Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

A comprehensive proteomic analysis was carried out to evaluate leaf proteome changes of Brassica napus cultivars as an important oilseed crop inoculated with the bacterium Pseudomonas fluorescens FY32 under salt stress. Based on the physiochemical characteristics of canola, Hyola308 was a tolerant and Sarigol was a salt sensitive cultivar. Gel-based proteomics indicated that proteins related to energy/metabolism, cell/membrane maintenance, signalins, stress, and development respond to salt stress and bacterial inoculation in both cultivars. Under salt stress, Hyola308 launches mechanisms similar to Sarigol, but the tolerance was related to consuming less energy consumption than Sarigol for launching the proper pathway/mechanism. Inoculation with plant growth promoting bacteria promotes relative growth rate and net assimilation rate; causes increase in soluble sugar content (12–32% varing to cultivars and salt treatments), as an osmo-protectant, in leaves of Sarigol and Hyola308 in control and salt stress conditions. The groups of proteins that are affected due to inoculation (18 and14 functional groups in Hyola308 and Sarigol, respectively) are varying to stress-influenced groups (10 and 6 functional groups in Hyola308 and Sarigol, respectively) that might be because of regulating tolerance mechanism of plant and/or plant-growth promoting bacteria inoculation. Furthermore, it is recognized that P. fluorescens FY32 has a dual effect on the cultivars including a pathogenic effect and a growth promoting effect on both cultivars under salt stress.

Employee Engagement (EE) has become a crucial element for the success of any organization. While psychology has conducted extensive research on EE, accounting has yet to give it the attention it deserves. This systematic literature review (SLR) was conducted to explore the linkage between corporate governance (CG) practices and factors that drive employee engagement (EED) in greater detail. The review identified several key factors influencing EED: board composition, audit committees, top leadership expertise, network power, other stakeholders’ supervision, and above-board application. In particular, the study revealed that effective CG practices have positive EE consequences, such as increased staff morale, job satisfaction, and productivity, and decreased employee turnover rates. The study underlined the importance of an employee-centric culture that encourages empowerment and fosters a sense of belonging in achieving maximum business success. It suggests that companies must create a work environment that promotes EE, provides opportunities for growth and development, recognizes employee contributions, and fosters open communication and collaboration. By leveraging these insights, companies can enhance their business operations and achieve maximum success by fostering a culture of EE and empowerment. The study concludes that businesses prioritizing EE as a core value can create a sustainable competitive advantage, increase their reputation, and attract and retain top talent.

This study delves into the concept of the “cultural bomb” within the framework of non-military defense empowerment strategies in Indonesia. This approach can potentially change society’s views and attitudes towards various security threats as a realization of strengthening the defense and security system of the universal people (Sishankamrata) per article 30 paragraph (2) of the 1945 constitution. By leveraging media, education, and information technology, the cultural bomb acts as a social weapon that operates powerfully in the “space of mind,” shaping behavior and actions nonviolently. The issue of cultural threats pertains to the infiltration and imposition of foreign cultural values and practices that undermine local traditions and national identity, leading to social fragmentation and weakness. This study proposes the concept of a “cultural bomb” as a policy framework to address and mitigate these cultural threats. The research employs a qualitative approach using the Delphi technique, engaging experts from cultural studies and defense strategies to reach a consensus on the strategic application of the cultural bomb. The results indicate that the cultural bomb can effectively strengthen national identity and awareness of national defense by promoting local values and cultural resilience, thus enhancing societal cohesion and mitigating the impact of foreign cultural influences. The paper outlines the components of a cultural bomb, analyzes its application in international contexts, and discusses its implications in efforts to strengthen national identity and foster a sense of national defense awareness. Focusing on the “war over space of mind” ideology, it introduces “cultural hacking” as a strategic initiative to address cultural power imbalances in the post-truth era.