National Soil Organic Carbon Stocks Inventories under Different Mangrove Forest Types in Gabon

Gabonese’s estuary is an important coastal mangrove setting and soil plays a key role in mangrove carbon storage in mangrove forests. However, the spatial variation in soil organic carbon (SOC) storage remain unclear. To address this gap, determining the SOC spatial variation in Gabonese’s estuarine is essential for better understanding the global carbon cycle. The present study compared soil organic carbon between northern and southern sites in different mangrove forest, Rhizophora racemosa and Avicennia germinans. The results showed that the mean SOC stocks at 1 m depth were 256.28 ± 127.29 MgC ha−1. Among the different regions, SOC in northern zone was significantly (p p < 0.001). The deeper layers contained higher SOC stocks (254.62 ± 128.09 MgC ha−1) than upper layers (55.42 ± 25.37 MgC ha−1). The study highlights that low deforestation rate have led to less CO2 (705.3 Mg CO2e ha−1 - 922.62 Mg CO2e ha−1) emissions than most sediment carbon-rich mangroves in the world. These results highlight the influence of soil texture and mangrove forest types on the mangrove SOC stocks. The first national comparison of soil organic carbon stocks between mangroves and upland tropical forests indicated SOC stocks were two times more in mangroves soils (51.21 ± 45.00 MgC ha−1) than primary (20.33 ± 12.7 MgC ha−1), savanna and cropland (21.71 ± 15.10 MgC ha−1). We find that mangroves in this study emit lower dioxide-carbon equivalent emissions. This study highlights the importance of national inventories of soil organic carbon and can be used as a baseline on the role of mangroves in carbon sequestration and climate change mitigation but the variation in SOC stocks indicates the need for further national data.

Exploiting Data Science for Measuring the Performance of Technology Stocks

The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.

Growth, Population Parameters and Stock Status of Sarotherodon galilaeus in Samandeni Reservoir, Burkina Faso

Mango tilapia, Sarotherodon galilaeus is one of the most caught fish species in the Samandeni multi-species fishing sites of which, few data on its biology and exploitation are available. The study aimed to Assess the stock status of S. galilaeus. Sampling was conducted from March, 2021 to February 2022 based on commercial fish catches to analyze growth parameters, first sexual maturity size and harvest status of the stock. A total of 572 specimens including 297 females and 275 males were examined. The stock assessment was performed by using the Length based Bayesian method of Biomass (LBB) and that of growth by the ELEFAN method. The growth parameters showed a seasonality of growth and females appeared to grow faster than males. On the other hand, males had a greater asymptotic length than females. Results on the estimated length of fish at first maturity showed that females firstly reached the maturity compared to males. The relative biomass (B/B0) estimated for the stock was higher than the relative biomass that produces maximum sustainable yield (BMSY/B0) indicating healthy biomass. In addition, the length at first sexual maturity was less than the length at the first catch, indicating the absence of overfishing of growth. In addition, extending the study to the various stocks of the reservoir would be important for the sustainable management of the Samandeni high economic fishing area.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Design Strategies and Practice Paths for Improving Urban Quality in the Stock Era

In the middle and later stages of urbanization development,the growth of the real estate industry will stagnate,and urban renewal will become the mainstream.With the advancement of urban renewal,there are still problems in improving the quality of cities in the stock era and their design strategies.This paper analyzed the Linping Old City organic renewal project and the Xishui River ecological governance project in the stock era of urban quality improvement by sorting out the current development status,historical background,planning types,and design strategies of quality improvement in the stock era from the perspective of urban renewal,combining with project overview,main problems,design methods,and design content.Urban renewal is the leading direction for promoting urban development and construction on a global scale,and countries formulate different plans and practices based on their local characteristics.Urban renewal strategies should be diversified,and focus on livable environments,urban characteristics,etc.,while considering human factors,green innovation,etc.,in order to achieve smart community management and enhance the economic and social benefits brought by urban attractiveness.For successful cases such as the Linping Old City and Xishui River ecological governance project,corresponding urban quality improvement strategies and implementation plans should be formulated according to local conditions,with emphasis on social participation and people’s livelihood improvement.This study can help urban planning pay more attention to rational utilization and upgrading of existing urban resources,adapt to the current urban development situation,and promote sustainable urban development.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.

Soil Organic Carbon Stock Variation under Different Soil Types and Land Uses in the Sub-Humid Noun Plain, Western Cameroon

This study was conducted to assess the current stock of soil organic carbon under different agricultural land uses, soil types and soil depths in the Noun plain in western Cameroon. Three sites were selected for the study, namely Mangoum, Makeka and Fossang, representative of the three dominant soil types of the noun plain (Andosols, Acrisols and Ferralsols). Three land uses were selected per site including natural vegetation, agroforest and crop field. Soil was sampled at three depths;0 - 20 cm, 20 - 40 cm, and 40 - 60 cm. Analysis of variance showed that soil type did not significantly influence carbon storage, but rather land uses and soil depth. SOCS decreased significantly with depth in all the sites, with an average stock of 66.3 ± 15.8 tC/ha at 0 - 20 cm, compared to an average stock of 33.3 ± 7.4 tC/ha at 40 - 60 cm. SOCS was significantly highest in the natural formation with 57.2 ± 19.7 tC/ha, and lowest in cultivated fields, at 37.7 ± 10.6 tC/ha. Andosols, with their high content of coarse fragments, stored less organic carbon than Ferralsols and Acrisols.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

室温下Yb^(3+)和Er^(3+)掺杂的NaYS_2粉末材料的Stocks和Anti-Stocks发光

合成了NaYS2:0.20Yb3+,0.03Er3+和NaYS2:0.03Er3+粉末材料,通过材料的Stocks和Anti-Stocks光谱,研究了Er3+和Yb3+跃迁几率对激发方式的依赖特征,以及局域环境对S2-Re3+(Re3+=Yb3+或Er3+)电荷迁移态的影响,探讨了在共掺杂和单掺杂状态下Yb3+和Er3+间的能量传递和Er3+的Stocks和Anti-Stocks辐射跃迁机制。

二维Stocks流动的一般解

采用分离变量法将二维斯托克斯流动的纳维－斯托克斯方程，在球坐标系进行推导，推导出二维斯托克斯流动的流函数的一般解。

Dynamics of ecosystem carbon stocks during vegetation restoration on the Loess Plateau of China

In the last few decades, the Loess Plateau had experienced an extensive vegetation restoration to reduce soil erosion and to improve the degraded ecosystems. However, the dynamics of ecosystem carbon stocks with vegetation restoration in this region are poorly understood. This study examined the changes of carbon stocks in mineral soil （0-100 cm）, plant biomass and the ecosystem （plant and soil） following vegetation restoration with different models and ages. Our results indicated that cultivated land returned to native vegetation （natural restoration） or artificial forest increased ecosystem carbon sequestration. Tree plantation sequestered more carbon than natural vegetation succession over decades scale due to the rapid increase in biomass carbon pool. Restoration ages had different effects on the dynamics of biomass and soil carbon stocks. Biomass carbon stocks increased with vegetation restoration age, while the dynamics of soil carbon stocks were affected by sampling depth. Ecosystem carbon stocks consistently increased after tree plantation regardless of the soil depth; but an initial decrease and then increase trend was observed in natural restoration chronosequences with the soil sampling depth of 0-100 cm. Moreover, there was a time lag of about 15-30 years between biomass production and soil carbon sequestration in 0-100 cm, which indicated a long-term effect of vegetation restoration on deeper soil carbon sequestration.

Dynamics of forest biomass carbon stocks from 1949 to 2008 in Henan Province,east-central China

We estimated forest biomass carbon storage and carbon density from 1949 to 2008 based on nine consecutive forest inventories in Henan Province,China.According to the definitions of the forest inventory,Henan forests were categorized into five groups: forest stands,economic forests,bamboo forests,open forests,and shrub forests.We estimated biomass carbon in forest stands for each inventory period by using the continuous biomass expansion factor method.We used the mean biomass density method to estimate carbon stocks in economic,bamboo,open and shrub forests.Over the 60-year period,total forest vegetation carbon storage increased from34.6 Tg(1 Tg = 1×10;g) in 1949 to 80.4 Tg in 2008,a net vegetation carbon increase of 45.8 Tg.By stand type,increases were 39.8 Tg in forest stands,5.5 Tg in economic forests,0.6 Tg in bamboo forests,and-0.1 Tg in open forests combine shrub forests.Carbon storageincreased at an average annual rate of 0.8 Tg carbon over the study period.Carbon was mainly stored in young and middle-aged forests,which together accounted for 70–88%of the total forest carbon storage in different inventory periods.Broad-leaved forest was the main contributor to forest carbon sequestration.From 1998 to 2008,during implementation of national afforestation and reforestation programs,the carbon storage of planted forest increased sharply from 3.9 to 37.9 Tg.Our results show that with the growth of young planted forest,Henan Province forests realized large gains in carbon sequestration over a 60-year period that was characterized in part by a nation-wide tree planting program.

Does tree species composition control the soil carbon stocks of the Hyrcanian forest in the Northern Iran?(A case study in Guilan province,Iran)

This work studied the effects of tree species composition on soil carbon storage in five mixed stands dominated by oriental beech and grown in the western Caspian region in Guilan province, called Astara, Asalem, Fuman, Chere and Shenrud. The thickness of the litter layer, soil characteristics, tree composition and percentage of canopy coverage were measured in each stand. Total soil organic carbon differed significantly by stand. Total （organic） carbon stores at Fuman, which had the lowest tree species richness with 2 species and least canopy coverage （75%）, were significantly （p〈0.05） higher than at other locations. Carbon stor-age in topsoil （0-10 cm） was significantly lower in Shenrud, which had the highest tree species richness with 5 species and highest canopy cov-erage （95%）. The high percentage of canopy coverage in Shenrud proba-bly limited the conversion of litter to humus. However, in the second soil layer （10-25 cm）, Asalem, with high tree species richness and canopy coverage, had the highest carbon storage. This can be explained by the different rooting patterns of different tree species. In the Hyrcanian forest. According to the results, it can be concluded that not only tree composi-tion but also canopy coverage percentage should be taken under consid-eration to manage soil carbon retention and release.

The inventory of the carbon stocks in sub tropical forests of Pakistanfor reporting under Kyoto Protocol

The United Nations Framework Convention on Climate Change （UNFCCC） requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the status of carbon stocks in sub tropical forests of Pakistan. There are two major sub types in subtropical forests of Pakistan viz a viz Subtropical Chir Pine and Subtropical broadleaved forests. A network of sample plots was laid out in four selected site. Two sites were selected from sub tropical Chir Pine （Pinus roxburghii） forests and two from Subtropical broadleaved forests. Measurement and data acquisition protocols were developed specifically for the inventory car- ried out from 2005 to 2010. In total 261 plots （each of lha.） were established. Estimation of diameter, basal area, height, volume and biomass was carried out to estimate carbon stocks in each of the four carbon pools of above- and below-ground live biomass. Soil carbon stocks were also determined by doing soil sampling. In mature （-100 years old） pine forest stand at Ghoragali and Lehterar sites, a mean basal area of 30.38 and 26.11 m2.ha-1 represented mean volume of 243 and 197 m3·ha-1, respectively. The average biomass （t.ha-1） was 237 in Ghoragali site and 186 tha-1 in Lehterar site, which is equal to 128 and 100 t·ha-1 including soil C. However, on average basis both the forests have 114.5± 2.26 t.ha-1 of carbon stock which comprises of 92% in tree biomass and only 8% in the top soils. In mixed broadleaved evergreen forests a mean basal area （m2.ha-1）was 3.06 at Kherimurat with stem volume of 12.86 and 2.65 at Sohawa with stem volume of 11.40 m3.ha-1. The average upper and under storey biomass （t·ha-1） was 50.93 in Kherimurat site and 40.43 t.ha-1 in Sohawa site, which is equal to 31.18 and 24.36 t ·ha-1 including soil C stocks. This study provides a protocol monitoring biomass and carbon stocks and valuable baseline data for in Pakistan＇s managed and unmanaged sub-tropical forests.

Carbon stocks of different land uses in the Kumrat valley, Hindu Kush Region of Pakistan

Changes in land use cover, particularly from forest to agriculture, is a major contributing factor in increasing carbon dioxide（CO2） level in the atmosphere.Using satellite images of 1999 and 2011, land use and land use changes in the Kumrat valley KPK, Pakistan, were determined： a net decrease of 11.56 and 7.46 % occurred in forest and rangeland, while 100 % increase occurred in agriculture land（AL）. Biomass in different land uses,forest land（FL）, AL, and range land（RL） was determined by field inventory. From the biomass data, the amount of carbon was calculated, considering 50 % of the biomass as carbon. Soil carbon was also determined to a depth of 0–15and 16–30 cm. The average carbon stocks（C stocks） in all land uses ranged from 28.62 ± 13.8 t ha-1in AL to486.6 ± 32.4 t ha-1in pure Cedrus deodara forest. The results of the study confirmed that forest soil and vegetation stored the maximum amount of carbon followed by RL. Conversion of FL and RL to AL not only leads to total loss of about 56 %（from FL conversion） and 37 %（RL conversion） of soil carbon in the last decades but also the loss of a valuable carbon sink. In order to meet the emissions reduction obligations of the Kyoto Protocol, Conservation of forest and RL in the mountainous regions of the Hindu Kush will help Pakistan to meet its emissions reduction goals under the Kyoto Protocol.

Impact of nutrient losses from agricultural lands on nutrient stocks in Dianshan Lake in Shanghai, China

Abstract： The water quality of Dianshan Lake in Shanghai Municipality, China, is impacted by nutrient losses from agricultural lands around the lake. In this study, nine types of agricultural land use were monitored in 2010 and 2011, and a correlation analysis between nutrient losses from agricultural non-point sources （NPS） and nutrient stocks in the lake was conducted over monthly and seasonal time periods. The results indicate that the monthly average concentration of total nitrogen （TN） ranged from 1.41 to 7.34 mg/L in 2010 and from 1.52 to 5.90 mg/L in 2011, while the monthly average concentration of total phosphorous （TP） ranged from 0.11 to 0.26 mg/L in 2010 and from 0.13 to 0.30 mg/L in 2011. The annual loss of TN from agricultural NPS was 195.55 tons in 2010 and 208.40 tons in 2011. The cultivation of water oat made the largest contribution to the loss of TN. The annual loss of TP was 44.58 tons in 2010 and 48.12 tons in 2011, and multi-vegetable cultivation made the largest contribution to the loss of TP. The results of correlation analysis show that the monthly stocks of TN and TP in the lake have a positive correlation with the monthly losses of TN and TP from agricultural NPS. According to the seasonal data, the stocks of TN and TP in the lake both have a much stronger correlation with the losses of TN and TP from agricultural NPS in summer than in other seasons. Agricultural NPS pollution control should be the main focus for the water resource conservation in this area.

Assessing current stocks and future sequestration potential of forest biomass carbon in Daqing Mountain Nature Reserve of Inner Mongolia, China

Assessment of regional forest carbon stocks and underlying controls is critical for guiding forest management in the context of carbon sequestration. We investigated the variations in tree biomass carbon stocks relating to forest types, and estimated the total tree biomass carbon stocks and projected gains through natural stand development by 2020 and 2050 in the Daqing Mountain Nature Reserve based on Category II data of the Forest Inventory of Inner Mongolia for the period ending 2008. Over a total area of 388,577 ha,this nature reserve currently stores an estimated 2221 Gg C in tree aboveground biomass alone, with potential to grow by more than 30 % to reach 2938 Gg C by 2020 and nearly double to 4092 Gg C by 2050 through natural development of the existing forest stands. The tree biomass carbon density and potential gain in tree biomass carbon stocks vary markedly among forest types and with stand development.The variations in the potential change of tree biomass carbon density for the periods 2008–2020 and 2008–2050 among forest types partly reflect the varying relationships of tree biomass carbon density with stand age for different tree species, and partly are attributable to variations in the stand age structure among different forest types. Of the major forest types, the ranking of projected changes in tree biomass carbon density are not consistent with variations in the relationship between tree biomass carbon density and stand age, neither are they explainable by variations in stand age structures, implying the interactive effect between forest type and stand dynamics on temporal changes in tree biomass carbon density. Birch rank highest for future biomass carbon sequestration because of its dominance in cover area and better age structure for potential gain in tree biomass carbon stocks. Poplar and larch were out-performers compared to other forest types given their greater contribution to total tree biomass carbon stocks relative to their distributional areas. Findings in this study illustrate that protection and proper management of under-aged forests can deliver marked gains in biomass carbon sequestration. This is of great importance to policy-makers as well as to scientific communities in seeking effective solutions for adaptive forest management and mitigation of anthropogenic greenhouse gases emissions using forest ecosystems.

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on shorttime stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

The impact of the COVID‑19 outbreak on Chinese‑listed tourism stocks

This research explored the effects of the coronavirus disease(COVID-19)outbreak on stock price movements of China’s tourism industry by using an event study method.The results showed that the crisis negatively impacted tourism sector stocks.Further quantile regression analyses supported the non-linear relationship between the government’s responses and stock returns.The results present that the resurgence of the virus in Beijing did bring about a short-term negative impact on the tourism industry.The empirical results can be used for future researchers to conduct a comparative study of cultural differences concerning government responses to the COVID-19.