Numerical simulation of surface pressure and temperature distribution along a cone at supersonic Mach numbers using CFD

The primary focus of this study is to use numerical simulations to analyze the static temperature and surface pressure distribution along the slant length of a cone at different Mach numbers and a range of semi-cone angles. Computational fluid dynamics (CFD) analysis numerically simulates temperature and surface pressure distribution. This research considers parameters such as supersonic Mach numbers, semi-cone angles, and different locations along the slant length of a cone. The study examines Mach numbers of 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0, along with cone angles ranging from 3° to 21°. The static temperature and pressure (P/Pa) results are measured at different locations (x/L) along the slant length of the cone, ranging from 0.1 to 1. The results for static temperature and pressure distribution obtained by CFD analysis are compared with results obtained by regression model at various Mach numbers and constant semi-cone angle (θ) = 12°. The results from the CFD analysis and the findings of the regression methodology are in agreement. This study found that the Mach number, semi-cone angle, and the various locations along the cone's slant length significantly impact the variation of static temperature and surface pressure distribution. As the Mach number and the semi-cone angle increase, the temperature and pressure distribution along the slant length of the cone also increase

Path loss prediction for V2I communications systems: a performance analysis of propagation models

This paper presents a comprehensive analysis of path loss prediction models for V2I communication in urban environments, focusing on the impact of non-line-of-sight (NLOS) conditions. Field tests conducted in Bologna, Italy, provided a dataset encompassing four distinct NLOS scenarios. Linear regression and random forest (RF) models were trained and evaluated using meticulously prepared data. Our findings demonstrate the superior performance of the RF model in capturing complex data relationships, as evidenced by lower RMSE, MSE, and MAE values compared to both the linear regression and the standard 3GPP model. Furthermore, the application of a Kalman filter significantly enhanced the RF model's accuracy, achieving near-zero error levels in certain scenarios. In contrast, the 3GPP model exhibited limited improvement, revealing its inadequacy in accurately modeling path loss under complex urban conditions. This research underscores the potential of advanced machine learning techniques, like RF, combined with noise reduction strategies for achieving highly accurate and reliable path loss predictions for V2I communication systems

M630L mutation disrupts the structure conformation of Bruton tyrosine kinase (BTK) domain in patient with x-linked agammaglobulinemia: insights from in silico

X-Linked Agammaglobulinemia (XLA) is a rare inherited disease, attributed to mutations found in the Bruton Tyrosine Kinase (BTK) gene. This research outlines the application of Molecular Modelling and Simulation to predict the effects of a novel non-synonymous Single Nucleotide Polymorphism (nsSNP) reported in the BTK kinase domain protein of a male XLA patient. The mutation at position c.1888A>T causes a substitution of p.M630L within the kinase domain of the BTK protein. Functional assessment using in silico prediction tools (SIFT, Polyphen-2, PROVEAN, MutationAssessor, PANTHER, FATHMM, MutPred and MutationTaster) predicted the mutation to be deleterious, potentially disrupting both the structure and function of the protein. Nevertheless, the mutation is not located at the active site of the kinase domain. Consequently, the molecular dynamics (MD) simulations were performed to investigate the impact of amino acid substitution to the three-dimensional (3D) conformational structure of the BTK protein, contributing to the disease phenotype observed in the reported patient. MD analysis, clustering analysis and Principle Component Analysis (PCA) revealed that the 3D conformational structure of the kinase domain of mutant protein to be more compact and rigid compared to the wildtype. Given that the alterations in protein structure can influence their functional characteristics, the p.M630L mutation within the BTK protein kinase domain might disrupt the protein function, potentially impeding the maturation of B cells and contributing to the onset of XLA disease

Natural remedies for hypertension: a systematic review

Hypertension is a chronic condition contributing significantly to global morbidity and mortality. Increasingly, natural remedies are being considered for hypertension management due to their accessibility, lower cost, and fewer perceived side effects compared to conventional medications. This systematic review aimed to evaluate the scientific evidence on the effectiveness and safety of various natural remedies for treating hypertension. The review included remedies such as garlic, celery, hibiscus, hawthorn, and herbal formulations used in Traditional Chinese Medicine, among others. Following the PRISMA guidelines, eligible studies were selected from databases such as Cochrane Library and PubMed, with quality assessed using the SIGN methodology checklist. Eighteen studies were included, totalling 1915 participants. While many studies showed that these remedies effectively reduced blood pressure, no study demonstrated superior effectiveness of natural remedies over conventional antihypertensive medications. Safety profiles were generally favourable, with few minor side effects reported. However, due to limited and variable safety data, further independent, high-quality research is needed to comprehensively evaluate the long-term safety and efficacy of these remedies for hypertension management

A conceptual edu4youth business model: empowering underserved youths through TVET and digital platform

This paper aims to develop a conceptual business model (BM) for Edu4Youth, complete with its digital platform that align with the Sustainable Development Goals (SDG) by promoting equitable education (SDG 4), fostering economic growth (SDG 8), and encouraging partnerships for collaboration (SDG 17). A digital project is designed to address key challenges, extreme pains and create essential gains for various customer segments (CS) - underserved youths, professional tutors, and organisations including educational institutions and community organizations. Many youths face extreme pains such as high course fees, limited access to personalized guidance and insufficient opportunities for real-world application. Educational institutions and community organisations have challenges in reaching underserved youths, measuring impacts and managing resources. Professional tutors struggle with limited tools for interactive teaching, balancing administrative responsibilities and providing personalized feedback. The methodology utilize design thinking (DT) by conducting a literature review (LR) and benchmarking, supplemented by interviews and surveys to understand and define the key challenges, pains, gains, and important jobs-to-do of various customer segments (CS). An initial Business Model (BM) is ideated and developed using business modelling tools such as Business Model Canvas (BMC) and Value Proposition Canvas (VPC). The initial Business Model (BM) together with the digital platform/app prototype is tested and validated by the various customer segments (CS) to establish the validated Edu4Youth Business Model (BM). A Strategy Canvas (SC) is created to compare the validated Business Model (BM) with other market players to identify Edu4Youth’s “purple cow” features. This paper offers validated conceptual Edu4Youth Business Model (BM) with digital platform/ app as pain reliever and gain creator. A comprehensive Project Management Plan (PMP) will be developed in the future for Edu4Youth’s development and implementation; and improve its impact on marginalised areas while fostering sustainable economic growt

Penilaian aktiviti antimikrob dan kandungan antioksidan pigmen biji kesumba melalui ujian bioesei = Evaluation of antimicrobial activity and antioxidant content of annatto seed pigment through bioassay testing

Kajian ini bertujuan untuk menilai potensi farmakologi pigmen semula jadi daripada biji kesumba (Bixa orellana) melalui analisis aktiviti antimikrob dan kandungan antioksidan. Pigmen utama yang dikenali sebagai bixin telah diekstrak menggunakan kaedah pelarut dan dianalisis untuk menilai keberkesanannya terhadap beberapa strain mikroorganisma patogenik. Mikrob yang dipilih dalam kajian ini terdiri daripada kumpulan bakteria gram-positif dan gram-negatif, serta kulat yis dan dermatofit. Antara strain yang diuji termasuklah Staphylococcus aureus, Methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa, Candida albicans, Microsporum canis dan Trichophyton mentagrophytes. Semua kultur mikroorganisma diperoleh daripada American Type Culture Collection (ATCC) dan diuji menggunakan kaedah Kepekatan Inhibitori Minimum (Minimum Inhibitory Concentration, MIC) untuk menentukan keberkesanan antimikrob bixin. Selain daripada itu, kandungan antioksidan pigmen kesumba dinilai menggunakan kaedah DPPH (2,2-diphenyl-1-picrylhydrazyl), yang mengukur keupayaan bahan untuk menstabilkan radikal bebas. Hasil kajian menunjukkan bahawa pigmen kesumba mempamerkan aktiviti antimikrob yang ketara, khususnya terhadap bakteria gram-positif seperti S. aureus dan dermatofit seperti T. mentagrophytes. Aktiviti antioksidan yang tinggi turut menyumbang kepada kestabilan struktur molekul bixin dan meningkatkan potensinya sebagai agen terapeutik. Secara keseluruhannya, penemuan ini menyokong potensi penggunaan bixin sebagai bahan semula jadi dalam formulasi farmaseutikal moden. Kajian ini juga mengukuhkan lagi nilai perubatan kesumba yang telah lama diamalkan dalam perubatan tradisional Melayu, sekaligus menunjukkan bahawa pengetahuan etnobotani tradisi masih relevan dalam pembangunan ubat-ubatan masa kini

Analyzing cloud size using weather radar data for improved flood disaster prediction

Cloud size and rain cell analysis are essential to meteorological research and flood risk prediction, especially in regions vulnerable to heavy rainfall events and flooding. By leveraging weather radar data, which captures reflectivity values indicating precipitation intensity, researchers can derive cloud size and better understand rainfall's spatial and temporal patterns. This paper introduces a comprehensive approach for analyzing cloud size using weather radar data, incorporating a series of systematic steps that enhance the detection and evaluation of rain cells. The process begins with data acquisition, wherein raw radar data is obtained from weather monitoring stations or agencies. Following acquisition, preprocessing techniques are applied to convert dBZ values into reflectivity values, remove non-meteorological noise, and organize data into structured grids. These preprocessing steps ensure data accuracy and facilitate analysis across different spatial regions and time intervals. The next phase involves thresholding and cloud boundary definition, where a reflectivity threshold (e.g., 30 dBZ) is used to create a binary cloud mask, identifying significant rain cells within the radar scans. This binary mask provides a foundation for further analysis, allowing the delineation of cloud boundaries and the isolation of specific rain cell regions. Feature extraction is then performed to quantify critical attributes, such as cloud size, maximum reflectivity, and rain cell movement patterns, which are crucial for accurate flood prediction. Finally, visualization methods, including time series plots, allow for assessing rain cell evolution over time, providing real-time insights into rainfall dynamics. Collectively, these steps enhance the predictive accuracy of flood risk models and offer valuable data for disaster mitigation strategies, contributing to more effective and timely responses in flood-prone area

SafaVoyage: a toolkit for Muslim-friendly adventure tourism

The growing demand for Muslim-friendly tourism highlights a gap in standardized frameworks to help adventure tourism businesses meet the unique needs of Muslim travelers. To address this, we propose SafaVoyage, a comprehensive digital platform designed to guide tourism operators in aligning their services with Muslim-friendly standards. The toolkit will offer best practices and resources on key areas such as halal food sourcing, prayer facilities, privacy measures, and gender-specific services, while also addressing adventure-specific needs like safety gear and family- or women-only packages. It will simplify the certification process for operators, providing step-by-step instructions and training modules on local cultural sensitivities to ensure a respectful and supportive environment for Muslim tourists. By offering actionable guidelines and tools, this innovation will empower tourism providers to better serve Muslim adventurers, foster inclusivity, and build trust with the Muslim market, ultimately creating a more sustainable and ethical adventure tourism industry