journal port science research

This study aims to analyze and measure the impact of corporate social responsibility (CSR) dimensions on the financial and equity performance indicators of a sample of Iraqi private banks for the period 2015–2024. This is particularly relevant given the growing global and local interest in integrating CSR practices into banking strategies as a key component of sustainability and long-term growth. The study employed a descriptive-analytical approach, analyzing the financial data of the banks surveyed to investigate the extent to which CSR dimensions were implemented. A range of statistical tools were used to measure the relationship and impact between CSR, on the one hand, and equity turnover, market capitalization, and financial performance, on the other. The study concluded that there is a significant positive correlation and impact between CSR dimensions and financial and equity performance indicators in Iraqi private banks. Banks adopting CSR practices demonstrated higher levels of profitability, better financial efficiency, and more stable market capitalization. The study also revealed that the economic dimension of social responsibility has the strongest impact on financial performance, followed by the ethical dimension, while the philanthropic dimension has an indirect impact by improving the bank's reputation and enhancing public trust.

In the healthcare sector, fostering deep customer (patient) commitment is critical for improving patient outcomes, ensuring satisfaction, and building long-term loyalty. Human-Centered Design offers a strategic framework for achieving this by prioritizing the needs, experiences, and perspectives of patients throughout service delivery. While patient views are often studied, the perceptions of employees who implement and embody these principles are a crucial indicator of an organization's true adoption of Human-Centered Design. This study aims to investigate the impact of Human-Centered Design initiatives on customer commitment from the perspective of employees at the Royal Hospital. It seeks to understand employees' awareness of Human-Centered Design principles, their perception of its application in hospital processes, and their views on how these practices influence patient loyalty and trust. A quantitative cross-sectional survey research design was employed. A structured questionnaire was distributed to a stratified random sample of 90 clinical and non-clinical employees at the Royal Hospital. The survey measured variables across key dimensions: (1) employee understanding of Human-Centered Design, (2) perceived integration of Human-Centered Design in patient care workflows, (3) observed patient feedback and satisfaction, and (4) overall customer commitment. Data were analyzed using descriptive statistics, correlation analysis, and multiple regression to determine the relationship between Human-Centered Design adoption and customer commitment. The results indicate a strong positive correlation between employees' perceived use of Human-Centered Design and their assessment of customer commitment. Employees who reported a higher integration of Human-Centered Design principles—such as empathy, co-creation, and iterative feedback—in their departments also reported higher levels of patient trust, satisfaction, and intention to return. However, the study also identified variability in the implementation of Human-Centered Design across different hospital units, suggesting opportunities for more consistent training and application.

Hyperparameter optimization and feature selection are closely related aspects of biomedical classification systems, especially when samples are high-dimensional and limited in sample size. Traditional pipelines normally tackle these components separately, though they interact heavily in kernel-based classifiers like Support Vector Machines (SVMs). This paper presents a unified optimization model that concurrently selects informative biomedical features and optimizes SVM hyperparameters, and compares three high-level binary metaheuristic algorithms (Binary Harris Hawks Optimization (BHHO), Binary Differential Evolution (BDE), and Binary Marine Predators Algorithm (BMPA)) in the same set of experimental conditions. Both candidate solutions combine a binary feature-selection vector with continuous SVM parameters to allow exploration of the mixed search space. The proposed framework is tested against various biomedical data to perform different diagnostic tasks. The evaluation is performed experimentally in terms of AUC, F1-score, accuracy, and the feature reduction rate. Results demonstrate consistent improvements over baseline methods, with the highest overall discriminative performance recorded with BHHO, while BMPA produces the most compact feature subsets. This result shows that combined optimization using the proposed binary metaheuristics provides a powerful and interpretable method of diagnosing biomedical problems.

This research aims to explore the role of the DMAIC methodology as a systematic analytical framework in enhancing the assessment of Industry 4.0 maturity within the banking sector, through a case study of Al-Rafidain Bank in Baghdad. The Industry 4.0 Maturity Model (MM) was employed to determine the bank's level of digital readiness, integrating the steps of the DMAIC methodology to provide a structured path for gap diagnosis, performance measurement, root cause analysis, improvement proposals, and results monitoring. The study relied on quantitative and qualitative tools, including structured questionnaires, statistical analysis of means, relative weights, and standard deviations, as well as the use of quality tools such as the Pareto chart. The results showed that the bank possesses a medium to good level of maturity in several dimensions, such as partnerships and digitalization. However, clear gaps emerged in the areas of exceptions, training, and digital infrastructure. The study confirmed that integrating the DMAIC methodology with the maturity model effectively contributes to improving the diagnosis of the digital landscape and guiding institutional transformation plans, thus supporting digital transformation strategies in the banking environment. The research recommends adopting flexible, data-driven evaluation methodologies, expanding digital awareness programs, and building capacity within government banks to keep pace with the requirements of the Fourth Industrial Revolution and promote financial inclusion.

This research aims to analyze the extent to which the Fama and Hexafrance factors affect stock returns in the Iraq Stock Exchange during the period from 2014 to 2023, the research problem stems from the observed shortcomings of traditional asset pricing models such as the CAPM model in interpreting the Iraqi market dynamics, and to achieve this goal, the research relied on a quantitative analytical approach using the secondary data of a sample of 10 companies, with the application of multiple regression analysis to test the hypotheses. The existence of a unique behavior of some factors in the Iraqi market, where the factors of profitability and investment were compatible with the prevailing financial theories, on the other hand, the factors of volume, value, momentum, and liquidity showed inconsistent behavior with these theories, and this is due to the fact that investors in this market tend to prefer large companies, growth stocks, and liquid assets, and the research concludes that the Fama and French hexagonal model have a much greater explanatory capacity than the CAPM model. Traditional in understanding the returns of equities in the Iraqi market, which makes it a more effective tool for investors and decision makers.

The current research aims to understand the importance of the role and impact of data collection and data mining on proactive entrepreneurial performance. The research problem was represented by an important question: "What is the impact of business intelligence on entrepreneurial performance?" To explain the relationship between the main research variables, two main hypotheses were formulated for correlation and impact, from which sub-hypotheses were derived. To achieve the research objectives, the descriptive analytical approach was used to survey the opinions of employees at Al-Isra Private University. The sample included (74) out of a total population of (96). The intentional sampling method was used, and their opinions were surveyed using a validated questionnaire. To analyze the data, the statistical programs SPSS and Excel were used. Among the statistical methods used were Spearman's correlation coefficient and simple linear regression coefficient. The research reached a number of conclusions, the most important of which is the role and impact of data collection and data mining on proactive entrepreneurial performance, contributing to increasing the efficiency of market analysis and reading with various capabilities and tools that support and advance entrepreneurial performance.

Fluoroquinolone resistance is a major challenge in managing bacterial infections and motivates the development of new derivatives with improved activity and pharmacokinetic properties . In this study, a series of levofloxacin–thiourea hybrids (L1–L3) were synthesized via esterification of levofloxacin, conversion to the corresponding hydrazide, and condensation with para substituted phenyl isothiocyanates under mild conditions. The structures of the intermediates and final products were confirmed by melting point, ATR FTIR, and ¹H NMR spectroscopy, which demonstrated introduction of the hydrazide and thiourea moieties while preserving the levofloxacin core. Antibacterial activity was assessed against Escherichia coli (G–) and Staphylococcus aureus (G+) using the agar well diffusion method, with levofloxacin as reference; the p chloro derivative L2 showed inhibition zones comparable to or greater than levofloxacin at higher concentrations, whereas L1 and L3 retained substantial activity. SwissADME predictions indicated that all derivatives display high gastrointestinal absorption, acceptable polarity (TPSA 122.96 Ų), and only one Lipinski rule violation, with moderate decreases in solubility relative to levofloxacin but overall favourable drug likeness profiles. Molecular docking into the Streptococcus pneumoniae DNA gyrase–DNA complex (PDB ID: 4Z2D) showed that L1–L3 adopt poses similar to levofloxacin, preserving the key Mg²⁺ mediated contacts and base stacking while forming additional hydrogen bond and π–π interactions through the thiourea–aryl fragment; L2 exhibited the most favourable docking score. Together, the experimental and in silico results identify levofloxacin–thiourea derivatives, particularly the p chloro analogue L2, as promising leads for further optimization against resistant bacterial pathogens.

Embedded system-integrated technology for the greenhouse protects the system through self-regulation and temperature control. Sensors and a microcontroller measure the temperature and activate the heating or cooling components accordingly. These services are provided in private housing, agricultural, and industrial facilities, and are intended to enhance energy efficiency and reduce energy consumption. The temperature regulation project, developed on the Arduino platform with an integrated PID controller, is an efficient and effective temperature control tool. The system automatically operates the heating and cooling mechanisms using a PID algorithm. Bluetooth-enabled remote XY switches can be used to monitor the temperature. The system adjusts the temperature sensor settings, ensuring continuous control of the PID indicator. This is the ideal way to regulate temperature while optimizing energy consumption and improving system efficiency.

Background: Dental caries still the most prevalent chronic oral disease among children, that sometimes need complex rather than simple preventive therapy in developing countries. Assessing dental treatment trends in teaching clinics is essential to enhance children oral health. Aim: To assess distribution and frequency of dental treatments accomplished to children attending the Diagnostic and Pediatric Dental Clinic at Uruk University's College of Dentistry in Baghdad city. Methods: A retrospective cross-sectional design was accomplished using archived clinical records at diagnostic and pediatric dental clinics from two academic years (2023-2024 and 2024-2025). Children aged 3 to 14 years with complete records were encompassed. Dental procedures were divided into preventive, restorative, vital pulp therapy, extractions, and other therapies for oral ulcers. Data was examined with descriptive statistics (number and percentage). Chi-square test represent the inferential statistics. A p-value of < 0.05 was considered statistically significant.Results: The overall number of procedures reported was 4,189, up from 1,807 to 2,382 in the second academic year. Restorative treatments were the most common treatment (48.86% and 44.12%, respectively). followed by extraction that increased progressively (30.94% to 38.83%). Pulpotomy represent about one-fifth of treatments but reduced slightly in the next year. Fluoride prophylaxis represented the highest percentage of preventive therapy, while fissure sealant application increased in the next year. Overall, curative therapy exceeds preventive approachs. Conclusion: Pediatric dental services at diagnostic and pediatric dental clinics are mostly treatment-oriented, reflecting late presentation and a significant caries burden among youngsters. Although preventive techniques have improved, notably the use of fissure sealants, early diagnosis and prevention programs must be strengthened in order to shift care to minimally invasive dentistry and enhance pediatric oral health outcomes.

The high world rate of mortality around is attributable to Coronary Artery Disease (CAD). Therefore, in order to prevent extreme outcomes (including coronary heart attacks and sudden death, early detection is essential. A diagnostic (non-invasive) tool commonly used to identify early symptoms of CAD is Electrocardiography (ECG). However, the interpreter's knowledge may overlook diffuse patterns, thus affecting the accuracy. Artificial Intelligence (AI) is a promising solution that has emerged recently. That is because it can utilize advanced algorithms to analyze large ECG datasets and identify previously hidden indicators of early-stage disease. This research demonstrates the significance of ECG in CAD diagnosis and investigates how AI can enhance diagnostic accuracy to improve preventive measures for cardiac disease.

This study aims to examine the impact of digital maturity, with its dimensions (digital strategy, digital market, digital operations, digital culture, and digital technology), on enhancing competitive cooperation, with its dimensions (interorganizational interaction, knowledge exchange, joint performance, influence on competition, and mutual trust), in Iraqi private banks affiliated with the Iraqi Private Banks Association. The study adopted a descriptive analytical approach through a questionnaire distributed to a sample of (150) managers out of (250), and the data were analyzed using (SPSS). The results showed a significant impact of digital maturity on enhancing competitive cooperation, indicating that improving the level of digital maturity contributes to raising the level of competitive cooperation in the studied banks.