With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threat...With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.展开更多
Hatchery release is a common and effective practice for protecting and restoring wild resources,and the success of the practice is commonly assessed using mark-recapture technology.We investigated the use of different...Hatchery release is a common and effective practice for protecting and restoring wild resources,and the success of the practice is commonly assessed using mark-recapture technology.We investigated the use of different dimensional X-ray imaging techniques for the strontium(Sr)marking of fish fin rays for stocking.Megalobrama amblycephala juveniles were marked by culturing specimens in 800-mg/L SrCl_(2)·6 H_(2)O solution,the cross-sections of dorsal fin rays were subsequently obtained,and the concentrations Sr was analyzed by 2 D imaging using an electron probe X-ray microanalyzer.Our preliminary findings indicate that the immersion marking method is effective for the Sr marking of fin rays in experimental fish.Moreover,we generated a bird’s-eye-view 3 D mesh image of the Sr concentrations,which can provide a more comprehensive information for fish stocking than that using normal 2 D imaging.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
Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon...Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon stocks in the sacred forests of Bandrefam and Batoufam (western Cameroon). The floristic inventory and the stand structures were carried out in 25 m × 25 m plots for individuals with diameters greater than 10 cm;5 m × 5 m for individuals with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and allometric equations. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam sacred forest and 45 species divided into 42 genera and 27 families in the Batoufam sacred forest. In the Bandrefam, the most important families are Phyllanthaceae (53.98%), Moraceae (21.69%), Lamiaceae (20.15%). At Batoufam, the most important families are Phyllanthaceae (39.73%), Fabaceae (28.47%), Araliaceae (23.77%). Malacantha alnifolia (55.14%), Vitex grandifolia (18.43%), Bosqueia angolensis (15.06%) were the most important species in Bandrefam. Otherwise, Malacantha alnifolia (28%), Polyscias fulva (22.73%), Psychotria sp. (21.28%) were the most important in Batoufam. The Bandrefam sacred forest has the highest tree density (2669 stems/ha). Total carbon stock is 484.88 ± 2.28 tC/ha at Batoufam and 313.95 ± 0.93 tC/ha at Bandrefam. The economic value varies between 5858.04 ± 27.62 USD/ha in Batoufam sacred forest and 3788.51 ± 11.26 USD/ha in Bandrefam sacred forest. The number of individuals and small-diameter trees has little influence on the carbon stocks in the trees. Medium-diameter trees store the most carbon, and very large-diameter trees, which are very poorly represented, store less carbon. In another way, wood density and the basal areas influence the carbon storage of the trees.展开更多
Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The ma...Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.展开更多
Collaborative forest management (CFM) is a form of forest governance in which local communities are involved in the management and decision-making processes related to forest resources. It is believed that forests und...Collaborative forest management (CFM) is a form of forest governance in which local communities are involved in the management and decision-making processes related to forest resources. It is believed that forests under such management are better in tree diversity and conservation status and thus hold more carbon stocks. The study assessed the impact of CFM on carbon stocks, tree species diversity & tree species density in Mabira Central Forest Reserve. Data were collected from plots that were systematically laid in the different purposively selected forest areas. The study findings show that there is no difference in stem density and carbon stocks between CFM and non-CFM areas. CFM areas had lower species richness compared to non-CFM areas. CFM areas, however, exhibited more species diversity than non-CFM areas. Climax colonization may favor a few dominant species over others, hence lowering species diversity despite the number of species being many in the understory, hence at the same time increasing species richness. Likewise, disturbance in CFM area may affect natural colonization and favor the emergency of many species either naturally or through assisted regeneration by reforestation, hence increasing diversity, whereas artificial selection of preferred species through harvesting may lower species richness, as observed. Recommendations for improving collaborative forest management (CFM) areas include implementing targeted interventions to enhance carbon sequestration, such as promoting reforestation and afforestation with high-carbon-storing species and strengthening monitoring and evaluation frameworks to assess carbon stock changes over time. Additionally, efforts should focus on enhancing biodiversity conservation by implementing more stringent protection measures and reducing human disturbance while encouraging community participation in biodiversity monitoring and conservation education.展开更多
The COVID-19 pandemic and its subsequent eradication precipitated a rare and unexpected stock market crash.This crisis presents a unique opportunity to evaluate environmental,social,sustainable,and management(ESSM)pol...The COVID-19 pandemic and its subsequent eradication precipitated a rare and unexpected stock market crash.This crisis presents a unique opportunity to evaluate environmental,social,sustainable,and management(ESSM)policy choices.This study contributes by demonstrating the application of ESSM concerns and investment decision investment horizons and showing the significance of the long-term perspective in ESSM investment decision-making.From January 2020 to December 2022,stocks with higher ESSM ratings had significantly greater returns,less return arbitrariness,and larger operational profit margins.ESSM ventures with more advertising expenditures had higher stock returns;however,investors more predisposed toward ESSM saw less return arbitrariness during market crises.This study emphasizes the importance of investor and consumer loyalty to the elasticity of ESSM stocks.展开更多
Necessary actions should be taken to ensure stock market efficiency;thus,financial innovation-based criteria that affect stock market efficiency should be improved.However,simultaneously improving all criteria is diff...Necessary actions should be taken to ensure stock market efficiency;thus,financial innovation-based criteria that affect stock market efficiency should be improved.However,simultaneously improving all criteria is difficult;therefore,performing priority analysis is important for carrying out this process effectively and efficiently.Accordingly,this study aims to evaluate the financial innovation-based characteristics of stock market efficiency.This study’s main research question within this framework is identifying which factors should be prioritized to improve the stock market.In this scope,we created a novel fuzzy decision-making model consisting of two stages.First,selected criteria for the financial innovation-based characteristics of stock market efficiency are weighted.In this process,quantum spherical fuzzy sets based on DEMATEL are considered.In the second stage,selected economies are ranked using the technique for order of preference by similarity to ideal solution(TOPSIS)approach.This study’s main contribution is that the DEMATEL technique in calculating criterion weights in the decision-making analysis process provides some advantages.With the help of this situation,the causal directions between these items can be considered;thus,it is possible to determine the most accurate strategies.The findings demonstrate that providing tax advantages is the most important factor in ensuring stock market efficiency.Moreover,the excellence of the financial system is critical in ensuring stock market efficiency.In this context,it is possible to provide tax advantages,especially for long-term investments.Thus,long-term investments can be increased,significantly increasing the market’s stability.展开更多
This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dyn...This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.展开更多
In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the d...In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the dynamic bi-directional relationship between the trading behavior of investor groups(institutional and noninstitutional)and stock index fluctuations in different positions(long and short)and market conditions(the pre-COVID-19 and COVID-19 periods)in the Turkish stock market.The results indicate a bidirectional relationship between the stock index return(SIR)and the trading behavior of online individual traders(OIT)and equity mutual and pension funds(EMPF).However,this relationship varies depending on the trading positions of different investor groups.Also,there is a unidirectional relationship between the SIR and the trading behavior of the diversified equity funds(DEF).During the pandemic period,the role of online traders became more prominent,coinciding with their increased participation in the market,significantly affecting and being affected by stock index fluctuations.We also evaluated some behavioral biases(including overconfidence and asymmetric reaction)and the trading strategy of investor groups(with their performance).Results suggest that the online individual traders were less(more)overconfident than other groups in the prepandemic(pandemic)period.Additionally,all groups had an asymmetric reaction to the positive and negative SIR shocks.This research,contributing to the field of financial innovation and aligning with behavioral finance principles,reveals a fascinating finding:individual investors react to stock index fluctuations,largely driven by institutional investors,despite lacking access to new fundamental information about their portfolio stocks.These findings have significant implications for investors and market regulators.Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions.It is concluded that the surge in retail investment is a phenomenon;hence,more effort is required for their investment stability in the Turkish stock market.展开更多
Tibetan Plateau,as one of the most carbon intensive regions in China,is crucial in the carbon cycle,and accurately estimating its vegetation carbon density(C_(VEG))is essential for assessing regional and national carb...Tibetan Plateau,as one of the most carbon intensive regions in China,is crucial in the carbon cycle,and accurately estimating its vegetation carbon density(C_(VEG))is essential for assessing regional and national carbon balance.However,the spatial distribution of regional C_(VEG)is not available remains highly uncertain due to lack of systematic research,especially for different organs.Here,we investigated the spatial distribution patterns and driving factors of C_(VEG)among different plant organs(leaf,branch,trunk and root)by systematically field grid-sampling 2040 field-plots of plant communities over the Tibetan Plateau from 2019 to 2020.The results showed that the carbon content of plant organs ranged from 255.53 to 515.58 g kg^(-1),with the highest in branches and the lowest in roots.Among the different plant functional groups,the highest C_(VEG)was found in evergreen coniferous forests,and the lowest in desert grasslands,with an average C_(VEG)of 1603.98 g m^(-2).C_(VEG)increased spatially from northwest to southeast over the Tibetan Plateau,with MAP being the dominant factor.Furthermore,the total vegetation carbon stock on the Tibetan Plateau was estimated to be 1965.62 Tg for all vegetation types.Based on the comprehensive field survey dataset,the Random Forest model effectively predicted and mapped the spatial distribution of C_(VEG)(including aboveground,belowground,and the total biomass carbon density)over the Tibetan Plateau with notable accuracy(validation R2 values were 71%,56%,and 64%for C_(AGB),C_(BGB),and C_(VEG),respectively)at a spatial resolution of 1 km×1 km.Our findings can help improve the accuracy of regional carbon stock estimations and provide parameters for carbon cycle model optimization and remote sensing calibration in the future.展开更多
This study selected 45 A-share listed companies that have paid dividends for five consecutive years from 2019 to 2024,with an average dividend yield of at least 3%,as the sample.Using a panel data model,the effect of ...This study selected 45 A-share listed companies that have paid dividends for five consecutive years from 2019 to 2024,with an average dividend yield of at least 3%,as the sample.Using a panel data model,the effect of the cash dividend ratio on stock pricing was analyzed.The empirical results indicated a significant positive relationship between the cash dividend ratio and stock price.Furthermore,stocks with high dividend payouts demonstrated greater resilience during macroeconomic downturns,while notable differences were observed across industries.These findings provide a theoretical foundation for investors in making informed decisions and offer practical guidance for listed companies in formulating effective dividend policies.展开更多
Agroforestry,as a platform for harmonizing agriculture and forestry is a win-win approach for the farming community and environmental sustainability.However,its potential is not well studied and quantified in Northwes...Agroforestry,as a platform for harmonizing agriculture and forestry is a win-win approach for the farming community and environmental sustainability.However,its potential is not well studied and quantified in Northwestern highland.Thus,this study aimed to investigate the woody species diversity,and carbon stock potential of traditional agroforestry practices in Northwestern Highlands(NWH)of Ethiopia.A total of 120 households were selected using stratified sampling for household(HH)surveys,and vegetation inventory was conducted in the winter season of 2023 on systematically laid 400 m2 sample quadrats.Shannon-Weiner diversity index(H’),Simpson’s diversity index(1-D)and Shannon evenness(E)were calculated to estimate woody species diversity.Variation in species diversity and carbon stock within and between agroforestry practices was assessed by 1-way ANOVA and rank differences were separated by post-hoc,Tukey HSD multiple comparison test.The result showed that four different agroforestry practices were identified,consisting of 44 woody species belonging to 23 families.Homegarden was the richest in terms of woody species composition(30),followed by boundary planting(25),while parkland agroforestry had the poorest species composition(12).The total carbon stock of the agroforestry practices in the study ranged from 92.51±29.21 to 143.52±47.83 Mg/ha),with soil organic carbon accounting for about 57.66%,followed by aboveground biomass carbon with 32.1%.Homegardens agroforestry had contributed more to the total carbon stocks than the other agroforestry practices.The total CO_(2)sequestration by above and below ground biomass of woody species in the traditional agroforestry practices of the NWH was estimated to be 519.97 and 104.01 Mg/ha,respectively.The study confirmed that the traditional agroforestry practices of the NWH of Ethiopia maintain a high diversity of woody species and are remarkably important for biodiversity conservation and climate change mitigation.展开更多
Pelagic fish are the most abundant species in upwelling regions,contributing 25%of total global fisheries production.Climate-driven changes in the marine environment play a crucial role in their population dynamics.Us...Pelagic fish are the most abundant species in upwelling regions,contributing 25%of total global fisheries production.Climate-driven changes in the marine environment play a crucial role in their population dynamics.Using Chilean jack mackerel(Trachurus murphyi)as an example,this study conducted simulations to quantify the impacts of environmental variations on the stock assessment.A habitat-based surplus production model was developed by integrating suitable habitat area into the model parameters carrying capacity(K)and intrinsic growth rate(r),with a suitable habitat area serving as the proxy for the environmental conditions for Chilean jack mackerel in the Southeast Pacific Ocean.The dynamics of Chilean jack mackerel stock and fisheries data were simulated,and four assessment models with different configurations were built to fit simulated data,with or without considering environmental effects.The results indicated that Joint K-r model,which integrated both parameters with the suitable habitat area index,outperformed the others by coming closest to the‘true'population dynamics.Ignoring habitat variations in the estimation model tended to overestimate biomass and underestimate harvest rate and reference points.Without observation and process error,the results were estimated with bias,while FMSY is relatively sensitive.This research illustrates the importance to consider random errors and environmental influences on populations,and provides foundation guidelines for future stock assessment.展开更多
Natural forests are the primary carbon sinks within terrestrial ecosystems,playing a crucial role in mitigating global climate change.China has successfully restored its natural forest area through extensive protectiv...Natural forests are the primary carbon sinks within terrestrial ecosystems,playing a crucial role in mitigating global climate change.China has successfully restored its natural forest area through extensive protective measures.However,the aboveground carbon(AGC)stock potential of China's natural forests remains considerably uncertain in spatial and temporal dynamics.In this study,we provide a spatially detailed estimation of the maximum AGC stock potential for China's natural forests by integrating high-resolution multi-source remote sensing and field survey data.The analysis reveals that China's natural forests could sequester up to 9.880.10 Pg C by 2030,potentially increasing to 10.460.11 Pg C by 2060.Despite this,the AGC sequestration rate would decline from 0.190.001 to 0.080.001 Pg C·yr^(-1)over the period.Spatially,the future AGC accumulation rates exhibit marked heterogeneity.The warm temperate deciduous broadleaf forest region with predominantly young natural forests,is expected to exhibit the most significant increase of 26.36%by 2060,while the Qinghai-Tibet Plateau Alpine region comprising mainly mature natural forests would exhibit only a 0.74%increase.To sustain the high carbon sequestration capacity of China's natural forests,it is essential to prioritize protecting mature forests alongside preserving and restoring young natural forest areas.展开更多
The study determined the carbon stocks and litter nutrient concentration in tropical forests along the ecological gradient in Kenya.This could help understand the potential of mitigating climate change using tropical ...The study determined the carbon stocks and litter nutrient concentration in tropical forests along the ecological gradient in Kenya.This could help understand the potential of mitigating climate change using tropical forest ecosystems in different ecological zones,which are being affected by climate change to a level that they are becoming carbon sources instead of sinks.Stratified sampling technique was used to categorize tropical forests into rain,moist deciduous and dry zone forests depending on the average annual rainfall received.Simple random sampling technique was used to select three tropical forests in each category.Modified consistent sampling technique was used to develop 10 main 20 m×100 m plots in each forest,with 202 m×50 m sub-plots in each plot.Systematic random sampling technique was used in selecting 10 sub-plots from each main plot for inventory study.Non-destructive approach based on allometric equations using trees’diameter at breast height(DBH),total height and species’wood specific gravity were used in estimating tree carbon stock in each forest.Soil organic carbon(SOC)and litter nutrient concentration(total phosphorus and nitrogen)were determined in each forest based on standard laboratory procedures.The results indicated that,whilst trees in rain forests recorded a significantly higher(p<0.001)DBH(20.36 cm)and total tree height(12.1 m),trees in dry zone forests recorded a significantly higher(p<0.001)specific gravity(0.67 kg m^(−3)).Dry zone tropical forests stored a significantly lower amount of total tree carbon of 73 Mg ha^(−1),compared to tropical rain forests(439.5 Mg ha^(−1))and moist deciduous tropical forests(449 Mg ha^(−1)).The SOC content was significantly higher in tropical rainforests(3.9%),compared to soils from moist deciduous(2.9%)and dry zone forests(1.8%).While litter from tropical rain forests recorded a significantly higher amount of total nitrogen(3.4%),litter from dry zone forests recorded a significantly higher concentration of total phosphorus(0.27%).In conclusion,ecological gradient that is dictated by the prevailing temperatures and precipitation affects the tropical forests carbon stock potential and litter nutrient concentration.This implies that,the changing climate is having a serious implication on the ecosystem services such as carbon stock and nutrients cycling in tropical forests.展开更多
This study investigates the weak-form efficiency and asymmetric multifractal scaling behavior of rare earth stock indices in the global,U.S.and Chinese markets during the trade war and the COVID-19 period.We examine t...This study investigates the weak-form efficiency and asymmetric multifractal scaling behavior of rare earth stock indices in the global,U.S.and Chinese markets during the trade war and the COVID-19 period.We examine the scaling behavior across overall,upward(bullish),and downward(bearish)market states from 2013 to 2021,employing an asymmetric multifractal detrended fluctuation analysis approach.Our findings indicate asymmetric multifractality in U.S.rare earth stock prices,caused by fat tails and long-range correlations.Weak-form price inefficiency and asymmetry in U.S.rare earth stock prices are prominent during market downturns,such as the trade war and COVID-19 periods.Chinese rare earth stocks demonstrate greater efficiency than U.S.and global stocks;thus,the latter markets provide arbitrage opportunities during upward and downward trends.展开更多
Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achiev...Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achieving the aspiration level)as an important aspect of decision-making.Motivated by these theories,this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month.This study uses portfolio-level and firm-level analyses,revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns,even after controlling for common risk factors and various characteristics.Additional analyses support the behavioral theory of the firm,which posits that firms act to achieve short-term aspiration levels.展开更多
The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of...The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.展开更多
Understanding livestock performance in typical steppe ecosystems is essential for optimizing grassland-livestock interactions and minimizing environmental impact.To assess the effects of different stocking rates on th...Understanding livestock performance in typical steppe ecosystems is essential for optimizing grassland-livestock interactions and minimizing environmental impact.To assess the effects of different stocking rates on the growth performance,energy and nitrogen utilization,methane(CH_(4))emissions,and grazing behavior of Tan sheep,a 2-year grazing experiment in the typical steppe was conducted.The grazing area was divided into 9 paddocks,each 0.5 ha,with 3 spatial replicates for each stocking rate treatment(4,8,and 13 sheep per paddock),corresponding to 2.7,5.3,and 8.7 sheep ha^(–1).The results showed that the neutral detergent fiber(NDF)and acid detergent fiber(ADF)contents of herbage varied between grazing years(P<0.05),with a positive correlation between stocking rate and crude fiber content in the herbage(P<0.05).Dry matter intake(DMI)decreased with increasing stocking rate(P<0.05),and the average daily gain(ADG)was highest at 2.7 sheep ha^(–1)(P<0.05).Compared to 2.7 and 8.7 sheep ha^(–1),the5.3 sheep ha^(–1)treatment exhibited the lowest nutrient digestibility for dry matter,nitrogen,and ether extract(P<0.05).Fecal nitrogen was lowest at 8.7 sheep ha^(–1)(P<0.05),while retained nitrogen as a proportion of nitrogen intake was highest.Digestive energy(DE),metabolic energy(ME),and the ratios of DE to gross energy(GE)and ME to GE were highest at 8.7 sheep ha^(–1)(P<0.05).In contrast,CH_4 emissions,CH_4 per DMI,and CH_(4)E as a proportion of GE were highest at 2.7 sheep ha^(–1)(P<0.05).Stocking rate and grazing year did not significantly affect rumen fermentation parameters,including volatile fatty acids,acetate,propionate,and the acetate/propionate ratio.At 8.7sheep ha^(–1),daily grazing time and inter-individual distance increased,while time allocated to grazing,walking,and ruminating/resting decreased as stocking rates increased(P<0.05).This study highlights the importance of adjusting stocking rates based on the nutritional value of forage and grazing year to optimize grazing management.展开更多
基金supported by National Key Research and Development Program of China(No.2021YFD2200405(S.R.L.))Natural Science Foundation of China(Grant No.31971653).
文摘With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.
基金Supported by the National Key Research and Development Program of China(No.2022 YFF 0608203)the Graduate Student Scientific Research Innovation Projects in Jiangsu Province(No.KYCX 22-0706)。
文摘Hatchery release is a common and effective practice for protecting and restoring wild resources,and the success of the practice is commonly assessed using mark-recapture technology.We investigated the use of different dimensional X-ray imaging techniques for the strontium(Sr)marking of fish fin rays for stocking.Megalobrama amblycephala juveniles were marked by culturing specimens in 800-mg/L SrCl_(2)·6 H_(2)O solution,the cross-sections of dorsal fin rays were subsequently obtained,and the concentrations Sr was analyzed by 2 D imaging using an electron probe X-ray microanalyzer.Our preliminary findings indicate that the immersion marking method is effective for the Sr marking of fin rays in experimental fish.Moreover,we generated a bird’s-eye-view 3 D mesh image of the Sr concentrations,which can provide a more comprehensive information for fish stocking than that using normal 2 D imaging.
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Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon stocks in the sacred forests of Bandrefam and Batoufam (western Cameroon). The floristic inventory and the stand structures were carried out in 25 m × 25 m plots for individuals with diameters greater than 10 cm;5 m × 5 m for individuals with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and allometric equations. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam sacred forest and 45 species divided into 42 genera and 27 families in the Batoufam sacred forest. In the Bandrefam, the most important families are Phyllanthaceae (53.98%), Moraceae (21.69%), Lamiaceae (20.15%). At Batoufam, the most important families are Phyllanthaceae (39.73%), Fabaceae (28.47%), Araliaceae (23.77%). Malacantha alnifolia (55.14%), Vitex grandifolia (18.43%), Bosqueia angolensis (15.06%) were the most important species in Bandrefam. Otherwise, Malacantha alnifolia (28%), Polyscias fulva (22.73%), Psychotria sp. (21.28%) were the most important in Batoufam. The Bandrefam sacred forest has the highest tree density (2669 stems/ha). Total carbon stock is 484.88 ± 2.28 tC/ha at Batoufam and 313.95 ± 0.93 tC/ha at Bandrefam. The economic value varies between 5858.04 ± 27.62 USD/ha in Batoufam sacred forest and 3788.51 ± 11.26 USD/ha in Bandrefam sacred forest. The number of individuals and small-diameter trees has little influence on the carbon stocks in the trees. Medium-diameter trees store the most carbon, and very large-diameter trees, which are very poorly represented, store less carbon. In another way, wood density and the basal areas influence the carbon storage of the trees.
文摘Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.
文摘Collaborative forest management (CFM) is a form of forest governance in which local communities are involved in the management and decision-making processes related to forest resources. It is believed that forests under such management are better in tree diversity and conservation status and thus hold more carbon stocks. The study assessed the impact of CFM on carbon stocks, tree species diversity & tree species density in Mabira Central Forest Reserve. Data were collected from plots that were systematically laid in the different purposively selected forest areas. The study findings show that there is no difference in stem density and carbon stocks between CFM and non-CFM areas. CFM areas had lower species richness compared to non-CFM areas. CFM areas, however, exhibited more species diversity than non-CFM areas. Climax colonization may favor a few dominant species over others, hence lowering species diversity despite the number of species being many in the understory, hence at the same time increasing species richness. Likewise, disturbance in CFM area may affect natural colonization and favor the emergency of many species either naturally or through assisted regeneration by reforestation, hence increasing diversity, whereas artificial selection of preferred species through harvesting may lower species richness, as observed. Recommendations for improving collaborative forest management (CFM) areas include implementing targeted interventions to enhance carbon sequestration, such as promoting reforestation and afforestation with high-carbon-storing species and strengthening monitoring and evaluation frameworks to assess carbon stock changes over time. Additionally, efforts should focus on enhancing biodiversity conservation by implementing more stringent protection measures and reducing human disturbance while encouraging community participation in biodiversity monitoring and conservation education.
文摘The COVID-19 pandemic and its subsequent eradication precipitated a rare and unexpected stock market crash.This crisis presents a unique opportunity to evaluate environmental,social,sustainable,and management(ESSM)policy choices.This study contributes by demonstrating the application of ESSM concerns and investment decision investment horizons and showing the significance of the long-term perspective in ESSM investment decision-making.From January 2020 to December 2022,stocks with higher ESSM ratings had significantly greater returns,less return arbitrariness,and larger operational profit margins.ESSM ventures with more advertising expenditures had higher stock returns;however,investors more predisposed toward ESSM saw less return arbitrariness during market crises.This study emphasizes the importance of investor and consumer loyalty to the elasticity of ESSM stocks.
文摘Necessary actions should be taken to ensure stock market efficiency;thus,financial innovation-based criteria that affect stock market efficiency should be improved.However,simultaneously improving all criteria is difficult;therefore,performing priority analysis is important for carrying out this process effectively and efficiently.Accordingly,this study aims to evaluate the financial innovation-based characteristics of stock market efficiency.This study’s main research question within this framework is identifying which factors should be prioritized to improve the stock market.In this scope,we created a novel fuzzy decision-making model consisting of two stages.First,selected criteria for the financial innovation-based characteristics of stock market efficiency are weighted.In this process,quantum spherical fuzzy sets based on DEMATEL are considered.In the second stage,selected economies are ranked using the technique for order of preference by similarity to ideal solution(TOPSIS)approach.This study’s main contribution is that the DEMATEL technique in calculating criterion weights in the decision-making analysis process provides some advantages.With the help of this situation,the causal directions between these items can be considered;thus,it is possible to determine the most accurate strategies.The findings demonstrate that providing tax advantages is the most important factor in ensuring stock market efficiency.Moreover,the excellence of the financial system is critical in ensuring stock market efficiency.In this context,it is possible to provide tax advantages,especially for long-term investments.Thus,long-term investments can be increased,significantly increasing the market’s stability.
文摘This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.
文摘In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the dynamic bi-directional relationship between the trading behavior of investor groups(institutional and noninstitutional)and stock index fluctuations in different positions(long and short)and market conditions(the pre-COVID-19 and COVID-19 periods)in the Turkish stock market.The results indicate a bidirectional relationship between the stock index return(SIR)and the trading behavior of online individual traders(OIT)and equity mutual and pension funds(EMPF).However,this relationship varies depending on the trading positions of different investor groups.Also,there is a unidirectional relationship between the SIR and the trading behavior of the diversified equity funds(DEF).During the pandemic period,the role of online traders became more prominent,coinciding with their increased participation in the market,significantly affecting and being affected by stock index fluctuations.We also evaluated some behavioral biases(including overconfidence and asymmetric reaction)and the trading strategy of investor groups(with their performance).Results suggest that the online individual traders were less(more)overconfident than other groups in the prepandemic(pandemic)period.Additionally,all groups had an asymmetric reaction to the positive and negative SIR shocks.This research,contributing to the field of financial innovation and aligning with behavioral finance principles,reveals a fascinating finding:individual investors react to stock index fluctuations,largely driven by institutional investors,despite lacking access to new fundamental information about their portfolio stocks.These findings have significant implications for investors and market regulators.Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions.It is concluded that the surge in retail investment is a phenomenon;hence,more effort is required for their investment stability in the Turkish stock market.
基金supported by CAS Project for Young Scientists in Basic Research(YSBR-037)the National Natural Science Foundation of China(42141004,32430067)by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK060602).
文摘Tibetan Plateau,as one of the most carbon intensive regions in China,is crucial in the carbon cycle,and accurately estimating its vegetation carbon density(C_(VEG))is essential for assessing regional and national carbon balance.However,the spatial distribution of regional C_(VEG)is not available remains highly uncertain due to lack of systematic research,especially for different organs.Here,we investigated the spatial distribution patterns and driving factors of C_(VEG)among different plant organs(leaf,branch,trunk and root)by systematically field grid-sampling 2040 field-plots of plant communities over the Tibetan Plateau from 2019 to 2020.The results showed that the carbon content of plant organs ranged from 255.53 to 515.58 g kg^(-1),with the highest in branches and the lowest in roots.Among the different plant functional groups,the highest C_(VEG)was found in evergreen coniferous forests,and the lowest in desert grasslands,with an average C_(VEG)of 1603.98 g m^(-2).C_(VEG)increased spatially from northwest to southeast over the Tibetan Plateau,with MAP being the dominant factor.Furthermore,the total vegetation carbon stock on the Tibetan Plateau was estimated to be 1965.62 Tg for all vegetation types.Based on the comprehensive field survey dataset,the Random Forest model effectively predicted and mapped the spatial distribution of C_(VEG)(including aboveground,belowground,and the total biomass carbon density)over the Tibetan Plateau with notable accuracy(validation R2 values were 71%,56%,and 64%for C_(AGB),C_(BGB),and C_(VEG),respectively)at a spatial resolution of 1 km×1 km.Our findings can help improve the accuracy of regional carbon stock estimations and provide parameters for carbon cycle model optimization and remote sensing calibration in the future.
文摘This study selected 45 A-share listed companies that have paid dividends for five consecutive years from 2019 to 2024,with an average dividend yield of at least 3%,as the sample.Using a panel data model,the effect of the cash dividend ratio on stock pricing was analyzed.The empirical results indicated a significant positive relationship between the cash dividend ratio and stock price.Furthermore,stocks with high dividend payouts demonstrated greater resilience during macroeconomic downturns,while notable differences were observed across industries.These findings provide a theoretical foundation for investors in making informed decisions and offer practical guidance for listed companies in formulating effective dividend policies.
基金financed by Debre Markose University Burie Campus.
文摘Agroforestry,as a platform for harmonizing agriculture and forestry is a win-win approach for the farming community and environmental sustainability.However,its potential is not well studied and quantified in Northwestern highland.Thus,this study aimed to investigate the woody species diversity,and carbon stock potential of traditional agroforestry practices in Northwestern Highlands(NWH)of Ethiopia.A total of 120 households were selected using stratified sampling for household(HH)surveys,and vegetation inventory was conducted in the winter season of 2023 on systematically laid 400 m2 sample quadrats.Shannon-Weiner diversity index(H’),Simpson’s diversity index(1-D)and Shannon evenness(E)were calculated to estimate woody species diversity.Variation in species diversity and carbon stock within and between agroforestry practices was assessed by 1-way ANOVA and rank differences were separated by post-hoc,Tukey HSD multiple comparison test.The result showed that four different agroforestry practices were identified,consisting of 44 woody species belonging to 23 families.Homegarden was the richest in terms of woody species composition(30),followed by boundary planting(25),while parkland agroforestry had the poorest species composition(12).The total carbon stock of the agroforestry practices in the study ranged from 92.51±29.21 to 143.52±47.83 Mg/ha),with soil organic carbon accounting for about 57.66%,followed by aboveground biomass carbon with 32.1%.Homegardens agroforestry had contributed more to the total carbon stocks than the other agroforestry practices.The total CO_(2)sequestration by above and below ground biomass of woody species in the traditional agroforestry practices of the NWH was estimated to be 519.97 and 104.01 Mg/ha,respectively.The study confirmed that the traditional agroforestry practices of the NWH of Ethiopia maintain a high diversity of woody species and are remarkably important for biodiversity conservation and climate change mitigation.
基金supported by the National Key Research and Development Program of China(No.2019YFD0901404)。
文摘Pelagic fish are the most abundant species in upwelling regions,contributing 25%of total global fisheries production.Climate-driven changes in the marine environment play a crucial role in their population dynamics.Using Chilean jack mackerel(Trachurus murphyi)as an example,this study conducted simulations to quantify the impacts of environmental variations on the stock assessment.A habitat-based surplus production model was developed by integrating suitable habitat area into the model parameters carrying capacity(K)and intrinsic growth rate(r),with a suitable habitat area serving as the proxy for the environmental conditions for Chilean jack mackerel in the Southeast Pacific Ocean.The dynamics of Chilean jack mackerel stock and fisheries data were simulated,and four assessment models with different configurations were built to fit simulated data,with or without considering environmental effects.The results indicated that Joint K-r model,which integrated both parameters with the suitable habitat area index,outperformed the others by coming closest to the‘true'population dynamics.Ignoring habitat variations in the estimation model tended to overestimate biomass and underestimate harvest rate and reference points.Without observation and process error,the results were estimated with bias,while FMSY is relatively sensitive.This research illustrates the importance to consider random errors and environmental influences on populations,and provides foundation guidelines for future stock assessment.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFF1300203)the National Natural Science Foundation of China(Grant No.42371329s).
文摘Natural forests are the primary carbon sinks within terrestrial ecosystems,playing a crucial role in mitigating global climate change.China has successfully restored its natural forest area through extensive protective measures.However,the aboveground carbon(AGC)stock potential of China's natural forests remains considerably uncertain in spatial and temporal dynamics.In this study,we provide a spatially detailed estimation of the maximum AGC stock potential for China's natural forests by integrating high-resolution multi-source remote sensing and field survey data.The analysis reveals that China's natural forests could sequester up to 9.880.10 Pg C by 2030,potentially increasing to 10.460.11 Pg C by 2060.Despite this,the AGC sequestration rate would decline from 0.190.001 to 0.080.001 Pg C·yr^(-1)over the period.Spatially,the future AGC accumulation rates exhibit marked heterogeneity.The warm temperate deciduous broadleaf forest region with predominantly young natural forests,is expected to exhibit the most significant increase of 26.36%by 2060,while the Qinghai-Tibet Plateau Alpine region comprising mainly mature natural forests would exhibit only a 0.74%increase.To sustain the high carbon sequestration capacity of China's natural forests,it is essential to prioritize protecting mature forests alongside preserving and restoring young natural forest areas.
基金funded by the Kenya National Research Fund(NRF-Kenya,2018).
文摘The study determined the carbon stocks and litter nutrient concentration in tropical forests along the ecological gradient in Kenya.This could help understand the potential of mitigating climate change using tropical forest ecosystems in different ecological zones,which are being affected by climate change to a level that they are becoming carbon sources instead of sinks.Stratified sampling technique was used to categorize tropical forests into rain,moist deciduous and dry zone forests depending on the average annual rainfall received.Simple random sampling technique was used to select three tropical forests in each category.Modified consistent sampling technique was used to develop 10 main 20 m×100 m plots in each forest,with 202 m×50 m sub-plots in each plot.Systematic random sampling technique was used in selecting 10 sub-plots from each main plot for inventory study.Non-destructive approach based on allometric equations using trees’diameter at breast height(DBH),total height and species’wood specific gravity were used in estimating tree carbon stock in each forest.Soil organic carbon(SOC)and litter nutrient concentration(total phosphorus and nitrogen)were determined in each forest based on standard laboratory procedures.The results indicated that,whilst trees in rain forests recorded a significantly higher(p<0.001)DBH(20.36 cm)and total tree height(12.1 m),trees in dry zone forests recorded a significantly higher(p<0.001)specific gravity(0.67 kg m^(−3)).Dry zone tropical forests stored a significantly lower amount of total tree carbon of 73 Mg ha^(−1),compared to tropical rain forests(439.5 Mg ha^(−1))and moist deciduous tropical forests(449 Mg ha^(−1)).The SOC content was significantly higher in tropical rainforests(3.9%),compared to soils from moist deciduous(2.9%)and dry zone forests(1.8%).While litter from tropical rain forests recorded a significantly higher amount of total nitrogen(3.4%),litter from dry zone forests recorded a significantly higher concentration of total phosphorus(0.27%).In conclusion,ecological gradient that is dictated by the prevailing temperatures and precipitation affects the tropical forests carbon stock potential and litter nutrient concentration.This implies that,the changing climate is having a serious implication on the ecosystem services such as carbon stock and nutrients cycling in tropical forests.
文摘This study investigates the weak-form efficiency and asymmetric multifractal scaling behavior of rare earth stock indices in the global,U.S.and Chinese markets during the trade war and the COVID-19 period.We examine the scaling behavior across overall,upward(bullish),and downward(bearish)market states from 2013 to 2021,employing an asymmetric multifractal detrended fluctuation analysis approach.Our findings indicate asymmetric multifractality in U.S.rare earth stock prices,caused by fat tails and long-range correlations.Weak-form price inefficiency and asymmetry in U.S.rare earth stock prices are prominent during market downturns,such as the trade war and COVID-19 periods.Chinese rare earth stocks demonstrate greater efficiency than U.S.and global stocks;thus,the latter markets provide arbitrage opportunities during upward and downward trends.
文摘Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achieving the aspiration level)as an important aspect of decision-making.Motivated by these theories,this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month.This study uses portfolio-level and firm-level analyses,revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns,even after controlling for common risk factors and various characteristics.Additional analyses support the behavioral theory of the firm,which posits that firms act to achieve short-term aspiration levels.
文摘The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.
基金supported by the National Natural Science Foundation of China(32161143028)the Key Technology of Grassland Ecological Civilization Demonstration Area in Ningxia Hui Autonomous Region,China(20210239)the Northwest Shelterbelt Construction Bureau of the National Forestry and Grassland Administration,China。
文摘Understanding livestock performance in typical steppe ecosystems is essential for optimizing grassland-livestock interactions and minimizing environmental impact.To assess the effects of different stocking rates on the growth performance,energy and nitrogen utilization,methane(CH_(4))emissions,and grazing behavior of Tan sheep,a 2-year grazing experiment in the typical steppe was conducted.The grazing area was divided into 9 paddocks,each 0.5 ha,with 3 spatial replicates for each stocking rate treatment(4,8,and 13 sheep per paddock),corresponding to 2.7,5.3,and 8.7 sheep ha^(–1).The results showed that the neutral detergent fiber(NDF)and acid detergent fiber(ADF)contents of herbage varied between grazing years(P<0.05),with a positive correlation between stocking rate and crude fiber content in the herbage(P<0.05).Dry matter intake(DMI)decreased with increasing stocking rate(P<0.05),and the average daily gain(ADG)was highest at 2.7 sheep ha^(–1)(P<0.05).Compared to 2.7 and 8.7 sheep ha^(–1),the5.3 sheep ha^(–1)treatment exhibited the lowest nutrient digestibility for dry matter,nitrogen,and ether extract(P<0.05).Fecal nitrogen was lowest at 8.7 sheep ha^(–1)(P<0.05),while retained nitrogen as a proportion of nitrogen intake was highest.Digestive energy(DE),metabolic energy(ME),and the ratios of DE to gross energy(GE)and ME to GE were highest at 8.7 sheep ha^(–1)(P<0.05).In contrast,CH_4 emissions,CH_4 per DMI,and CH_(4)E as a proportion of GE were highest at 2.7 sheep ha^(–1)(P<0.05).Stocking rate and grazing year did not significantly affect rumen fermentation parameters,including volatile fatty acids,acetate,propionate,and the acetate/propionate ratio.At 8.7sheep ha^(–1),daily grazing time and inter-individual distance increased,while time allocated to grazing,walking,and ruminating/resting decreased as stocking rates increased(P<0.05).This study highlights the importance of adjusting stocking rates based on the nutritional value of forage and grazing year to optimize grazing management.
