An efficient approach for low latency processing in stream data.

COVID - 19 and its consequential impact on the economic fundamentals are highly discussed topics among the researchers. However, there is a literature void in the context of the Indian commodity and financial markets. To bridge this gap, the current study tried to investigate the influence of COVID -19 on the Indian commodity and financial markets by taking the data of the National Stock Exchange of India representing financial markets and the gold prices & oil prices representing commodity markets. The data were segregated into three-time lines, that is, whole period (April 1, 2020 – April 10, 2021), first wave (June 6 – September 30, 2020), and second-wave (February 2 – April 10, 2021) and to investigate the above relationship, the autoregressive distribution lag (ARDL) approach was employed. The findings suggested that during the whole period of the study and the first wave, coronavirus spread had a significant negative influence on the oil prices and the stock market. However, the impact was significant and positive for gold prices. The Wald test also confirmed a long-run cointegration among the variables in both periods of the study. In the context of the second wave, the study contradicted the above findings and concluded that during the second wave, the spread of COVID-19 cases had a positive impact on oil prices and stock markets; whereas, there was a negative impact on the gold prices. The findings highlighted the issue of uncertainty of pandemic, symmetry, demand theory and also highlighted the inverse relationship of gold and equity instruments, which will help in making appropriate policy-oriented decisions.

Purpose : This study aimed to investigate the causative relationship between oil, gold, and the stock market and the effects of gold and oil prices on the stock market, focusing on the BSE SENSEX. Methodology : Daily data were used in the study for the sampling period spanning from January 1996 to October 2022. The factors under investigation were the price of gold, the BSE SENSEX, and WTI crude oil. The most closely followed bellwether indicator in India is the BSE SENSEX. SENSEX aims to assess the performance of the 30 largest, most liquid, and soundest financial companies listed on the BSE Ltd. in India's several economic sectors. Gold, oil, and stock prices were all analyzed using the vector autoregression (VAR) model. When one or more variables in a model change, the impulse response function (IRF) has been used to explain how the variables in the model change as well. Findings : No long-term correlation was found in this analysis between the prices of stocks, gold, and oil. The long-term relationship (co-integration) between stock, gold, and oil prices was not found in this study. The oil price influenced both the gold price and the SENSEX in the short term. The analysis also showed that the price of gold and the SENSEX were causally related in a one-way fashion. Originality : The short- and long-term effects of gold prices on macroeconomic factors like inflation, growth rates, exchange rates, employment effects, and monetary policy have all been studied in economic research. However, little research has been done on how changes in the price of gold and oil affected the SENSEX.

Qatar and other Middle Eastern countries stock market are influenced by oil prices. The goal of this paper is to compare how stock prices in the GCC are affected by shocks in oil prices and comparing the results with other stock markets in other oil exporting countries. The relationship between oil and stock prices has been analyzed extensively in the recent literature. This paper aims to shed light on the volatility spillover dynamics running from the oil market into stock markets volatility for eight selected Middle East/African frontier markets. Middle East countries account for 31? of all crude oil production, while approximately 69? of all crude oil is produced by only ten countries. The methodology adopted in this paper is based on the VAR-GARCH approach of Engle and Kroner (1995), which allows to test for the presence of volatility spillover in both directions (i.e., from oil prices to stock prices as well as in the opposite direction). We use weekly data for GCC stock markets, plus three frontier stock markets (Algeria, Morocco and Namibia), WTI oil prices and stock prices were sourced by the U.S. Energy Information Administration and Bloomberg, respectively. Weekly indices, Wednesday to Wednesday, were preferred in order to overcome the different stock markets days closure across the eight countries considered in this study. We define weekly returns as logarithmic differences of oil and stock prices. Following Caporale et al. (2006) and Al-Maadid et al. (2016), we use a multivariate GARCH-BEKK model to test for volatility spillover by placing restrictions on the relevant parameters. We consider the following two null hypotheses: i) Tests of no stock price volatility spillover to oil price volatility (H0: Stock → Oil: a21?g21?0) and ii) Tests of no oil price volatility spillover to stock price volatility (H0: Oil → Stock: a12?g12?0). The results indicate that there is volatility spillover from oil prices volatility into stock market returns volatility. There is evidence of significant conditional volatility spillover, measured by g12, running from oil towards UAE (0.130), Qatar (0.134) and Oman (0.259). These results are consistent with other findings which show significant volatility spillovers between oil and stock markets in the GCC region. However volatility spillover from stock market returns volatility into oil prices volatility.The conclusion of this paper helps with moving to a more diversified and knowledge based economy because it identifies the effects of oil prices volatility on stock markets volatility for eight oil exporter countries (GCC and non GCC counties). By using weekly data for the 2004–2015 period and using Wednesday to Wednesday weekly prices to overcome the different weekend effect, and by using the US stock market because it is a proxy for the business cycle and an international stock market, we model the relationship between oil and stock prices using a multivariate GARCH-BEKK model. We find evidence of co-movement between oil and stock markets, especially in the GCC region, whereas results for volatility spillovers are quite mixed. Consequently, general policies aimed at stabilizing stock prices in oil exporting countries should be formulated by diversification. The specific linkages between different markets need to be taken into account in order to devise appropriate policy measures.

Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.

Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

The early Internet of Things stream processing platforms were mainly designed to collect and display real‐time raw sensor measurements. Data often has to go through several phases of processing to lead to actionable automatic or human decision making. This chapter discusses five main operations performed on streaming data: compression, dimensionality reduction, summarization, learning, and visualization. It distinguishes two main types of stream data processing systems. The first type, also called a data stream management system, is based on relational database principles and introduced the concept of continuous queries. The second type does not enforce a relational view and enables the creation of custom operators. Based on batch and stream processing paradigms, two data processing architectures emerged: the Lambda architecture, which enables both batch and stream processing, and the simpler Kappa architecture, which enables stream processing.

This paper examines the impact of the exchange rate, oil price and gold price on the Kuwaiti stock market using a wavelet analysis, namely, cross-wavelet coherency and partial cross-wavelet coherency. This method is used to test for nonlinear causality and decompose the data into various time frequencies to better understand various investment horizons. These interactions were examined based on daily observations from 02 January 1996 to 28 September 2017. The findings show a positive relationship between stock market and exchange rate in all frequencies. This relationship is being remarkably weak once the impact of oil price is removed. Besides, the correlation between the stock market and oil price is positive in low-frequency bands and will be reduced after eliminating the effect of the exchange rate. Regarding gold price, there is only a negative short-term relationship with the stock market during crisis periods. In summary, the impact of oil price is indirectly positive on the stock market by leading the movement of the exchange rate.

Stream processing has been widely used in big data analytics because it provides real-time information on continuously incoming data streams with low latency. As the volume of data increases and the processing logic becomes more complicated, the size of internal states in stream processing applications also increases. To deal with large states efficiently, modern stream processing systems support storing internal states on solid state drives (SSDs) by utilizing persistent key-value (KV) stores optimized for SSDs. For example, Apache Flink and Apache Samza store internal states on RocksDB. However, delegating state management to persistent KV stores degrades the performance, because the KV stores cannot optimize their state management strategies according to stream query semantics as they are not aware of the query semantics. In this paper, we investigate the performance limitations of current state management approaches on SSDs and show that query-aware optimizations can significantly improve the performance of stateful query processing on SSDs. Based on our observation, we propose a new stream processing system design with static and runtime query-aware optimizations. We also discuss additional research directions on integrating emerging storage technologies with stateful stream processing.

We estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multisalinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty‐five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time ( of 0.69 and 0.33 and normalized root mean square errors (NRMSE) of 0.16 and 0.21, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE=0.23). We demonstrate that that permeability estimates from the joint‐NMR‐CC model (NRMSE=0.13) compare favorably to estimates from the Katz and Thompson model (NRMSE=0.074). This model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

This paper explores the dependence of emerging European stock markets (Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania, Russia, Turkey and Ukraine) on global risk factors (changes in gold prices, US implied volatility index and oil prices) based on daily data from 6 January 2004 to 31 December 2013. We employ a quantile regression model to analyse how the global factors affect stock returns under different market circumstances, such as bearish (lower quantiles), normal (intermediate quantile) and bullish (higher quantiles) times. Empirical results reveal that the response of stock markets is heterogeneous; larger equity markets, such as Poland, Russia and Turkey, are highly sensitive to the global factors while Bulgaria is the least sensitive. Overall, the dependence on gold and oil prices is positive while the dependence on US stock market uncertainty is negative. Additionally, in most of the cases, the dependence intensifies during bear market conditions, in which stock prices fall.

Existing stream processing frameworks operate either under data stream paradigm processing data record by record to favor low latency, or under operation stream paradigm processing data in micro-batches to desire high throughput. For complex and mutable data processing requirements, this dilemma brings the selection and deployment of stream processing frameworks into an embarrassing situation. Moreover, current data stream or operation stream paradigms cannot handle data burst efficiently, which probably results in noticeable performance degradation. This paper introduces a dual-paradigm stream processing, called DO (Data and Operation) that can adapt to stream data volatility. It enables data to be processed in micro-batches (i.e., operation stream) when data burst occurs to achieve high throughput, while data is processed record by record (i.e., data stream) in the remaining time to sustain low latency. DO embraces a method to detect data bursts, identify the main operations affected by the data burst and switch paradigms accordingly. Our insight behind DO's design is that the trade-off between latency and throughput of stream processing frameworks can be dynamically achieved according to data communication among operations in a fine-grained manner (i.e., operation level) instead of framework level. We implement a prototype stream processing framework that adopts DO. Our experimental results show that our framework with DO can achieve 5x speedup over operation stream under low data stream sizes, and outperforms data stream on throughput by 2.1x to 3.2x under data burst.

An effective method for the rapid detection of microplastics in soil

Purpose: The purpose of this study is to examine how fluctuations in oil and gold prices have influenced Saudi Arabia's GDP from 2001 to 2023. Given the country's status as one of the world's largest oil exporters, its economic stability is closely tied to changes in oil prices. While gold plays a less central role, it still impacts the economy by shaping investor confidence and serving as a safe haven during times of uncertainty. Materials and Methods: The study employs a detailed analysis of historical trends and economic indicators to explore the relationship between oil and gold prices and their effects on GDP. A multiple linear regression model is used to examine these relationships. Factors such as inflation and investment patterns are considered to assess the strength and implications of these correlations. Findings: The findings reveal that oil prices have a significant and direct impact on Saudi Arabia's GDP growth, with an increase in oil prices being associated with a substantial decrease of 6.52 units in GDP. This underscores the country's economic reliance on oil exports. On the other hand, gold prices positively contribute to GDP, with an increase of 2.20 units, highlighting their role in influencing economic factors such as inflation, investment trends, and investor sentiment. These results underscore the vulnerabilities associated with commodity price fluctuations and their substantial impact on economic performance. Implications to theory, Policy and Practice: To address these challenges, the study recommends that policymakers focus on stabilizing oil and gold prices to mitigate economic volatility. Additionally, achieving the goals of Vision 2030 is essential, emphasizing the need to diversify the economy and reduce dependence on oil. Strengthening economic resilience and fostering sustainable growth strategies are critical steps toward ensuring long-term prosperity.

Soil water content indicators, particularly field capacity (FC) and permanent wilting point (PWP), are fundamental parameters influencing irrigation management, water retention capacity, infiltration, drainage, hydraulic conductivity, plant water stress, and solute dynamics. Their direct measurement is often costly. To address this, pedotransfer functions (PTFs) have emerged as efficient, rapid, and economical alternatives for estimating these characteristics. This study evaluates the capability of multiple linear regression, classification and regression trees (C&RT), artificial neural networks (ANN), k-nearest neighbor (k-NN), and adaptive neuro-fuzzy inference system (ANFIS), for estimating soil FC and PWP. A total of 240 soil samples were collected and split into training and testing datasets. Key input variables such as clay, silt, sand, and bulk density were considered, while FC and PWP served as the outputs. Sensitivity analysis and statistical validation were used to assess model performance, primarily based on the determination coefficient (R2) and normalized root mean square error (NRMSE). Among the models tested, ANFIS (for FC; R2 = 0.77, NRMSE = 0.138 and PWP; R2 = 0.79, NRMSE = 0.149) demonstrated superior predictive accuracy, making it the most suitable approach for estimating soil water content in diverse soil textures. Consequently, the integration of ANFIS into the development of soil PTFs is recommended for soils in northwestern Iran.

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model