WHAT IS THEORETICAL CONTRIBUTION? A NARRATIVE REVIEW

Theoretical contribution is a process which is based on the theory development and advancement in existing theory with some logics and facts. This study has focused on some theoretical contribution related question and their answers through the narrative review of literature. This study will highlight what is the theory? And what are the major building blocks of theory? How authors can contribute in theory? The answers for these questions during theoretical studies will enhance the impact of paper and also increase the chance of publication. This study also suggested how theoretical concepts can be practical implemented in the society and organizations to enhance organizational performance and validate the theory.  Â

AI-Powered Home Automation: A Simple and Smart Living Solution

This paper explains a smart home system that uses artificial intelligence (AI) to make life easier, safer, and more comfortable. The system uses voice commands, face recognition, and augmented reality (AR) so people can control home devices in simple ways. It also processes data quickly and securely. This kind of smart home can save energy, improve safety, and provide a better experience for users. There are many systems developed previously, but everyone has its own merits and demerits. The proposed system is power efficient, cost-effective and computationally intensive. The deployment of the system is very simple, and the adaptability to the new features is very flexible. The adaptability of the proposed system makes it most prominent and is gaining attention from the community

Studying Angiographic Disease Pattern In Patients With Left Bundle Branch Block Undergoing Coronary Angiography

Objective: To observe the relationship between left bundle branch block (LBBB) and coronary artery disease (CAD) severity using the SYNTAX score and to evaluate the impact of CAD severity on left ventricular ejection fraction (LVEF) in patients undergoing coronary angiography. Methods: A prospective, comparative, cross-sectional study was conducted at the Rawalpindi Institute of Cardiology. Patients with symptomatic LBBB, defined by European Society of Cardiology criteria, who underwent coronary angiography were included. Echocardiography was used to assess LVEF, dividing patients into two groups: Group A (LVEF <45%) and Group B (LVEF ≥45%). CAD severity was evaluated using the SYNTAX score, categorising patients into low (0–22), intermediate (23–32), and high (>32) groups. Statistical analysis was performed using SPSS version 24, with a p-value of <0.05 considered significant. Results: A total of 140 patients were included (57% male, mean age 57.14 ± 10.42 years). Hypertension (60%) and multi-risk factors (50%) were predominant. Angiography revealed left anterior descending artery (LAD) involvement in 60% of patients. Group A exhibited significantly higher intermediate and high SYNTAX scores than Group B (p<0.001). Male patients and those with multiple risk factors were more likely to show abnormal angiographic findings (p<0.05). LAD involvement was notably higher in patients with high SYNTAX scores (p=0.002). Conclusion: Patients with LBBB show a strong correlation with severe CAD and reduced LVEF, highlighting the need for routine coronary angiography in high-risk cases. The study emphasizes the importance of addressing cardiovascular risks aggressively in this population to improve clinical outcomes

Electrochemical Water Splitting Using NiO-NiFe2O4/MWCNTs Nanocomposite as Electrocatalyst

Escalating energy demands, scarcity of conventional energy resources and environmental concerns are the key to fuel production through water splitting. Various electrocatalysts have been reported, considering the cost effectiveness, stability and OER (oxygen evolution reaction) activity. In the same context, porous hybrid NiO-NiFe2O4/MWCNTs based nanocomposite as an OER electrocatalyst, has been investigated in the current study. The synthesis has been accomplished via co-precipitation using Tween as a surfactant. Characterization and electrochemical study for water electrolysis using synthesized electrocatalyst deposited glassy Carbon (GC) electrode as anode was carried out using relevant tools. Iron-doped Nickel oxide nanoparticles were synthesized recognizing excellent oxygen evolution activity of NiO and its increase in conductivity with Fe incorporation due to its higher electropositivity. Nanocomposites were synthesized by incorporating upto 20% weight percent MWCNT (Multiwall carbon nanotubes). High surface to volume ratios, stability and excellent conductivity of MWCNTs furthermore, reduction of crystallite sized due to their incorporation enhanced the performance of the electrocatalyst significantly. Hybrid formation of NiO and NiFe2O4 at a certain calcination temperature was also found to be the reason for enhanced OER activity due to the increased grain boundaries. Porous NiO-NiFe2O4/MWCNTs with 10% MWCNTs concentration outperformed with 35mA/cm2 of current density at 1.8V in alkaline media

WHAT IS THEORETICAL CONTRIBUTION? A NARRATIVE REVIEW

Theoretical contribution is a process which is based on the theory development and advancement in existing theory with some logics and facts. This study has focused on some theoretical contribution related question and their answers through the narrative review of literature. This study will highlight what is the theory? And what are the major building blocks of theory? How authors can contribute in theory? The answers for these questions during theoretical studies will enhance the impact of paper and also increase the chance of publication. This study also suggested how theoretical concepts can be practical implemented in the society and organizations to enhance organizational performance and validate the theory. 

Ecological Applications of Enzymes in Plants Based Textile Dyeing

Biotechnology has a foremost role in the textile industry by enhancing ecofriendly, cost-effective, and energy-efficient manufacturing processes. The use of enzymatic biotechnology is one of the sustainable newly developed state-of-the-art processes for textile processing. To reduce the use of toxic and hazardous chemicals, enzymes have been proposed as one of the finest promising alternatives. Many enzymes have been used widely in textile processes such as lipase, laccase, pectinase, cellulase, catalase, amylase, and protease. The enzymatic use in the textile industry is very promising because they produce top-class goods, and give way to the reduction of water, time, and energy. The increasing demand for natural dyes especially with the incorporation of enzymes makes process more sustainable and eco-friendlier to suppress the toxicity of synthetic dyes. In the first part of the chapter, particular attention has been given to the source and extraction of natural dyes. In the second part of the chapter, different enzymes and their possible roles in the textile industry have been discussed. It is expected that this chapter will provide an innovative direction to the academic researchers, the community of textile and traders as well as artisans who are working in the area of biotechnological applications for the betterment of textile processing

A new ensemble-based intrusion detection system for Internet of Things

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios