This research study examines the impact of information technology on firm profitability and stock returns. Using a comprehensive dataset of firms across various industries, this research employs rigorous statistical analysis techniques to investigate the relationship between IT investments, firm profitability metrics, and stock returns. The study focuses at how IT investments affect financial performance measures including return on assets (ROA) and return on equity (ROE), with P-values of 0.34 and 0.12, respectively. Furthermore, the study investigates the influence of IT on stock returns, taking into account market capitalization, industry trends, and macroeconomic variables. This study's conclusions center on the beneficial association between IT investments and corporate profitability. The T-value for the IT investment has risen to 6.5. The analysis reveals that firms that strategically leverage IT investments tend to experience higher profitability metrics. Additionally, the research demonstrates the impact of IT on stock returns, highlighting the significance of IT as a driver of firm value and investor confidence. Moreover, this study delves into the mechanisms through which IT investments contribute to firm profitability and stock returns. It investigates the mediating role of factors such as process innovation, customer relationship management, and supply chain optimization, which facilitate the translation of IT investments into improved financial performance. The implications of this research are significant for both practitioners and policymakers. The findings provide valuable insights for firms seeking to enhance their profitability and create shareholder value through strategic IT investments. Additionally, policymakers can use these findings to formulate policies and initiatives that promote the adoption and effective utilization of IT in businesses across various sectors
Fourier Transform-Infrared (FT-IR) spectroscopy was used to analyze gasoline engine oil (SAE 5W20) samples that were exposed to seven different oxidation times (0 h, 24 h, 48 h, 72 h, 96 h, 120 h, and 144 h) to determine the best wavenumbers and wavenumber ranges for the discrimination of the oxidation times. The thermal oxidation process generated oil samples with varying total base number (TBN) levels. Each wavenumber (400–3900 cm−1) and wavenumber ranges identified from the literature and this study were statistically analyzed to determine which wavenumbers and wavenumber ranges could discriminate among all oxidation times. Linear regression was used with the best wavenumbers and wavenumber ranges to predict oxidation time.
... Show MoreBackground: Bacteriocin is a peptidic toxin has many advantages to bacteria in their ecological niche and has strong antibacterial activity. Objective: The aim of this study was to evaluation of bacteriocin using Streptococcus sanguinis isolated from human dental caries.
Subjects and Methods: Thirty five streptococcus isolates were diagnosed and tested for their production of bacteriocin, and then the optimal conditions for production of bacteriocin were determined. After that, the purification of bacteriocin was made partially by ammonium sulfate at 95% saturation levels, followed by and gel filtration chromatography
... Show MoreThis study is the first investigation in Iraq dealing with genotyping of
Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in
... Show MoreUnderstanding how abiotic influences affect organisms and their interactions within ecological communities has long been of interest to ecologists. The significance of this comprehension has increased given rapid human-induced climate change. Wind speed variation is one of the less researched effects of climate change, as wind speeds worldwide are generally falling. Although wind is a fairly universal component of the environment, relatively little work has been conducted to integrate our knowledge of how wind affects interactions between different species. In this study, the influence of wind on bottom-up trophic interactions in the plant–ungulate–wolf food chain has been investigated through mathematical modeling. Positivity and bound
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