013 to 2017 at the regional level showed a significant inverse correlation with the cartographic estimates of soil I and combined (Co + Cu + I) status with due consideration of soil structure (12 regions, except for those affected by technogenic radioiodine contamination and Moscow urbanized regions). The urban population suffered from thyroid diseases to a higher extent in comparison with the rural population, which corresponds to our previous estimates. The results confirmed the possibility of assessing the geochemical risk of endemic diseases based on geochemical soil maps and identifying the negative contribution of micronutrient deficiency in the environment to endemic morbidity in the population.

Supervised intelligent prediction of shear strength of rockfill materials based on data driven and a case study

The rockfill materials (RFM) are emerging and regarded waste reuse product in construction and mining engineering. In this paper, six distinctive supervised machine learning (SML) models, namely artificial neural network (ANN), extreme learning machine (ELM), random forest (RF), relevance vector regression (RVR), support vector regression (SVR), and extreme gradient boosting (XGBoost), are adopted to predict the RFM shear strength using 165 data cases with 13 features. To improve model performance, an improved physics meta-heuristic algorithm, named snow ablation optimizer with Logistic mapping (LogSAO), is utilized to select the optimal hyperparameter combination of the proposed models. Four popular statistical indices, including coefficient of determination (R2), root mean squared error (RMSE), Willmott’s index (WI), and Scatter index (SI) are employed to quantify the model performance. The model evaluation results show that the LogSAO-SVR model is the best prediction model with the highest values of R2 and WI while the lowest values of RMSE and SI in both training and testing phases (0.9985, 0.0257, 0.9996, and 0.0380; 0.9802, 0.0812, 0.9950, and 0.1335). Normal stress (Ns) is the most important feature with the highest importance score of 0.925 in predicting the RFM shear strength. Interestingly, the negative contribution of Ns to the RFM shear strength prediction will be transformed into the positive contribution when coefficients of curvature (Cu) values are small. The validation results demonstrate that the predicted values of the proposed LogSAO-SVR model are highly consistent with the measured values from the direct shear tests based on a case study.

Response of Land Surface Temperature to Heatwave-Induced Bio-Geophysical Changes in Tropical Forests on Hainan Island from 2010 to 2022

Land surface temperature plays an important role in the water cycle and surface energy balance. Using data collected by a vorticity covariance tower from 2010 to 2022, the relative threshold method and TRM method were employed to study the land–atmosphere exchange of water and the heat flux of rubber forest ecosystems under heatwave and non-heatwave conditions. The results show that the latent heat flux, sensible heat flux, and incoming and outgoing radiation increase from non-heatwave to heatwave conditions. In addition, the multi-year average LST was 6.7 °C higher under HW conditions than under non-HW conditions at the 99% confidence level. Further attribution analysis demonstrates that heatwave-induced land surface temperature change is mainly governed by atmospheric factors rather than by land surface factors. Specifically, radiative forcing shows the largest positive contribution, which is partly offset by the negative contributions of air temperature and relative humidity. In particular, the contributions of radiative forcing, air temperature, relative humidity, and atmospheric pressure to LST were 14.70 K, −4.76 K, −5.86 K, and −0.04 K, respectively. Moreover, surface resistance contributed to LST by 2.42 K, aerodynamic resistance by −0.23 K, and soil heat flux by −0.91 K.

Patterns and Changes in Life Expectancy in Korea, 1990-2020

Objectives: The purpose of this study is to measure the contribution of life expectancy to changes in mortality due to age, sign in Korea.Methods: This study used the life table data (1990-2020) and analyzed the data of the death statistics of the National Statistical Office in 1990 and 2020 (Categorization data by ICD-10). The contribution of differen ages, and causes of death to the change in life expectancy at birth during the 1900-2020 was estimated with a decomposition technique developed by Arriaga.Results: The contribution to the average life span of 1990-2007 was 7.50 years, and 2.91 years between 2007-2015. From 1990 to 2007, the positive contributing causes of death were cerebrovascular disease (0.781 years), hypertensive disease (0.599 years), and heart disease (0.475 years), and the negative contributing causes were intentional self-harm (-0.304 years), pneumonia (-0.209 years), and colon cancer (-0.092 years). From 2007 to 2015, the positive contributing causes were malignant neoplasms (0.548 years), cerebrovascular disease (0.529 years), and diabetes (0.162 years), and the negative contributing causes were pneumonia (-0.198 years), Alzheimer’s (-0.060 years), sepsis (-0.038 years), etc. From 2015 to 2020, the positive contributing causes of death were malignant neoplasms (0.474 years), cerebrovascular disease (0.301 years), and heart disease (0.475 years), and the negative contributing causes were intentional self-harm (-0.304 years), pneumonia (-0.209 years), and colon cancer (-0.092 years). In general, pneumonia was shown to be the cause of death with the largest negative contribution due to increased mortality, and the pattern by age group showed that the positive contribution was relatively large in the age group after 65 years of age. In common, the positive contribution of most causes decreased as time passed.Conclusions: The age group that contributed the most to the increase in life expectancy after 1990 was found to be 70-74 years old (0.74 years) from 1990-2007. In addition, it was found to have contributed significantly to those aged 70-74 (0.42 years) from 2007-2015 and those aged 75-79 (0.27 years) from 2015-2020. In the overall period, the contribution to average life expectancy appears to be large in the age group of 65 years or older, and in recent years, the contribution to average life expectancy from age 0 gradually decreases, while the contribution to average life expectancy in the middle-aged and older age group increases. This study evaluated the increase in life expectancy by age and cause of death by applying join point regression analysis, which can partially complement the limitations of existing models, to evaluate and analyze the amount of life expectancy according to changes in mortality level.

Multi-spectroscopic and molecular docking studies for the pH-dependent interaction of β-lactoglobulin with (−)-epicatechin gallate and/or piceatannol: Influence on antioxidant activity and stability

(−)-Epicatechin gallate (ECG) and piceatannol (PIC) are commonly polyphenols with excellent biological activities. β-Lactoglobulin (BLG) is a food-grade globule protein and its morphologies are sensitive to pH. This study used experimental and computational methods to determine the interaction of single or combined ECG and PIC with BLG at different pHs. The static quenching process was determined through fluorescence and ultraviolet–visible spectroscopy. Compared with ECG, PIC could significantly bind to BLG with higher affinity. Their binding affinity for BLG with different morphologies followed the tendency of monomer > dimer > tetramer. The negative contribution of van der Waals forces, electrostatic interactions, and hydrogen bonds to ΔHo exceeded the positive contribution of hydrophobic interactions in the spontaneous and exothermic process. The reduced binding affinity in the ternary systems demonstrated the competitive binding between ECG and PIC on BLG, and the hinder effect of ECG or PIC was enhanced with increasing pH. Molecular docking studies revealed the same binding sites of ECG and PIC on various conformations of BLG and identical driven forces as thermodynamic results. Tryptophan and tyrosine were the main participators in the BLG + ECG and BLG + PIC systems, respectively. The conformational changes in the binary and ternary systems could be ascertained through synchronous fluorescence, circular dichroism, and dynamic light scattering. Furthermore, the effects of pH and BLG encapsulation on the antioxidant capacity and stability of ECG or PIC were also implemented. ECG or PIC was the most stable in the (BLG + PIC) + ECG system at pH 6.0. This study could clarify the interaction mechanism between ECG/PIC and BLG and elucidate the pH effect on their binding information. The results will provide basic support for their usage in food processing and applications.

Flavor analysis and region prediction of Chinese dishes based on food pairing

There are thirty-four provincial administrative regions in China, and each region possesses its own distinct food culture. However, existing studies on flavor-based research often treat Chinese dishes from various regions as a single entity, resulting in biases when examining regional distinctions and the variety of recipes. To explore the potential correlation between food ingredients and regions, this paper performs flavor analysis and region prediction for Chinese typical dishes. A new dataset including 1534 recipes combined with a compound-ingredient list to construct a compound-ingredient-recipe network. The results indicate that in terms of food ingredient selection, Hainan shows the highest degree of flavor compound sharing, while Shanghai does the lowest. While all the regions studied tend to exhibit negative food pairing according to the food pairing hypothesis, a region with a high degree of flavor sharing is more able to become prominent representation of the corresponding flavor branch through the analysis of representative ingredients. Across the 14 regions studied, the frequency of ingredient usage within a region plays a crucial role in maintaining its distinct flavor profile. Concerning the individual contributions of ingredients to flavor compound sharing, the positive contributions are linked to rice and natural spices, including Welsh onion, ginger, and garlic, while the negative contributions are primarily associated with flour, pork, and traditional Chinese condiments such as soy sauce, cooking wine, and grain vinegar. Multiple deep learning experiments demonstrate the effectiveness of combining ingredient and flavor information to region prediction for recipes.