Higher order compact finite difference method for the solution of 2-D time fractional diffusion equation

The main purpose of this study is to work on the solution of two-dimensional time fractional diffusion equation In this research work we apply the HOC scheme to approximate the second order space derivative. To obtain a discrete implicit scheme, Grunwald-Letnikov descritization is used in sense to approximate the Riemann-Liouville time fractional derivative. The scheme thus obtained is based on block pentadiagonal matrix and each matrix has five-point stencil in order to reduce the computational cost we use AOS method. In AOS method, before taking the average of two solutions first we split the n-dimensional problems into a sum of n-one dimensional problem

Multigrid solution for the cauchy problem associated with helmholtz type equation on non uniform grids

In this paper, an HOC scheme with multigrid algorithm is developed for solving the Cauchy problem associated with two dimensional Helmholtz type equations. The suggested scheme has up to fourth order accuracy. Lastly, some numerical experiments are given to show the accuracy and performance of the proposed scheme

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and $F1$ -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and $99\%~F1$ -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and $F1$ Measure score is 80%

Kala dalam bahasa Arab: analisis program minimalis

Kala dalam bahasa Arab wujud dalam kata kerja. Oleh yang demikian, kajian ini lebih memberi tumpuan terhadap sistem kala dalam bahasa Arab Baku. Berdasarkan data yang telah dibentuk dan dikumpul berpandukan sebuah buku berjudul Mulakhos Qowa’id Al-lughah Al-‘arabiah dengan bantuan seorang informan menggunakan kaedah kualitatif berorientasi analisis deskriptif, kala bahasa Arab bergabung terus dengan kata kerja dan ianya tidak mempunyai waktu yang tepat seperti kala masa dalam bahasa Inggeris. Kala bahasa Arab hanya terbahagi kepada dua jenis iaitu kala kini dan kala lampau. Kala masa akan datang pula tidak ditekankan kerana dianggap sama seperti kala masa kini dengan hanya menambahkan imbuhan ‘sa’ atau ‘sawfa’. Dapatan kajian mendapati bahawa kata kerja bahasa Arab merangkumkan sekali penggunaan subjek dan kala dalam pada satu kata yang sama mengikut suatu bentuk ayat itu juga. Oleh itu, kajian ini cuba menjelaskan kala dan mengkategorikannya dalam kata kerja bahasa Melayu Standard. Hal ini bagi membantu dalam memahami penggunaan kala dalam kata kerja transitif, kata kerja tak transitif dan kata kerja dwitransitif. Kajian ini juga akan menghuraikan kala bahasa Arab melalui analisis sintaksis berpandukan kerangka Program Minimalis

Multigrid Method for 2D Helmholtz Equation using Higher Order Finite Difference Scheme Accelerated by Krylov Subspace

ABSTRACT A sixth-order compact difference scheme is applied with uniform mesh sizes in different coordinate directions to discretize a two dimensional Helmholtz equation. Multigrid method is designed to solve the resulting sparse linear systems. Numerical results are conducted to test the accuracy and performance of the sixth-order compact difference scheme with multigrid method and to compare it with the standard second-order difference scheme and fourth-order compact difference scheme. The errors norms L 2 is used to establish efficiency and accuracy of the proposed scheme with multigrid method

Disposition of youth in predicting sustainable development goals using the Neuro - fuzzy and random forest algorithms

This paper evaluates the inclination of Asian youth regarding the achievement of Sustainable Development Goals (SDGs). As the young population of a country holds the key to its future development, the authors of this study aim to provide evidence of the successful application of machine learning techniques to highlight their opinions about a sustainable future. This study’s timing is critical due to rapid developments in technology which are highlighting gaps between policy and the actual aspirations of citizens. Several studies indicate the superior predictive capabilities of neuro-fuzzy techniques. At the same time, Random Forest is gaining popularity as an advanced prediction and classification tool. This study aims to build on the previous research and compare the predictive accuracy of the adaptive neuro-fuzzy inference system (ANFIS) and Random Forest models for three categories of SGDs. The study also aims to explore possible differences of opinion regarding the importance of these categories among Asian and Serbian youth. The data used in this study were collected from 425 youth respondents in India. The results of data analysis show that ANFIS is better at predicting SDGs than the Random Forest model. The SDG preference among Asian and Serbian youth was found to be highest for the environmental pillar, followed by the social and economic pillars. This paper makes both a theoretical and a practical contribution to deepening understanding of the predictive power of the two models and to devising policies for attaining the SDGs by 2030

Crisis averted: a world united against the menace of multiple drug-resistant superbugs -pioneering anti-AMR vaccines, RNA interference, nanomedicine, CRISPR-based antimicrobials, bacteriophage therapies, and clinical artificial intelligence strategies to safeguard global antimicrobial arsenal

The efficacy of antibiotics and other antimicrobial agents in combating bacterial infections faces a grave peril in the form of antimicrobial resistance (AMR), an exceedingly pressing global health issue. The emergence and dissemination of drug-resistant bacteria can be attributed to the rampant overuse and misuse of antibiotics, leading to dire consequences such as organ failure and sepsis. Beyond the realm of individual health, the pervasive specter of AMR casts its ominous shadow upon the economy and society at large, resulting in protracted hospital stays, elevated medical expenditures, and diminished productivity, with particularly dire consequences for vulnerable populations. It is abundantly clear that addressing this ominous threat necessitates a concerted international endeavor encompassing the optimization of antibiotic deployment, the pursuit of novel antimicrobial compounds and therapeutic strategies, the enhancement of surveillance and monitoring of resistant bacterial strains, and the assurance of universal access to efficacious treatments. In the ongoing struggle against this encroaching menace, phage-based therapies, strategically tailored to combat AMR, offer a formidable line of defense. Furthermore, an alluring pathway forward for the development of vaccines lies in the utilization of virus-like particles (VLPs), which have demonstrated their remarkable capacity to elicit a robust immune response against bacterial infections. VLP-based vaccinations, characterized by their absence of genetic material and non-infectious nature, present a markedly safer and more stable alternative to conventional immunization protocols. Encouragingly, preclinical investigations have yielded promising results in the development of VLP vaccines targeting pivotal bacteria implicated in the AMR crisis, including Salmonella, Escherichia coli, and Clostridium difficile. Notwithstanding the undeniable potential of VLP vaccines, formidable challenges persist, including the identification of suitable bacterial markers for vaccination and the formidable prospect of bacterial pathogens evolving mechanisms to thwart the immune response. Nonetheless, the prospect of VLP-based vaccines holds great promise in the relentless fight against AMR, underscoring the need for sustained research and development endeavors. In the quest to marshal more potent defenses against AMR and to pave the way for visionary innovations, cutting-edge techniques that incorporate RNA interference, nanomedicine, and the integration of artificial intelligence are currently under rigorous scrutiny

Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial

Background Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects. Methods FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762. Findings Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months. Interpretation Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function. Funding UK Stroke Association and NIHR Health Technology Assessment Programme