Islamic Azad University, Omidieh Branch

This study investigates and proposes a model of digital transformation that affects the quality of accounting information in Iran's capital market. In terms of purpose, this research is categorized as exploratory. From the research philosophy perspective, the study adopts a holistic approach that encompasses both positivism and interpretivism. Moreover, the research follows a mixed-methods strategy, integrating both quantitative and qualitative methodologies. Initially, to collect data and propose a preliminary model, library research and a systematic literature review were conducted. Subsequently, using the fuzzy Delphi method—including a semi-structured questionnaire distributed among 18 experts in the fields of accounting and digital transformation—the key components of the study were identified. Then, to validate the developed model, a researcher-made questionnaire was designed based on the dimensions confirmed in the second phase for quantitative validation. A statistical sample of 384 individuals, including accountants, professionals involved in accounting activities, corporate managers, accounting and finance faculty members, and IT personnel, was selected, and the questionnaire was distributed among them to collect quantitative data. The data were analyzed using quantitative methods and the Smart PLS software, and the final research model was developed. The results revealed that the following are the key components influencing the quality of accounting information: integration of artificial intelligence to reduce errors (5 subcomponents), blockchain for data validation (4 subcomponents), strong internal controls (5 subcomponents), enhanced security of financial data (4 subcomponents), real-time financial reporting (5 subcomponents), cloud-based systems for timeliness (5 subcomponents), artificial intelligence for process acceleration (5 subcomponents), impact of digital platforms on timeliness (3 subcomponents), data visualization tools (5 subcomponents), user-friendly financial reporting platforms (5 subcomponents), and stakeholder-focused data usability (5 subcomponents). The findings align with established theoretical frameworks, including the Technology Acceptance Model, Diffusion of Innovations, and Institutional Theory, highlighting the importance of understanding the opportunities and challenges organizations face amid the complexities of digital transformation.

Bank sampah memainkan peran penting dalam mengelola sampah dengan cara berkelanjutan. Masyarakat dapat menukarkan sampahnya dengan imbalan berupa uang atau barang berguna. Hal ini tidak hanya membantu mengurangi jumlah sampah yang mencemari lingkungan, tetapi juga merangsang kesadaran akan pentingnya daur ulang dan pengelolaan sampah yang bijaksana. Desa Simbur Naik dipilih sebagai lokasi pengabdian dikarenakan Pemerintah Desa sebagai mitra pertama menyadari bahwa simbur naik merupakan daerah pesisir yang berpotensi mendapatkan sampah kiriman dari daerah lain, selain itu kesadaran masyarakat dalam menjaga lingkungan masih rendah sehingga Pemerintah Desa Simbur Naik berkolaborasi dengan Program Studi Ilmu Pemerintahan Universitas Jambi dan TPBIS Omah Sinau Jambi sebagai mitra kedua yang sudah berpengalaman dalam mengelola bank sampah di Kota Jambi. Adapun metode yang dilaksanakan pada pengabdian ini tahapan pertama memberikan sosialisasi edukasi dan berdiskusi sebagai pengetahuan awal, menggiring pemahaman masyarakat agar peduli terhadap lingkungan, kedua pendampingan pendirian bank sampah diawali penandatanganan perjanjian kerjasama dan nota kesepahaman sebagai bentuk landasan komitmen pelaksanaan pengabdian secara berkelanjutan di Desa Simbur Naik. Hasil evalusi kegiatan menunjukkan bahwa aparatur desa dan peserta yang mengikuti kegiatan pengabdian mendukung penuh proses diskusi serta pembentukan bank sampah. Harapannya dapat menciptakan lingkungan yang bersih, sehat dan dapat mengelola sampah dengan bijak.

This study investigates countercurrent air-water two-phase flow in vertical pipes with inner diameters of 26 mm and 44 mm and a height of 2000 mm, under controlled conditions to eliminate heat and mass transfer. Cutting-edge techniques were employed to measure the liquid film thickness (δ) and entrainment (e) within the annular flow pattern. The methodology involved a systematic comparative analysis of experimental results against established models, identifying the most accurate methods for predicting flow behavior. Specifically, the Schubring et al. correlation was found to most accurately predict e in 26 mm pipes, while the Wallis correlation was more accurate for 44 mm pipes. Additionally, interfacial shear stress was analyzed, confirming the high precision of the δ and e parameters. This research enhances the understanding of countercurrent air-water two-phase flow by providing reliable estimation methods for different pipe diameters and emphasizes the significance of accurately determining interfacial shear stress. Key findings include the identification of the most accurate models for different pipe sizes and addressing challenges in measuring δ and e under controlled conditions. The study’s novelty lies in its comprehensive comparative analysis of existing models, leading to improved predictions of flow dynamics in vertical pipes, thereby contributing valuable insights into two-phase flow behavior in geosciences and environmental engineering.

Injection of surfactant and polymer at the same time is one of the challenges that reduce their performance in reservoirs. Using a polymeric surfactant (PS) as a single new material can be the best alternative to solve the existing challenges. Considering the basic problems of chemical injection, this work focuses on the surface adsorption of polymer/surfactant on carbonate reservoirs (CRs). In this work, polyacrylamide was synthesized using zwitterionic as a hydrophobic polymeric surfactant (i.e. HZPAA). For a better comparison, this substance was compared with hydrolyzed polyacrylamide (HPAA) in concentrations of 40 to 1000 mg in CRs. Examining the results states that with increasing polymer concentration, the surface absorption of HZPAA and HPAA on dolomite reservoir rocks (DRRs) with positive surface charge increases. The attendance of COO and SO3− functional groups in the matrix of the new polymer (i.e. HZPAA) creates electrostatic forces and superior surface absorption. Their mechanism works in such a way that negative functional groups attract positively charged surfaces and increase the surface absorption of the polymer. Investigations show that the new synthesized material (i.e. HZPAA) can provide a new approach to improving surface absorption in carbonate reservoirs.

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous.

This study aims to use a static-based solubility method for measuring the solubility of lumiracoxib at a temperature of 308–338 K and pressure of 120–400 bar for the first time. The obtained solubility data for lumiracoxib is between 4.74 × 10−5 and 3.46 × 10−4 (mole fraction) for the studied ranges of pressure and temperature. The solubility values reveal that the lumiracoxib experiences a crossover pressure of about 160 bar. Moreover, the measured solubility data of these two drugs are correlated with density-based semi-empirical correlations namely Bartle et al., Mendez-Santiago-Teja, Kumar and Johnstone, Chrastil and modified Chrastil models with an average absolute relative deviation of 10.7%, 9.5%, 9.8%, 7.8%, and 8.7% respectively for lumiracoxib. According to these findings, it is obvious that all of the examined models are rather accurate and there is no superiority between these models for both examined drugs although the Chrastil model is slightly better in the overall view.

Gas hydrate (GH) reservoirs play an important role in the economy of countries and various industries depend on it. Since these reservoirs display specific behavior, understanding their thermodynamic aspects can help engineers to find their behavior. In this study, 1005 data points were collected from an Iranian gas reservoir for the construction and development of three novel machine learning techniques: Extra Tree (ET), Gene Expression Programming (GEP), and Light Gradient Boosting Machine (LightGBM) to predict gas hydrate pressure (P<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</inf>). From the total data, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$70{\rm{\% }}$</tex> was utilized for constructing artificial intelligence algorithms, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15{\rm{\% }}$</tex> for testing, and another <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15{\rm{\% }}$</tex> for validating outputs. Based on analyzing the algorithm results and comparing statistical errors, it is concluded that the LightGBM algorithm exhibits higher performance accuracy compared to other algorithms (RMSE = 1.875 and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R2 = 0.97$</tex> ). The LightGBM algorithm has several advantages, including high accuracy, high speed, and high efficiency, as well as a histogram-based approach that enhances performance accuracy through short memory retention. This algorithm utilizes parallel computations, making it suitable for large datasets. Through gradient-based learning, LightGBM captures complex relationships and provides accurate predictions, even with nonlinear patterns. Additionally, it seamlessly handles classification features, eliminates preprocessing needs, and limits excessive internal regularization. Its ability to determine matching metaparameters and handle unbalanced data enhances its performance.

Drilling mud (DM) plays an important role for exploration of oil and gas reservoirs. Lost circulation (LC) has highly influenced drilling costs due to the waste and loss of the drilling fluid (DF), which stems from some variables such as mud composition, permeability, and mud pressure. To predict this important parameter, a dataset includes 1003 data points was collected from two oil field wells in southern Iran. For the prediction of LC in this article, three hybrid algorithms were used, combining three optimization algorithms with one network algorithm, including Social Ski-Driver (SSD), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) with the Multilayer Perceptron Network (MLP). Analysis shows the combination of the MLP with SSD give good and higher accuracy than other algorithms. The statical error meteoric for the test dataset, as indicated by an RMSE of 30.85 and an impressive R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.94, show their results are better and over than other competing algorithms.