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Mohan Prasath Mani,1,2 Hemanth Ponnambalath Mohanadas,3 Ahmad Athif Mohd Faudzi,4,5 Ahmad Fauzi Ismail,6 Nick Tucker,7 Shahrol Mohamaddan,8 Manikandan Ayyar,9 Tamilselvam Palanisamy,1 Rajasekar Rathanasamy,10 Saravana Kumar Jaganathan11– 13 1Department of Mechanical Engineering, SNS College of Technology, Coimbatore, TN, India; 2School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, JB, Malaysia; 3Device Development Group, New Product Research and Development, Abbott Diabetes Care, Alameda, CA, USA; 4School of Electrical Engineering, Faculty of Engineering, UniversitiTeknologi Malaysia, Skudai, JB, Malaysia; 5Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Kuala Lumpur, SG, Malaysia; 6Advanced Membrane Technology Research Centre (AMTEC), School of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Skudai, JB, Malaysia; 7School of Engineering and Physical Sciences, College of Health and Sciences, University of Lincoln, Lincoln, LS, UK; 8Innovative Global ProgramCollege of Engineering Shibaura Institute of Technology Tokyo, Tokyo, Japan; 9Department of Chemistry, Centre for Materials Chemistry, Karapagam Acdemy of Higher Education, Coimbatore, TN, India; 10Department of Mechanical Engineering, Kongu Engineering College, Perunduari, TN, India; 11Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; 12School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam; 13Biomaterials and Tissue Engineering, School of Engineering and Physical Sciences, College of Health and Sciences, University of Lincoln, Lincoln, LS, UKCorrespondence: Saravana Kumar Jaganathan, Institute of Research and Development, Duy Tan University, Da Nang, Vietnam, Email jaganathansaravanakumar@dtu.edu.vn; sjaganathan@lincoln.ac.ukPurpose: Wound patches are essential for wound healing, yet developing patches with enhanced mechanical and biological properties remains challenging. This study aimed to enhance the mechanical and biological properties of polyurethane (PU) by incorporating magnesium chloride (MgCl2) into the patch.Methodology: The composite patch was fabricated using the electrospinning technique, producing nanofibers from a mixture of PU and MgCl2 solutions. The electrospun PU/MgCl2 was then evaluated for various physico-chemical characteristics and biological properties to determine its suitability for wound healing applications.Results: Tensile strength testing showed that the mechanical properties of the composite patch (10.98 ± 0.18) were significantly improved compared to pristine PU (6.66 ± 0.44). Field scanning electron microscopy (FESEM) revealed that the electrospun nanofiber patch had a smooth, randomly oriented non-woven structure (PU – 830 ± 145 nm and PU/MgCl2 – 508 ± 151 nm). Fourier infrared spectroscopy (FTIR) confirmed magnesium chloride’s presence in the polyurethane matrix via strong hydrogen bond formation. Blood compatibility studies using coagulation assays, including activated partial thromboplastin time (APTT), prothrombin time (PT), and hemolysis assays, demonstrated improved blood compatibility of the composite patch (APTT – 174 ± 0.5 s, PT – 91 ± 0.8s, and Hemolytic percentage – 1.78%) compared to pristine PU (APTT – 152 ± 1.2s, PT – 73 ± 1.7s, and Hemolytic percentage – 2.55%). Antimicrobial testing showed an enhanced zone of inhibition (Staphylococcus aureus – 21.5 ± 0.5 mm and Escherichia coli – 27.5 ± 2.5 mm) compared to the control, while cell viability assays confirmed the non-cytotoxic nature of the developed patches on fibroblast cells.Conclusion: The study concludes that adding MgCl2 to PU significantly improves the mechanical, biological, and biocompatibility properties of the patch. This composite patch shows potential for future wound healing applications, with further studies needed to validate its efficacy in-vivo.Keywords: wound dressings, polyurethane, MgCl2, biomaterial, nanofibers

Due to the complexity of autonomous mobile robot's requirement and drastic technological changes, the safe and efficient path tracking development is becoming complex and requires intensive knowledge and information, thus the demand for advanced algorithm has rapidly increased. Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning and motion control. Among those, motion control is a vital part of a fast, secure operation. Yet, current approaches face problems in managing unstructured gain data and producing accurate local planning due to the lack of formulation in the knowledge on the gain optimization. Therefore, this research aims to design a new gain optimization approach to assist researcher in identifying the value of the gain's product with a qualitative comparative study of the up-to-date controllers. Gains optimization in this context is to classify the near perfect value of the gain's product and processes. For this, a domain controller will be developed based on the attributes of the Fuzzy-PID parameters. The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). It is expected that the domain controller will give a positive impact to the path planning position and angular PID controller algorithm that meet the autonomous demand., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Razali, Mohd Faudzi, Shamsudin and Mohamaddan.)

Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Here, we report the nearly complete genome sequences of nine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the D614G mutation. These viruses were detected from various infected individuals with different levels of severity from Pahang, Malaysia. In addition, this study described the presence of lineage B.1.351 as a type of variant of concern (VOC) and lineages B.1.466.2 and B.1.524 as local variants.