Continued Refinement Research Articles

Electrochemical and impedance analysis of nickel oxide nanoflakes-based electrodes for efficient chromo supercapacitors

In response to the dynamic advancement of our growing world, there is a tenacious need for sophisticated technologies and continuous refinement of existing knowledge through rigorous scientific endeavors, all with a focus on achieving sustainable development goals for a cleaner and more prosperous future. In this context, this study involves a novel approach to the synthesis of highly porous and stable nickel oxide (NiO) nanoflakes assembled with stacked nanosheet thin films through hydrothermal methods. These films are in-detail examined for their redox-active chromo-supercapacitive properties. The hydrothermal synthesis technique yields thin films with exceptional adhesion to the electrode surface, remarkable chemical stability, and suitable porous structure. These characteristics are strategically employed to facilitate rapid ion intercalation and deintercalation processes, thereby enhancing electrochemical activity. Notably, films deposited for 6-hour reaction times deliver a higher areal capacitance of 140 mF/cm² (and capacity of 70 mC/cm2) at 0.5 mA/cm², which is higher than other electrodes and earlier reports. In-situ, optical investigations further underscore the high coloration efficiency of 44.14 cm²/C, coupled with large optical modulation of 56.5 % and enduring the electrochemical and electrochromic stability. Further research and optimization of NiO-based materials hold significant potential for the development of efficient, smart, and sustainable energy storage solutions in the evolving field of electrochromic supercapacitors to meet the demands of next-generation electronic systems.

Engaging with AI painting: exploring motivations and challenges in laypeople's creative information practices

Introduction. The advent of generative AI has democratized art creation, enabling individuals without formal training to produce visually appealing digital artworks. However, it is not yet well-understood why and how laypeople engage with AI in creative information practice. Method. This study conducted semi-structured interviews with 17 participants, aiming to understand the motivations for laypeople's engagement with AI painting and the challenges they encounter. Analysis. The interviews were analysed using open coding and thematic analysis, with two independent coders achieving a substantial inter-coder reliability score. Results. Our findings reveal that laypeople's engagement with AI painting is a practice-oriented information practice, influenced by social and contextual factors. The engagement process is iterative, starting with a user prompt and AI-generated initial output, followed by continuous refinement. Laypeople engage with AI painting for emotional motivations, personal utilitarian motivations, and social interaction. Notable challenges include image quality, technological limitations, and personal constraints. Conclusion. These findings provide empirical evidence of the potential and limitations of AI in creative information practice. This understanding is vital for informing the design of future AI tools to enhance the human-AI collaborative experience in creative endeavours.

Occlusion therapy for amblyopia, a historical report from 9th century Persian scholar, Ali ibn Sahl ibn Rabban al-Tabari (838–870 CE)

ABSTRACT Introduction This study reevaluates the historical origins of occlusion therapy for amblyopia, focusing on the contributions of the 9th-century Islamic scholar, Ali ibn Sahl ibn Rabban al-Tabari (838–870 CE). Methods The investigation delved into al-Tabari’s writings, particularly ”Firdous al-Hikma,” to extract insights into his approach to addressing reduced vision in one eye.Additionally, the study examined subsequent advancements in occlusion therapy by scholars such as Thabit ibn Qurrah and Rhazes, building upon al-Tabari’sfoundational work. Results Al-Tabari’s reports contain significant insights into occlusion therapy for amblyopia, predating commonly attributed origins of the treatment. Within ”Firdous al-Hikma,” he outlines methods for addressing reduced vision, advocating for the covering of the healthier eye to promote the function of the weaker eye. These findings highlight the pioneering efforts of al-Tabari and his contemporaries in the Islamic civilization and challenge the conventional narrative surrounding the history of occlusion therapy. Subsequent advancements by scholars such as Thabit ibn Qurrah and Rhazes expanded upon al-Tabari’s work, advocating for similar therapeutic approaches within the Islamic civilization. Their contributions further solidified the practice of occlusion therapy, laying the groundwork for its continued evolution and refinement in subsequent centuries. Discussion Al-Tabari’s contributions to occlusion therapy underscore the rich heritage of scientific inquiry in theIslamic civilization during the medieval period. This historical perspective sheds light on the diverse contributions to medical knowledge and practice outside of Western contexts and emphasizes the importance of recognizing and honoring these contributions in the broader history of medicine.

CRISPR-based gene editing in plants: Focus on reagents and their delivery tools

Introduction: CRISPR-Cas9 technology has revolutionized plant genome editing, providing precise and efficient methods for genetic modification. This study focuses on the advancements and delivery of CRISPR-Cas9 in plant gene editing. Methods: A comprehensive search in scientific databases, including PubMed, ScienceDirect, and Google Scholar, was conducted to gather information on CRISPR-Cas9 gene editing and its delivery in precise gene modification in plants. Results: The evolving landscape of CRISPR nucleases has led to the development of innovative technologies, enhancing plant research. However, successful editing is contingent on efficient delivery of genome engineering reagents. CRISPR-based gene editing in plants utilizes diverse delivery methods: Agrobacterium-mediated transformation for bacterial transfer, biolistic transformation for physical gene insertion, electroporation for direct gene entry, expression of developmental regulators for gene expression modulation, and tobacco rattle virus as a viral vector, each offering distinct advantages for precise and efficient genetic modification in plants. Conclusion: CRISPR-Cas9 gene editing stands as a pivotal advancement in plant genetics, offering precise gene manipulation with applications in agriculture and biotechnology. The continuous refinement of reagent delivery tools reinforces CRISPR-Cas9's transformative role in plant genome editing, with significant implications for broader scientific applications.

The spiking neural network based on fMRI for speech recognition

The structure of the human brain has evolved to achieve extraordinary computing power through continuous refinement by natural selection. At present, the topology of brain-like model lacks biological plausibility. In this paper, a new brain-like model is proposed, called fMRI-SNN, which is a spiking neural network (SNN) constrained by the topology of a functional brain network from human functional Magnetic Resonance Imaging (fMRI) data. To verify its performance, this fMRI-SNN is applied to speech recognition. Our results indicate that the recognition accuracy of fMRI-SNN is superior to that of other SNNs and reported methods, and exhibits stronger performance on more difficult speech recognition tasks. Our discussion on recognition mechanism finds the advantage of fMRI-SNN is that the differences in its neuronal firing patterns are greater than those of other SNNs, since it has better information transmission ability.

Utilizing Neural Networks for Early Prediction of Pneumococcal Disease: A Case Study in Bonny Island, Nigeria

Pneumococcal disease, caused by Streptococcus pneumoniae, poses a significant health challenge, particularly in resource-limited settings like Bonny Island, Nigeria. This study employs neural networks and artificial intelligence to predict pneumococcal disease, addressing the critical need for early diagnosis and intervention. Methodologically, the research encompasses data collection, cleaning, correlation analysis, and model development, ensuring a robust system for early disease prediction. By analyzing demographic, clinical, and environmental factors, the study identifies significant predictors of pneumococcal disease risk. In comparison with Random Forest and Support Vector Machines trained on the same data, the neural network achieved 100 percent accuracy, recall, precision, and f1 scores. The integration of the neural network model into a web application facilitates real-time predictions, enabling healthcare providers to input symptoms and receive immediate diagnostic insights. This approach enhances timely interventions, potentially reducing morbidity and mortality associated with pneumococcal disease. Despite challenges like data quality and integration, the findings demonstrate the efficacy of AI-driven models in improving public health outcomes. The deployment of such models in Bonny Island underscores their practicality and scalability, paving the way for broader applications in similar contexts. Ultimately, this study not only advances understanding of pneumococcal disease epidemiology in Bonny Island but also contributes to global efforts in enhancing healthcare delivery through innovative technological solutions. Future research should focus on continuous model refinement and validation with larger datasets to further improve accuracy and reliability.

Enhancing team dynamics through digital coaching: the role of managerial and peer support

PurposeAlthough the literature on the effectiveness of team interventions is constantly expanding, there has been a strong focus on the process mechanisms that could explain their success, often overlooking the contextual aspects in which these interventions are carried out. Based on the Context-Mechanism-Outcome framework, this study aims to investigate the influence of contextual factors on the effectiveness of digital team coaching interventions that use social network visualisation to enhance team coordination and reduce interpersonal conflicts.Design/methodology/approachUsing a multi-wave, longitudinal design, this research analysed 38 work teams from three organisations over three-time points. Data collection focused on manager and peer support, the mechanisms of training transfer and action plan implementation and the outcomes of these interventions. Surveys were administered in three organisations, involving 317 respondents across different phases. The intervention spanned six to eight months, incorporating three to four structured online group sessions. Each session involved a multi-stage process, concluding with a result-oriented action plan about work-related goals. The intervention included social network visualisation, discussions, coaching and continuous refinement of action plans.FindingsThe analysis highlights how manager support significantly correlates with team coordination and performance, mainly when teams are less engaged in implementing action plans. Peer support did not show a mediating effect on training transfer or outcomes but had direct positive impacts on team coordination and performance.Practical implicationsTeams actively implementing action plans may require less immediate managerial support for effective coordination and high performance. The manager’s role becomes crucial, particularly in the early stages of the intervention or in those teams where online coordination alone may not be adequate for action plan implementation. Peer support for training transfer could enhance the effectiveness of the intervention in achieving desired team outcomes; therefore, cultivating a supportive peer environment is crucial for the success of such interventions. Monitoring and assessing team dynamics are vital to maximise the benefits of digital team coaching interventions.Originality/valueThis study stands out for its innovative exploration of the interplay between managerial and peer support in the context of digital team coaching, using social network visualisation as a novel approach to enhancing team dynamics.

Machine learning-based comparative analysis of weather-driven rice and sugarcane yield forecasting models

This study investigates the use of various machine learning algorithms for predicting rice and sugarcane yields for Navsari district of Gujarat, India. Recognizing the critical role of weather in crop productivity, accurate forecasting becomes essential for effective resource management. In methodology, weekly averages and weighted weather indices were computed based on daily weather data to develop forecast models using machine learning algorithms such as Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), XGBoost (XGB), Gradient Boost Regression (GBR), and Decision Tree (DT). Results show that RF and GBR algorithms outperform others in rice yield forecasting, while Gradient Booster and XGBoost demonstrate high accuracy in sugarcane yield prediction. However, the Mean Absolute Percentage Error (MAPE) values remained above 8%, indicating room for improvement. The study also emphasizes the importance of tuning hyperparameters for each machine learning algorithms (MLA) to achieve the most accurate predictions. Overall, the findings contribute valuable insights for stakeholders, including agricultural planners, policymakers, and researchers, emphasizing the need for continued refinement and validation of models to optimize agricultural planning and decision-making in this region. MLA highlight that features associated with temperature and relative humidity (RH) play a crucial role as the most significant contributors to the forecasting models for both rice and sugarcane yield. Introducing additional features, particularly remote sensing data, holds the potential to decrease the current error range of 8 to 10% to a more favourable and lower value.

Regulatory Framework of P2P Lending in India: Market Stability and Financial Inclusion Implications

Peer-to-peer (P2P) lending has revolutionized the financial industry by offering an alternative to conventional banking. It facilitates direct connections between lenders and borrowers via online platforms. This research paper examines the regulatory framework governing P2P lending in India, introduced by the Reserve Bank of India (RBI) in 2017, and its implications for market stability and financial inclusion. The study highlights the benefits of P2P lending, including greater acceptance, regional inclusivity, reduced costs, and faster loan approvals. It also explores the challenges posed by regulatory measures, such as caps on lending and borrowing, compliance costs, and operational hurdles, which can limit the impact on financial inclusion. The role of transparency in borrower information is analyzed, demonstrating how it influences loan interest rates, reduces adverse selection, and facilitates the formation of borrower groups based on credit ratings and social capital. Despite the significant benefits of enhanced transparency and regulatory oversight, the paper identifies ongoing challenges, including privacy concerns and information asymmetry. The findings underscore the need for continued regulatory refinement and collaboration among stakeholders to fully leverage the benefits of P2P lending in promoting financial inclusion and market stability in India. By addressing these challenges, P2P lending platforms can more effectively meet the credit needs of underserved populations, thereby contributing to a more inclusive financial system.

Optimizing Talent Acquisition: Strategies for Efficiency and Employee Engagement

This research study aims to analyze the resource-intensive nature of current recruitment practices in selected companies in Mysore City and develop strategies for sustaining employee engagement and loyalty. By examining the challenges faced in talent acquisition and retention, the study seeks to identify opportunities for enhancing organizational efficiency and effectiveness in attracting and retaining skilled individuals. This study conducts a thorough examination of recruitment practices and employee engagement strategies in both manufacturing and service sectors by employing a descriptive research design. A sample size of 100 HR managers and executives are selected using simple random sampling, with primary data collected through structured questionnaires. Statistical tools such as ANOVA are utilized to analyze the data, providing insights into industry-wise variations and uniformities in recruitment practices and employee engagement initiatives. The analysis reveals consistent employee engagement scores across manufacturing and service sectors, with non-significant differences in variables such as employee satisfaction scores, retention rates, and participation in training and development programs. Moreover, industry-wise variations in employee engagement strategies, including communication effectiveness and mentorship program participation, are found to be statistically insignificant, This study unfolds the importance of adopting a holistic approach to employee engagement and recruitment practices. The findings suggest that certain engagement initiatives can be implemented uniformly across industries, although organizations should remain attentive to sector-specific needs. Future research could delve deeper into understanding the impact of organizational culture on employee engagement and explore innovative strategies to address evolving workplace dynamics. Ultimately, fostering a resilient and loyal workforce requires continuous adaptation and refinement of engagement strategies in diverse organizational settings.

The transition from traditional to online education for the adult learner

Most educational institutions have been forced to transition to online education, prompting the adaptation of instructional approaches to ensure the diverse student population is accommodated. Digital learning platforms have created educational barriers for many adult students as the shift towards online education has accelerated due to the global COVID-19 pandemic. Decades of traditional classroom experiences have enabled learned behaviour and the shift to online may no longer align with the students’ preferences, decreasing motivation and resulting in suboptimal outcomes. Exploration of the transition from traditional to online education has found technological barriers and the development of inclusive learning environments as transitional complexities for the adult learner. Examination of advantages and disadvantages of age-appropriate inclusive learning environments enables adaptation to equivalent learning opportunities. The transition to online education presents challenges and opportunities; however, through continuous evaluation and refinement, adaptation of digital learning can enhance the adult learner experience.

The Nuances of Plant Disease Severity Estimation Using Quantitative Ordinal Scales-Lessons Learned over Four Decades.

We revisit the foundations of the Horsfall-Barratt (HB) scale, a widely cited and applied plant disease visual assessment tool introduced in 1945, a full 37 years prior to T. T. Hebert's 1982 critique that raised concerns regarding the scale's rationale, particularly its reliance on the Weber-Fechner law and visual perception assumptions. Although use of the HB scale and similar ordinal scales persists, comprehensive studies have revealed that direct visual estimation using percentage scales often proves more accurate and reliable. Challenges remain, such as biases due to estimator subjectivity and the potential for misclassification. The logarithmic assumptions of the HB scale have been debunked, and the importance of choosing appropriate interval sizes and numbers of classes in developing ordinal scales is emphasized. Analyzing ordinal scale data appropriately is crucial, and recent advances offer promising methods that reduce type II error rates. The closely related disease severity index is noted to have its shortcomings and potential for misuse. The letter underscores the need for continuous refinement and critical evaluation of disease assessment methodologies.

The Effect of Green Innovation on Corporate Financial Performance: A Literature Review

Heightening environmental challenges, increasing environmental awareness, and continuous refinement of environmental regulations have prompted corporations to increasingly focus on the development of green innovation in recent years. However, previous research has not reached a conclusive understanding of how green innovation affects corporate financial performance. In this case, this study seeks to categorize and summarize research conducted over the past five years on the relationship between green innovation and corporate financial performance, aiming to illustrate the reasons behind varied research conclusions.

A Comprehensive Review of the Global Epidemiology, Clinical Management, Socio-Economic Impacts, and National Responses to Long COVID with Future Research Directions.

Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID's epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management; Conclusions: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Addressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.

Empowering Mental Well-being: AI-guided Self-Recognition and Support

In an era marked by the convergence of technology and mental health, the integration of artificial intelligence (AI) holds promise for revolutionizing how individuals engage with their own well-being. This abstract explores the concept of AI-guided self-recognition and support as a transformative approach to empower mental wellness. Through sophisticated algorithms, AI can analyze diverse data streams, including digital behaviors and physiological markers, to detect subtle shifts indicative of mental health challenges. Leveraging this capability, AI becomes a proactive companion in individuals' journeys toward self-awareness. Furthermore, AI augments traditional support systems by offering personalized interventions tailored to individual needs and preferences. From empathetic chatbots providing emotional solace to curated resources and mindfulness exercises promoting resilience, the AI ecosystem becomes a versatile toolkit for mental health enhancement. Importantly, this paradigm shift emphasizes continuous learning and refinement, ensuring that interventions evolve in tandem with users' experiences and feedback. However, the implementation of AI-guided mental wellness initiatives necessitates careful consideration of privacy and ethical principles. Striking a balance between data utilization and user confidentiality remains paramount to fostering trust and engagement. Moreover, transparency in AI algorithms' functioning and accountability in decision-making processes are imperative to uphold ethical standards.

Light-Mediated Contact Printing of Phosphorus Species onto Silicon Using Carbene-Based Molecular Layers.

The ability to deposit pattern-specific molecular layers onto silicon with either regional p-/n-doping properties or that act as chemoselective resists for area-selective deposition is highly sought after in the bottom-up manufacturing of microelectronics. In this study, we demonstrate a simple protocol for the covalent attachment and patterning of a phosphorus-based dopant precursor onto silicon(100) functionalized with reactive carbene species. This method relies on selective surface reactions, which provide terminal functionalities that can be photochemically modified via ultraviolet-assisted contact printing between the carbene-functionalized substrate and an elastomeric stamp inked with the inorganic dopant precursor. X-ray photoelectron spectroscopy (XPS) analysis combined with scanning electron microscopy (SEM) imaging was used to characterize the molecule attachment and patterning ability of this technique. XPS spectra are indicative of the covalent bonding between phosphorus-containing molecules and the functionalized surface after both bulk solution-phase reaction and photochemical printing. SEM analysis of the corresponding printed features demonstrates the effective transfer of the phosphorus species in a patterned orientation matching that of the stamp pattern. This simple approach to patterning dopant precursors has the potential to inform the continued refinement of thin-film electronic, photonic, and quantum device manufacturing.

A Structural Framework for Effective Time Management in Dynamic Work Environments

This paper presents a structural framework to enhance time management profi- ciency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The evaluation demonstrates significant improvements in time management efficiency and client satisfaction across various professional contexts. For instance, by employing the eisenhower Matrix and Pareto Principle, project managers achieved a 20% im- provement in project completion times. The framework’s adaptability is further highlighted by a 25% reduction in project turnaround time in a marketing agency and a 30% increase in project visibility in a startup. These findings underscore the framework’s practical implementation as a holistic approach to managing time effectively and achieving long-term success. Continuous refinement, real- time feedback integration, and exploring the impact of emerging technologies are recommended for further enhancing the framework’s effectiveness. This research contributes valuable insights for organizations aiming to navigate the complexities of modern work environments.

THE REAL TIME HIGH PRECISION CRIMINAL FACE RECOGNITION FOR ATM SECURITY IN DEEP LEARNING

The proposed ATM security system, utilizing composable deep face recognition, emphasizes user protection during transactions with a specific focus on eye retina accuracy. In the event of a potential threat identified through real-time face detection and tracking, an automatic alert is initiated. Prioritizing precision, the system employs advanced face detection algorithms for accurate location and tracking, even in dynamic scenarios. A down sampling technique ensures efficient processing of facial data, crucial for meticulous identification in crowded ATM locations. The integration of a face tracking ID unit enhances accuracy, providing continuous monitoring and persistence in identification, particularly focusing on the distinctive patterns in the eye retina. The scoring method, rooted in face tracking and embedding distance, significantly elevates user identification reliability, minimizing the risk of false positives, especially in criminal identification based on unique eye retina features. In response to potential threats, the system promptly generates automatic alerts and dispatches secure emails to relevant entities, including the affected card user, the respective bank, and cybersecurity authorities. These emails include comprehensive information, such as a high-precision image of the detected criminal's face, detailed facial recognition data, and a thorough summary of their known criminal history. This accuracy-driven and retina focused approach enhances safety, emphasizing continuous refinement and adherence to privacy and ethical considerations during implementation. KEYWORDS: Crime prevention, down-sampling, face recognition.

International Business Expansion Strategies: A Data-Driven Approach with IBM SPSS

This paper presents a structural framework to enhance time management proficiency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The methodology involves the application of the Eisenhower Matrix, Pareto Principle, and time-blocking techniques, supported by case studies in diverse professional settings. Results indicate a 20% improvement in project completion times, a 25% reduction in project turnaround time, and a 30% increase in project visibility. These findings underscore the framework’s effectiveness in enhancing time management and achieving long-term success. Implications include recommendations for continuous refinement and integration of emerging technologies.

STYLESYNC: A FASHION RECOMMENDATION SYSTEM

Our project focuses on an AI-powered Fashion Recommendation System tailored to assist users in curating outfits for diverse occasions. Users upload clothing images to create individualized wardrobes. Employing machine learning and deep learning, our system accurately classifies clothing types and identifies colors within these images. The core of our innovation lies in a sophisticated recommendation algorithm. By analyzing users' existing wardrobes, including garment types and colors, our algorithm delivers personalized outfit suggestions aligned with users' style preferences and specific event needs. The interface prioritizes user-friendliness, enabling seamless wardrobe management and presenting users with well-matched outfit recommendations. Continuous refinement through user feedback ensures ongoing enhancement of recommendation accuracy, user experience, and machine learning model performance. In summary, our AI-driven Fashion Recommendation The system aims to streamline the process of dressing for diverse occasions, offering personalized outfit suggestions based on individual wardrobes, thus empowering users with convenient and stylish clothing choices. Keyword – fashion-recommendation, Feature extraction, recommendation system, data analysis, transfer-learning, javascript, python, bootstrap, npm, machine learning, computer vision, deep-learning, reactjs, material-ui, sklearn, Collaborative Filtering, Content-Based Filtering, Deep Learning, Feature Extraction, Natural Language Processing (NLP), Image Recognition.