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Homestay ecotourism in Malaysia has been extensively examined in terms of its concepts, approaches, activities, and community engagement. However, a comprehensive assessment of the sustainability factors pertaining to host families remains a critical area awaiting exploration. This is paramount for ensuring the long-term viability of homestays and fostering economic benefits within rural communities. The present study seeks to establish direct subjective measurements for evaluating the interplay between local communities, tourism, and resources in safeguarding sustainable homestays. Utilizing the Delphi approach, this research conducted interviews with 51 experts who were actively involved in six homestays located on the East Coast of Peninsular Malaysia. The objective was to identify key evaluation indicators pertinent to the homestay industry. The findings underscored the pivotal roles played by community resources and tourism in the sustainability of homestays. Additionally, environmental, economic, and social factors emerged as crucial components for maintaining the industry's sustainability. This innovative assessment methodology offers a valuable instrument for enhancing the sustainability of the homestay sector, especially in the wake of the COVID-19 pandemic. By embracing this approach, homestay operators can fortify their sustainable management practices and prepare themselves for future pandemics. This study represents a significant contribution to the field of homestay ecotourism, emphasizing the imperative for continued research in this dynamic domain.

Sentinel-2 is high-resolution multispectral imagery that launched by the European Space Agency on June 23, 2015 for Sentinel-2A and March 7, 2017 for Sentinel-2B. The two satellites were launched with the aim of land monitoring studies, including vegetation, soil, and water cover, as well as the observation of inland waterways and coastal areas. In 2018, Sentinel-2 produced bottom-of-atmosphere (L2A) imagery derived from top-of-atmosphere (L1C), which has been atmospherically corrected using Sen2Cor algorithm. However, there is an overcorrection effect due to inaccuracies of digital elevation model, over-detection of clouds over bright targets, and miss-classification of topographic shadows. This research aims to explore the application of Sentinel-2 imagery for mangrove mapping by comparing two levels of data, including L1C and L2A. L2A is divided into two, namely L2A atmospherically corrected using the Sen2Cor method (L2A_Sen2Cor) and dark object subtraction method (L2A_DOS). The classification scheme was built based on in-situ data containing seven objects: water, clouds, built-up, cloud shadows, bare land, mangroves, and land vegetation using random forest classification. The comparison of each level of data is analyzed based on the spectral signature and accuracy assessment using confusion matrix. The result shows that there are differences in the spectral signature between L1C and L2A data because of atmospheric impacts. L2A outperforms L1C, as shown by the higher coefficient of determination (R2). The accuracy is in the range of 93.7 – 95.4%, with the best accuracy shown by L2A_Sen2Cor.

Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.

Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average–least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

Abstract In the present paper, using the discrete analogue of the operator d 6 / d x 6 − 1 $\mathrm{d} ^{6}/\mathrm{d} x^{6}-1$ , we construct an interpolation spline that minimizes the quantity ∫ 0 1 ( φ ‴ ( x ) + φ ( x ) ) 2 d x $\int _{0}^{1}(\varphi {'''}(x)+\varphi (x))^{2}\,\mathrm{d}x$ in the Hilbert space W 2 ( 3 , 0 ) $W_{2}^{(3,0)}$ . We obtain explicit formulas for the coefficients of the interpolation spline. The obtained interpolation spline is exact for the exponential-trigonometric functions e − x ${{e}^{-x}}$ , e x 2 cos ( 3 2 x ) ${{e}^{\frac{x}{2}}}\cos ( \frac{\sqrt{3}}{2}x)$ , and e x 2 sin ( 3 2 x ) ${{e}^{\frac{x}{2}}}\sin ( \frac{\sqrt{3}}{2}x )$ .

Crimean–Congo hemorrhagic fever (CCHF) disease cases are registered annually in endemic regions of Kazakhstan. To study the prevalence of various Crimean–Congo hemorrhagic fever virus (CCHFV) genotypes, a total of 694 ticks were collected from southern regions of Kazakhstan in 2021. Hyalomma marginatum (n = 323) (46.5%), Hyalomma anatolicum (n = 138) (19.9%), Hyalomma asiaticum (n = 126) (18.2%), Hyalomma scupense (n = 80) (11.5%) and Ixodes ricinus (n = 27) (3.9%) were collected using the standardized flagging technique from the environment. All the tick samples were analyzed for the presence of CCHFV RNA by RT-PCR. The CCHF-positive samples were found within three Hyalomma asiaticum and one Ixodes ricinus tick sample. For the first time in Kazakhstan, infection of the Ixodes ricinus tick with CCHFV was detected. The results of sequencing and analysis of the S-gene fragment showed that the Asia 1 and Asia 2 CCHF genotypes circulate in the southern regions of Kazakhstan. Viruses isolated in the Zhambyl and Turkestan regions are assigned to the Asia-2 genotype, whereas the virus isolated in the Kyzylorda region to the Asia-1 genotype.

The anniversary of the publication of 'One Flew Over the Cuckoo's Nest' by Ken Kesey offers an opportunity for reflection on the use of neurosurgery in psychiatry. We used a narrative, historical and dialectical method to deliver an account of the controversial subject. A balanced representation of the negative and positive aspects, acknowledging some of the questionable ethical practices while describing well-reasoned applications is provided. It includes neurosurgeons, psychiatrists who have embraced these procedures with unwarranted enthusiasm and those who have opposed. Neurosurgical techniques for the treatment of severe mental disorders have evolved from rudimentary procedures which were used to 'correct' unwanted behaviours associated with a wide range of severe mental disorders to more refined and selective approaches used as a last resort to treat specific mental health conditions. In the absence of specific aetiological models to guide ablative surgical targets, non-ablative, stimulatory techniques have more recently been developed to allow reversibility when surgical treatment fails to obtain a sizeable improvement in quality of life. The subject is concretely illustrated by two eloquent clinical images: one on a series of brain computed tomography scans carried out on a Canadian population of subjects, who underwent leukotomy decades ago, and the other more contemporary on an implantation surgery to epidural stimulation. Alongside technical advances in psychosurgery, a regulatory framework has gradually developed to ensure vigilance in the appropriateness of patients' selection. Nevertheless, harmonisation of protocols around the world is necessary to ensure consistency in obtaining and maintaining the highest possible ethical standards for the benefit of patients. If the neurosciences promise today, in their new, better framed, and reversible applications, to provide answers to unmet therapeutic needs, we still must remain attentive to drifts linked the introduction of intrusive technologies for purposes of domination or behaviour modification that would impede our individual freedom. [ABSTRACT FROM AUTHOR]

To evaluate the role of transarterial chemoembolization “TACE”, radiofrequency ablation “RFA” and combination of both techniques in management of hepatocellular carcinoma “HCC”. Patients and methods: This study was carried out at Al-Azhar University Hospital (New Damietta) during the period from May 2013 to June 2015. It included forty patients were included in this study (27 males and 13 females), with age ranged from 43 to 68year (mean age 51year). They were divided into three groups after laboratory and radiological assessment: group 1 treated with TACE, group 2 treated with RFA and group 3 treated with combined therapy. The response was evaluated by triphasic MDCT, alpha-fetoprotein as well as clinical follow-up and classified as complete and partial response, and the overall survival was recorded. Results: There were no major complications detected. At 3month follow-up, there was complete response “CR” in 32 patients (80%), and partial response “PR” in 8 patients (20%). At 6months, there was complete response in 34 patients (85%), and partial response in 3 patients (7.5%). At 12months, there was complete response in 33 patients (82.5%), and partial response in one patient (2.5%). It is proved that complete response with high percentage achieved by combined therapy than either by TACE or by RFA (100%, 88.46% and 75% respectively), with the overall survival was 85%. Conclusion: The study showed that combined therapy achieved better results regarding complete response and overall survival rate than RF or TACE alone.

Immunotherapies that modulate T cell function have been firmly established in cancer immunotherapy, whereas the potential for B cells in the antitumor immune response is not established. B cell–activating factor (BAFF) is a cytokine belonging to the TNF ligand family that activates B cells, and is linked to autoimmunity. Our preliminary data identified BAFF as a target for enhancing antitumor immunity and formation of tertiary lymphoid structures (TLS) which confers favorable clinical prognosis. BAFF has three known receptors (BAFF-R, TACI and BCMA), but the relative contribution of these receptors to BAFF-mediated antitumor immunity is unknown. We hypothesize that the BAFF-R specifically mediates the positive effects of BAFF on antitumor immunity. We generated BAFF-overexpressing cell lines using genomic editing [KPC-BAFF (pancreas), B16F10-BAFF (melanoma), and PancO2-BAFF (pancreas)] and examined their tumor growth in syngeneic immunocompetent host. We studied the effect of BAFF-overexpression on tumor response when individual receptors are knocked out (KO) using BAFF-R-KO, TACI-KO and BCMA-KO mice. Tumor infiltrating lymphocytes were profiled by CyTOF analysis. Additionally, we characterized the native B cell phenotype in these genetic models. We tested the impact of genetic absence of these individual BAFF receptors as well as BAFF ligand in a TLS formation model. In complementary experiments we characterized the function of candidate cytokines, including BAFF, in the context of de novo TLS induction. BAFF overexpression suppressed tumor growth in immunocompetent host. Antibody mediated B cell depletion abrogated this effect. Characterization of tumor infiltrating lymphocytes showed BAFF overexpression increased the proportion of total B cells with a shift towards matured phenotype and increased antigen presentation. BAFF ligand knockout abrogated TLS formation in a hepatoma model. Tumor response experiments in receptor knockout mice and characterization of broader immune phenotype is currently in progress. We have created a preclinical platform to study the anti-tumor effects of BAFF. Successful identification of the BAFF-R as the effector of BAFF-mediated anti-tumor responses will have important implications for clinical translation. Development of agonists for the specific receptor may allow selective modulation of this particular B cell function in context of cancer therapy without perturbation of broad systemic humoral immunity. [ABSTRACT FROM AUTHOR]

A theoretical investigation of the modulational stability of the ion temperature gradient (ITG) mode in electron–ion plasma is presented. To examine linear features of dynamic species in plasma, we used reduction perturbation to solve Braginskii’s transportation equations and get phase and group velocities. The results reveal that ion species velocities are affected by plasma factors such as ion temperature, density, and ion temperature gradient coefficient, among others. We also find a nonlinear Schrodinger equation. This equation shows that the plasma dynamics depends on the coefficients of nonlinearity and dissipation of the nonlinear Schrodinger equation. These coefficients are strongly related to the plasma variables. The present investigation may be helpful in space and laboratory plasma, e.g., fusion confinement devices. [ABSTRACT FROM AUTHOR]

Ion temperature gradient mode nonlinear structures are studied with stationary charged dust. The dynamical role here is played by heavy ions. The small amplitude limit influence of various plasma parameters shows that the phase velocity of the mode modifies the overall plasma dynamics. Further, from the solution of the model equations we obtain Kortweg-de-Vries (KdV) and Korteweg-de-Vries Burger (KdVB) like equations, the solution of which gives solitary and shock wave nonlinear structures. Numerical analysis of these nonlinear structures shows that dust number density and polarity have a substantial impact on these structures. The present observation may be beneficial in space and laboratory plasma investigations. [ABSTRACT FROM AUTHOR]