PAHs and nickel contaminated soil containment and stabilization with silica grout

The migration of contaminants from a site in the soil represents a real threat to the environment and to human health because contaminants might leach to groundwater and humans could be affected directly and indirectly. Therefore, the objective of this research was to evaluate the vulnerability of silica based chemical grout barriers to protect leaching of contaminants to groundwater. Thirty five experiments were done using calcium chloride (SC), formamide (SF) and combined reagents (SFC) with sodium silicate, along with three different types of soil. Sand, silty sand and sandy silt soils were contaminated with nickel (1000 ppm) or phenanthrene (600 ppm) or mixture of both contaminants. A resistance-based methodology was developed to find out the internal grout changes of grouted specimens. Grouted specimens were submerged in water which was simulated low-mineralized and high-mineralized groundwater. The results show that soil resistivity went up in all specimens. The highest coefficient of stability was found to be 6% in mixed (nickel-phenanthrene) contaminated soil grouted with SF. There was no phenanthrene release from grouted contaminated soil. The nickel release was as low as 4~5 ppm. Furthermore, a complex of sodium formate was found in water based on FT-IR analysis. Lastly, the values of pH and redOx confirmed the stabilization process. This research showed that to reduce contamination with heavy metal, silica formamide (SF) grout should be used. Contamination with phenanthrene was best treated with formamide (SF grout) and a combined (SFC grout) reagent. It was also found that a combination of both reagents made setting faster and created more stable conditions. A model was developed for prediction of contaminant stability. The results of this research can be applied to stabilize any kind of soil with particle size from 1.0 mm to 0.053 mm, contaminated with PAHs and heavy metals

Effect of Technology on Service Quality Perception and Patient Satisfaction- A study on Hospitals in Bangladesh

This study investigates the intricate dynamics of technology's influence on service quality perception and patient satisfaction in Bangladesh's healthcare sector, a vital component of emerging economies. Focusing on the interplay between technology, service quality, and patient satisfaction, the research employs a quantitative approach, utilizing a structured questionnaire survey conducted both online and in-person among patients in private hospitals in Dhaka city. Statistical analyses, including correlation and regression, were applied to the gathered data. Key findings reveal significant correlations between service quality dimensions (responsiveness, assurance, communication, and discipline) and patient satisfaction. Notably, technology-related factors, specifically the use of tips, negatively impact satisfaction levels. Regression analysis identifies responsiveness, assurance, communication, and tips as significant predictors of patient satisfaction. Limitations, including the exclusive focus on Dhaka city's private hospitals, underscore the necessity for broader research across diverse healthcare settings to enhance generalizability. Practical implications recommend healthcare providers prioritize improving responsiveness, assurance, and communication, considering patient preferences on technology-related practices like tipping. From a societal perspective, the study emphasizes the broader importance of optimizing technology to elevate patient experiences, contributing to overall well-being. The research's originality lies in its nuanced examination of technology's influence on service quality and patient satisfaction within the distinctive context of Bangladeshi private hospitals. Acknowledging its limitations, this study encourages future research to explore technology's impact on service quality across various healthcare settings, providing valuable insights for ongoing improvements

The Impact of User Participation on the Success of Enterprise Resource Planning (ERP) Adoption in Bangladesh

The successful adoption of Enterprise Resource Planning (ERP) systems is crucial for organizations to enhance operational efficiency and gain a competitive edge. User participation has been recognized as a key factor in determining the success of ERP implementation. This study aims to investigate the impact of user participation on ERP adoption success in the context of Bangladesh. The specific objectives include assessing the relationship between user participation and work performance, understanding/proficiency, user-friendliness, and training/support. Additionally, the influence of organizational factors, such as organizational value, guidelines/procedures, and resource/support availability, on user participation is examined. The study also explores the impact of user participation on compatibility with existing organizational processes and alignment with strategic goals. The findings reveal that user participation significantly influences work performance, understanding/proficiency, user-friendliness, and training/support. Organizational factors and strategic alignment play important roles in facilitating user participation. The results emphasize the need to foster user participation, provide adequate training and support, promote organizational values, and align strategic goals for successful ERP adoption in Bangladesh. These insights contribute to a better understanding of the factors that drive ERP implementation success and provide guidance for organizations in Bangladesh and similar contexts

Byzantine-Resilient Federated Learning Leveraging Confidence Score to Identify Retinal Disease

Federated learning is a distributed machine learning paradigm that enables multiple actors to collaboratively train a common model without sharing their local data, thus addressing data privacy issues, especially in sensitive domains such as healthcare. However, federated learning is vulnerable to poisoning attacks, where malicious (Byzantine) clients can manipulate their local updates to degrade the performance or compromise the privacy of the global model. To mitigate this problem, this paper proposes a novel method that reduces the influence of malicious clients based on their confidence. We evaluate our method on the Retinal OCT dataset consisting of age-related macular degeneration and diabetic macular edema, using InceptionV3 and VGG19 architecture. The proposed technique significantly improves the global model's precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) for both InceptionV3 and VGG19. For InceptionV3, precision rises from 0.869 to 0.906, recall rises from 0.836 to 0.889, and F1 score rises from 0.852 to 0.898. For VGG19, precision rises from 0.958 to 0.963, recall rises from 0.917 to 0.941, and F1 score rises from 0.937 to 0.952