Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the lit...With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.展开更多
Background Conditional relative survival(CRS),the probability of survival given that an individual has already survived a certain period post-diagnosis,is a more clinically relevant measure for long-term survival than...Background Conditional relative survival(CRS),the probability of survival given that an individual has already survived a certain period post-diagnosis,is a more clinically relevant measure for long-term survival than standard relative survival(RS).This study aims to evaluate the 5-year CRS among adolescent and young adult(AYA)breast cancer patients by age,tumor stage,and receptor subtype to guide disclosure periods for insurance.Methods Data of all females aged 18–39 years and diagnosed with invasive breast cancer between 2003 and 2021(n=13,075)were obtained from The Netherlands Cancer Registry(NCR).The five-year CRS was calculated annually up to 10 years post-diagnosis using a hybrid analysis approach.Results For the total AYA breast cancer study population the 5-year CRS exceeded 90%from diagnosis and increased beyond 95%7 years post-diagnosis.Patients aged 18–24 reached 95%9 years post-diagnosis,those aged 25–29 after 5 years,and those aged 30–34 and 35–39 after 8 years.For stage I,the 5-year CRS reached 95%from diagnosis,for stage II after 6 years,while the 5-year CRS for stages III and IV did not reach the 95%threshold during the 10-year follow-up.Triple-negative tumors exceeded 95%after 4 years,human epidermal growth factor receptor 2(HER2)positive tumors after 6 years,while hormone receptor(HR)positive tumors did not reach 95%.Conclusion Excess mortality among AYA breast cancer patients tends to be little(CRS 90%–95%)from diagnosis and becomes minimal(CRS>95%)over time compared to the general population.These results can enhance expectation management and inform policymakers,suggesting a shorter disclosure period.展开更多
Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pest...Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pests and their natural enemies is required to minimize the pest population and yield losses.In the current study,analysis of the seasonal population trend of pests and natural enemies and their relative occurrence on cultivars of three cotton species in Central India has been carried out.Results A higher number and diversity of sucking pests were observed during the vegetative cotton growth stage(60 days after sowing),declining as the crop matured.With the exception of cotton jassid(Amrasca biguttula biguttula Ishida),which caused significant crop damage mainly from August to September;populations of other sucking insects seldom reached economic threshold levels(ETL)throughout the studied period.The bollworm complex populations were minimal,except for the pink bollworm(Pectinophora gossypiella Saunders),which re-emerged as a menace to cotton crops during the cotton cropping season 2017–2018 due to resistance development against Bt-cotton.A reasonably good number of predatory arthropods,including coccinellids,lacewings,and spiders,were found actively preying on the arthropod pest complex of the cotton crop during the early vegetative growth stage.Linear regression indicates a significant relationship between green boll infestations and pink bollworm moths in pheromone traps.Multiple linear regression analyse showed mean weekly weather at one-or two-week lag periods had a significant impact on sucking pest population(cotton aphid,cotton jassid,cotton whitefly,and onion thrips)fluctuation.Gossypium hirsutum cultivars RCH 2 and DCH 32,and G.barbadense cultivar Suvin were found susceptible to cotton jassid and onion thrips.Phule Dhanvantary,an G.arboreum cotton cultivar,demonstrated the highest tolerance among all evaluated cultivars against all sucking pests.Conclusion These findings have important implications for pest management in cotton crops.Susceptible cultivars warrant more attention for plant protection measures,making them more input-intensive.The choice of appropriate cultivars can help minimize input costs,thereby increasing net returns for cotton farmers.展开更多
Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmen...Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.展开更多
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.展开更多
Gas-bearing shales have become a major source of future natural gas production worldwide.It has become increasingly urgent to develop a reliable prediction model and corresponding workflow for identifying shale gas sw...Gas-bearing shales have become a major source of future natural gas production worldwide.It has become increasingly urgent to develop a reliable prediction model and corresponding workflow for identifying shale gas sweet spots.The formation of gas-bearing shales is closely linked to relative sealevel changes,providing an important approach to predicting sweet spots in the Wufeng-Longmaxi shale in the southern Sichuan Basin,China.Three types of marine shale gas sweet spots are identified in the shale based on their formation stages combined with relative sea-level changes:early,middle,and late transgression types.This study develops a prediction model and workflow for identifying shale gas sweet spots by analyzing relative sea-level changes and facies sequences.Predicting shale gas sweet spots in an explored block using this model and workflow can provide a valuable guide for well design and hydraulic fracturing,significantly enhancing the efficiency of shale gas exploration and development.Notably,the new prediction model and workflow can be utilized for the rapid evaluation of the potential for shale gas development in new shale gas blocks or those with low exploratory maturity.展开更多
Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristic...Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.展开更多
Discrete fracture network(DFN)commonly existing in natural rock masses plays an important role in geological complexity which can influence rock fracturing behaviour during fluid injection.This paper simulated the hyd...Discrete fracture network(DFN)commonly existing in natural rock masses plays an important role in geological complexity which can influence rock fracturing behaviour during fluid injection.This paper simulated the hydraulic fracturing process in lab-scale coal samples with DFNs and the induced seismic activities by the discrete element method(DEM).The effects of DFNs on hydraulic fracturing,induced seismicity and elastic property changes have been concluded.Denser DFNs can comprehensively decrease the peak injection pressure and injection duration.The proportion of strong seismic events increases first and then decreases with increasing DFN density.In addition,the relative modulus of the rock mass is derived innovatively from breakdown pressure,breakdown fracture length and the related initiation time.Increasing DFN densities among large(35–60 degrees)and small(0–30 degrees)fracture dip angles show opposite evolution trends in relative modulus.The transitional point(dip angle)for the opposite trends is also proportionally affected by the friction angle of the rock mass.The modelling results have much practical meaning to infer the density and geometry of pre-existing fractures and the elastic property of rock mass in the field,simply based on the hydraulic fracturing and induced seismicity monitoring data.展开更多
Magnesium and magnesium alloys,serving as crucial lightweight structural materials and hydrogen storage elements,find extensive applications in space technology,aviation,automotive,and magnesium-based hydrogen industr...Magnesium and magnesium alloys,serving as crucial lightweight structural materials and hydrogen storage elements,find extensive applications in space technology,aviation,automotive,and magnesium-based hydrogen industries.The global production of primary magnesium has reached approximately 1.2 million tons per year,with anticipated diversification in future applications and significant market demand.Nevertheless,approximately 80%of the world’s primary magnesium is still manufactured through the Pidgeon process,grappling with formidable issues including high energy consumption,massive carbon emission,significant resource depletion,and environmental pollution.The implementation of the relative vacuum method shows potential in breaking through technological challenges in the Pidgeon process,facilitating clean,low-carbon continuous magnesium smelting.This paper begins by introducing the principles of the relative vacuum method.Subsequently,it elucidates various innovative process routes,including relative vacuum ferrosilicon reduction,aluminum thermal reduction co-production of spinel,and aluminum thermal reduction co-production of calcium aluminate.Finally,and thermodynamic foundations of the relative vacuum,a quantitative analysis of the material,energy flows,carbon emission,and production cost for several new processes is conducted,comparing and analyzing them against the Pidgeon process.The study findings reveal that,with identical raw materials,the relative vacuum silicon thermal reduction process significantly decreases raw material consumption,energy consumption,and carbon dioxide emissions by 15.86%,30.89%,and 26.27%,respectively,compared to the Pidgeon process.The relative vacuum process,using magnesite as the raw material and aluminum as the reducing agent,has the lowest magnesium-to-feed ratio,at only 3.385.Additionally,its energy consumption and carbon dioxide emissions are the lowest,at 1.817 tce/t Mg and 7.782 t CO_(2)/t Mg,respectively.The energy consumption and carbon emissions of the relative vacuum magnesium smelting process co-producing calcium aluminate(12CaO·7Al_(2)O_(3),3CaO·Al_(2)O_(3),and CaO·Al_(2)O_(3))are highly correlated with the consumption of dolomite in the raw materials.When the reduction temperature is around 1473.15 K,the critical volume fraction of magnesium vapor for different processes varies within the range of 5%–40%.Production cost analysis shows that the relative vacuum primary magnesium smelting process has significant economic benefits.This paper offers essential data support and theoretical guidance for achieving energy efficiency,carbon reduction in magnesium smelting,and the industrial adoption of innovative processes.展开更多
At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since th...At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.展开更多
To explore the effects of relative humidity(RH)on the quality of walnut kernels and establish a rapid,effective method/model for identifying their deterioration degree,walnut kernels were stored at 45℃for 90 days und...To explore the effects of relative humidity(RH)on the quality of walnut kernels and establish a rapid,effective method/model for identifying their deterioration degree,walnut kernels were stored at 45℃for 90 days under different RH conditions(35%,50%,65%and 80%)in this study.Every 15 days,changes in the kernels'color,acid values(AV),peroxide values(POV),fatty acid composition,contents of total phenols and soluble quinones,synchronous fluorescence spectra,and the compositions/contents of volatile organic compounds(VOCs)were analyzed.Partial least squares discriminant analysis(PLS-DA)and variable importance in projection(VIP)were used to conduct differential analysis of VOCs.The deterioration degree of walnut kernels was predicted using Pearson correlation analysis and a Back Propagation Neural Network(BPNN)model.The results showed that RH had a significant effect on the quality of walnut kernels,with 65%RH being the suitable storage condition for them.According to gas chromatography-mass spectrometry(GC-MS)analysis,a total of 40,34,23 and 17 characteristic VOCs were identified in the walnut kernels stored at RH of 35%,50%,65%and 80%,respectively.Among these VOCs,hexanal,1-octen-3-ol,4,5-dimethyltetrahydrofuran-2-one and DL-pantolactone were identified as potential volatile deterioration markers(PVDMs).Based on the POV limit standard of 1.0 mmol/kg for walnut oil,the threshold concentrations of these four PVDMs were 500-1000,50-100,10-15,and 30-60μg/100g,respectively.This research provides a reference for the quality monitoring and evaluation of walnut kernels during storage.展开更多
The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they h...The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.展开更多
While the fire protection function of tree bark has been extensively documented,other critical functions,including storage and mechanical support,have received less attention.In this study we examined:(1)the allometry...While the fire protection function of tree bark has been extensively documented,other critical functions,including storage and mechanical support,have received less attention.In this study we examined:(1)the allometry of bark thickness(and biomass)against wood radius(and biomass)at a disc level,(2)differences in bark allocation between the ratio and the regression approaches,(3)differences between bark thickness and biomass as metrics of bark allocation,and(4)how bark allocation is associated with the evolution of wood from non-porous to diffuse-porous and ring-porous types.Thickness and biomass of bark and wood were measured using trunk discs of 88 individual trees of 36 species in a temperate forest characterized by a long fire interval.Allometric relationships of bark thickness(and biomass)against wood radius(and biomass)explained why both relative bark thickness and biomass decreased with increasing stem diameter.Variations in both among species varied by factors of 3.5 to 7.5 depending on the measurement methods.The ratio approach produced higher estimates of both relative bark thickness and biomass compared to the regression approach,while relative bark thickness was significantly lower than relative bark biomass.Ring-porous species exhibited higher bark thickness based on the ratio approach,which might reflect evolutionary adaptations where ring-porous species have developed thicker bark as protection:thermal insulation against freeze-thaw embolism coupled with carbohydrate reservoirs for hydraulic repair.The regression slope of bark allocation against wood density increased along the wood porosity gradient,demonstrating evolutionary biomechanical coordination between bark and wood.These findings highlight systematic coupling between bark and xylem multifunctionality.展开更多
In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hour...In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.展开更多
AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives ...AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.展开更多
The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.G...The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.Grounded in information asymmetry theory,this study investigates the impact of geographical distance on stock price synchronization in the Chinese stock market.Using the data from the Shanghai and Shenzhen Stock Exchanges,we find that a greater geographical distance between mutual funds and firms considerably increases stock price synchronization,highlighting a strong positive relationship.Additional analysis show that firms in the regions with better external and internal governance,benefit more from reduced information asymmetry,than those in less regulated or transparent regions.These results have key implications for institutional investors and policymakers aiming to enhance information dissemination and market integration in China.展开更多
Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation ...Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation between stock price and nine key financial indicators selected from three dimensions:profitability,development capability,and operating capability,including fixed asset growth rate,price-to-book ratio(P/B ratio),and gross profit margin.Through correlation analysis,multiple regression analysis,and curve fitting,the study finds that:fixed asset growth rate,P/B ratio,and gross profit margin show a significant positive correlation with stock price;return on equity(ROE),operating income,and accounts receivable turnover days show a significant negative correlation with stock price;earnings per share(EPS)and net profit growth rate do not show a significant correlation with stock price.The research results indicate that the stock price of Bengang Steel Plates Co.Ltd.is greatly affected by its asset scale and market valuation,while some profitability indicators have not been effectively transmitted to the stock price.Finally,countermeasures and suggestions are put forward from the aspects of cost control,technological innovation,market expansion,and financial structure optimization,so as to provide references for corporate operation and investment decisions.展开更多
Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,...Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.展开更多
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
文摘With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.
基金supported by The Netherlands Organization for Scientific Research VIDI(grant number:198.007).
文摘Background Conditional relative survival(CRS),the probability of survival given that an individual has already survived a certain period post-diagnosis,is a more clinically relevant measure for long-term survival than standard relative survival(RS).This study aims to evaluate the 5-year CRS among adolescent and young adult(AYA)breast cancer patients by age,tumor stage,and receptor subtype to guide disclosure periods for insurance.Methods Data of all females aged 18–39 years and diagnosed with invasive breast cancer between 2003 and 2021(n=13,075)were obtained from The Netherlands Cancer Registry(NCR).The five-year CRS was calculated annually up to 10 years post-diagnosis using a hybrid analysis approach.Results For the total AYA breast cancer study population the 5-year CRS exceeded 90%from diagnosis and increased beyond 95%7 years post-diagnosis.Patients aged 18–24 reached 95%9 years post-diagnosis,those aged 25–29 after 5 years,and those aged 30–34 and 35–39 after 8 years.For stage I,the 5-year CRS reached 95%from diagnosis,for stage II after 6 years,while the 5-year CRS for stages III and IV did not reach the 95%threshold during the 10-year follow-up.Triple-negative tumors exceeded 95%after 4 years,human epidermal growth factor receptor 2(HER2)positive tumors after 6 years,while hormone receptor(HR)positive tumors did not reach 95%.Conclusion Excess mortality among AYA breast cancer patients tends to be little(CRS 90%–95%)from diagnosis and becomes minimal(CRS>95%)over time compared to the general population.These results can enhance expectation management and inform policymakers,suggesting a shorter disclosure period.
基金Funding support for the Crop Pest Surveillance and Advisory Project(CROPSAP)。
文摘Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pests and their natural enemies is required to minimize the pest population and yield losses.In the current study,analysis of the seasonal population trend of pests and natural enemies and their relative occurrence on cultivars of three cotton species in Central India has been carried out.Results A higher number and diversity of sucking pests were observed during the vegetative cotton growth stage(60 days after sowing),declining as the crop matured.With the exception of cotton jassid(Amrasca biguttula biguttula Ishida),which caused significant crop damage mainly from August to September;populations of other sucking insects seldom reached economic threshold levels(ETL)throughout the studied period.The bollworm complex populations were minimal,except for the pink bollworm(Pectinophora gossypiella Saunders),which re-emerged as a menace to cotton crops during the cotton cropping season 2017–2018 due to resistance development against Bt-cotton.A reasonably good number of predatory arthropods,including coccinellids,lacewings,and spiders,were found actively preying on the arthropod pest complex of the cotton crop during the early vegetative growth stage.Linear regression indicates a significant relationship between green boll infestations and pink bollworm moths in pheromone traps.Multiple linear regression analyse showed mean weekly weather at one-or two-week lag periods had a significant impact on sucking pest population(cotton aphid,cotton jassid,cotton whitefly,and onion thrips)fluctuation.Gossypium hirsutum cultivars RCH 2 and DCH 32,and G.barbadense cultivar Suvin were found susceptible to cotton jassid and onion thrips.Phule Dhanvantary,an G.arboreum cotton cultivar,demonstrated the highest tolerance among all evaluated cultivars against all sucking pests.Conclusion These findings have important implications for pest management in cotton crops.Susceptible cultivars warrant more attention for plant protection measures,making them more input-intensive.The choice of appropriate cultivars can help minimize input costs,thereby increasing net returns for cotton farmers.
基金supported by the National Key Research and Development Program of China(2023YFF0805602)National Natural Science Foundation of China(32225032,32001192,32271597)+1 种基金the Innovation Base Project of Gansu Province(2021YFF0703904)the Science and Technology Program of Gansu Province(24JRRA515,22JR5RA525,23JRRA1157).
文摘Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
文摘Gas-bearing shales have become a major source of future natural gas production worldwide.It has become increasingly urgent to develop a reliable prediction model and corresponding workflow for identifying shale gas sweet spots.The formation of gas-bearing shales is closely linked to relative sealevel changes,providing an important approach to predicting sweet spots in the Wufeng-Longmaxi shale in the southern Sichuan Basin,China.Three types of marine shale gas sweet spots are identified in the shale based on their formation stages combined with relative sea-level changes:early,middle,and late transgression types.This study develops a prediction model and workflow for identifying shale gas sweet spots by analyzing relative sea-level changes and facies sequences.Predicting shale gas sweet spots in an explored block using this model and workflow can provide a valuable guide for well design and hydraulic fracturing,significantly enhancing the efficiency of shale gas exploration and development.Notably,the new prediction model and workflow can be utilized for the rapid evaluation of the potential for shale gas development in new shale gas blocks or those with low exploratory maturity.
基金the National Natural Science Foundation of China (Nos. 42302143, 42172159)China Geological Survey Project (No. DD20211350)support from the G. Albert Shoemaker endowment
文摘Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.
基金Australian Research Council Linkage Program(LP200301404)for sponsoring this researchthe financial support provided by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology,SKLGP2021K002)National Natural Science Foundation of China(52374101,32111530138).
文摘Discrete fracture network(DFN)commonly existing in natural rock masses plays an important role in geological complexity which can influence rock fracturing behaviour during fluid injection.This paper simulated the hydraulic fracturing process in lab-scale coal samples with DFNs and the induced seismic activities by the discrete element method(DEM).The effects of DFNs on hydraulic fracturing,induced seismicity and elastic property changes have been concluded.Denser DFNs can comprehensively decrease the peak injection pressure and injection duration.The proportion of strong seismic events increases first and then decreases with increasing DFN density.In addition,the relative modulus of the rock mass is derived innovatively from breakdown pressure,breakdown fracture length and the related initiation time.Increasing DFN densities among large(35–60 degrees)and small(0–30 degrees)fracture dip angles show opposite evolution trends in relative modulus.The transitional point(dip angle)for the opposite trends is also proportionally affected by the friction angle of the rock mass.The modelling results have much practical meaning to infer the density and geometry of pre-existing fractures and the elastic property of rock mass in the field,simply based on the hydraulic fracturing and induced seismicity monitoring data.
基金supported by the China Postdoctoral Science Foundation(No.2023T160088)the Youth Fund of the National Natural Science Foundation of China(No.52304324).
文摘Magnesium and magnesium alloys,serving as crucial lightweight structural materials and hydrogen storage elements,find extensive applications in space technology,aviation,automotive,and magnesium-based hydrogen industries.The global production of primary magnesium has reached approximately 1.2 million tons per year,with anticipated diversification in future applications and significant market demand.Nevertheless,approximately 80%of the world’s primary magnesium is still manufactured through the Pidgeon process,grappling with formidable issues including high energy consumption,massive carbon emission,significant resource depletion,and environmental pollution.The implementation of the relative vacuum method shows potential in breaking through technological challenges in the Pidgeon process,facilitating clean,low-carbon continuous magnesium smelting.This paper begins by introducing the principles of the relative vacuum method.Subsequently,it elucidates various innovative process routes,including relative vacuum ferrosilicon reduction,aluminum thermal reduction co-production of spinel,and aluminum thermal reduction co-production of calcium aluminate.Finally,and thermodynamic foundations of the relative vacuum,a quantitative analysis of the material,energy flows,carbon emission,and production cost for several new processes is conducted,comparing and analyzing them against the Pidgeon process.The study findings reveal that,with identical raw materials,the relative vacuum silicon thermal reduction process significantly decreases raw material consumption,energy consumption,and carbon dioxide emissions by 15.86%,30.89%,and 26.27%,respectively,compared to the Pidgeon process.The relative vacuum process,using magnesite as the raw material and aluminum as the reducing agent,has the lowest magnesium-to-feed ratio,at only 3.385.Additionally,its energy consumption and carbon dioxide emissions are the lowest,at 1.817 tce/t Mg and 7.782 t CO_(2)/t Mg,respectively.The energy consumption and carbon emissions of the relative vacuum magnesium smelting process co-producing calcium aluminate(12CaO·7Al_(2)O_(3),3CaO·Al_(2)O_(3),and CaO·Al_(2)O_(3))are highly correlated with the consumption of dolomite in the raw materials.When the reduction temperature is around 1473.15 K,the critical volume fraction of magnesium vapor for different processes varies within the range of 5%–40%.Production cost analysis shows that the relative vacuum primary magnesium smelting process has significant economic benefits.This paper offers essential data support and theoretical guidance for achieving energy efficiency,carbon reduction in magnesium smelting,and the industrial adoption of innovative processes.
文摘At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.
基金Anhui Province Scientific and Technological Research Project(202423110050025)National Key Research and Development Project(2023YFD2100400)。
文摘To explore the effects of relative humidity(RH)on the quality of walnut kernels and establish a rapid,effective method/model for identifying their deterioration degree,walnut kernels were stored at 45℃for 90 days under different RH conditions(35%,50%,65%and 80%)in this study.Every 15 days,changes in the kernels'color,acid values(AV),peroxide values(POV),fatty acid composition,contents of total phenols and soluble quinones,synchronous fluorescence spectra,and the compositions/contents of volatile organic compounds(VOCs)were analyzed.Partial least squares discriminant analysis(PLS-DA)and variable importance in projection(VIP)were used to conduct differential analysis of VOCs.The deterioration degree of walnut kernels was predicted using Pearson correlation analysis and a Back Propagation Neural Network(BPNN)model.The results showed that RH had a significant effect on the quality of walnut kernels,with 65%RH being the suitable storage condition for them.According to gas chromatography-mass spectrometry(GC-MS)analysis,a total of 40,34,23 and 17 characteristic VOCs were identified in the walnut kernels stored at RH of 35%,50%,65%and 80%,respectively.Among these VOCs,hexanal,1-octen-3-ol,4,5-dimethyltetrahydrofuran-2-one and DL-pantolactone were identified as potential volatile deterioration markers(PVDMs).Based on the POV limit standard of 1.0 mmol/kg for walnut oil,the threshold concentrations of these four PVDMs were 500-1000,50-100,10-15,and 30-60μg/100g,respectively.This research provides a reference for the quality monitoring and evaluation of walnut kernels during storage.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT)(RS-2025-16067531:Kwangwon Ahn)Hankuk University of Foreign Studies Research Fund(0f 2025:Sihyun An).
文摘The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.
基金supported by the National Natural Science Foundation of China(32171765)。
文摘While the fire protection function of tree bark has been extensively documented,other critical functions,including storage and mechanical support,have received less attention.In this study we examined:(1)the allometry of bark thickness(and biomass)against wood radius(and biomass)at a disc level,(2)differences in bark allocation between the ratio and the regression approaches,(3)differences between bark thickness and biomass as metrics of bark allocation,and(4)how bark allocation is associated with the evolution of wood from non-porous to diffuse-porous and ring-porous types.Thickness and biomass of bark and wood were measured using trunk discs of 88 individual trees of 36 species in a temperate forest characterized by a long fire interval.Allometric relationships of bark thickness(and biomass)against wood radius(and biomass)explained why both relative bark thickness and biomass decreased with increasing stem diameter.Variations in both among species varied by factors of 3.5 to 7.5 depending on the measurement methods.The ratio approach produced higher estimates of both relative bark thickness and biomass compared to the regression approach,while relative bark thickness was significantly lower than relative bark biomass.Ring-porous species exhibited higher bark thickness based on the ratio approach,which might reflect evolutionary adaptations where ring-porous species have developed thicker bark as protection:thermal insulation against freeze-thaw embolism coupled with carbohydrate reservoirs for hydraulic repair.The regression slope of bark allocation against wood density increased along the wood porosity gradient,demonstrating evolutionary biomechanical coordination between bark and wood.These findings highlight systematic coupling between bark and xylem multifunctionality.
基金funded in part by Grant No.DF-091-135-1441 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
基金supported by the grant of the Russian Science Foundation(RSF Code:23-18-01065).
文摘AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.
基金supported by the National Natural Science Foundation of China(72141304,72201190).
文摘The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.Grounded in information asymmetry theory,this study investigates the impact of geographical distance on stock price synchronization in the Chinese stock market.Using the data from the Shanghai and Shenzhen Stock Exchanges,we find that a greater geographical distance between mutual funds and firms considerably increases stock price synchronization,highlighting a strong positive relationship.Additional analysis show that firms in the regions with better external and internal governance,benefit more from reduced information asymmetry,than those in less regulated or transparent regions.These results have key implications for institutional investors and policymakers aiming to enhance information dissemination and market integration in China.
基金Innovation Team Project of Liaoning Institute of Science and Technology:“Smart Economy Practice and Innovation Team”College Students’Innovation and Entrepreneurship Training Program Projects:“Research on the Application of Big Data Analysis Tools”and“Zhice Quantitative Investment Studio”+2 种基金Teaching and Research Project:“Research on the Path of AI-Enabled Undergraduate Education and Teaching Reform Based on the Needs of Liaoning’s Revitalization and Development(Project No.:LKJY202510)”Teaching Reform Project:“Research and Practice on the Evaluation of Digital Talents in Application-Oriented Universities(Project No.:LNKJ202412)”Project of Liaoning Federation of Social Sciences:“Research on the Key Elements and Practical Paths of Educational Digital Transformation(Project No.:2025lslybkt-050)”。
文摘Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation between stock price and nine key financial indicators selected from three dimensions:profitability,development capability,and operating capability,including fixed asset growth rate,price-to-book ratio(P/B ratio),and gross profit margin.Through correlation analysis,multiple regression analysis,and curve fitting,the study finds that:fixed asset growth rate,P/B ratio,and gross profit margin show a significant positive correlation with stock price;return on equity(ROE),operating income,and accounts receivable turnover days show a significant negative correlation with stock price;earnings per share(EPS)and net profit growth rate do not show a significant correlation with stock price.The research results indicate that the stock price of Bengang Steel Plates Co.Ltd.is greatly affected by its asset scale and market valuation,while some profitability indicators have not been effectively transmitted to the stock price.Finally,countermeasures and suggestions are put forward from the aspects of cost control,technological innovation,market expansion,and financial structure optimization,so as to provide references for corporate operation and investment decisions.
文摘Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.
