Quantification of the Beneficial Effects of Compression Stockings on Symptoms of Exercise and Orthostatic Intolerance in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis Patients

Chronic fatigue syndrome and myalgic encephalomyelitis (CFS/ME) are, amongst others, characterized by exercise intolerance, pain, post exertional malaise and orthostatic intolerance. It has been shown in venous disease and sport participation that compression stockings may improve exercise performance and reduce post exercise muscle soreness. Moreover, its use is advocated in orthostatic hypotension. Therefore, it was hypothesized that compression stockings may reduce symptomatology in CFS/ME patients. Methods: 100 patients used compression stockings class II for minimally 3 weeks and thereafter filled in a questionnaire, based on the Rand 36 physical activity questions (n = 9), whether compression stockings changed perceived symptoms or not. Moreover, 7 questions referring to prolonged standing and sitting, to recovery post exercise, muscle pain during or immediately post exercise, and to dizziness/light-headedness during or immediately post exercise, while standing and during prolonged sitting were added. Questions were scored as 1: able to perform activity much less while wearing the stockings, 2: perform activity somewhat less, 3: no perceived change in activity, 4: perform activity slightly better, 5: able to perform activity much better while wearing the stockings. Results: In patients able to answer the question, all mean scores per activity were significantly higher than 3, being no perceived change in activity while wearing the stockings. Subgroup analysis showed that patients with orthostatic intolerance reported higher effects than patients without orthostatic intolerance. Conclusion: This pilot study suggests that compression stockings may be useful to reduce symptomatology of physical activities in CFS/ME patients, especially in patients with orthostatic intolerance. Larger prospective studies with hard endpoints are warranted.

Comparative Study on Various Methods of Pressure in Compression Stockings

A series of self-designed and woven compression stockings were used in this pressure comparison experiment. In order to compare the differences of the garment pressure values exerted by the compression stockings with different structure parameters among three methods. The experiments were carried out with Flexi force sensors. The pressure value of nine pairs of compression stockings on five subjects and the model leg was collected, and the tensile force of the every section of these stockings was collected to calculate the pressure according to Laplace's equation. The data analysis results show that the pressure values of the compression stockings obtained by comparing the three methods have great differences in various testing methods. There is a significant correlation between processing parameters and stitch density(SD). The pressure design of the compression stockings should be based on the actual wearing of the pressure.

Parkland trees on smallholder farms ameliorate soil physical-chemical properties in the semi-arid area of Tigray,Ethiopia

Proposed agroforestry options should begin with the species that farmers are most familiar with,which would be the native multipurpose trees that have evolved under smallholder farms and socioeconomic conditions.The African birch(Anogeissus leiocarpa(DC.)Guill.&Perr.)and pink jacaranda(Stereospermum kunthianum Cham.)trees are the dominant species in the agroforestry parkland system in the drylands of Tigray,Ethiopia.Smallholder farmers highly value these trees for their multifunctional uses including timber,firewood,charcoal,medicine,etc.These trees also could improve soil fertility.However,the amount of soil physical and chemical properties enhanced by the two species must be determined to maintain the sustainable conservation of the species in the parklands and to scale up to similar agroecological systems.Hence,we selected twelve isolated trees,six from each species that had similar dendrometric characteristics and were growing in similar environmental conditions.We divided the canopy cover of each tree into three radial distances:mid-canopy,canopy edge,and canopy gap(control).At each distance,we took soil samples from three different depths.We collected 216 soil samples(half disturbed and the other half undisturbed)from each canopy position and soil depth.Bulk density(BD),soil moisture content(SMC),soil organic carbon(SOC),total nitrogen(TN),available phosphorus(AP),available potassium(AK),p H,electrical conductivity(EC),and cation exchange capacity(CEC)were analysed.Results revealed that soil physical and chemical properties significantly improved except for soil texture and EC under both species,CEC under A.leiocarpus,and soil p H under S.kunthianum,all the studied soils were improved under both species canopy as compared with canopy gap.SMC,TN,AP,and AK under canopy of these trees were respectively 24.1%,11.1%,55.0%,and 9.3% higher than those soils under control.The two parkland agroforestry species significantly enhanced soil fertility near the canopy of topsoil through improving soil physical and chemical properties.These two species were recommended in the drylands with similar agro-ecological systems.

Auto-Detection Method Using Convolution Neural Network for Bottom-Simulating Reflectors

In studies on gas hydrate,bottom-simulating reflectors(BSR)are used to determine the potential hydrate-bearing sedimentary layers.Usually,BSR detection is performed manually by experienced interpreters.Therefore,a method for implementing an auto-matic BSR detection process should be established.In this study,we develop a novel architecture for BSR characterization using the convolutional neural network(CNN)technique.We propose the use of Stokes’transform(ST)to obtain a time-frequency spectrum for the input of CNN.ST fully uses the frequency content of the seismic data,and a part of the 3D seismic data collected from the Blake Ridge is utilized to train the CNN.Synthetic seismic records with variable signal-to-noise ratios(SNR),as well as Blake Ridge seismic data,were used to validate the detection effect of the CNN.Results show that the CNN trained by this method exhibits excellent performance in noise-resistant testing and achieves an accuracy of more than 89% in field seismic data detection.

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.

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.

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.

Study of Some Biodemographic Parameters of Caryedon serratus Olivier (Coleoptera: Chrysomelidae) Insect Pest of Tamarind (Tamarindus indica Lin.) Fruit, in Burkina Faso

The lack of food security leads us to turn to the riches of the forest, namely non-timber forest products (NTFP) and timber forest products (TFP). In Burkina, these products are a source of income for families, improving their living conditions. Tamarindus indica L. that is a NFTP, is widely used in both rural and urban areas. Unfortunately, tamarind is subject to attack by Caryedon serratus. Its biological activity begins on the fruit before the harvest. The aim of our study is to evaluate some biodemographic parameters of the insect pest C. serratus Olivier. First100 healthy T. indica pods were placed in contact with ten pairs of C. serratus for 24 hours. 25 pods, each carrying a maximum of two eggs, were divided into five batches of 5 pods. Then a pair of C. serratus aged less than 24 h was placed in contact with 4 healthy pods for 24 h. Every day, the pair was removed and transferred to another box containing 4 new healthy pods. A total of 20 replicates were performed. The results showed that C. serratus has an average lifespan of 14 days. However, the female (16 days) lived longer and laid an average of 34 eggs during her lifetime. The egg-laying period lasted 14 days, the number of eggs increased until reaching a maximum peak on the 4th day. C. serratus has an embryonic development time of 3 days and an emergence rate of 66.63%. However, the weight of males and females differed significantly (p = 0.0108). We found an intrinsic rate of natural increase of 0.12 and a population doubling time was 5.68 days. These results have enabled us to gain a better understanding of its development cycle and its capacity for regeneration. This allows us to implement control strategies for better stock protection.

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.

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.

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.

Christmas Stockings

Many families will hang large brightly colored Christmas stockings over the fireplace or on the walls of their homes during Christmastime in the hopes that Santa Claus will fill them with toys,treats,and goodies.In fact,families have been long as there have been hanging stockings for as stockings!

Dietary organic acids ameliorate high stocking density stress-induced intestinal inflammation through the restoration of intestinal microbiota in broilers

Background:High stocking density(HSD)stress has detrimental effects on growth performance,intestinal barrier function,and intestinal microbiota in intensive animal production.Organic acids(OA)are widely used as feed addi-tives for their ability to improve growth performance and intestinal health in poultry.However,whether dietary OA can ameliorate HSD stress-induced impaired intestinal barrier in broilers remains elusive.In this study,a total of 528 one-day-old male Arbor Acres broilers were randomly allocated into 3 treatments with 12 replicates per treatment including 10 birds for normal stocking density and 17 birds for HSD.The dietary treatments were as follows:1)Normal stocking density+basal diet;2)HSD+basal diets;3)HSD+OA.Results:HSD stress can induce increased levels of serum corticosterone,lipopolysaccharides,interleukin-1β,tumor necrosis factor-α,and down-regulated mRNA expression of ZO-1,resulting in compromised growth performance of broilers(P<0.05).Dietary OA could significantly reduce levels of serum corticosterone,lipopolysaccharides,interleukin-1β,and tumor necrosis factor-α,which were accompanied by up-regulated interleukin-10,mRNA expres-sion of ZO-1,and growth performance(P<0.05).Moreover,OA could down-regulate the mRNA expression of TLR4 and MyD88 to inhibit the NF-κB signaling pathway(P<0.05).Additionally,HSD stress significantly decreased the abundance of Bacteroidetes and disturbed the balance of microbial ecosystems,whereas OA significantly increased the abundance of Bacteroidetes and restored the disordered gut microbiota by reducing competitive and exploita-tive interactions in microbial communities(P<0.05).Meanwhile,OA significantly increased the content of acetic and butyric acids,which showed significant correlations with intestinal inflammation indicators(P<0.05).Conclusions:Dietary OA ameliorated intestinal inflammation and growth performance of broilers through restor-ing the disordered gut microbial compositions and interactions induced by HSD and elevating short-chain fatty acid production to inhibit the TLR4/NF-κB signaling pathway.These findings demonstrated the critical role of intestinal microbiota in mediating the HSD-induced inflammatory responses,contributing to exploring nutritional strategies to alleviate HSD-induced stress in animals.

COVID‑19 and tourism sector stock price in Spain:medium‑term relationship through dynamic regression models

The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

Effect of blockchain technology initiatives on firms’market value

Despite blockchain’s potential to transform corporations by providing new ways of organizing business processes and handling information,extant research pays inadequate attention to how and under what conditions blockchain technology provides additional financial value for shareholders.Drawing on the efficient market hypothesis and signaling theory,we examined the relationship between firms’blockchain use,development announcements,and stock market reactions.We used the event study methodology to analyze a sample of blockchain projects initiated by US firms between 2016 and 2019.The sample contains 114 firm-event observations.The findings show that the average abnormal return over a 2 days event period(including the day of the announcement and the day after the announcement)was positive.This positive stock market reaction is even more substantial when firms announce blockchain projects that focus on saving cost or time.Our findings also indicate that blockchain announcements tend to elicit more positive market reactions from smaller firms.We analyzed 249 firm-event observations containing firms from around the world and conclude that blockchain technology has a non-significant long-term impact on operating performance.The contingency approach adopted in our research provides advice for selecting the right mix of blockchain investment initiatives that is most suitable for a given organizational context.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Forecasting Stock Prices with an Integrated Approach Combining ARIMA and Machine Learning Techniques ARIMAML

Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.

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.