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we published new research article (Project #59) in journal of Natural Products communication.
Hawks International Training Institute

We are thrilled to announce the publication of our latest paper titled 'Design of novel isoxazole derivatives as tubulin inhibitors using computer-aided techniques: QSAR modeling, in silico ADMETox, molecular docking, molecular dynamics, biological efficacy, and retrosynthesis' in the prestigious Journal of Biomolecular Structure and Dynamics with an Impact Factor of 5.235.

This achievement is the result of dedicated teamwork and collaboration. I extend my sincere gratitude to my esteemed international collaborator, from the Molecular Chemistry and Natural Substances Laboratory (MCNSL), whose competence and diligence significantly contributed to the success of this study. Together, as a team of researchers, we have executed this task with excellence and precision.

This paper represents a significant milestone in our ongoing research efforts, and we are excited about the potential impact it may have in advancing our understanding of isoxazole derivatives as tubulin inhibitors. We look forward to further contributions and collaborations all around the world.


Hawks International Training Institute

Our new publication

New paper 2024
We are working on multiple research domains. Individuals can contact us for fast publication and research collaborations

Our recent publication







ABSTRACT
Rainbow is a salmonid species that benefit aquaculture, wildlife fisheries, and commercial fish farming. Because of their economic worth, rainbow 2trout are extensively used as model fish in various scientific fields. From an evolutionary genomic standpoint, the rainbow genome is truly intriguing because of a whole duplication event along salmon fishing lines 88–96 million years ago. Because of the development of nextgeneration sequencers, a wide variety of genomic technologies, including whole-genome sequencing, are now accessible in this species. Several instances of whole-genome duplication (WGD) have affected the history of vertebrate animals, and they are often related to adaptive radiation or developmental progress. Due to an additional cycle of WGD, the rainbow genome offers a chance to study the early evolutionary destiny of the same vertebrate genome, and researchers found that the subgenomes of the two forebears remained remarkably identical after 100 million years of evolution. MiRNAs, on the other hand, were essentially wholly duplicated copies of their genome. The completion of genome sequencing by researchers opens up new opportunities for aquaculture, which is a critical step toward addressing specific issues in trout farming, such as disease resistance and identifying specific strains with higher growth potential or better adapted to marine growing conditions. The presence of various viral diseases in the salmon and trout industry has highlighted the possibility of naturally resistant rainbow trout reared in seawater for the generation of smoked filets from larger fish weighing over 3 kg. This chapter presents a synoptic assessment of current knowledge on rainbow trout genomics and what molecular evidence has already been discovered due to functional genomics.

Our Team New Publication
Congratulations to whole team 👏

Title: Climate-Resilient Forest Economics: Adapting Tree Species Rotation for Changing Times

Abstract
Background:
The economic implications of rotation periods in tree plantation ventures are often underestimated. This study aims to determine the most financially rewarding rotation cycles for three tree species—Vachellia nilotica, Populus deltoides, and Dalbergia sissoo—in diverse tehsils of District Jhang, Pakistan, employing various economic assessment tools.
Methods:
Data were gathered on the quantities of the selected tree species, along with associated costs and benefits. Tree valuations beyond five years were determined by local farmers. Net present worth (NPW) and benefit-cost ratio (BCR) were used as economic indicators to identify optimal rotation cycles.
Results: The most favorable rotation cycle for V. nilotica was found to be six years, yielding a NPW of Rs. 1768.1 and a BCR of 222.01. Similarly, P. deltoides exhibited optimal economic outcomes within a six-year rotation, with a NPW of Rs. 1356.8 and a BCR of 170.61. For D. sissoo, the ideal rotation was also six years, resulting in a NPW of Rs. 2191.2 and an impressive BCR of 274.91 (equivalent to 273.91%).
Conclusion:
This study concludes that a six-year rotation cycle is recommended for cultivating V. nilotica, P. deltoides, and D. sissoo, to maximize economic returns. These findings suggest the viability of these species for neighboring regions and districts, underlining their potential for sustainable growth and substantial economic benefits.

Congratulations to All team members




Abstract

Remote diagnosis and health management are made possible by intelligent healthcare. The advent of deep learning (DL) techniques opens the way to better healthcare by utilizing cutting-edge concepts. DL approaches are widely employed in healthcare frameworks to enable automated processes in modern technology breakthroughs. DL has a proven track record of performance and is essential to logistics process automation and supply optimization. Due to its ability to manage enormous data volumes with little human help, DL was beneficial in medical systems. The current healthcare system heavily utilizes DL to provide patients, physicians, and other healthcare practitioners with better service and more innovative health. Acute illness identification, picture analysis, medication development, biopharmaceuticals, and intelligent surveillance systems have all been demonstrated successful uses for DL.

This book chapter offers a cutting-edge perspective on current developments in DL and how they are being implemented in healthcare systems to achieve several objectives. The chapter also addresses difficulties and possibilities in DL, notably in medicine. This will contribute to developing reference data that may be valuable for the eventual use of DL in other sectors of the healthcare infrastructure.

Congratulations to whole team for New publication
Waqas Goraya
Mahar Imran Zafar

Hawks International Training Institute
Mahar Imran Zafar
Waqas Goraya

Our new accepted research
Congratulations to whole team
Hawks International Training Institute
Mahar Imran Zafar
Waqas Goraya
Aysha Fatima Ashi

We need coauthors and collaborators
Hawks International Training Institute
Mahar Imran Zafar

Allahamdulliha Our long A waited Research is Published in PakistanJournal of Botany. Congratulations to whole team
Hawks International Training Institute
Mahar Imran Zafar

Allahamdulliha Our team new research efforts
Research proposal, research paper and thesis preparation Group
Research Paper and Methodology
Research Methodology

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New Publication
Hawks International Training Institute
Research proposal, research paper and thesis preparation Group
Research Paper and Methodology
Research Methodology

Allahamdulliha New Article Published




Link: https://www.sciencedirect.com/science/article/pii/S0013935123010940?via%3Dihub

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Our team research efforts
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Our team recent research efforts 3.1 to 8.023 Impact Factor
Congratulations to All team members for excellent contributions and achievements

Multifunctional Roles of Genetic Programing and Computational Intelligence in Wireless Sensor Networks for interpretation of Complex Biological Diseases

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Our research team efforts Hawks International Training Institute

Allahamdulliha our team published article with 5+ impact factor

Allahamdulliha our team published article with 5+ impact factor

Research Internship for Article Publication
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Our new publication

Link: https://ieeexplore.ieee.org/document/9960917

Abstract:
Machine learning (ML) is a strong means to bring perspectives concealed on the internet of things (IoT) data. Such composite fields act cleverly to improve the decision‐making procedure in several domains, such as teaching, security, industry, and the biomedical sector. ML enables the IoT to explain obscured structures in bulk data for best forecasting and recommendation systems. Medicine has adopted IoT, ML, and deep learning (DL); hence, automated machines could build medical records, diagnose diseases, and, overmuch significantly, carry on timely observing patients. ML and DL algorithms act diversely on various datasets. Since the predictive outcomes differ, this could affect the general outcomes. The difference in forecasting outcomes seems significant in the clinical decision‐making procedure. Thus, it is all‐important to realize the variant ML and DL algorithms applied to deal with IoT data in medicine. This chapter spotlights known ML and DL algorithms for classification and forecasting and presents their applications in medicine. The aim of this chapter is to show a broad summary of current ML and DL techniques and their application in IoT biomedical data. In a careful investigation, we find that diverse ML and DL forecasting algorithms have different defects. Counting on the form of IoT datasets, we postulate to select the best way to forecast vital biomedical data. This chapter gives several cases of IoT‐based ML and DL. While DL procedures have assisted these tasks, the integration with IoT still requires further development

Research Title
Deep learning approaches for intelligent healthcare System in genomics and biomedicine

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